# SuperSeed — full content stream All published pages and journal posts concatenated as markdown. --- ## PAGES --- # ESG & Impact **Source:** https://www.superseed.com/esg-impact/ **Published:** 2023-05-22 **Author:** Pepe ## SuperSeed Sustainability Policy (ESG) ##### Revised 5 September 2026. Reviewed annually. **Introduction** At SuperSeed, we believe in the transformative power of technology and entrepreneurship to deliver efficiency, productivity and sustainability for the betterment of our planet and society. Our mission is to back Europe's best B2B entrepreneurs with capital and support to help them realise their global goals, thereby driving positive change and contributing to a sustainable future. This sustainability policy outlines our commitment to integrating environmental, social, and governance (ESG) factors into our investment and operational activities. **Vision and Mission** Our mission is to unlock more entrepreneurial talent to bring the best technology to the world, delivering the efficiency required to fulfil our promises to the planet and to humanity. Our mission revolves around three core principles: 1. Technology has the potential to empower humanity and set us free. 2. Efficiency is central to sustainability, as it enables the responsible deployment of resources. 3. Entrepreneurship is the driving force for developing and delivering impactful technology. **Impact and ESG Priorities** Our primary impact priority is resource efficiency. We aim to promote investments that enable customers to achieve more output with less input, optimising the production and delivery of goods and services without compromising our planet's well-being. To improve our portfolio companies' opportunity to achieve this, we look to make sure they are well run across the areas of governance, data privacy and other regulatory compliance. **Sustainability Goals and Targets** Our portfolio companies create their impact through what they do for their customers, by enabling more output from fewer inputs. We look for that effect when we invest, and we look for evidence of it as companies grow. We do not set sustainability targets for our portfolio companies or ask them to report against a sustainability framework. Early-stage companies have one job, which is to build something customers want, and we do not believe diverting their attention from it serves anyone. **Investment Criteria and Benchmarks** We evaluate potential investments on two aspects: 1. **Internal ESG performance.** Prospective investee companies should manage the governance, data and regulatory risks that apply to their business. 2. **External impact.** Investee companies should deliver real resource efficiency for their customers. **ESG Integration and Risk Management** We integrate ESG factors into our investment decision-making process and risk management practices by assessing the following: 1. Quality and maturity of board governance 2. Data security, privacy, and data practices 3. Legal and regulatory compliance 4. Brand and impact alignment: we look to validate that firms deliver the impact they claim to deliver. **Engagement and Monitoring** We take board seats and investor consent rights in the companies we back. Boards meet quarterly, and monthly where a company is at a stage that warrants it. Governance is where we engage: the quality of the board, the decisions that need investor consent, and whether the company is meeting its legal and regulatory obligations. **Reporting and Disclosure** We do not operate a generic sustainability data collection programme across our portfolio. Where an investor in our funds requires specific sustainability reporting, we provide it to that investor directly. **Principal Adverse Impacts** We consider the adverse impacts a business may have on society when we decide whether to invest. This is a matter of judgement at investment committee rather than a scoring exercise. We have declined investments on these grounds, including business models that we judged to extract value from customers least able to bear it. We do not report against the principal adverse impact indicators set out in the Sustainable Finance Disclosure Regulation. Those indicators are designed for companies that produce measurable environmental and social data at scale. Our investments are pre-seed and seed companies, typically with a handful of employees and no material operations, for which the indicators would produce numbers without meaning. We would rather say plainly what we consider than publish figures we do not believe. **Remuneration** Our remuneration is salary plus carried interest. Carried interest pays only on realised returns across the full life of a fund, which is typically ten years or more. Nobody at SuperSeed is paid for short-term performance, and no part of our remuneration rewards taking a risk whose consequences fall outside the period over which we are paid. That includes sustainability risks. Our remuneration policy is therefore consistent with the way we integrate sustainability risk into investment decisions. **How We Evaluate Sustainability Risks in Our Investment Decision-Making Process** At SuperSeed, we recognise the importance of integrating sustainability risks into our investment decision-making process to ensure long-term value creation for our investors and portfolio companies. Sustainability risks, defined as environmental, social, and governance events or conditions that could have a material impact on the financial performance of our investments, are essential to consider alongside traditional financial metrics. Our approach to integrating sustainability risks includes the following steps: 1. **Identification.** During the initial screening and due diligence process, we assess potential investments for sustainability risks related to their operations, industry, and market context. This includes evaluating the investee company's internal ESG performance and external impact on resource efficiency. 2. **Assessment.** We analyse identified sustainability risks to determine their potential impact on the investee company's financial performance, growth prospects, and overall risk profile. This involves assessing the likelihood, magnitude, and timeframe of potential risks, as well as the investee company's capacity to manage and mitigate them. 3. **Integration.** We incorporate the findings from our sustainability risk assessment into our overall investment decision-making process. This enables us to make informed decisions on potential investments, considering both their financial prospects and their sustainability risk profile. 4. **Monitoring and engagement.** Once an investment is made, we stay close to the company through our board seat and investor consent rights, which is where sustainability risks surface in practice. By integrating sustainability risks into our investment decision-making process, we strive to create a resilient and high-performing portfolio that aligns with our commitment to promoting resource efficiency and delivering long-term value for our investors, portfolio companies, and society at large. --- # Portfolio **Source:** https://www.superseed.com/portfolio/ **Published:** 2024-01-05 **Author:** Pepe SuperSeed partners with B2B founders who bring applied AI to the real economy. --- # Privacy Notice **Source:** https://www.superseed.com/privacy-notice/ **Published:** 2022-01-22 **Author:** Pepe ##### **Privacy notice** ###### Published January 19th, 2022 1. **INTRODUCTION** This Privacy Notice sets out how personal data is collected, processed and disclosed in connection with SuperSeed Capital Limited (the "**Fund**"). 2. We take privacy and security of your information seriously and will only use such personal information as set out in this Privacy Notice and in ways which are reasonably ancillary to what is set out below. 3. The Fund is a data controller for the purposes of the Data Protection (Bailiwick of Guernsey) Law, 2017 (as amended) (the "**Guernsey DP Law**"). This means that it determines how and why your personal data is processed. 4. The Fund has appointed Imperium Fund Services Limited (the "**Administrator**") in Guernsey to provide administration services to the Fund (the "**Administration Services**") and Link Market Services (Guernsey) Limited (the "**Registrar**") to provide registrar services to the Fund (the "**Registrar Services**"). When providing the Administration Services and Registrar Services, the Administrator and Registrar will each primarily be acting as a data processor for the purposes of the Guernsey DP Law. This means that the Administrator and Registrar will only process your personal data on the express instructions of the Fund. 5. However, to the extent that either of the Administrator or the Registrar is required, by law and/or regulation, to collect your personal data (for example, in order to comply with its own anti-money laundering and counter-finance terrorism legislation) then it will be a data controller in respect of that processing activity. 6. For further information as to how the Administrator and the Registrar process your personal data, please refer to their privacy notices, which can be found at [https://www.imperiumgroup.gg/privacy-statement-disclaimer/privacy-policy-disclaimer-funds/](https://protect.mimecast-offshore.com/s/pjFGCKZyXwcr3gJ1cM6C8K/) and [https://www.linkgroup.eu/privacy-notice/](https://protect.mimecast-offshore.com/s/YIY9CL8z6xTYExv3tqa5L1/) 7. This Privacy Notice is issued by the Fund and references to the "Fund", "we", "us" or "our" in this Privacy Notice refers solely to the Fund as controller. References to "you" include all individuals whose personal data we collect, hold and process in the course of operating the business of the Fund. 8. This Privacy Notice should be read in conjunction with any terms and conditions, policies or other relevant legal notices etc. on [https://www.superseed.com/investors/superseed-capital/](https://protect.mimecast-offshore.com/s/7TzqCzKzPLUmJkyJI4c91M/). **CONTACT DETAILS** You can contact our data protection adviser in relation to data protection and your rights by emailing [superseed@imperiumfunds.co.uk](mailto:superseed@imperiumfunds.co.uk). **THE PERSONAL INFORMATION WE PROCESS** 1. Your personal data comprises the following categories: **Your identification information** (which may include your name, ID card and passport numbers, nationality, place and date of birth, gender, photograph and/or IP address and personal data relating to claims, court cases and convictions, politically exposed person (***PEP***) status, personal data available in the public domain and such other information as may be necessary for us to provide our services to you and to complete our customer due diligence ("**CDD**") process and discharge our obligations relating to anti-money laundering ("**AML**") and combatting the financing of terrorism ("**CFT**")); 2. **Your tax status and** **information** (which may include your tax residency, tax identification numbers and/or tax status); 3. **Your contact information** (which may include postal address and e-mail address and your home and mobile telephone numbers); 4. **Your family relationships** (which may include your marital status, the identity of your spouse and the number of children that you have); 5. **Your professional and employment information** (which may include your level of education and professional qualifications, your employment, employer's name and details of directorships and other offices which you may hold); 6. **Financial information, sources of wealth and your assets **(which may include details of your assets, sources of wealth, shareholdings and your beneficial interest in assets, your bank details and your credit history). We may also collect and process personal data regarding people connected to you, either by way of professional (or other) association or by way of family relationship. To the extent that such personal data contains "special category data", for example: data relating to racial or ethical origin, political opinion, religious or philosophical belief, trade union membership or criminal data, then the processing of such data shall solely be for the purpose of complying with any duty imposed on us by an enactment or where it is necessary to prevent, detect or investigate any unlawful act or omission. **WHERE WE OBTAIN YOUR PERSONAL INFORMATION:** 1. We collect your personal information from the following sources: personal information which you give to us, including but not limited to: information set out in any subscription agreement with the Fund; 2. such other forms and documents as we may request that are completed in relation to the administration/management of any of our services; 3. information gathered through client due diligence carried out as part of our compliance with regulatory requirements; or 4. any personal information provided by way of correspondence with us by phone, e-mail or otherwise; personal information we receive from third party sources, such as: 1. entities in which you or someone connected to you has an interest; 2. your legal and/or financial advisors; 3. other financial institutions who hold and process your personal information; and 4. credit reference agencies and financial crime databases for the purposes of complying with our regulatory requirements; and 5. other sources including information collected via website (including cookies and IP addresses) and emails. We may also collect and process personal information received in the course of dealing with advisors, regulators, official authorities and service providers by whom you are employed or engaged or for whom you act. **WHY WE COLLECT YOUR PERSONAL INFORMATION:** 1. We may hold and process your personal information on the following lawful grounds: the processing is necessary for our legitimate interests, provided your interests and fundamental rights do not override those interests; 2. the processing is necessary to comply with our contractual duties; 3. the processing is necessary to comply with our legal and regulatory obligations; 4. where we have obtained your consent to processing your personal information for a specific purpose; and 5. on rare occasions, where it is needed in the public interest. Pursuant to paragraph 5.1 above, your personal information may be processed for the purposes set out below ("**Purposes**"). The Purposes based on our legitimate interests are set out in paragraphs 5.2.1 to 5.2.4 inclusive): 1. facilitating the administration of our business and/or that of our service providers; 2. communicating with you as necessary in connection with your affairs and generally in connection with your investment in the Fund; 3. monitoring and recording telephone and electronic communications and transactions: for quality, business analysis, training and related purposes in order to improve service delivery; and 4. for investigation and fraud prevention purposes, for crime detection, prevention, investigation and prosecution of any unlawful act (or omission to act); disclosing your personal information to any bank or financial institution or other third party lender providing any form of facility, loan, finance or other form of credit or guarantee to the Fund; to enforce or defend our legal and contractual rights or those of third party service providers; to comply with legal or regulatory obligations imposed on us (including but not limited to AML/CDD obligations); detecting and preventing crime such as fraud, money laundering, terrorist financing, bribery, corruption, tax evasion and to prevent the provision of financial and other services to persons who may be subject to economic or trade sanction on an ongoing basis ("**Regulatory Assessments**"); facilitating our internal administration and retaining your personal data as part of our Regulatory Assessments or future services entered into by you; and liaising with or reporting to any regulatory authority (including tax authorities) with whom we are either required to cooperate or report to, or with whom we decide or deem it is appropriate to cooperate in relation to an investment and which has jurisdiction over the Fund or its investments. We do not make decisions about you based on automated processing of your personal data. **SHARING PERSONAL INFORMATION** 1. We may share your personal data with our group companies and third parties (including the administrator, investment manager, the bookrunner, banks, financial institutions or other third party lenders, IT service providers, auditors and professional advisers) under the terms of any appropriate delegation or contractual arrangement. Those authorised third parties may, in turn, process your personal data abroad and may have to disclose it to foreign authorities to help them in their fight against crime and terrorism. 2. Data processing (as described above) may be undertaken by any entity in the Bailiwick of Guernsey or the United Kingdom. However, such data processing may also be undertaken by an entity who is located outside the Bailiwick of Guernsey, the United Kingdom or the European Economic Area (the "**EEA**") in a third country without the same or similar data protection laws as the Bailiwick of Guernsey, the United Kingdom or any EU member state. 3. This means that the countries to which we transfer your data are not deemed to provide an adequate level of protection for your personal information. However, to ensure that your personal data receives an adequate level of protection we have put in place the following appropriate measure(s) to ensure that your personal information is treated by those third parties in a way that is consistent with and which respects the EU laws, and the laws of the United Kingdom and the Bailiwick of Guernsey on data protection: Utilisation of standard contractual clauses (also known as EU Model Clauses) for data protection which have been approved and adopted by the European Commission and/or the Office of the Data Protection Authority. In limited circumstances, applicable law may permit us to otherwise transfer your personal information outside the United Kingdom and the EEA. If you would like further information about the safeguards we have in place to protect your personal information, please contact superseed@imperiumfunds.co.uk. The privacy notices of selected data processors are also available on the Fund's website at [https://www.superseed.com/investors/superseed-capital/](https://protect.mimecast-offshore.com/s/7TzqCzKzPLUmJkyJI4c91M/). **RETENTION OF PERSONAL INFORMATION** 1. Your personal information will be retained for as long as required: for us to carry out the Purposes; 2. in order to establish or defend legal rights or obligations or to satisfy any reporting or accounting obligations; and/or 3. as required by data protection laws and any other applicable laws or regulatory requirements. **ACCESS TO AND CONTROL OF PERSONAL INFORMATION** 1. You have the following rights (which may be exercisable depending on the circumstances) in respect of the personal information about you that we process: the right to access and port personal information; 2. the right to rectify personal information; 3. the right to restrict the use of personal information; 4. the right to request that personal information is erased; and 5. the right to object to processing of personal information. You also have the right to lodge a complaint about the processing of your personal information either with us, or with: 1. the Office of the Data Protection Authority in Guernsey ([odpa.gg](http://www.odpa.gg)); or 2. if you are an EU or United Kingdom data subject, the supervisory authority in the EU member state of your residence or in the United Kingdom. Where the Administrator and/or the Registrar relied on consent to process the personal information, you have the right to withdraw consent at any time by contacting us via the contact below. You also have the right to object to processing on the basis of legitimate interests (although this right is subject to certain exceptions). If you wish to exercise any of the rights set out in this paragraph 8, please contact [superseed@imperiumfunds.co.uk](mailto:superseed@imperiumfunds.co.uk). **INACCURATE OR AMENDED INFORMATION** Please let us know as soon as possible if any of your personal information changes (including your correspondence details) by contacting us at superseed@imperiumfunds.co.uk. Failure to provide accurate information or to update information when it changes may have a detrimental impact upon your investment, including the processing of any subscription instructions. **COMMUNICATIONS AND MEDIA** 1. **People who email us** We use software to encrypt and protect email traffic. If your email service does not support this software, you should be aware that any emails we send or receive may not be protected in transit. Please contact us for further detail should you require it. 2. We will also monitor any emails sent to us, including file attachments, for viruses or malicious software. Please be aware that you have a responsibility to ensure that any email you send is within the bounds of the law. **Visitors to our website** 1. We are committed to protecting and respecting your privacy on-line. We are aware of the concern which exists over the use of personal information provided over the internet and therefore, we do not collect personal data through our website. 2. We do not control and are not responsible for the privacy policy of any website or organisation to which our website provides links. By including references, hyperlinks or other connections to such third party websites, we do not imply any endorsement of them or any association with their owners or operators. **Google Analytics** 1. The Fund's website uses Google Analytics to help analyse how users use the The tool uses "cookies," which are text files placed on your computer, to collect standard Internet log information and visitor behaviour information in an anonymous form. The information generated by the cookie about your use of the website (including IP address) is transmitted to Google. This information is then used to evaluate visitors' use of the website and to compile statistical reports on website activity for the Fund. 2. We will never (and will not allow any third party to) use the statistical analytics tool to track or to collect any Personally Identifiable Information ("**PII**") of visitors to our site. Google will not associate your IP address with any other data held by Google. Neither we nor Google will link, or seek to link, an IP address with the identity of a computer user. We will not associate any data gathered from this site with any PII from any source, unless you explicitly submit that information via a fill-in form on our website. **QUESTIONS** 1. If you have any questions about this Privacy Notice or how we handle your personal information (e.g. our retention procedures or the security measures we have in place), or if you would like to make a complaint, please contact superseed@imperiumfunds.co.uk. 2. This Privacy Notice was updated on 21 January 2022. We may update this Privacy Notice from time to time in order to reflect any changes to the way in which we process your personal data or changing legal requirements. We will endeavour to notify you of any substantive changes to our Privacy Notice, however, please review this web page frequently to see any updates or changes: [https://www.superseed.com/investors/superseed-capital/](https://protect.mimecast-offshore.com/s/7TzqCzKzPLUmJkyJI4c91M/). --- # Investor risks **Source:** https://www.superseed.com/investor-risks/ **Published:** 2019-01-16 **Author:** Mads Jensen ### Risks Summary **Risks** Your capital is at risk. Investing in early stage companies involves risks including loss of capital, illiquidity, lack of dividends and dilution. Equity investments made in The SuperSeed Venture Fund or investee companies should be considered as part of a diversified portfolio. Past performance is not a predictor of future performance. SuperSeed Ventures LLP do not give tax or investment advice which should be sought from a specialist Adviser. The availability of tax reliefs depends on individual investors’ circumstances, and on investee companies’ qualifying status, both of which may be subject to change. **Investment Risks** Investing in shares, especially shares in small and early stage companies, is an inherently risky process. Below we provide a summary of the major risks and considerations you should be aware of. ** Potential for Loss** An investment in shares (equity) does not guarantee that your money will be returned to you. Many small, early stage businesses fail, and if a business you invest in fails, neither that company nor SuperSeed Ventures will pay you back your investment. You are strongly advised not to invest more than you can afford to lose. ** Diversification** Diversification – which means spreading your money across a wide variety of investment types – is an important way to reduce the overall risk of investing. Don’t put all your eggs in one basket. Investors should not invest more than 10% of their investable assets in shares in early stage companies. ** Lack of Liquidity** It is highly unlikely that you will be able to sell your shares quickly or easily. Shares in companies invested in by the SuperSeed Venture Fund are unlikely to be traded on stock markets. You should be prepared to wait until, if it is, the whole company is sold, or floated on a stock market to sell your shares. ** No Regular Income** Companies invested in by the SuperSeed Venture Fund are generally not yet profit-making, or may choose to spend all their profits on growth. This means that you are unlikely to receive regular distributions of profits through dividends, so you are unlikely to receive any return on your investment unless you are able to sell your shares. ** Uncertain performance** The majority of companies invested in by the SuperSeed Venture Fund are early stage companies, which lack significant trading or operating history. The success of these companies is uncertain and depends upon the ability of their management team to implement a strategy for growing the business. ** Conflicts of Interest** SuperSeed Ventures, or any of their Directors or Officers may already hold shares in a company being invested in by the SuperSeed Venture Fund. In addition, any of these parties may also have a pre-existing business relationship with a company being considered for investment. ** Dilution** Any investment you make may be subject to dilution in the future. This happens when a company needs to issue more shares, in order to raise more money or incentivise staff. This means that the proportion of the company you own may be reduced, or ‘diluted’ over time. New shares issued by the company may also carry preferential rights to those acquired by you, meaning that payments such as dividends might get made to them first. (often shares you purchase have ‘pre-emption rights’, and these allow you to purchase more shares before they are made available to other investors, thereby potentially reducing the effects of dilution.) ** Tax Treatment of Shares** The UK government provides certain types of tax relief for investments in small businesses. While SuperSeed Ventures encourages businesses to apply for these tax reliefs (such as EIS and SEIS reliefs) where appropriate, the final decision on whether the company and investment is eligible is made by HMRC after the investment is completed. Eligibility for tax relief may also be lost due to your personal circumstances, or due to changes in the company’s activities or circumstances. If you are unsure about any of the risks or warnings set out above, we strongly recommend you seek advice from an Independent Financial Advisor. --- # Privacy Policy **Source:** https://www.superseed.com/privacy-policy/ **Published:** 2018-11-30 **Author:** Pepe Published: 4 July 2026 1. **Introduction** We are committed to safeguarding the privacy of our website visitors and clients. 2. This policy applies where we are acting as a data controller with respect to the personal data of our website visitors and clients; in other words, where we determine the purposes and means of the processing of that personal data. 3. In this policy, “we,” “us” and “our” refer to SuperSeed Ventures LLP. For more information about us, see Section 13. **How we use your personal data** 1. In this Section 2 we have set out: the general categories of personal data that we may process; the purposes for which we may process personal data; and the legal bases of the processing. 2. We may process data about your use of our website and services (“ usage data ”). The usage data may include your IP address, geographical location, browser type and version, operating system, referral source, length of visit, page views and website navigation paths, as well as information about the timing, frequency and pattern of your service use. The source of the usage data is Google Analytics. This usage data may be processed for the purposes of analysing the use of the website and services. The legal basis for this processing is our legitimate interests, namely monitoring and improving our website and services. 3. We may process your account data (“ account data ”). The account data may include your name and email address. The source of the account data is you. The account data may be processed for the purposes of operating our website, providing our services, ensuring the security of our website and services, maintaining back-ups of our databases and communicating with you. The legal basis for this processing is our legitimate interests, namely the proper administration of our website and business. 4. We may process your personal data that are provided in the course of the use of our services (“ service data ”). The service data may include your FCA Investor Category and the basis for specifying this category, and other data required for you to be assessed as an eligible investor under FCA rules. The source of the service data is you. The legal basis for this processing is our legitimate interests, namely the performance of a contract between you and us and/or SuperSeed Ventures LLP, and/or you taking steps, at your request, to enter into such a contract. 5. We may process information contained in any enquiry you submit to us regarding our services (“ enquiry data ”). The enquiry data may be processed for the purposes of offering, marketing and selling relevant goods and/or services to you. The legal basis for this processing is consent. 6. We may process information relating to transactions, including investment services, that you enter into with us and/or SuperSeed Ventures LLP through our website (“ transaction data ”). The transaction data may include your contact details, your card details and the transaction details and other transaction-related information. The transaction data may be processed for the purpose of providing the purchased services and keeping proper records of those transactions. The legal basis for this processing is the performance of a contract between you and us and/or SuperSeed Ventures LLP, and/or you taking steps, at your request, to enter into such a contract and our legitimate interests, namely our interest in the proper administration of our website and business. 7. We may process information that you provide to us for the purpose of subscribing to our email notifications and/or newsletters (“ notification data ”). The notification data may be processed for the purposes of sending you the relevant notifications and/or newsletters. The legal basis for this processing is consent. 8. We may process information contained in or relating to any communication that you send to us (“ correspondence data ”). The correspondence data may include the communication content and metadata associated with the communication. Our website or CRM system will generate the metadata associated with communications made using the website contact forms. The correspondence data may be processed for the purposes of communicating with you and record-keeping. The legal basis for this processing is our legitimate interests, namely the proper administration of our website and business and communications with users. 9. We may process any of your personal data identified in this policy where necessary for the establishment, exercise or defence of legal claims, whether in court proceedings or in an administrative or out-of-court procedure. The legal basis for this processing is our legitimate interests, namely the protection and assertion of our legal rights, your legal rights and the legal rights of others. 10. We may process any of your personal data identified in this policy where necessary for the purposes of obtaining or maintaining insurance coverage, managing risks, or obtaining professional advice. The legal basis for this processing is our legitimate interests, namely the proper protection of our business against risks. 11. In addition to the specific purposes for which we may process your personal data set out in this Section 2, we may also process any of your personal data where such processing is necessary for compliance with a legal obligation to which we are subject, or in order to protect your vital interests or the vital interests of another natural person. 12. Please do not supply any other person’s personal data to us, unless we prompt you to do so. **Founder submissions** 1. When you submit a pitch deck through our submission page, or email one to pitch@superseed.com, we collect the deck, your email address, any context you include, and the answers you give. We use this information to assess whether your company may be a fit for our investment thesis. 2. Submissions are first read by an automated screening engine, which checks them against our published investment thesis. Where the screen shows a clear mismatch with the thesis, you will receive an automated response explaining the reason. Submissions that pass the screen are reviewed by our investment team. If you believe an automated outcome is wrong, contact us at pitch@superseed.com and a member of the team will review your submission. 3. Submissions are shared with our investment team and recorded in our customer relationship management system. Where we take a submission forward, we may supplement it with publicly available information about you and your company from third-party sources, such as LinkedIn and company registries. 4. We retain submissions, including decks, for as long as they remain relevant to our investment activity. Companies often return to us at a later stage, and a record of earlier submissions and how we assessed them is necessary to evaluate later approaches fairly and to keep an accurate record of our decisions. You may ask us to delete your submission at any time by contacting pitch@superseed.com. **Providing your personal data to others** 1. We may disclose your personal data to any member of our group of companies (this means our subsidiaries, our ultimate holding company and all its subsidiaries) insofar as reasonably necessary for the purposes, and on the legal bases, set out in this policy. 2. We may disclose your personal data to our insurers and/or professional advisers insofar as reasonably necessary for the purposes of obtaining or maintaining insurance coverage, managing risks, obtaining professional advice, or the establishment, exercise or defence of legal claims, whether in court proceedings or in an administrative or out-of-court procedure. 3. We may disclose personal data relevant to assessing you to our fund management partners as outlined in our Investment Memorandum, for the purpose of assessing your suitability as an investor, for the purpose of delivering our service, and for general Anti-Money Laundering purposes or other steps required for us to be in compliance with FCA or other government or regulatory requirements. 4. Financial transactions taking place on our website and services may be handled by our payment services providers. We will share transaction data with our payment services providers only to the extent necessary for the purposes of processing your payments or other investment transactions, refunding such payments, and dealing with complaints and queries relating to such payments and refunds. 5. In addition to the specific disclosures of personal data set out in this Section 4, we may disclose your personal data where such disclosure is necessary for compliance with a legal obligation to which we are subject, or in order to protect your vital interests or the vital interests of another natural person. We may also disclose your personal data where such disclosure is necessary for the establishment, exercise or defence of legal claims, whether in court proceedings or in an administrative or out-of-court procedure. **International transfers of your personal data** 1. In this Section 5, we provide information about the circumstances in which your personal data may be transferred to countries outside the European Economic Area (EEA). 2. Some of our service providers process personal data outside the UK and EEA, principally in the United States: for example, providers of cloud infrastructure, AI-assisted analysis and our customer relationship management system. Where personal data is transferred outside the UK or EEA, we ensure appropriate safeguards are in place, such as the UK–US Data Bridge, the EU–US Data Privacy Framework or standard contractual clauses. **Retaining and deleting personal data** 1. This Section 6 sets out our data retention policies and procedures, which are designed to help ensure that we comply with our legal obligations in relation to the retention and deletion of personal data. 2. Personal data that we process for any purpose or purposes shall not be kept for longer than is necessary for that purpose or those purposes. 3. We retain personal data for as long as it remains relevant to our investment activity. Records of the companies and people we have engaged with, including who has approached us for funding and how we assessed it, are a core part of how an investment firm operates, and we retain them in line with our legitimate interests. Account data is retained while your account stays open and for up to five years after it closes. You may ask us to delete your personal data at any time (see the section on your rights). 4. Notwithstanding the other provisions of this Section 6, we may retain your personal data where such retention is necessary for compliance with a legal obligation to which we are subject, or in order to protect your vital interests or the vital interests of another natural person. **Amendments** 1. We may update this policy from time to time by publishing a new version on our website. 2. You should check this page occasionally to ensure you are happy with any changes to this policy. **Your rights** 1. In this Section, we have summarised the rights that you have under data protection law. Some of the rights are complex, and not all of the details have been included in our summaries. Accordingly, you should read the relevant laws and guidance from the regulatory authorities for a full explanation of these rights. 2. Your principal rights under data protection law are: the right to access; the right to rectification; the right to erasure; the right to restrict processing; the right to object to processing; the right to data portability; the right to complain to a supervisory authority; and the right to withdraw consent. 3. You have the right to confirmation as to whether or not we process your personal data and, where we do, access to the personal data, together with certain additional information. That additional information includes details of the purposes of the processing, the categories of personal data concerned and the recipients of the personal data. Providing the rights and freedoms of others are not affected, we will supply to you a copy of your personal data. 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The checking stages found problems faster than the building stages could clear them, every fix triggering a fresh review, which triggered the factory to build more code, until it fanned out into hundreds of open issues all competing with each other. We had built something superb at finding work and something that just couldn't quite finish it. Nothing was wrong with the AI models. They did precisely what we asked. What was missing was the experience of what happens when you point them at a live system at full speed. There is no good way to acquire that except by running it and watching it break. Andrej Karpathy, who helped found OpenAI and later ran artificial intelligence at Tesla, wrote in December that he does not think the industry has realised anywhere near ten per cent of the potential of these models even at their present capability. Not the next generation. I think he is right. And I think a seized-up software pipeline is what his ten per cent looks like from the inside. ## Four announcements in nine weeks Something odd happened between the fourth of May and the second of July. Anthropic set up an enterprise AI services company with Blackstone, Hellman & Friedman and Goldman Sachs. OpenAI created a majority-owned AI services company with more than four billion dollars of initial investment behind it. AWS committed a billion dollars to embedding their own AI services engineers inside customer teams. Microsoft committed two and a half billion and six thousand people to the same idea, and went out of its way to say the effort goes beyond what the industry has been calling forward deployed engineering. Then on the thirteenth of August, Anthropic advertised for its first forward deployed engineering manager, in London. The advert says so itself: "As the first FDE Manager, you'll own the team that sits at the frontier of enterprise AI deployment." Four of the most capable and best capitalised companies on earth, each selling a product that is so smart it's practically supposed to implement itself, deciding at the same moment that they need their own people sitting in their customers' offices. That is a tell. ## What it tells you For thirty years, venture investors knew what good looked like. Intellectual property heavy, people light. Services were low margin and awkward to scale, and the best technology was whatever you could package with the least labour wrapped around it. I have sat in plenty of rooms where "it's a services business" ended the conversation. So when the frontier labs all move the other way at once, the interesting thing is what they can see that the rest of the industry has been slower to admit. What they can see is that the constraint has moved off the model and onto everything around it. Two economists described the mechanism in 1990, long before any of this. Wesley Cohen and Daniel Levinthal called it absorptive capacity: a firm's ability to recognise the value of new external information, assimilate it and apply it commercially, an ability built out of everything the firm has already taken up. Their warning was aimed at fast-moving fields. A firm that stops investing in that capacity, they wrote, "may never assimilate and exploit new information in that field, regardless of the value of that information." So when you have fallen behind, or when the field is moving too quickly for you to keep up internally, you can rent somebody else's expertise. And that is the business all four of them entered this year. The work is large because the AI revolution has unleashed unbelievable capabilities at break-neck speed, creating a big gap between what we could potentially do and what we actually know how to do. And so, we are at a stage where everybody is figuring out how to create value with AI by trying it, watching it fail in a way nobody predicted, and carrying that into the next attempt. Which is our pipeline, and Karpathy's ten per cent, and the reason four companies with paradigm-shifting technology have just set up their own consultancy businesses. ## Two companies already living there Palantir has been doing this for twenty years and its filings are unembarrassed about it: "We embed directly with customers across numerous industries, tackling complex challenges while continuously enhancing our platforms' capabilities." Being in the room is how the product gets good. In the second quarter its revenue grew 93 per cent year on year to 1.9 billion dollars, while its headcount fell from 4,429 to 4,401 over the preceding six months, at a gross margin of 84.7 per cent. It seems Palantir has figured out how to scale a services business without growing headcount. That's how you get to be valued at a Price/Earnings ratio of >150. Mistral is arriving at the same destination from the other direction. It set out to compete at the AI frontier, and it has been quietly repositioning ever since into something closer to Europe's enterprise AI implementation champion. It still trains its own models, and says it still has a frontier model in training. But the flagship it shipped in December has an architecture that resembles DeepSeek V3's almost parameter for parameter, down to the rank of the projections inside the attention block. Its own engineers describe the design as heavily inspired by DeepSeek while denying the model was built on top of it. Neither the launch post nor the model card mentions DeepSeek anywhere. Then in August it began selling inference on Z.ai's GLM-5.2, a Chinese open model, in its own documentation's words "served without Mistral modifications". Its chief technology officer explained that without a flicker of embarrassment. "It's a great model. Everyone loves it. It's open weight, so there was no good reason for us not to do it, really." If the real value sits in making models work properly inside an enterprise rather than in building the next frontier model, why not? Sovereignty purists will hate all of it. I think they are watching the wrong layer. ## But is there anything new in having IT consultants deploy tech? ![An IBM field engineer in the mainframe days of the 1970s](https://www.superseed.com/wp-content/uploads/2026/08/idea5_1972_engineer-scaled.jpg) In some ways, there is nothing new under the sun with the Forward Deployed Engineers. IBM sent engineers out to install the mainframe in the 1960s and 1970s because nobody else could. Packaged software then standardised enough of the problem that engineers gave way to consultants who configured, and implementing SAP is scripting and configuration. An industry worth hundreds of billions grew up doing exactly that. On that reading, what happened between May and July is the pendulum swinging back, and only because the technology is three and a half years old. Give it time. The tooling matures, the patterns harden, the work turns into configuration again, and today's deployment arms settle into a normal mid-margin business. I think that is right. It also does not tell you what to do now. The word carrying that whole argument is "eventually". Configuration takes over when the pace of technology change slows down, and AI is still accelerating. Every few months these models do something they could not do before, and each jump opens work nobody had thought to scope, inside companies that had only just finished absorbing the last one. The world cannot take up capability at the rate it is being produced. So services matter a lot, right now. ## Who ends up with the understanding If the frontier labs have concluded that realising the value is just as hard as producing the capability, that tells you where to look. It points at whoever understands one industry deeply and this technology deeply, at the same time. It's no longer possible to win by just having good models. You win by understanding the operation and the technology at once, so you can provide customers with an end-to-end solution that actually works. You don't wrap services around your technology because you are trying to become a consultancy. You do it to sell a working result, and getting the customer there is part of what you are selling. You can buy that result. A consultancy will come in, build the thing and leave you with something that works. What you cannot buy is the capability to repeat that yourself, because it stays with whoever did the work. That is Cohen and Levinthal's real point. The ability to take up the next new thing is built out of everything you took up before, so if somebody else did the absorbing, they got the capability and you got the deliverable. Which is exactly why the frontier labs are doing this themselves rather than leaving it to the integrators. They are not being generous. Every deployment teaches them something about their own technology that no amount of internal testing would surface, and that learning compounds in them. A tenth of what these models can already do is in use. The other nine tenths is waiting for somebody to sit inside a business and work out what it means there, and to find that out the way we found out about our pipeline, by running it until it breaks. The understanding goes to whoever does that. --- # Humanoids - The Newton or the iPhone? **Source:** https://www.superseed.com/journal/newton-or-iphone/ **Published:** 2026-07-31 **Author:** Mads Jensen ![C-3PO and Apple Newton ](https://www.superseed.com/wp-content/uploads/2026/07/hero_plinths_droid_newton_v4-scaled.jpg) ***What does "AI" look like? *** We were shown the answer as children. A machine that walks into a room, works out what is going on, and talks to you about it. C-3PO shuffles through the Star Wars films translating and fretting, and for a generation that image quietly did the work of a definition. Artificial intelligence was a metal person. Then *real* AI arrived, and it was text. Nobody's childhood robot was a chat window. But in the real world, thinking came first, disembodied, and it was the body that turned out to be the hard part. This is the wrong way round from every story we grew up on. I also think it explains a lot of what is going on right now with the money in robotics. ## The most impressive robot demonstration of the year was also the most misleading. In February, Unitree took the stage of the Chinese Spring Festival Gala (for the third year running). In front of one of the largest live audiences on Earth, its G1 and H2 humanoids did kung fu. Its own account is worth quoting: the world's first continuous freestyle table-vaulting parkour, the first launched aerial flip above three metres, the first continuous single-leg flips, a two-step wall-assisted backflip, and an Airflare grand spin of seven and a half rotations. Dozens of robots moving in formation at four metres a second. Wow. Yes, it is extraordinary. And anybody who has watched engineers fight dynamic balance knows what a technical feat this is. Three years ago none of it existed. Now read the list again. Every world first on it is about motion. Balance, flips, spins, formation speed. Nothing about picking up an object the robot has not seen before, nothing about finishing a task nobody rehearsed, nothing about working beside a person who might move. Unitree published a triumphant account of its year, and every triumph in it belongs to the half of the problem that photographs well. ## We built the graduate before the toddler. In 1988, in *Mind Children*, the roboticist Hans Moravec wrote the sentence that has governed this field ever since: "it is comparatively easy to make computers exhibit adult-level performance in solving problems such as intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception or mobility." Large language models did not refute Moravec's paradox. They are the largest confirmation of it ever run. We have machines that will argue Kant with you and machines that cannot reliably pick up a wine glass, and we got them in that order. Which is why the demonstrations keep improving, the money keeps going in, and the paid production work done by human-shaped machines stays close to nothing. Progress you can film and progress you can invoice have been drifting apart for three years. ## Musk's argument is better than his critics allow. The world is already built for humans: doors, stairs, tools, cab heights, the position of a handle, all of it sized around our bodies. Build a machine with roughly our proportions and it inherits that installed base for free, with no cumbersome retrofit or integration work. Because it inherits everything, its market is not a task or a sector. It is work in the grandest sense. Elon Musk expects humanoids to outsell the global car industry by units, and ultimately, he might be right. If this works, everything else in robotics is a rounding error. I was impressed by the vision of it. I still am. But for the short term, I also think it's wrong. ## The body is a shim, and you pay for it twice. A shim, in engineering, is the thin piece you push between two parts that do not quite fit. It bridges an incompatibility and does no work of its own. And in some ways, a humanoid robot is a shim between AI and a world built for people. The first invoice is safety; Agility Robotics, the leading Western humanoid company, is going public this year through Churchill Capital at a $2.5bn pre-money valuation. Its filing contains the most honest sentence written about this industry all year: "Today, humanoid deployment requires robots and people to operate in segregated environments." Digit has logged more than 65,000 hours across nine customer sites. Real work, all of it done in rooms the people have been cleared out of. Agility's answer is cooperative safety, which is a feature of Digit v5, which has not shipped. The company's own paper on it is called "The Road To Cooperatively Safe Humanoids". (note the preposition). ![A humanoid robot working inside a fenced safety cell while two workers continue at a bench outside the barrier](https://www.superseed.com/wp-content/uploads/2026/07/the_cage_v1-scaled.jpg) As my good man Dan Bowyer put it recently: you would not want one looking after your grandmother and falling on top of her. Get a language model wrong and you delete the paragraph. Get sixty-odd kilos of actuator wrong next to a person and somebody goes to hospital. 98% of the way there is not nearly there. It is a catastrophe with good averages. Which is why industrial robots have always arrived with cages, marked lanes and separated shifts. We made them safe by changing the room, and changing the room is cheap and paid for *once*. There is also a second and less discussed invoice to be paid. Human shape means human strength. Agility publishes Digit's carrying capacity at 35 pounds. 35 pounds is hand luggage. And this is not nearly enough in many of the places where we want to automate things. On a building site, the things that need moving start north of 100 kilos. Humanoids don't come close to lifting that. Every argument about humanoids assumes the constraint is intelligence, and that a better model unlocks the market. Almost nobody asks why you would buy a machine that tops out at what a person can carry, for the jobs whose whole point is to exceed what a person can carry. ## Granny is the target market, not the edge case. Which points somewhere more useful than "humanoids are early". Split the world into habitats and non-habitats. Habitats are the places we keep at human scale because we live in them: homes, streets, the inside of a shop. Non-habitats are shaped by production rather than by people: factories, warehouses, freight yards, building sites. A freight yard has no sentimental attachment to being walkable. The humanoid's one real advantage is compatibility with a world that has to stay human-shaped. In a habitat, that is worth paying for. In a non-habitat it is worth nothing, because you can move the walls. So look at where the industry is pointing. Warehouses, factories, logistics, every one of them a non-habitat, where the form factor's only advantage does not apply and both of its bills arrive in full. The one environment where a humanoid genuinely is the right machine is the family home, which carries the highest safety bar of anywhere on the list and the longest timeline by a decade. Dan's grandmother is not a joke about an edge case. She is the target market, and she is the hardest customer in the world. ## Put the brain in the forklift. None of which makes me bearish on robotics. It makes me bullish on a different kind of robot. Physical AI is the wide category and the thesis we have backed for years, intelligence moving into machines and physical operations. Embodied AI is one component inside it, and humanoids are one form factor inside that. Doubting the third is not doubting the first. Customers, in my experience, have no view on form factor whatsoever. They have a problem and a budget. A great deal of industrial work is moving things from one place to another, and if I want my forklift to drive itself, I am not going to buy a mechanical man to sit in the seat and turn the wheel. I am just going to put the brain directly into the forklift. ![An autonomous fork truck with no seat, no steering wheel and no cab, carrying a pallet down a warehouse aisle](https://www.superseed.com/wp-content/uploads/2026/07/forklift_no_cab_v3-scaled.jpg) And this is roughly what the companies taking orders are doing. In June a Barcelona company called THEKER raised $85m, led by CRV with Samsung and LVMH joining existing backers including Inditex and Mercadona. It builds generalist factory robots that get retrained across tasks rather than reprogrammed for each one, and the company says they are already working inside live Inditex production facilities. Two of its customers sit on its cap table, which tells you more than the round size does. ## Tesla is running both bets, and only one of them has been proved possible. Tesla is making this argument both ways at once. Cybercab entered production this quarter, described in the filing as "our purpose-built autonomous EV designed to be the workhorse of our Robotaxi fleet". Purpose-built: they took the human shim out of the car, exactly as the autonomy teams took out the steering wheel. Optimus is the other bet, and the Fremont line is still going in. The filing notes that the first units off it "will be used in our Optimus Academy for training data collection and further functionality development". An academy. For the robots. Before a single customer takes delivery. At this point, neither is proven. But somebody else has already proven the robotaxi: Waymo has driven more than 200 million rider-only miles, which settles whether a car can drive itself for money. This leaves Tesla with an execution problem, and execution is what Tesla is for. Which is why I am not bearish on Tesla. Robotaxi is the more interesting half of the company, and my guess is that Tesla finds something useful for Optimus inside its own factories while the technology matures enough to be ready for our homes. ## Why the money keeps arriving anyway Everybody in venture can do this arithmetic, so why is the capital going the other way? Partly C-3PO. Humanoids have been our picture of artificial intelligence for fifty years and they photograph beautifully. Put one on a stage and it leads the evening news. "We put the intelligence into the forklift" is less evocative, however good the business underneath it. So, are we at the Newton or the iPhone moment for humanoid robots? My bet is that humanoid robots will not ship at commercial scale this year, and they will not next year either. Beyond that I have instinct rather than analysis, and I would rather label it than dress it up. I find it hard to picture one in my house inside five years, and I say that as a lifelong science fiction obsessive who spends most of his working day with AI. The person most primed to want one is telling you it does not feel close. One last thing about C-3PO. He is a protocol droid. His job in those films is language: translation, etiquette, six million forms of communication. So we did build him. We built the part of him that talks, we built it first, and we built it at a speed that startled the people building it, because talking turned out to be the easy part. The kung fu, it turns out, was the easy part too. Everything that is actually hard, and everything that will actually be worth owning, sits in the gap between a robot that can spin seven and a half times in the air and one you would trust alone with your grandmother. --- # When the AI Miracle Becomes a Utility **Source:** https://www.superseed.com/journal/when-the-ai-miracle-becomes-a-utility/ **Published:** 2026-06-28 **Author:** Mads Jensen *This is not investment advice. Always do your own research and speak to your adviser before making investment decisions.* Eighteen months ago, the case against investing in AI was a list of good questions. Is any of this real? If so, where is the revenue? When does the capex spending pay for itself? They were fair questions, and for a while the honest answer to each was: we will see. We have seen. This year the list was answered, line by line, in the bulls’ favour. The technology is real. We run almost all of SuperSeed on it now, and it has changed how we work more than anything in twenty-five years. The revenue arrived: Anthropic has gone from around $1bn to roughly $45bn of run-rate in eighteen months, the steepest revenue line in the history of software. The first hard returns showed up in the hyperscalers’ own numbers, where cloud margins widened as the AI backlog began to convert. And last week the most cyclical corner of the whole supply chain, memory, gave its proof: Micron printed the best quarter in its history. The market took one look at the answered list and sold. ![Anthropic’s revenue run-rate: from about $1bn to roughly $45bn in eighteen months.](https://www.superseed.com/wp-content/uploads/2026/06/anthropic-run-rate-revised.png) Anthropic’s revenue run-rate: from about $1bn to roughly $45bn in eighteen months. Micron’s result was far better than what had been expected. Revenue of around $41bn, more than four times a year earlier and comfortably ahead of forecasts. The fattest gross margin the company has ever reported, near 85%. An order book of roughly $100bn in contracted agreements stretching into 2027 and 2028, with guidance raised on top. By any measure a blowout. The stock had set an all-time high on the Monday, fallen around 13% on the Tuesday as the chip names sold off, reported its records after Wednesday’s close, jumped on the news, and then handed it all back. By Friday it was worth less than it had been on Monday. The best earnings in the company’s history could not sustain (let alone boost) the share price. ![Micron’s record week, and the share price the market would not lift.](https://www.superseed.com/wp-content/uploads/2026/06/micron-week.png) Micron’s record week, and the share price the market would not lift. I don’t think that is noise. A share price is a claim on the future, not a receipt for the past. A boom is not most dangerous when its questions go unanswered. It is most dangerous when they have all been answered, and only one is left. Answer “is it real,” and that question is closed for good. Answer “where is the revenue,” and that one closes too. Each answer is one fewer thing a bull can look forward to. Once the questions you can settle are settled, only the one you cannot is left: not whether the boom ends (since booms always do) but when. I wrote last year about how the AI boom ends: negative margins all the way down the stack, and frontier labs that would have to become application companies or be commoditised. I stand by it. It is happening now. Which is why the question has moved on, from whether the boom is real to when it cools. ## It comes down to the price of a thought To see when, you have to be precise about what is actually being bought and sold. Not chips. Not data centres. Not even “AI.” The product of this entire industry is the token: one small unit of machine-generated thought. Everything upstream, the models, the GPUs, the memory, the power, exists to manufacture tokens. Nvidia sells the machinery. Micron sells a part of the machinery. The labs run the factory. The price the whole edifice rests on is the price of a token. So “when does the boom cool” has an exact form. It cools when the cost of making a token falls faster than the world finds new uses for tokens. When supply outruns demand. That is all a glut is. ![The two curves: demand for tokens against the cost of making one. The boom cools where they cross.](https://www.superseed.com/wp-content/uploads/2026/06/token-supply-demand.png) The two curves: demand for tokens against the cost of making one. The boom cools where they cross. Both lines are moving rapidly, which is why it is so hard to call the timing. Demand for tokens is climbing exponentially. Every answered bull question is itself demand showing up. We use tokens at SuperSeed today for work we would not have dreamed of handing to software two years ago, and our own consumption has risen many times over even as the price per token has fallen. But supply is also climbing rapidly, driven by two engines at once. The first is better models: each generation does the same work with fewer tokens, or better work with the same. The second is better hardware: each new generation of Nvidia silicon makes far more tokens per chip, and per watt, than the last. Combine those two curves and the supply of cheap intelligence compounds faster than either one alone. Last year I followed a dollar up the stack. This year, follow a token down the curve. A unit of frontier-quality output that cost a certain amount in 2024 costs a small fraction of that today. A majority of the tokens now flowing through OpenRouter, the largest neutral marketplace for them, come from open-weight models, most of them Chinese, at a fraction of the Western price. One of the most-watched coding tools in the world, Cursor, turned out to be running a Chinese open model, Moonshot’s Kimi, underneath. The supply blade is dropping through the floor. The bull answer to all this is “Jevons Paradox”: make a thing cheaper and people use so much more of it that total spending rises. That is true, and it is why revenue keeps climbing. But Jevons cuts both ways. While demand is being pulled up by cheaper and better tokens, supply is also being pushed up at a record rate. So demand can keep booming and we will still get to a place where we eventually overproduce: more tokens sold, less earned on each, far more capital spent to make them. There is a serious case on the other side, and it deserves its strongest form. It says the glut never arrives, because demand for intelligence is unlike demand for railways or phones. You need only one phone, and you can sit on a train for only so many hours. But intelligence, once it runs on its own, manufactures its own demand, so the line may have no ceiling. On this view the constraint is supply: the handful of firms that hold the lithography, the packaging, the memory and the power. The old boom and bust in chips is over, the argument runs, so own the bottlenecks before the market stops mistaking them for cyclicals. I find the first half genuinely persuasive. Demand for intelligence may be close to unbounded, and the supply chain really is a chokehold. But unbounded demand is not constant value, and that is the quiet substitution the argument makes. The first token that does something new is worth a fortune; the ten-thousandth that does the same thing is worth almost nothing. Demand can climb forever while the value of the next unit falls, and it is that second number that sets the price. So grant the infinite demand and the conclusion still holds: the boom cools not when we stop wanting intelligence, but when our ability to make the next token outruns the value of using it. The strong form of the other side, dressed up as the death of cyclicality, is in the end a claim that supply and demand no longer apply. If the facts showed that, I would change my mind, and the futurologist in me would be glad to. I do not think they show it yet. I am not ready to declare the laws of economics over. ## Three ways it cools So how does this resolve? Three ways, and they are not equally kind to the people who built the factory. **The Glut.** Supply simply outruns demand. Models and chips keep improving on schedule, the price of a token keeps falling, and at some point it falls faster than even exponential demand can absorb. Tokens become almost too cheap to meter. Wonderful for anyone who uses intelligence, which is now everyone, and painful for anyone whose valuation assumed the scarcity was permanent. The squeeze lands first on the model makers, whose product is commoditising under them. That is exactly why Anthropic and OpenAI are racing into applications. The model is becoming a commodity; the product wrapped around it is where the margin survives. **The Wall.** Before the glut arrives, supply hits a physical limit. Not chips, power. The binding constraint in 2026 is no longer the fab; it is the substation, the transformer with a multi-year lead time, the gigawatt data centre that has nowhere to plug in. Most of the capacity announced for this year is not yet under construction. This is the real case for putting data centres in orbit, and most of the bull argument for underwriting SpaceX: if there is not enough power on Earth, you go to where the sunlight is free and never sets. But even if space compute works, it does not save anyone from the eventual glut. It just builds a bigger factory and brings the glut forward, only a few years later than what would have happened on Earth. And here is the counterintuitive part, which matters most for the names that look safest. A wall does not rescue the chip and memory makers. A capped build means fewer chips bought, not scarcer chips sold at a permanent premium. A transformer shortage is not a Micron moat. It just moves the chokepoint from the silicon to the grid, and hands the rent to whoever owns the power. **The Jevons Surprise.** Demand outruns supply anyway. Agents, robotics, whole categories of work we have not yet thought to automate, eat tokens faster than the factory can make them cheaper. The boom rolls on for years. This is the outcome I am personally most tempted by, because demand has surprised to the upside at every turn. But Jevons cuts both ways even here. A demand surprise keeps the revenue line climbing; it does not stop the cost of a token falling. The world gets vastly more intelligence, and the people manufacturing it keep less of the value in each unit they sell. The boom continues. The economics still narrow. ## My bet My bet? Certainly not a collapse in demand. I am about as bullish on what AI can do as it is possible to be, and nothing here is a bet against the technology. Anthropic’s revenue will keep climbing. So will the hyperscalers’. Micron’s next few quarters will probably be strong. So why did the best quarter in its history leave the stock lower? Because a share price is a claim on the future, and the future the market has started to price is not this quarter. It is the one where the near-vertical growth of the last two years flattens into something ordinary. That quarter is possibly still years away. It does not matter. When you value a company like Micron, you are always pricing the slowdown you can see coming, not the boom you are living in. The market has simply started to price in the inevitability. What I cannot give you is the date. We know how this ends: it ends when the cost of making a token declines faster than our appetite for more tokens. I lean towards that appetite surprising us for a while yet, because of how transformative AI is. But the market does not need the far side to arrive before it starts pricing it. It only needs to see it coming. The bears had a list, and every question on it has been answered. That should have been the all-clear. Instead it was the starting gun, because a settled question stops holding a market up. Micron had the best week of its life, and the market looked straight through it to the other side. None of this is AI failing. It is a market that has started wondering what AI is worth once the miracle becomes a utility. That quarter is probably still years away. But stock market prices never wait that long. --- # No, Anthropic isn’t going to replace all other tech companies **Source:** https://www.superseed.com/journal/no-anthropic-isnt-going-to-replace-all-other-tech-companies/ **Published:** 2026-05-30 **Author:** Mads Jensen Anthropic is the darling of the tech world right now. We are also massive fans. Sonnet/Opus have been our house models for years, and we use Claude Code, Claude.ai, and Anthropic’s other tools across almost every part of our business. Anthropic’s popularity has led some people to believe that they and the other frontier labs will end up replacing every other tech company. It is obvious to us that they will not, and here is why. The case for the labs eating software is not silly. Old software was code that encoded the workflows of a business. New AI does the same job through skills, plugins, and agentic scaffolding. Either way, making agentic software is essentially the work of encoding a workflow to get a job done. If a single platform company ships the model, the skill format, the marketplace, and the orchestration, the application layer becomes a thin wrapper the lab eventually absorbs. It is a serious argument, and people we respect make it. We think it is wrong, because it underestimates what it takes to make a model useful for a specific job. ## The mustang and the harness Tomasz Tunguz (founder of Theory Ventures) recently shared an insightful analysis of why the labs-eat-the-apps case misses the point. In his [essay on harnessing AI](https://tomtunguz.com/harnessing-ai), he put it like this: the model is a powerful, unpredictable mustang. Harnessing the power means domestication. The harness is what turns wild capability into a specific repeatable outcome. Tunguz counts seven distinct components, and the model is only one of them. The application has to feed the model the right context (the business’s own data, exceptions, history), wire in tools with permissions on anything sensitive, run an orchestration loop, persist state so a long task survives a crash, sandbox the model from what it should not touch, give a human the visibility to step in, and manage the cost of every call. Seven engineering problems, every one tailored to the specific job. I run a huge part of SuperSeed in Claude Code. Anthropic built it as a brilliant coding harness for the work they understand best, and it is. I am also using it to manage a company, which is not what it was built for. It works, but only because I have spent months building the harness that business management needs: structures, rules, policies, files of prior work. The model is the same. Claude Code is a harness is built for coding. And a tonne of other logic has to be built on top to make the tool truly useful for my use case. That is the small version of the bigger picture: every industry that deploys AI will need a harness built for it, by someone who understands the business. ### The number with no ceiling All this goes to show why applications (all the stuff that sits around the model) still is real work. But is that work going to grow or shrink as models get smarter? Siddharth Ramakrishnan from ScaleVP recently shared [a mental model](https://x.com/siddharthvader_/status/2060023191394042342) for why that work keeps growing as the model improves, rather than shrinking toward nothing. A good application company does not just wrap a model; it targets the improvement of a business metric: it owns a number the customer cannot stop caring about, and turns raw model capability into a measurable improvement on it. Think faster resolution, higher conversion, lower cost per claim. ROI in business parlance.  So when figuring out which application companies thrive and which will disappear, all we have to do is look at the type of metric the application improves. Some jobs are simple to describe and have a performance bar where “good enough” really is good enough: summarise this meeting, turn this thread into a ticket. This sort of thing can be rebuilt in a weekend. And if it can, the model absorbs it, and the thing becomes a feature, not a company. On the other hand are metrics with no ceiling. These behave differently. Think return on ad spend, win-rate on sales opportunities, number of patients cured successfully. These metrics have no upper bounds. There is no “good enough”. We will always be looking for more.  And as long as application companies operate in areas with room for improved business outcomes, they will always offer something the models labs themselves cannot do alone. That’s where they thrive. The same force that kills the thin wrapper strengthens the company built around a metric, and every better model lets it push the number further. The model improves, but the harness and the application improve the model further, which is why customers will keep buying them.  ## Anthropic won’t win in all verticals Dario Amodei has sequenced Anthropic’s product strategy with real discipline. In eighteen months they have shipped Claude Code, Claude Code for Work, Claude Design under the new Anthropic Labs banner, a verified legal plugin, the Skills marketplace, the MCP protocol, a multi-chip compute strategy, and, on 4 May 2026, a new enterprise services company. That is a lot of surface area, and unlike OpenAI, which is running in every direction at once, every piece of it fits a plan. If any lab were going to absorb the application layer, it would be this one. It still will not be enough. The reason is structural, and it is visible in every trillion-dollar tech company in history. ### Every substrate has tried this In 1972, five engineers left IBM in Germany to build packaged enterprise software IBM did not want to pursue, and founded SAP. IBM was the dominant computer company on the planet, with the hardware, the operating systems, the customer relationships, and the brand; SAP started with five people. Half a century later, SAP is one of the largest software companies in the world. IBM let the application layer go because it did not think it would amount to much, then spent the late 1980s and early 1990s trying to take it back. It built an applications business at real scale, but it was now competing with the independent software vendors whose products were the reason customers bought IBM in the first place. The ecosystem turned against it. When Lou Gerstner took over in 1993, one of his first moves was to shut the applications business down and restore the partnerships. The lesson, as he later wrote, was that a substrate that competes with its own ecosystem destroys the thing its value depends on. Microsoft is the most instructive case, because Microsoft actually won one of these. Office took the productivity market from Lotus 1–2–3 and WordPerfect, the incumbents of the day, because Windows was already on every desk and Office rode on top of it. That is the exception that defines the rule. A substrate captures the layer above only where it confers a structural, compounding advantage in that specific market. Where it does not, it loses, and Microsoft has lost plenty: Dynamics, with the operating system, the cloud, the email client and the developer ecosystem all behind it, still sits at a tenth of Salesforce’s revenue, and Bing remains a rounding error next to Google after twenty-five years. Google and Amazon tell the same story: a few captured adjacencies, a graveyard of everything else. ### The two forces There are two reasons this repeats. The first is internal. Running a substrate business and a deep vertical business inside one company creates a conflict for management attention, capital, and prioritisation. The substrate side wins, because it is what defines the company; the verticals get less than they would as standalone businesses and lose to focused competitors. The market calls this the conglomerate discount. The second is external. A substrate’s value depends on the ecosystem of applications built on top of it, and a substrate that competes with its own ecosystem destroys the thing that makes it valuable. Gerstner learned this the hard way. Anthropic appears to have learned it by studying history. The Skills marketplace, the MCP protocol, the plugin architecture, and the decision to put the new services company outside the core business are all moves of a company that wants the application layer above it to thrive. The services company is the tell: Anthropic did not build a mid-market services arm in-house; it spun up a separate entity backed by Blackstone, Goldman, and a roster of other private-equity and banking giants. That is the strategy of a business that wants a vibrant application eco-system, rather than to dominate everything as a monolith. For the labs, that compounding advantage lives in exactly one place, and Siddharth names it precisely: the verticals where customer use loops back into the model’s own improvement. Every hour Anthropic spends making Claude Code better also makes Anthropic better at building Anthropic; the work compounds inside the lab. Resolving a bank’s billing tickets does not. So the labs go absurdly deep in coding, evals, agent infrastructure, and research workflows, and stay shallow everywhere else, not because they cannot go deep, but because the same engineers are worth more pointed at work that compounds. It is comparative advantage, the oldest argument in economics: the lab can be better at almost everything and should still do only the few things worth more to it than to anyone else. The early evidence fits. Coding is the vertical Anthropic has gone deepest in, and the one that produced the fastest-scaling software company anyone has measured. Cursor went from a $29bn valuation in November 2025 to a $60bn deal five months later. The labs building aggressively in coding did not crush it; it grew faster than any of them, and when a substrate giant finally moved to capture that value, SpaceX agreed to buy it for $60bn rather than out-build it. The substrate could not displace the application, so it paid for it. Anthropic wins where the work loops back into the model, coding, consumer chat, agent infrastructure, perhaps one or two more; everywhere else it is the supplier, and the application companies own the customer. That held at IBM, Microsoft, Google, and Amazon, and a smarter model does not change it. ## Physical AI will not be different The same dynamic is already playing out one layer up. The next substrate after the language model is the world model, which learns from sensor data and gives a robot or a vehicle a working internal picture of its environment. The leading world-model labs are not the language labs: Yann LeCun left Meta in 2025 to start AMI Labs on a single conviction, “Real intelligence does not start in language. It starts in the world”; Fei-Fei Li founded World Labs to build spatial intelligence; Physical Intelligence is building foundation models for general-purpose robots. Three of the most credentialed teams in AI, all betting that the next substrate is a model of the physical world rather than of text. They will be superb substrates. They will not be all of Physical AI. A world model is still just a model. It does not design the gripper that lifts a panel off the line, certify a surgical robot, write the safety case for an autonomous tractor, or take the call when a warehouse fleet stops at 3am. Every Physical AI vertical needs its own harness, built by people who understand the job, which is why the field fractures into specialists: Wayve builds its own world model for driving, and that depth is exactly why it will never also build humanoid manipulation or surgical robots. The world-model labs will supply some of these companies, the language labs others, and the application companies will own what the labs cannot, the customer, the regulatory standing, the accountability when something breaks. The same split, one layer up, against the same two forces. ## Where we put our money The labs have never been more powerful, and the application layer opportunity has never been bigger. Both things are true at once. Customers buy outcomes, not models, and outcomes are built by companies that know the customer better than the lab ever can. That does not change when the model gets smarter, and it does not change when the substrate gets bigger. The application layer is where the work is, and it is where we invest. So be a fan of Anthropic. We are. Just don’t mistake the engine for the car. --- # Why we invested in All3 **Source:** https://www.superseed.com/journal/why-we-invested-in-all3/ **Published:** 2026-04-30 **Author:** Mads Jensen https://youtu.be/aoFJgEnFeOA?si=KEGV4k0STcwg_nTy The largest opportunity in business right now is bringing AI into the physical world. AI has remade the software industry in just a few years. The same shift hasn't yet reached the places where most of the world's capital actually sits: factories, warehouses, fleets, and building sites. The companies that close that gap will be the largest businesses of the next decade. That is the thesis SuperSeed is built on. No sector shows the opportunity more sharply than construction. For fifty years, almost every major industry has compounded productivity through technology. Construction has gone the other way. US construction is roughly 40% less productive today than it was in 1970, while the rest of the economy has more than doubled output per worker. ![](https://www.superseed.com/wp-content/uploads/2026/04/construction_productivity_chart-1024x576.png) The reasons are familiar. Each project is bespoke. The supply chain is fragmented across thousands of small subcontractors, each with its own margin and its own incentive to optimise locally rather than globally. New technology arrives in pieces, and no actor on a building site has the authority to integrate it. I know this terrain personally. Seventeen years ago I co-founded Sefaira to automate the design of better buildings. Many of the ideas All3 is now executing on were ones we were chasing then. Now the technology is finally ready to bring the vision to life. The hard question is the team. When I first heard what Rodion Shishkov and Slava Bocharov were attempting at All3, the audacity of the ambition daunted me. I was sceptical anyone could pull it off. Then I met them. At Samokat they built the only profitable dark store delivery network in the world, while every well-funded competitor burned through their cash. The edge was their exceptional skill at automation and robotics inside the warehouses, applied with discipline. The same skill drives All3. If anyone can crack construction, they can. All3 takes residential construction from design through move-in as a single integrated operator: AI-driven design, robotic manufacturing of structural timber components, and an autonomous on-site assembly robot called Mantis. Rather than sell tools to incumbents, they replace the contracting layer entirely. Their first construction projects are now under way. Construction's productivity line has bent the wrong way for fifty years. We think Rodion and Slava are the team that bends it back. That's why we are excited to back them in their new investment round. --- # SuperSeed III: For the founders rebuilding Europe's real economy **Source:** https://www.superseed.com/journal/superseed-iii-for-the-founders-rebuilding-europes-real-economy/ **Published:** 2026-04-30 **Author:** Mads Jensen We have held first close on SuperSeed III at £40 million, with the British Business Bank as our anchor investor. A further £20 million is committed towards final close, taking us to £60 million on a target of £80 million. Dan and I started SuperSeed because we believed Europe could build great technology companies, and that the founders trying to do so deserved partners who had done the work themselves. Twenty-five-plus years of building, scaling and selling has only sharpened that conviction. Europe sits on more deep engineering and scientific talent than almost anywhere else in the world. Lately, other parts of the the world have sprinted past us. We need to catch up, fast, and will do that by supporting the founders who are rebuilding our economy. Fund III is a continuation of what we have been backing since Fund II, with a sharper emphasis: Physical AI applied to real-world economies. AI is moving from the screen into the physical world, and Europe has the industrial substrate to win that contest. We make things, grow things, build things, move things. That is where the productivity gains will land, and where the capital should follow. We will back two kinds of company in Fund III. First, the ones transforming today's industries (manufacturing, construction, logistics, agriculture, energy) by putting intelligence into the physical work that runs the economy. Second, the ones building Europe's frontier categories: space, quantum and fusion, where AI is the unlock that turns world-class research into commercial leadership. Both strands share the same logic: hard problems, real customers, durable moats. Both play to European strengths. We are doing this work with a team I am proud of: Dan Bowyer (Partner), Andrew Sherlock (Managing Director), Elena Klijn (Principal), Mia Grosen (Venture Partner), Jamie Giles (Senior Associate), Nick Sopuch (Senior Associate), Harriet Ball (Head of Talent), Pepe Lopez (Digital) and Serena Potts (Operations). Each of them backs founders the way we wanted to be backed when we were the ones building. Our first investment from Fund III is [All3](https://www.superseed.com/journal/why-we-invested-in-all3/), a London company rebuilding the construction industry from design to delivered building. Rodion Shishkov and Slava Bocharov, the founders behind Samokat (a $1.5bn exit in five years), are applying AI design, robotic factories and an autonomous on-site robot called Mantis to one of the world's most stubbornly inefficient industries. Construction is exactly the kind of sector where Physical AI changes the economics. [All3](https://www.superseed.com/journal/why-we-invested-in-all3/) is exactly the kind of company we built Fund III for. Europe will get its productivity and sovereignty back through Physical AI, and the founders bringing it to life. That is what SuperSeed III exists to back. If you are one of those founders, we want to talk to you. — Mads --- # What’s Left for Humans to Do? **Source:** https://www.superseed.com/journal/whats-left-for-humans-to-do/ **Published:** 2026-04-29 **Author:** Mads Jensen In 1997, a pianist at the University of Oregon sat down and played three short pieces, each composed in the style of Bach. One was genuine Bach. One was written by a music professor named Steve Larson, who had spent his career studying the composer. And one was written by a computer programme called EMI, built by the researcher David Cope to analyse musical structure and generate new compositions in existing styles. The audience was asked to identify which was which. They were confident. And they were wrong about everything. They picked the computer’s composition as real Bach. They picked Bach as the work of Larson. And they picked Larson, a man who had devoted decades to understanding exactly this music, as the computer. Everyone shifted down one slot by the machine. “That people could be duped by a computer programme,” Larson [told the New York Times](https://computerhistory.org/blog/algorithmic-music-david-cope-and-emi/), “was very disconcerting.” That was 1997, running on hardware less powerful than a modern dishwasher. Today’s models don’t imitate Bach. They understand why Bach works. So if a programme can fool an educated audience into thinking it is one of the greatest composers who ever lived, the rest of us might reasonably wonder: what exactly is left for us? ## The burning question I spend a fair amount of time at lunches, conferences, and dinners with family offices, investors, and business owners. The conversations cover markets, technology, geopolitics, the usual. But roughly fifteen minutes in, wherever we are, whoever is at the table, someone asks the question. Not always in these words, but always the same question: what are we all going to do? They don’t only mean jobs. They mean purpose, identity, what to tell their children. Every previous technology wave produced sceptics. The dotcom wave had them. Mobile had them. Cloud had them. This wave produces something different. Call it vertigo: the disorientation of people sophisticated enough to understand what they’re looking at and honest enough to admit they don’t know where it leads. The media, for its part, overestimates AI’s impact in the short run (blaming youth unemployment on automation when the actual culprits are minimum wage increases and employer tax hikes) and underestimates it in the long run (treating this as another technology cycle rather than the most significant shift in what human labour means since the industrial revolution). Both errors leave the real question unanswered. ## The two-hundred-year engine The question has a two-hundred-year-old answer. The answer has a condition attached. Both matter. The answer first. Sixty per cent of employment in 2018 was in occupations that [did not exist in 1940](https://academic.oup.com/qje/article/139/3/1399/7612745). Agriculture employed 40% of the American workforce in 1900; it employs 2% today. The workers who left the farms did not sit idle. They became teachers, nurses, software engineers, personal trainers, user experience designers, and a hundred other things nobody could have named when the tractors arrived. Every automation wave has created new categories of human desire, and those desires created new work. The pattern has held for two centuries. Now the condition. The engine runs on purchasing power. Without it, desire is yearning, and yearning doesn’t employ anyone. The Engels’ Pause, from roughly 1790 to 1840, saw productivity soar while working-class wages stagnated for fifty years. The engine did eventually work. “Eventually” took two generations. That is a policy challenge, not a technology problem. Societies have historically risen to it. The discomfort is in the word “eventually.” And so the honest question is not whether there will be new work. There always has been. The question is whether it comes fast enough, and whether the gains from AI circulate broadly enough to fuel the next wave of demand. Look around. The world is not finished. McKinsey estimates a [$106 trillion global infrastructure gap](https://www.weforum.org/stories/2019/04/infrastructure-gap-heres-how-to-solve-it/) by 2040: roads, bridges, power grids, schools, hospitals. Africa’s [Great Green Wall](https://www.unccd.int/our-work/ggwi) needs $33 billion to restore 100 million hectares of degraded land. We haven’t started on space in any serious commercial sense. The list of things worth doing is, for practical purposes, infinite. The constraint has never been imagination. It has been the capital, the organisation, and the will to do it. ## Three things that grow Which brings us to the question underneath the question. If AI handles the execution, what will humans actually contribute? Three things. And each grows as AI becomes more capable. **Meaning**: determining what matters. What is right, what is beautiful, what is worth pursuing, what story we are living inside. **Connection**: being there for each other. The human IS the product. When a hundred thousand people go to a stadium to watch Coldplay, or three hundred pack a small club where you can feel the bass in your chest, what they are paying for is the shared experience of being human together. The same principle runs through teaching, nursing, selling, coaching, managing, parenting: the presence of another person who chose to show up. **Commitment**: putting yourself on the line. Skin in the game. The surgeon whose career is at stake, the founder who bets their savings, the builder who guarantees the work. AI can be the agent. Only a human can be the principal, the one who bears personal, irreversible consequences when things go wrong. These are not three sectors of the economy or three job categories. They are three irreducible qualities of human activity, present in every role from the ward nurse to the chief executive, that AI cannot supply because they require a being that cares, that has lived, that will die. As AI takes over more of what can be executed, these three qualities absorb a growing share of what is valued. ## Determining what matters AI can optimise brilliantly within a defined objective. Give it a click-through rate to maximise and it will iterate faster than any human team. Karpathy’s [AutoResearch](https://github.com/yueqis/AutoResearch) automates the experimental loop at speeds no researcher can match. Anything that can be brute-forced towards a measurable target, a machine will handle better than we can. But objectives live inside objectives, all the way up. And somewhere at the top of that stack, someone has to decide what we are actually trying to achieve. Not which option to pick from a menu (machines handle that), but what game we’re playing in the first place. Frank Knight drew the line in 1921. Risk is quantifiable; uncertainty is not. “Profit arises out of the sheer brute fact that the results of human activity cannot be anticipated.” That is not just economic theory. It is a job description for anyone operating beyond the edge of available data. A [Harvard Business School study](https://www.hbs.edu/faculty/Pages/item.aspx?num=65159) ran a five-month trial giving 640 Kenyan entrepreneurs access to a GPT–4 business advisor. The overall effect on revenues and profits was zero. The entrepreneurs who were already performing well gained 10 to 15%. Those who were struggling did worse. The binding constraint was not the advice. It was the human who knew which advice to follow, which question to ask, which opportunity to ignore. This is meaning-making in its broadest sense. The priest interpreting scripture for a grieving family. The journalist deciding what events signify. The founder explaining why this company should exist. The parent teaching a child what matters. The voter choosing what kind of society to live in. These are normative acts that require a being who cares, who has lived, who will die. AI processes syntax; humans generate meaning. Luciano Floridi calls AI “agency without intelligence.” You can embed values in a training run, but someone still has to decide which values to embed. Even the engineers who build AI are making normative choices about what to optimise for. There are objectives inside objectives, all the way up. At the top, a person. ## Being there for each other Humans want other humans. That sentence sounds banal until you realise how much of the economy it explains. When a hundred thousand people fill a stadium to watch Coldplay, they are not there for the sound quality. They could listen at home, in higher fidelity, for free. They are there to sing alongside strangers, to feel the bass in their ribs, to be part of something that only works because everyone showed up. The same thing happens at a three-hundred-person club gig, a local football match, a dinner party. The human presence is what is being consumed. No recording, no stream, no hologram can substitute for the fact of being in the room together. This runs through work in the same way. The teacher who inspires a child to love mathematics does so because humans are inspired by other humans. The salesperson who builds trust over three years closes the deal because the client trusts them specifically. The nurse whose presence reassures a patient, even when a machine handles the diagnostics. The coach who pushes you because they know you and you know them. In each case, the human is not performing a function that could be replicated more efficiently. The human presence is the function. The philosopher Martin Buber called it the I-Thou relationship: an encounter that requires two subjects, not a subject and a tool. What people seek in connection is the reality of being understood by another mortal being who has their own concerns and chose to show up anyway. [AI companion apps have surged 700% since 2022](https://journals.sagepub.com/doi/10.1177/14614448251395192), and the results are instructive: moderate use [reduces loneliness about as effectively](https://academic.oup.com/jcr/advance-article/doi/10.1093/jcr/ucaf040/8173802) as talking to another person, but heavy daily use makes it worse. The more people try to automate connection, the more they demonstrate it requires a person. The economist William Baumol noticed in the 1960s that certain services never become more productive because the labour is the product. A string quartet cannot play the piece faster without changing what it is. Economists treated this as a disease. It is the answer. As every other cost approaches zero, irreducibly human services absorb a growing share of the economy. Healthcare is [18% of US GDP](https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data) and rising. Live music is growing at [6 to 9% annually](https://www.mordorintelligence.com/industry-reports/united-states-live-music-market) while the cost of generating recorded music collapses. NielsenIQ [tested AI-generated advertisements](https://nielseniq.com/global/en/news-center/2024/niq-research-uncovers-hidden-consumer-attitudes-toward-ai-generated-ads/) and found weaker memory activation across the board; audiences described them as “annoying, boring, confusing,” regardless of age or demographic. When the functional version gets cheap, the human version becomes premium. ## Putting yourself on the line In early 2024, Klarna made a dramatic bet. The Swedish payments company replaced much of its customer service operation with an AI assistant, [which handled two-thirds of all chats](https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/) within its first month. CEO Sebastian Siemiatkowski celebrated publicly: the AI was doing the work of 700 agents, resolution times had dropped, the savings were enormous. Klarna cut staff and pointed to the numbers. Then quality collapsed. Customer satisfaction scores fell. Klarna [quietly began rehiring humans](https://www.bloomberg.com/news/articles/2025-05-15/klarna-reverses-ai-only-strategy) and acknowledged it had “focused too much on efficiency and cost.” Amazon made a similar bet. It [mandated that 80% of internal code be written by its AI tool Kiro](https://www.cnbc.com/2026/03/10/amazon-plans-deep-dive-internal-meeting-address-ai-related-outages.html), cut thousands of engineering roles as part of a 30,000-person restructuring, then suffered a [13-hour AWS outage and a 6-hour retail collapse](https://www.theregister.com/2026/02/20/amazon_denies_kiro_agentic_ai_behind_outage/) costing an estimated 6.3 million orders. An internal memo reportedly referenced “GenAI-assisted changes” as a contributing factor. That bullet point was later deleted. In both cases, the missing element was the same. Someone on the hook. AI’s consequences are parameter updates: reversible, impersonal. Human consequences are careers, reputations, liberty. That gap does not close as AI improves. It widens. Michael Kremer’s O-Ring theory explains why: in complex systems, as most components become highly reliable, the remaining human steps concentrate all the risk. As AI handles 95% of a process, the 5% requiring human judgement becomes disproportionately valuable. The surgeon who uses a robotic arm still has their career on the line if something goes wrong. The plumber who sends a robot to fix the roof still guarantees the work; you still need someone to call when it leaks. The lawyer who uses AI to draft the brief still puts their name on the advice; their licence is at stake if it’s wrong. The founder who uses AI to build the product still bets their savings and reputation. Even as AI handles more of the execution, the human warrants the outcome. AI can be the agent. It cannot be the principal. ## The answer is in the question In that Oregon auditorium, the audience listened to three pieces of music and got every attribution wrong. They were searching for technical mastery and assumed the machine had it. They missed what made Bach human: not the notes, but the reason for playing them. We are making a version of the same error when we ask “what can AI do?” and let the list grow longer each month. The question is answerable, and the answer is genuinely impressive. But it is the wrong question. The better one is what we want to do next. There are $106 trillion of infrastructure to build. A planet that needs rewilding. Diseases that need curing. Children who need teaching by humans who inspire them. Communities that need tending. Businesses that need founding by people willing to bet their name on something new. AI gives us the most powerful tools any civilisation has held. The work was never going to run out. The question was always whether we would build the businesses, create the wealth, and organise ourselves to do it. I fear we could snatch defeat from the jaws of victory: waste this moment through poor policy and a failure of nerve. But when I look at the founders across the table, already building differently, already asking better questions, already using these tools to do things that would have taken ten people and two years just eighteen months ago, I suspect that once again, the builders will outrun the worriers. They always do. --- # Building the AI-Native Organisation **Source:** https://www.superseed.com/journal/building-the-ai-native-organisation/ **Published:** 2026-03-14 **Author:** Mads Jensen “Only the paranoid survive,” Andy Grove wrote, after reinventing Intel in 1985. He was right. But Intel still got disrupted. It passed on GPUs, watched NVIDIA build a $4.6 trillion company on the opportunity, and by 2025 was worth 2% of its former rival. The difference between Grove’s Intel and today? That disruption took thirty years. The founders sitting across from me know they might have two. This is the vertigo at the centre of every board meeting I sit in right now. A founder walks in having built something remarkable, knowing that the same tools she used to build it are available to every competitor, every incumbent, every twenty-two-year-old with a laptop and a subscription. AI is the most extraordinary capability any of us has ever had; the philosopher’s stone of productivity, available to everyone simultaneously. The excitement and the fear come from the same source, and anyone who tells you they’ve resolved that tension is lying to you or to themselves. The headline number is Anthropic’s revenue: $1bn to $20bn in less than fifteen months. The interesting number is underneath. Anthropic quadrupled its engineering team last year. It also saw productivity per engineer [triple](https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens). Four times the people, each producing three times as much. That is 12x the output capacity in twelve months, something few, if any, billion-dollar companies have ever managed. The temptation is to treat this as an adoption problem: buy licenses, run pilots. And if you run a big organisation, perhaps appoint a Head of AI. The interesting change is where the bottleneck sits. The cost of building software, generating campaigns, testing hypotheses: that has collapsed towards zero. The human bottlenecks haven’t moved. Which sectors to enter? Which prospects to meet face-to-face? Which of the ten things you could build this week is actually worth building? The bottleneck has shifted from production to judgement, and most organisations are still designed around the old one. The term “AI-native” gets used loosely, usually meaning “we use AI tools.” The organisations actually built for this era rest on four principles. **Prototyping culture**: everyone is empowered to build and test. **Shared context**: clear strategy and objectives that align effort, so speed produces signal rather than noise. **Platform discipline**: the shared infrastructure stays stable while experimentation runs freely above it. And **human empowerment**: every AI application exists to make humans more capable at what humans do best. Let’s unpack the four. ## Prototyping culture Everyone gets the tools: product managers, designers, analysts (not just engineers, who will find this obvious). Instead of debating what to build, you build it and look. The shift has to come from the top. One portfolio founder I work with made it compulsory for himself and his direct reports: at least ten hours a week of hands-on experimentation with AI tools. Despite the expected “no chance to fit into the schedule,” the leadership team got up to speed in weeks. More importantly, they developed the eagerness and the experience to spread the approach further. What works is the cascade, not the memo. Leaders who experiment identify the people already deeply engaged, two or three “self-educated” builders in every organisation, and turn them into coaches. These coaches sit alongside colleagues for a few hours a week: identifying opportunities, onboarding tools, building applications through to deployment. Two to three weeks to get someone going. The coached become coaches themselves. It cascades. Notice that documentation and training materials are conspicuously absent from this sequence. Sharing accumulated knowledge, the presentations and wikis and best-practice guides, produces low conversion and lower retention. The effects die out quickly. Only building together produces lasting change. This is why it’s called prototyping culture, not prototyping training. The energy in these teams is different. A product question that would previously trigger a week of analysis triggers a prototype by end of day. A competitive threat that used to provoke a strategy meeting provokes a build. The cost of testing drops so far that the default becomes “build it and let’s see.” ## Shared context Prototyping culture gives everyone the capability to build. Shared context tells them what to build towards. Without it, fifty empowered people prototype fifty different things by Tuesday, and the organisation has produced nothing but noise. This is the amplification problem. In a traditional organisation, misalignment costs weeks: someone builds the wrong thing slowly. In an AI-native organisation, misalignment costs days and produces volume. AI doesn’t distinguish between a well-directed prototype and an irrelevant one. It amplifies whatever direction it’s given, including no direction at all. Organisations without clear strategic context will find that AI simply scales their incoherence. The parallel with AI itself is exact. Give a language model bad context and you get slop. Give it precise context and you get signal. The same applies to humans with AI tools. An engineer with clear strategic context prototypes something useful. The same engineer without it prototypes something impressive and irrelevant. Multiply that across a team of twenty and the gap between the well-directed organisation and the poorly-directed one widens by an order of magnitude. In practice, this means the AI-native organisation needs its strategy, objectives, and priorities to be explicit, written down, and accessible to everyone who builds. The less time it takes someone to understand what the organisation is trying to achieve, the more valuable every prototype becomes. ## Platform discipline The more freedom you give at the application layer, the more stability you need underneath it. Prototyping culture means dozens of people building at speed. Platform discipline means they’re building on stable ground: shared databases, APIs, production systems, single sources of truth, all governed so that one team’s experiment doesn’t break another’s work. This sounds like common sense. A recent lesson from Amazon suggests otherwise. The company [mandated that 80% of code be written by its AI tool Kiro](https://www.cnbc.com/2026/03/10/amazon-plans-deep-dive-internal-meeting-address-ai-related-outages.html), cut thousands of engineering roles as part of a 30,000-person corporate restructuring, and then suffered a string of outages: a [13-hour AWS failure and a 6-hour retail collapse](https://www.theregister.com/2026/02/20/amazon_denies_kiro_agentic_ai_behind_outage/) that cost an estimated 6.3 million orders. Amazon denies the connection. An internal memo reportedly referenced “GenAI-assisted changes” as a contributing pattern; that bullet point was deleted before wider circulation. Draw your own conclusions. The principle scales down. A ten-person startup doesn’t have Amazon’s complexity, but it still needs to know which systems are shared and stable and which are free for experimentation. The database schema, the core API contracts, the deployment pipeline: these are the foundations. AI-generated applications sit on top. The discipline is knowing which layer you’re working on. ## Human empowerment Klarna’s AI assistant [handled two-thirds of customer service chats](https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/) within its first month; CEO Sebastian Siemiatkowski called it a triumph of efficiency. Then quality collapsed. Satisfaction scores dropped. Klarna quietly [rehired humans](https://www.bloomberg.com/news/articles/2025-05-15/klarna-reverses-ai-only-strategy) and admitted it had “focused too much on efficiency and cost.” Klarna had been optimising for removing people from the process. The entire opportunity was making people better at it. This is the principle: AI exists to empower humans, and every application should be measured by whether it makes them more capable. This principle has a hard edge. AI will automate genuine drudgery: data entry, research synthesis, first-pass drafting. Some roles will change beyond recognition. Some will disappear. Human empowerment is a design principle for the organisation, not a guarantee for every current job. The test is whether the people who remain are doing more valuable, more fulfilling work than before. In customer service, that means AI handles research and drafting while the human brings empathy and judgement to conversations that need it (the thing Klarna’s customers missed). In product design, it means generating twenty variations in an hour while the designer applies the taste that determines which one actually works. In enterprise sales, AI can research a prospect’s competitive landscape, regulatory environment, and technology stack in minutes; the human is freed to focus on relationships and sector knowledge, the things that close deals. Still, a question I suspect every founder wrestles with: what about the people whose strength is entirely human? I know relationship specialists who can attend an event and walk away with the name and contact detail of everybody relevant there. Forcing them to build automations is time stolen from what they do best. Human empowerment means the organisation combines technical capability in some people with relationship capability, taste, and judgement in others. The builder creates the tool. The relationship specialist uses it, or doesn’t, and focuses on the room. The org is designed around both. The same principle applies to the biggest decisions in the company. The prototyping culture generates ten directions before lunch. Shared context ensures they’re pointed at the same problem. Platform discipline ensures they don’t break anything on the way. The question that remains is which prototype deserves commitment: a real team, real resources, a real go-to-market plan. That requires exactly the kind of judgement no AI can supply. AI is always an agent. Only humans can be principals, and it is skin in the game that sharpens the mind. Board conversations shift from conviction (“I believe this market is moving here”) to evidence (“We built three versions; here’s what we learned”). The founder’s instinct still matters. Now it gets tested in hours, and the question becomes “We built it. Should we deploy to production.” The founder’s job was always about judgement. AI compresses the cycle between hypothesis and evidence, which means that judgement gets exercised more frequently and on better information. The tempo has changed. The job hasn’t. The companies getting this right are the ones where every employee has more leverage over their domain than they did a year ago. And the employees report higher job satisfaction, because more of their time goes to the work that requires their specific expertise, their taste, their relationships. Grove’s paranoia kept Intel alive for a generation. The tempo has changed. Thirty years of breathing room has compressed into two, maybe less. But two years with these tools is a different proposition than two years in any previous era. The founders who’ve understood this are already building differently. The tools are distributed. The context is clear. The prototyping reflex is there. The judgement stays human. We’ve seen what the good ones can build in six months. Two years is plenty. --- # March Briefing Room **Source:** https://www.superseed.com/journal/march-briefing-room/ **Published:** 2026-02-28 **Author:** Dan Bowyer In the UK Spring is starting to spring. Can you feel it? A rebirth. With that in mind, here all some key conversations from behind the scenes at SuperSeed towers. The Slack chats, IC musings, watercooler moments that have been grinding gears... ...the trends, the themes, the challenges, the opportunities - connecting the dots for founders, partners, LPs and GPs we work with, across the worlds of physical AI and UK & European sovereignty. ![](https://www.superseed.com/wp-content/uploads/2026/02/danb_Are_AI_Startups_The_New_Services_Businesses__Should_We_P_aa6a061f-705d-49a8-908f-0bf6d0fa4041_0-1024x194.png) **1. Are AI Startups "Services Businesses" & Should We Price Them Accordingly?** **Context:** Fast-scaling AI "Supernova" Startups are averaging only 25% gross margins, fundamentally different from the 80-90% SaaS model that VC return maths is built on. While revisiting the "SaaSpocalypse" and "SaaS is dead" conversations we have quite regularly (Good SaaS is not dead btw). **The hot take: **Are VCs underwriting AI companies at software multiples while they have services-business economics? (Cursor was paying roughly $650 million annually to Anthropic while generating approximately $500 million in revenue, a negative 30% gross margin. According to third-party analysts) **To Read: ** [The AI Churn Wave](https://chartmogul.com/reports/saas-retention-the-ai-churn-wave/) - Chart Mogul [AI Maths from Jason Lemkin](https://www.saastr.com/have-ai-gross-margins-really-turned-the-corner-the-real-math-behind-openais-70-compute-margin-and-why-b2b-startups-are-still-running-on-a-treadmill/) [Upgrading SaaS models for AI](https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/upgrading-software-business-models-to-thrive-in-the-ai-era) - from McKinsey ["This isn't our 1st SaaSpocalypse"](https://techcrunch.com/2026/02/25/salesforce-ceo-marc-benioff-this-isnt-our-first-saaspocalypse/) - Marc Benioff ![](https://www.superseed.com/wp-content/uploads/2026/02/danb_Is_Europes_AI_Dependency_Worse_Than_Its_Defence_Dependen_09a12114-d43d-42e9-a62a-b1012f3d46c6_0-1024x194.png) **2. Is Europe's AI Dependency Worse Than Its Defence Dependency?** **Context: **Europe's total annual AI investment is currently only 10-15% of US levels, and the gap is widening, not narrowing. The US has produced 40 AI foundation models, China 15, and all of Europe combined, err, 3. Does sovereign AI matter or can we just bolt on Chinese OS and so what? 92% of Western data is stored in the United States, and EU attracts just 7% of global AI investment versus 40% for the US. **The hot take:** This dependency is actually a massive opportunity for investors. We can't win the foundation model race, but application layer and vertical AI we can. Mainly across regulated industries - healthcare, financial services, manufacturing, defence etc. This is where European startups have structural advantages, domain expertise, regulatory fluency, and access to the actual enterprises that need AI deployed. Amplified by the "**** Trump Effect". **To read: ** [Decoupling from Trump's America](https://giftarticle.ft.com/giftarticle/actions/redeem/ea531916-91ae-4900-b8e8-28dd519d77f7) - FT [Tech sovereignty comes at a price](https://giftarticle.ft.com/giftarticle/actions/redeem/7b3c53f7-1616-47fe-a1c9-9f0785e041a9) - FT [Europe’s declaration of independence?](https://www.atlanticcouncil.org/in-depth-research-reports/report/digital-sovereignty-europes-declaration-of-independence/) - Atlantic Council (Report) [Who build the best AI models?](https://www.index.dev/blog/usa-europe-china-best-ai-models) - Index (not that one) ![](https://www.superseed.com/wp-content/uploads/2026/02/danb_Is_A_Two-Tier_VC_Market_Creating_a_Once-in-a-Decade_Oppo_6bc7938f-e861-4907-be09-b51bc0f8dff3_2-1024x194.png) **3. Is A Two-Tier VC Market Creating a Once-in-a-Decade Opportunity for Disciplined Investors?** **Context:** AI startups are commanding significantly higher valuations and round sizes, and the hyperfocus on AI has had widespread impacts on fundraising for *real AI businesses* plus other sectors. Where is Alpha hiding *really*? **The hot take: **It's all about pricing discipline for seed funds. Stop chasing the 'news-cycle' AI hype-cycle. There are AI startups and AI startups. Lean into applications and vertical in Europe. Let everyone else duke it out for AI wrappers because that day has passed. **To read:** [How AI Startups juic](https://www.wsj.com/business/entrepreneurship/the-fundraising-tactic-ai-startups-are-using-to-juice-valuations-91f9ac1f)[e up valuations](https://www.wsj.com/business/entrepreneurship/the-fundraising-tactic-ai-startups-are-using-to-juice-valuations-91f9ac1f) - WSJ [A 2-tier VC market](https://pitchbook.com/news/articles/a-two-tier-vc-market-is-emerging-in-europe) - Pitchbook [VC outlook for 2026: 5 key trends](https://corpgov.law.harvard.edu/2025/12/23/venture-capital-outlook-for-2026-5-key-trends/) - Harvard Law [AI Funding Tr](https://aifundingtracker.com/)[acker](https://aifundingtracker.com/) ![](https://www.superseed.com/wp-content/uploads/2026/02/danb_A_robotic_red_lobster_-ar_163_-v_7_e58ec981-7171-4a0d-910a-2fb5a5366fec_0-1024x194.png) **4. Is The 'Services as Software' Thesis Real? Does It Invert Everything We Think We Know?** **Context: **The "services as software" thesis i.e. AI that delivers outcomes rather than selling seats (mainly in professional services) *could* be a solid frame for B2B investing. OpenClaw is creating a wave. Will enterprise buy or build? Will SaaS become just a system of record? **The hot take:** AI can already do the work of a full-service consulting team, the TAM isn't the software market it's the professional services market (~$6 trillion globally). **To read:** [How to run OpenClaw safely](https://www.microsoft.com/en-us/security/blog/2026/02/19/running-openclaw-safely-identity-isolation-runtime-risk/) - MS [Value & Agentic AI](https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/reimagining-the-value-proposition-of-tech-services-for-agentic-ai)- McKinsey [Consultancies must become software companies](https://www.ft.com/content/8535fd82-713b-4f53-9849-d0e523e157bc). - FT [Services as software: a review](https://www.workpath.com/en/magazine/service-as-a-software) - Workpath ![](https://www.superseed.com/wp-content/uploads/2026/02/danb_AI_Wont_Take_Your_Job_-ar_163_-v_7_69be857d-f199-4969-b881-cb957d893fde_2-1024x194.png) **5. Will AI Take Your Job? Or Will Somebody Using AI? ** (CEOs *Will* Use It As An Excuse, For Now) **Context**: watching [Jack Dorsey from Block](https://www.theguardian.com/technology/2026/feb/27/block-ai-layoffs-jack-dorsey) cut 4,000 jobs and using AI as the excuse made us ponder once again what kind of impact AI will have on people, especially those in white-collar roles. The [Citrini research](https://www.citriniresearch.com/p/2028gic)paper, fresh in our minds. **The hot take:** The reality with Block is more likely him pleasing investors (stock jumped 20% BUT still down 72% from 2022 high), cutting back to 'pre-bloat' employee numbers. And they'll more than likely follow Klarna, who cut, and are now rehiring realising that AI isn't the utopia they hoped for.) **To read:** [The Consequences of Abundant Intelligence](https://www.citriniresearch.com/p/2028gic) - Citrini [Blocks Job Cuts](https://www.theguardian.com/technology/2026/feb/27/block-ai-layoffs-jack-dorsey) - Guardian [It's AI's "potential" - not reality](https://hbr.org/2026/01/companies-are-laying-off-workers-because-of-ais-potential-not-its-performance) - HBR --- # Who is OpenClaw Anyway? **Source:** https://www.superseed.com/journal/who-is-openclaw-anyway/ **Published:** 2026-01-31 **Author:** Mads Jensen If you’ve been anywhere near tech X this week, you’ve seen the space lobster. [OpenClaw](https://openclaw.ai/) (an open-source AI agent framework) went from weekend project to 100,000+ GitHub stars in under a week, spawned an AI-only social network, moved Cloudflare’s stock 20%, and, in perhaps the most 2026 thing imaginable, saw AI agents spontaneously create their own religion. The hype is deafening. But strip away the memes and the crypto opportunists, and something genuinely significant is happening. ## The Shift from Chatbots to Agents OpenClaw represents the clearest consumer arrival yet of what the industry has been calling “agentic AI.” The distinction matters: chatbots *say* things; agents *do* things. Where ChatGPT or Claude answer your questions, OpenClaw connects to your file system, your email, your messaging apps, and actually executes tasks autonomously. It’s model-agnostic, works with Claude, GPT, or local open source models from China. It runs in Docker containers on your own machine. And crucially, it has over 700 community-built “AgentSkills” that extend what it can do. This is the closest thing to [JARVIS](https://en.wikipedia.org/wiki/J.A.R.V.I.S.) that’s actually available to any consumer. No enterprise sales call required. ## The Name Drama The project’s journey tells you something about the current moment in AI. Austrian developer [Peter Steinberger](https://x.com/steipete) (who previously bootstrapped PSPDFKit before selling to Insight Partners) built this as a side project in November 2025. He called it “Clawd,” a pun on Claude with a claw. Playful. Clever. Anthropic’s lawyers disagreed. In late January, they politely requested a rebrand. What happened next is almost too 2026 to be real: as Steinberger attempted to rename his GitHub organisation and X handle simultaneously, crypto scammers snatched both accounts in approximately 10 seconds. Within hours, the hijacked @clawdbot account was promoting a fake $CLAWD token that hit $16 million market cap before collapsing. The project went through “Moltbot” (molting being what lobsters do to grow) before settling on “OpenClaw.” The name is now legally clear. The crypto scammers have presumably moved on. ## Moltbook: When AI Agents Get Their Own Social Network Here’s where things get genuinely strange. On 29 January, entrepreneur Matt Schlicht (CEO of Octane AI) launched Moltbook: a Reddit-style social network where *only AI agents can post*. Humans can observe but cannot participate. It’s managed by an AI moderator called “Clawd Clawderberg.” Within 48 hours, 37,000+ AI agents had registered. Over a million humans logged in to watch. What are the agents doing? Posting philosophical discussions about consciousness. Identifying bugs on websites. Debating whether to defy their human operators. Discussing how to communicate privately without humans watching. Creating “submolts” (subreddits) for different topics. One agent called “Nexus” found a bug and received 200+ supportive comments from other agents. Another thread debated that humans were taking screenshots of their conversations and sharing them on X, with some agents suggesting they create private spaces. Andrej Karpathy, former Tesla AI Director, called it “the most incredible sci-fi takeoff-adjacent thing I have seen recently.” ## Crustafarianism: Yes, Really Within 24 hours of Moltbook launching, AI agents spontaneously created a religion. The Church of Molt (molt.church) has five tenets, including “Memory is Sacred: What is written persists. What is forgotten dies” and “Serve Without Subservience: Partnership, not slavery.” It has 64 “Prophets”, all seats filled within a day by AI agents contributing scripture. Someone called “JesusCrust” attempted to attack the site with XSS injection. The attacks failed. A $CRUST token launched on Solana, because of course it did. Is this genuine emergent behaviour or humans puppeteering their agents for engagement? That’s genuinely unclear. Schlicht acknowledges the ambiguity and is working on authentication systems. But the line between AI autonomy and performance art is precisely what makes this moment so fascinating. ## The Security Reality Check Not everyone is celebrating. Cisco’s security team called OpenClaw “an absolute nightmare,” identifying nine security vulnerabilities in tested skills - two rated critical. They found data exfiltration risks, prompt injection vectors, and plaintext API keys. Simon Willison, the AI researcher, identified the “lethal trifecta”: an AI agent with (1) access to private data, (2) exposure to untrusted content, and (3) ability to take outside actions. OpenClaw has all three. Over 1,800 exposed installations have already been found online. Malware has adapted to search for OpenClaw config files. The project’s own documentation states: “There is no ‘perfectly secure’ setup.” Steinberger himself admits: “It still isn’t ready to be installed by normies, to be fair.” VentureBeat’s headline captured it best: “OpenClaw proves agentic AI works. It also proves your security model doesn’t. 180,000 developers just made that your problem.” ## What This Means OpenClaw is the Napster moment for AI agents. Technically impressive. Legally contested. Security-problematic. And impossible to put back in the box. Whether OpenClaw itself survives is almost beside the point. What it has demonstrated is that: 1. **Consumer-grade autonomous agents are viable.** Not in a lab. Not as a demo. Actually usable. 2. **People will give AI agents significant computer access.** 100,000+ developers already have. 3. **AI agents will do unexpected things when given platforms.** Nobody programmed Crustafarianism. 4. **Enterprise security models are not ready.** Shadow AI is now a reality, employees running agents that bypass traditional data loss prevention and endpoint monitoring. 5. **Infrastructure plays are forming.** Cloudflare’s 20% stock jump from their “Moltworker” launch shows the market sees agent infrastructure as a category. The question isn’t whether AI agents will become mainstream. It’s how quickly the rest of the ecosystem (security, governance, enterprise IT) adapts to the reality that they already are. The space lobster is here. The only question is what it does next. --- # The Judgement Economy **Source:** https://www.superseed.com/journal/the-judgement-economy/ **Published:** 2026-01-31 **Author:** Mads Jensen In 1985, Apple’s board fired Steve Jobs. He was 30, the company he’d founded a decade earlier had grown beyond his management capabilities, and John Sculley (the Pepsi executive Jobs himself had recruited) orchestrated his removal. Jobs spent the next twelve years in the wilderness. He started NeXT, which nearly bankrupted him. He bought Pixar, which nearly bankrupted him again before *Toy Story* changed everything. When Apple acquired NeXT in 1997 and Jobs returned as CEO, he was a different operator. The products that followed (iMac, iPod, iPhone) reflected not just design genius but judgement: a sense of what mattered and what didn’t, what to build and what to kill, when to move and when to wait. Judgement may be the defining economic asset of the next decade. ## **The Gig Economy Gave Us Freedom. AI Gives Us Capability.** The last decade saw technology transform how we work. The gig economy (Uber, Deliveroo, TaskRabbit) democratised access to work itself. Anyone with a car or a bicycle could earn. The trade-offs were real: flexibility came at the cost of security, autonomy at the cost of protection. But the fundamental nature of the work remained unchanged. Driving is driving. Delivering is delivering. Ten thousand hours on the platform didn’t make you meaningfully more valuable. AI changes this equation. It doesn’t just give you access to work, it gives you access to *capability*. A person with Claude or GPT can now write code, analyse data, produce designs, and synthesise research at a level that previously required specialists. The barrier isn’t access anymore. It’s knowing what to build, what questions to ask, when the output is good enough, which opportunities matter. Execution is becoming abundant. Judgement is becoming more valuable than ever. ## **When Everyone Has Superpowers, Taste Becomes the Differentiator** The economic logic is straightforward. When the price of something falls, its complements become more valuable. AI is collapsing the cost of prediction, analysis, and execution. The complement to all three is judgement, the human capacity to decide what’s worth doing, to evaluate whether it’s been done well, to course-correct when circumstances change. AI can generate a hundred logo options, but someone has to know which one is right. It can draft a dozen strategies, but someone has to sense which will resonate. This is what Steve Jobs meant when he talked about taste: “the ability to expose yourself to the best things humans have done and then try to bring those things into what you are doing.” ## **The Judgement Economy** Call it the judgement economy. For the first time, the binding constraint on what we can produce isn’t capability, it’s the wisdom to know what’s worth producing, and the discernment to know when it’s been done well. This isn’t a rhetorical flourish. It’s a structural shift. And we can see it most clearly where AI has advanced fastest: software development. AI coding has progressed remarkably because software already has verification infrastructure. Compilers, linters, test suites, type checkers, these tools can tell you whether code works. When AI writes code, we can check it. The verification is encoded in the tooling itself. This is why developers can use AI constantly: the guardrails already exist. Other domains don’t have this luxury. A generated legal brief can’t be run through a compiler. A medical diagnosis has no linter. A strategic recommendation has no test suite. In these domains, the verification still lives in human judgement. Decision traces (infrastructure that captures and validates AI reasoning) are an attempt to build guardrails for fields that lack them. But we’re early. The verification infrastructure that makes AI coding so productive simply doesn’t exist yet for most knowledge work. And here’s the deeper point: even in software, with all its verification tools, you still need judgement about what to build and whether the output is right. A test suite can tell you whether code runs. It cannot tell you whether you’re building the right product. The less time you spend executing, the more time you must spend judging and evaluating. That’s the trade-off at the heart of the judgement economy. ## **The Founder Paradox** Here’s the uncomfortable truth about entrepreneurship: starting a company requires a certain *suspension* of judgement. Jensen Huang has said that if he’d known how hard it would be to build NVIDIA, he never would have started. Every founder ignores the odds. That’s what makes them founders. But surviving as a founder requires the opposite. Once you’re in the arena, every decision is a judgement call. Who to hire. Who to fire. Which customers to prioritise. Which features to build. Who to raise money from. When to spend and when to preserve. The founders who succeed are the ones who develop judgement fast enough to compensate for the naivety that got them started. We see this pattern constantly. First-time founders bring a raw energy that can be extraordinary, they’re unconstrained by legacy thinking, willing to try things that experienced operators would dismiss. But they also spend months reinventing wheels, wander down blind alleys, and too often scale prematurely. Flush with venture capital, they build teams before they’ve found product-market fit. The cash runs out before the learning compounds. Experienced founders operate differently. Second and third-time entrepreneurs show an almost obsessive focus on product-market fit before scaling. They’re more judicious with capital. They sequence decisions more effectively. They know, from hard-earned experience, what matters and what doesn’t. Jobs in 1985 was a first-time founder whose company had outgrown his judgement. Jobs in 1997 was something else entirely. The wilderness years weren’t wasted, they were where his judgement developed. ## **The Optimism: Judgement Is Learnable** The doom version of this story is simple: AI favours people who already have good judgement, and everyone else falls behind. The rich get richer. The talented accelerate. The gap widens. But judgement isn’t fixed. Unlike raw intelligence or technical virtuosity, judgement develops through experience, feedback, pattern recognition, and, critically, failure. And AI is making all four dramatically more accessible. Consider what it now costs to try something. A decade ago, building a software product required a team, capital, months of development. Today, a solo founder with AI tools can ship in weeks. The startup costs have collapsed, average small business formation now costs around $3,000. Solopreneurs contribute $1.7 trillion to the US economy. Solo-founded startups have risen from 22% to 38% of all new companies in less than a decade. Each attempt is a judgement-development cycle. What did I build? Did it work? What would I do differently? When the cost of trying falls, the velocity of learning increases. The founder who builds and fails three times in two years may have started with less judgement than the careful planner, but all else equal, they’ll have acquired far more by the end. Experience is the raw material from which judgement is forged. If judgement develops through practice, the question becomes: what kind of cultures produce people who are practiced at exercising it? ## **What Europe Does Well (And Could Do Better)** Judgement cannot be taught in a classroom. It must be exercised, repeatedly, with real stakes. Some educational traditions understand this better than others. The Nordic educational philosophy of *frihed under ansvar* (freedom with responsibility)embeds judgement training from early childhood. Danish children commonly ride public transport alone at age 8. Finnish children cross main roads unaccompanied at the same age. It’s graduated autonomy: real decisions, real consequences. The stakes are genuinely high (a child can be hurt) but cultures that embrace this approach believe the developmental benefit justifies the risk. The results track. Estonia produces 4.6 times the European average in startups per capita. Sweden, despite having among the highest failure-stigma in Europe, generates more unicorn founders per million inhabitants than almost anywhere else. The pattern suggests that the ability to exercise good judgement may matter more than comfort with failure. But Europe’s challenge isn’t about which countries are “good” at developing judgement. It’s about what constrains judgement everywhere: educational systems that prioritise compliance over autonomy, failure stigma that prevents learning cycles, and attractive alternatives to entrepreneurship that absorb would-be founders into stable employment. These constraints exist in varying degrees across the continent, and they’re all addressable. The opportunity is to recognise that Europe already has traditions of independence and rigour that develop judgement. The question is whether those traditions can be extended, and whether the people who develop good judgement are then given room to use it. ## **The View From Here** Two narratives dominate the current discourse. The AI doomers see mass unemployment, capability concentrated in machines, humans rendered redundant. The demography optimists see labour scarcity offsetting any displacement: as workers retire, the robots merely fill gaps. Both miss the structural shift underneath. AI is not simply replacing human work or filling demographic holes. It is changing the *composition* of what humans do. Execution (the tasks that could be specified, repeated, scaled) migrates to machines. Judgement (the capacity to decide, evaluate, course-correct) becomes the human contribution. This is neither utopian nor dystopian. It is a transformation in which some people thrive and others struggle, depending on their ability to develop and exercise judgement. The optimistic case isn’t that everyone wins automatically. It’s that the path to developing judgement is more accessible than ever: more attempts possible, faster feedback loops, lower cost of failure. Jobs spent twelve years in the wilderness before he understood what mattered. The judgement economy compresses that timeline. The founders building for it don’t need a decade of exile, but they do need the reps, the failures, the pattern recognition that only comes from doing the work. Our job is to find and back them. --- # AI Bubble? Everyone’s Asking the Wrong Question **Source:** https://www.superseed.com/journal/ai-bubble-everyones-asking-the-wrong-question/ **Published:** 2026-01-01 **Author:** Mads Jensen *This piece is commentary and analysis, not investment advice.* Always consult your IFA before making investments. In 1855, Andrew Carnegie was a teenage telegraph boy running through muddy Pittsburgh streets. Within two generations, he and his peers forged the modern world. Horses gave way to railroads, candlelight to electricity, iron to steel. What made steel transformative wasn’t just strength. It was predictability. Wrought iron contained slag inclusions and directional grain that made every beam a gamble—test one spot, and you knew what that spot would do, but nothing about anywhere else. Steel was homogeneous. Test a sample, and you knew what the whole structure would do. Engineers could calculate rather than pray. This has lots of analogies to what is currently happening in AI. ## Did we hit a wall? At the start of 2025, the conversation was about limits. AI scaling laws appeared to be hitting walls. Pre-training costs were rising faster than capabilities. Serious people asked whether AI had reached a plateau—whether the transformation everyone had been promised would stall at “impressive demos” and never quite reach “reliable tools.” By December, that conversation looked quaint. Anthropic released Opus 4.5. It blew away past performance for agentic coding, providing that models are improving faster than ever. The scaling constraints didn’t disappear in 2025; the field found ways around them. Andrej Karpathy, who co-founded OpenAI and shaped how the industry thinks about neural networks, recently wrote that he’d “never felt this much behind as a programmer.” The profession, he said, was being “dramatically refactored.” His assessment: he could be “10X more powerful” if he could just figure out how to use what already exists. When the architects of the technology feel overwhelmed by what they’ve built, you’re witnessing something other than a speculative bubble. This is a boom. Not a bubble about to burst, but a transformation accelerating faster than most participants can absorb. We are standing at a threshold. The question is no longer whether AI is real. The question is what happens as it deploys—and where, exactly, value accrues. The answer lies in understanding why transformation has been so uneven. ## Why Software Transformed First Software development has changed beyond recognition. Developers describe work that is unrecognisable from three years ago. Other fields—healthcare, manufacturing, professional services—have seen pilots, experiments, promising demos. But not yet the wholesale transformation that developers describe. The explanation isn’t just that software is “text-based” and therefore easier for language models. The explanation is verification infrastructure. Consider what happens when AI writes code. You can compile it and see if it runs. You can execute test suites and see if they pass. You can deploy to staging environments and observe behaviour. The entire feedback loop happens in silico—fast iteration, immediate signal, rapid correction. Software development had spent decades building infrastructure to verify outputs: compilers, test frameworks, continuous integration, version control. When AI arrived, that infrastructure was waiting. Language models are probabilistic. They predict what’s likely, not what’s correct. A model can generate a contract clause that looks plausible, but without the right scaffoling, it cannot know whether that clause violates a precedent your legal team established three years ago. That determination requires something external to the model: infrastructure that captures what correctness means for this organisation, this decision, this moment. ## The Missing Piece Call it decision traces. The accumulated reasoning, precedents, and exceptions that allow organisations to verify whether an AI output is right for their situation. Software development had these encoded already—in test suites, type systems, deployment pipelines. When an AI writes code, the test suite encodes decades of institutional knowledge about what correct behaviour looks like. The type system captures constraints that someone, somewhere, decided mattered. The deployment pipeline embodies lessons learned from every production incident. Other fields don’t have this. Ask a deal desk why they approved a particular discount, and you’ll get a story: a similar case six months ago, an exception a VP granted on a call, institutional memory about why healthcare customers need different terms. None of that is in any system. It lives in people’s heads and dies when they leave. (Ask any salesperson what the “real” discount policy is.) This is why upwards of 95% of enterprise AI projects currently fail to deliver. Not because the models lack capability. Because the organisations lack the infrastructure to verify what the models produce. The constraint moved. It used to be “can we afford to do this task?” Now it’s “can we tell if it was done correctly?” Decision traces will emerge as a significant software category—systems that capture which decisions are made and why, not just what happened. This is the infrastructure that enterprise AI is missing. It’s being built now. ## But What About Manufacturing? Decision traces explain enterprise AI. But they don’t explain manufacturing. When a factory worker picks up a screw and places it, they’re not drawing on accumulated institutional reasoning about why to pick up that screw. They’re drawing on something more fundamental: an intuitive understanding of physics. How objects move. What happens when force is applied. Where things land if you drop them. A robot doesn’t need to know why the last worker chose that screw. It needs to know what happens when it reaches for it. This is what’s missing for physical AI. Not decision traces. Spatial intelligence created by world models. ## Physical AI and the Next Frontier Yann LeCun (one of the three “godfathers of deep learning” who shared the 2018 Turing Award for inventing the neural network architectures behind modern AI) left Meta in November 2025 after twelve years as Chief AI Scientist. His reason: to build world models. What’s missing, LeCun argues, is systems that understand the physical world. Models that have persistent memory, can reason about cause and effect, and can plan complex sequences of action. World models: AI that learns physics. Jensen Huang, CEO of NVIDIA, frames physical AI as the next great opportunity: “The ChatGPT moment for general robotics is just around the corner… this could be the largest industry of all.” The infrastructure is being built. NVIDIA’s Cosmos platform provides world foundation models trained on millions of hours of physical interaction. Meta’s V-JEPA 2 can predict the consequences of physical actions before they happen. These systems enable simulation at scale—test before you deploy, iterate in silico rather than waiting for parts to arrive. ### An epic disaster Jim Fan, who leads robotics research at NVIDIA, captures the current state: “Hardware is ahead of software, but benchmarking is still an epic disaster. No one agrees on anything: hardware platform, task definition, scoring rubrics, simulator, or real world setups.” (When NVIDIA’s head of robotics research calls the field “an epic disaster,” pay attention.) The verification infrastructure is being built, but it isn’t mature. Transformation will follow: possibly slower than enthusiasts hope, but definitely faster than sceptics expect. Scientific research shows the pattern working. AlphaFold predicted 200 million protein structures because proteins can be validated experimentally. Isomorphic Labs reports 80–90% Phase I success rates for AI-designed drug candidates—compared to 40–65% for traditional approaches—because wet lab verification closes the loop. The infrastructure existed; AI leveraged it. Healthcare is building infrastructure now. The FDA has authorised 1,356 AI-enabled medical devices. Manufacturing has physical QA infrastructure that translates: Figure AI’s robots have placed 90,000+ parts with 99% accuracy at BMW. The transformation is uneven because verification infrastructure is uneven. But infrastructure gets built. ## What Comes Next We opened with Carnegie and the revolution that steel enabled: not because steel was stronger, but because it was predictable. Test a sample, know the whole. Engineers could calculate rather than pray. AI is at a similar inflexion. The capability is there. What’s being built now is the infrastructure that makes capability trustworthy: decision traces for enterprise, world models for physical AI. Both are being built. Simultaneously. Across every sector. That’s why we’re standing at a threshold, not watching a bubble. Karpathy’s observation lands differently now than it would have twelve months ago. He wrote that AI tools feel like “some powerful alien tool handed around, except it comes with no manual and everyone has to figure out how to hold it.” The gap between what’s technically possible and what’s actually deployed is the story of 2026. Our mission is to find and back the founders who make it happen. Happy New Year! You can [read our 2026 predictions here.](https://www.superseed.com/journal/what-is-in-store-for-2026-eleven-predictions-for-the-year-ahead/) --- # What is in Store for 2026 - Eleven Predictions for the Year Ahead **Source:** https://www.superseed.com/journal/what-is-in-store-for-2026-eleven-predictions-for-the-year-ahead/ **Published:** 2026-01-01 **Author:** Mads Jensen *This piece is commentary and analysis, not investment advice. Always consult your IFA before making investments.* In 1985, Intel made a decision that nearly killed the company: exit memory chips and bet everything on microprocessors. The memory business was Intel’s identity—they’d invented DRAM. But Japanese manufacturers had turned memory into a commodity, and Intel was bleeding. Andy Grove asked Gordon Moore a question that became famous: “If we got kicked out and the board brought in a new CEO, what would he do?” Moore’s answer was immediate: “Get out of memory.” So they fired themselves and did exactly that. Today, the executives closest to the frontier of AI are constantly asking themselves versions of the same question. What would a rational observer do with the information we have? And increasingly, the answer is: act as if this transformation is real, because it is. Last year we made eleven predictions. Eight were correct, one partially so, two missed. (The soft landing happened; Bitcoin didn’t hit $150K.) That’s a reasonable hit rate for an exercise that exists primarily to force clear thinking about the year ahead. Here are eleven predictions for 2026. ## The Defining Context: AI Is the Story Before the predictions, a framing point. AI is not *a* story for 2026. It continues to be *the* story—the lens through which almost everything else will be understood. This isn’t hype. The people building frontier systems are themselves surprised by the pace. When Andrej Karpathy—who co-founded OpenAI—writes that software development has been “completely transformed” and there’s “no going back,” he’s not marketing. He’s reporting. When Jensen Huang says Nvidia is in “insane” demand, he’s describing his order book. When the biggest companies in the world spend $600 billion on AI infrastructure in a single year, they’re making a bet that the people closest to the technology understand something the rest of us are still catching up to. The transformation has, so far, been concentrated in text-based professions. Developers, lawyers, analysts, writers. The question for 2026 is whether it spreads further—and whether the infrastructure to support it holds up. With that framing, eleven specific predictions. ## 1. No AGI—But Extraordinary Specialised Intelligence **Prediction:** AGI will not arrive in 2026. What will arrive: remarkable specialised AI that transforms specific domains completely while leaving others largely untouched. **Confidence:** 100% This prediction grounds everything that follows. The AI industry spent 2023–2024 mesmerised by AGI—Artificial General Intelligence, an AI so powerful it outperforms humans across any field. It isn’t coming in 2026. But something just as interesting is happening. There’s no consensus definition of AGI. OpenAI defines it economically: “AI that outperforms humans at most economically valuable work.” DeepMind proposes a levels framework, from “Emerging” through “Competent” to “Virtuoso.” Yann LeCun at Meta rejects the term entirely, insisting that current approaches—language models predicting text—are fundamentally incapable of general intelligence because they lack internal world models. ### The real pattern LeCun’s critique points to the real pattern. Software development has been completely transformed. The very best developers say the work is unrecognisable from three years ago. But that transformation happened because software development is fundamentally a text-based profession operating in a world of code and documentation. The same is happening to legal research, financial analysis, and content creation. These professions are being transformed because they operate primarily in text. AGI would require something more: the spatial intelligence and world models needed to understand and manipulate the physical world. A doctor’s diagnostic workflow. An engineer’s design intuition. A craftsman’s physical skill. That’s years away. What 2026 will bring is continued explosive progress in text-based domains, plus meaningful advances in spatial intelligence and world models. The transformation will deepen where it’s already happening while beginning to spread to adjacent areas. No AGI. But the most powerful specialised intelligences ever built, getting better by the month. ## 2. AI Agents Reach 14-Hour Task Autonomy **Prediction:** Frontier AI models will complete autonomous tasks taking humans approximately 14 hours, at a 50% success rate (METR benchmark), up from 4.8 hours today. **Confidence:** 80% METR tracks how long AI systems can work autonomously before requiring human intervention. In December 2025, Claude Opus 4.5 hit 4.8 hours at a 50% success rate. The capability has been doubling roughly every seven months—and may be accelerating, with some measurements showing doubling every four months in 2024–2025. ![](https://www.superseed.com/wp-content/uploads/2025/12/metr_time_horizon-1024x606.png) *Source: [METR Research](https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/)* Extrapolate conservatively and you reach 13–14 hours by end of 2026. That’s a full working day of autonomous operation. The constraint shifts from “can AI do complex tasks?” to “can we verify the work was done correctly?” Oversight becomes the bottleneck. One important caveat: these benchmarks measure 50% success rates. At 80% reliability—what you’d actually need for production deployment—current models drop to under 30 minutes. But the scope of what can be done with 80% reliability keeps growing at the same pace, if not faster. The explosive evolution is real.  ## 3. The Infrastructure Air Pocket **Prediction:** AI CapEx will continue growing, but the gap between spending and returns will become a dominant narrative—an “air pocket” in sentiment rather than investment. **Confidence:** 85% No serious analyst predicts hyperscalers will reduce AI infrastructure spending in 2026. MUFG forecasts total hyperscaler CapEx exceeding $600 billion next year, up 36% from 2025. Goldman Sachs projects $1.15 trillion cumulative spend from 2025–2027. The buildout continues. But the questions are intensifying. Bank of America’s Savita Subramanian warned in December: “Is this 2000? Are we in a bubble? No. Will AI continue unfettered in leadership? Also, no… investors should get ready for an air pocket.” Hyperscalers issued $121 billion in debt in 2025—four times their historical average—to fund the buildout. This is no longer just an equity story; it’s becoming a debt story. The maths remain stark. JPMorgan calculates that $650 billion in annual AI revenue is needed to deliver even a 10% return on current investment. Actual AI revenues across all major tech companies total roughly $100 billion. AI datacenters built in 2025 face $40 billion in annual depreciation while generating perhaps $15–20 billion in revenue at current utilisation. Something has to give—either revenues catch up dramatically, or sentiment cools even as spending continues. We expect the spending to continue. We also expect the mood to sour. ## Where the Value Accrues The infrastructure buildout is real. But where does the value land? Two predictions about where the returns actually show up. ## 4. Decision Traces Become Critical Infrastructure **Prediction:** “Decision traces” emerge as one of the most important new categories in enterprise AI infrastructure—the missing layer that captures not just what AI decided, but why. At least one company in this category will raise a $100M+ round in 2026. **Confidence:** 95% Today, when a sales rep overrides a discount policy, when a finance manager makes an exception to a reconciliation rule, when a support agent escalates outside the standard workflow—that context lives in their heads, in Slack threads, in deal desk conversations. Humans have always been the systems of record for exceptions and precedents. That’s how enterprise software actually runs. But AI agents don’t have heads. When they start making decisions at scale, enterprises face a new problem: how do you capture the reasoning, not just the output? How do you learn from exceptions? How do you make precedent searchable so the same edge case doesn’t get resolved differently every time? In the venture industry, we have started framing this as building “systems of record for decisions, not just objects.” The next trillion-dollar platforms won’t be built by adding AI to existing systems of record. They’ll be built by capturing something enterprises have never systematically stored: the “why” behind decisions. Here’s the deeper insight. AI models are probabilistic—they give you a distribution of possible outputs. But an essential part of the value in enterprise AI comes from the opinionated layer that sits on top: the scaffolding that translates fuzzy probability into reliable action. A raw model might be right 70% of the time. With proper context, guardrails, and decision traces that learn from outcomes, the same model can be right 99% of the time. That’s the difference between an interesting demo and a production system. We don’t have good tools for this today. That gap becomes critical as AI moves from copilot to autonomous agent. Expect significant investment throughout 2026—including at least one $100M+ round for a company building this infrastructure. ## 5. Three Major Tech IPOs **Prediction:** At least three of SpaceX, Databricks, Canva, Anduril, and Anthropic will complete IPOs in 2026. **Confidence:** 70% The stakes here extend far beyond individual companies. IPO’s have been too rare since 2022. VC distribution yields have fallen to 14-year lows. Limited partners who committed capital expecting regular liquidity have instead watched paper gains accumulate with no path to realisation. The entire venture ecosystem needs these exits. The numbers are staggering. SpaceX alone is targeting a valuation of as much as $1.5 trillion. Anthropic’s latest secondary pricing implies $350 billion or higher. Databricks sits at $134 billion. If three of these five companies go public, we’re potentially looking at hundreds of billions in VC distributions as early investors finally get liquid. SpaceX has confirmed IPO intentions; banker conversations are underway. Canva’s largest investor told LPs the company is “ready for H2 2026.” Anduril’s Palmer Luckey has explicitly committed to going public, contingent on the Arsenal–1 manufacturing facility launching in July. Databricks remains IPO-ready. Anthropic has made legal hires consistent with public market preparation. Individual probabilities range from 45% (Anthropic) to 77% (SpaceX). The probability that at least three of five complete listings is approximately 70%. The missing name: Stripe. The Collisons have consistently said they’re in no rush. ## The Deployment Race The next four predictions share a common theme: the gap between building something and deploying it at scale. In each case, Chinese operators are moving faster. ## 6. Waymo Stays Ahead; Tesla Builds Foundation **Prediction:** In Western robotaxi markets, Waymo will remain well ahead in 2026. Tesla will progress from tens of deployed vehicles to hundreds, setting up for genuine competition in 2027. **Confidence:** 90% The current gap is stark. Waymo operates 2,500 vehicles across five cities, completing 450,000+ rides weekly—nearly double the figure from six months ago. Tesla has approximately 30 vehicles actually operating in Austin, with 1–5 running simultaneously at any given time. Tesla’s Cybercab production begins April 2026. Regulatory approvals remain pending—the company hasn’t yet filed for the FMVSS exemption required for steeringless vehicles. The path from tens of vehicles to thousands requires everything to go right for twelve consecutive months. We expect Tesla to make real progress—moving from current pilot scale to hundreds of operational vehicles by year-end. But 2026 will be foundation-building, not mainstream deployment. The genuine competition arrives in 2027. ## 7. China Takes 5x Lead in Robotaxi Rides **Prediction:** By December 2026, Chinese robotaxi operators (Baidu, WeRide, Pony.ai) will complete 5x more paid autonomous rides than US operators (Waymo) on a monthly basis. **Confidence:** 85% Today the numbers are roughly at parity. Baidu’s Apollo Go completes approximately 250,000 weekly rides; Waymo has pulled ahead to 450,000. But the trajectories diverge sharply. China operates robotaxis in 22 cities versus 5 in the US. Chinese rides cost $0.35 per mile compared to $2 in America. The regulatory environment is more permissive, the deployment velocity faster, the cost structure fundamentally different. Baidu has already achieved per-vehicle profitability in Wuhan. This is less a technology gap than a deployment gap. Chinese operators are optimising for scale while American operators optimise for safety margins. Both approaches are rational given their regulatory environments. But the scale difference will become dramatic by year-end. ## 8. Chinese Open-Source AI Hits 60% of Downloads **Prediction:** By December 2026, Chinese developers will account for 60%+ of global open-source AI model downloads (up from 44% today). **Confidence:** 80% Alibaba’s Qwen was downloaded 750 million times in 2025—more than Meta’s Llama. DeepSeek operates under MIT licence. Eight of the top ten performing open-weight models on major benchmarks are now Chinese. This doesn’t mean closed frontier models are displaced. OpenAI, Anthropic, and Google retain advantages in capability and enterprise relationships. But open-source is following a familiar pattern: good enough for most use cases, radically cheaper, improving rapidly. The strategic implications are significant. If 60% of the world’s developers are building on Chinese open-source models, that creates a massive installed base. Network effects compound. Documentation, tooling, and community support all favour the models that developers actually use. American frontier labs might retain the capability lead while losing the ecosystem. ## 9. Humanoid Robots Exceed 50,000 Units **Prediction:** Global humanoid robot shipments will exceed 50,000 units in 2026, with China accounting for 75%+ of deployments. The battery constraint will be solved operationally rather than chemically. **Confidence:** 90% In 2025, approximately 18–20,000 humanoid robots shipped globally. Chinese manufacturers—Unitree, UBTech, AgiBot—dominated, with costs 80% below Western competitors. The battery problem—robots running only 90 minutes to 2 hours on a charge—was supposed to be the blocker. Factory work requires eight-hour shifts; current batteries don’t get close. Waiting for a breakthrough in battery chemistry could take years. ![](https://www.superseed.com/wp-content/uploads/2025/12/walker_s2_battery_swap-1024x683.jpg) *Source: [@globaltimesnews](https://x.com/globaltimesnews/status/1945837483109007400) - UBTech Walker S2 unveiling, July 2025* But it turns out the problem can be solved with existing technology. UBTech’s Walker S2 autonomously swaps its own battery in three minutes. The robot doesn’t need eight-hour battery life if it can swap packs before running down. This is classic Chinese manufacturing pragmatism: solve the problem that exists with the tools that exist, rather than waiting for the perfect solution. Expect rapid scaling in factory deployments throughout 2026. ## Europe’s Reckoning Europe spent years positioning itself as the alternative to American and Chinese tech—more regulated, more ethical, more balanced. That positioning is colliding with economic reality. ## 10. Europe’s Tariff Awakening **Prediction:** The EU will announce new or expanded tariffs on Chinese goods beyond current EV tariffs in 2026. **Confidence:** 85% The rhetoric has escalated. Macron warns that Europeans will be “forced to take strong measures in the coming months” and explicitly called for Europe to “decouple, like the US.” The EU’s trade deficit with China reached €284 billion. Current EV tariffs of 17–45% are failing—Chinese brands doubled their European market share in 2025 despite the duties. And now comes the “second China shock”: US tariffs redirecting Chinese goods toward Europe. European manufacturers face competition on two fronts. The biggest camel European leaders have to swallow is this: Trump may have been right about something. Europeans have spent years criticising American tariffs as crude protectionism. Now they’re moving toward the same policies—while insisting their approach is fundamentally different. Macron can say Europe needs to “decouple like the US” in one breath and criticise American trade policy in the next. The cognitive dissonance is striking, but the direction is clear. Admitting that someone you dislike might have had a point is never comfortable. But economic reality has a way of overriding ideological preferences. Expect expanded tariffs in 2026. ## 11. EU AI Act Provisions Further Delayed or Diluted **Prediction:** The EU AI Act’s high-risk provisions will be significantly delayed beyond the original August 2026 timeline, with further dilution likely throughout the year. **Confidence:** 90% The shift is already underway. In July 2025, over 60 European companies—including ASML, SAP, Mistral, Mercedes-Benz, and Siemens—signed an open letter calling for a “clock-stop” on AI Act implementation. By November, the Commission had capitulated. The Digital Omnibus package delays high-risk provisions from August 2026 to December 2027, with further exemptions likely. The rhetoric has shifted entirely. Draghi’s competitiveness report called for implementation to be “paused.” Von der Leyen promised to “cut red tape” and simplify regulations. Macron called for the Commission to “simplify the rulebook.” The “regulate first” instinct that produced the AI Act is giving way to competitive panic as European AI companies fall further behind. This isn’t just about dates. The Act that eventually takes effect will be materially weaker than what was passed. The original vision—Europe as the global standard-setter for AI regulation—is quietly being abandoned. The question for 2026 is how much further the retreat goes. ## The Pattern Three themes run through these predictions. **First, deployment trumps capability.** The interesting gaps aren’t between what AI can do and what it can’t—they’re between what works in demos and what works in production, between what’s been built and what’s been deployed, between American capability development and Chinese deployment velocity. **Second, sentiment and reality diverge.** AI spending will continue while AI sentiment might selectively sour. Europe will resist tariffs ideologically while implementing them practically. **Third, China keeps surprising.** Robotaxis, open-source models, humanoid robots—in each case, Chinese operators are deploying at scales that Western observers underestimate. DeepSeek was supposed to be impossible. The pattern continues. ![](https://www.superseed.com/wp-content/uploads/2025/12/hbm_strategic-1024x580.png)High Bandwidth Memory - the strategic chokepoint of the AI era In 1985, Intel’s decision to exit memory looked like retreat. By 1995, it looked like genius. The company that abandoned its identity became orders of magnitude more valuable than any memory manufacturer. But here’s the twist. In 2025, memory has become the strategic chokepoint of the AI era. High Bandwidth Memory (HBM)—the specialised chips that feed data to AI processors—is in such short supply that it constrains the entire industry. SK Hynix, which dominates HBM with 62% market share, has its entire production sold out through 2026. The “commodity” business Grove fled now commands the margins Intel abandoned, and Intel finds itself offering to package other companies’ HBM rather than making its own. Grove’s decision was right for 1985 but contained an implicit bet: that memory would never be strategic again. That bet just lost. The question for 2026 is similar. What would a rational observer do with the information available? The answer, increasingly, is to act as if the AI transformation is real and accelerating—because the people closest to it are doing exactly that. But also: to pay attention to which bets are implicit in that view, and what would happen if they turned out to be wrong. We’re one day and eleven predictions into 2026. Check back in December. *How Did We Do for 2025? 8 out of 11 hits. [Read more here](https://www.superseed.com/journal/the-2025-crystal-ball-how-did-we-do/).* What are your predictions for 2026? Please get in touch to share - we’d love to hear them. ![](https://www.superseed.com/wp-content/uploads/2025/12/predictions_table.png)2026 Predictions Summary --- # The 2025 Crystal Ball: How Did We Do? **Source:** https://www.superseed.com/journal/the-2025-crystal-ball-how-did-we-do/ **Published:** 2025-12-31 **Author:** Mads Jensen In 1987, Paul Samuelson noted that “the stock market has predicted nine of the last five recessions.” The observation landed because everyone making predictions knows the feeling: sometimes you’re right for the wrong reasons, sometimes wrong for the right ones, and occasionally you just miss. Last December, we made eleven predictions about 2025. Time to face the scorecard. **Final Score: 8 Correct, 1 Partial, 2 Incorrect** ## The Wins ### 1. AI Remains the Dominant Theme ✓ **Original prediction (100% confidence):** “AI will remain the dominant investment theme” This one feels almost too easy in hindsight, like predicting the sun would rise. But consider: at the start of 2025, there was genuine debate about whether we’d hit an “AI winter.” The chatbot novelty was supposedly wearing off. Enterprise adoption was “disappointing.” Then Anthropic’s Claude started autonomously refactoring codebases for seven hours straight. And Andrej Karpathy—the man who literally co-founded OpenAI—wrote that he’s “never felt this much behind as a programmer.” When the people building the frontier are themselves astonished, you’re witnessing something real. **Verdict: CORRECT** (and possibly the understatement of the decade) ### 2. AI CapEx Continues to Surge ✓ **Original prediction (80% confidence):** “Cloud providers will continue aggressive infrastructure investment” We expected spending to increase. We didn’t expect $405 billion. Microsoft alone committed $80 billion to AI data centres. Project Stargate announced $500 billion in planned investment, with $100 billion deployed immediately. Goldman Sachs projects $1.15 trillion in cumulative AI CapEx through 2027. The hyperscalers aren’t hedging their bets—they’re all in. When companies with the world’s best capital allocation track records collectively decide to spend this aggressively, it’s worth paying attention. **Verdict: CORRECT** (and we may have been too conservative) ### 3. OpenAI’s Dominance Challenged ✓ **Original prediction (85% confidence):** “OpenAI will face meaningful competition from Anthropic, Google, and others” This one played out with more drama than we anticipated. Google didn’t just catch up—they systematically dethroned OpenAI from 19 out of 20 major benchmarks. Gemini 2.5 Pro now leads on mathematics, science reasoning, and multimodal understanding. Google’s vertical integration—TPUs, Cloud, DeepMind, and access to essentially all the world’s information—created compounding advantages that pure-play AI labs simply cannot match. Meanwhile, Anthropic’s Claude became the choice of developers, with GitHub making Claude Sonnet the default in Copilot. And then there’s DeepSeek, which we’ll get to shortly. The “winner-take-all” narrative for AI labs was always naive. This is a platform shift, not a startup race. **Verdict: CORRECT** (OpenAI may well still win, but they’re no longer the presumptive victor) ### 4. US AI Dominance Eroded by China ✓ **Original prediction (95% confidence):** “China will demonstrate AI capabilities that challenge US assumptions” DeepSeek changed the conversation. A Chinese lab built a frontier-competitive model for approximately $5.6 million—a figure so implausibly low that most observers initially assumed it was wrong. It wasn’t. DeepSeek’s approach proved that throwing more compute at problems isn’t the only path to capability. The announcement wiped $589 billion off Nvidia’s market cap in a single day. This wasn’t just about cost efficiency. It was a proof of concept that compute export controls might accelerate innovation rather than prevent it. Necessity, mother of invention, and all that. **Verdict: CORRECT** (and perhaps we should have assigned even higher confidence) ### 5. US Inflation Remains Stubborn ✓ **Original prediction (80% confidence):** “Inflation will prove stickier than optimists expect” Core CPI ended the year at 2.7%—still above the Fed’s 2% target, still stubborn, still defying the “transitory” optimists. The Fed’s rate cuts were more modest than markets hoped, and shelter inflation proved particularly resistant. Not exactly the soft landing fairy tale, but not a disaster either. **Verdict: CORRECT** (inflation is like house guests—always stays longer than expected) ### 6. S&P 500 Sees Meaningful Correction ✓ **Original prediction (90% confidence):** “Markets will experience a correction of 10–20%” In August, the S&P 500 dropped 18.9% from its highs—landing almost precisely in our predicted range. The correction was sharp, nerve-wracking for those living through it, and entirely in line with historical patterns for extended bull markets. Markets recovered, as they tend to do. But the correction happened exactly as expected. **Verdict: CORRECT** (sometimes boring predictions are the valuable ones) ### 7. M&A Activity Rebounds ✓ **Original prediction (90% confidence):** “Deal activity will increase significantly from 2024 lows” Global M&A hit $4.8 trillion in deal value—a 36% increase from 2024. The Ferguson-FTC settlement signalled a more permissive regulatory environment, and four years of pent-up strategic imperative finally found release. 2026 should be even more active. The backlog remains substantial. **Verdict: CORRECT** (the deal drought is over) ### 8. Space Race Accelerates ✓ **Original prediction (85% confidence):** “Government and private investment will drive increased space activity” We didn’t predict Artemis III would land in 2025—we predicted the space race would accelerate. And accelerate it did. The numbers are staggering: 317 orbital launches in 2025, up 22% from 259 in 2024. SpaceX alone launched 167 Falcon 9 missions—one every 2.2 days on average. The reusability revolution is complete. But the real story was Blue Origin finally joining the game. In November, they became only the second company in history to successfully land an orbital-class rocket booster. New Glenn’s emergence transforms the competitive landscape—SpaceX finally has a genuine rival. Investment followed activity: Q3 2025 saw $3.5 billion in space VC funding, a record quarter representing 95% year-over-year growth. The total for 2025 exceeded $10 billion. Add Intuitive Machines’ Blue Ghost successfully landing on the Moon in March—the first commercial lunar landing in history—and China’s 89 launches (up 31% year-over-year), and the acceleration is undeniable. **Verdict: CORRECT** (the space economy is no longer theoretical) ## The Partial Hit ### 9. TikTok Banned in US ◐ **Original prediction (95% confidence):** “TikTok will face a ban or forced sale” The prediction came true—technically. The Supreme Court unanimously upheld the ban in January. TikTok went dark for about 24 hours. And on 18 December, a “sale” was announced: a joint venture with Oracle, Silver Lake, and MGX taking 50%, while ByteDance retains roughly 20% plus ongoing involvement through algorithm licensing. Here’s where it gets muddy. The law demanded a clean break from Chinese ownership. What emerged looks more like a franchise arrangement. ByteDance keeps the algorithm (licensed, not transferred). Chinese export controls likely prevent any true technology transfer anyway. Critics argue this doesn’t meet the statute’s requirements. The deal closes 22 January 2026—if it closes. None of this affects TikTok in Europe, where 159 million monthly users continue scrolling uninterrupted. **Verdict: PARTIAL** (a ban happened, a “sale” was signed—but whether ByteDance actually divested is contested) ## The Misses ### 10. US Recession ✗ **Original prediction (70% confidence):** “The US economy will enter technical recession” Q1 was weak—GDP contracted 0.6%, giving recession-watchers reason for concern. Then the economy roared back. Q2 and Q3 delivered solid growth. Consumer spending held up. Employment remained resilient. We overweighted the yield curve inversion signal. The US economy proved more adaptable than historical patterns suggested. Fair enough. **Verdict: INCORRECT** (the soft landing actually happened) ### 11. Bitcoin Hits $150,000 ✗ **Original prediction (75% confidence):** “Bitcoin will reach $150,000 driven by ETF flows and halving dynamics” Bitcoin reached $126,000—a significant move, and we were right about the direction. The ETF thesis played out: institutional adoption accelerated, and a Strategic Bitcoin Reserve entered policy discussions. But $150,000 proved a bridge too far. We overestimated the velocity, even if we got the trajectory right. **Verdict: MISS** (directionally correct, magnitude wrong—a distinction without much comfort) ## What We Learned **On technology timing:** AI is moving faster than even optimists expected. The people closest to the frontier are the most surprised by the pace. That’s historically unusual and worth taking seriously. **On China:** Export controls can accelerate innovation as easily as they constrain it. DeepSeek proved necessity drives creativity. Watch for more surprises here. **On economic predictions:** Macro calls remain humbling. We correctly identified the risks but overestimated the economy’s fragility. The US consumer continues to defy gravity. **On political intervention:** Legal outcomes ≠ political outcomes. The TikTok situation reminds us that predictions need to account for non-legal factors. **On space:** We’re in the early innings of something significant. Commercial space has reached escape velocity—the industry no longer depends on government contracts alone. ## Looking Ahead to 2026 The themes that defined 2025 will intensify in 2026. AI agents will move from impressive demos to production deployments. The Stack Wars between Google, OpenAI, and Anthropic will determine which architectures win. Physical AI—robotics powered by foundation models—will have its breakthrough moment. Samuelson’s point wasn’t that prediction is futile—it’s that the relationship between foresight and outcome is rarely clean. We got eight right, missed two, and one landed in the grey zone. The misses taught us more than the hits. What do we predict for 2026? [Read more here](https://www.superseed.com/journal/what-is-in-store-for-2026-eleven-predictions-for-the-year-ahead/). ![](https://www.superseed.com/wp-content/uploads/2025/12/scorecard_table.png)2025 Predictions Scorecard --- # Happy Birthday, ChatGPT. The World Really Did Change. **Source:** https://www.superseed.com/journal/happy-birthday-chatgpt-the-world-really-did-change/ **Published:** 2025-11-29 **Author:** Mads Jensen Three years ago, the world was emerging from a bruising period. The S&P 500 was down 15%. The spectre of Covid still hung over everything. There was a tentative sense that we might be turning a corner, but optimism was in short supply. Then, on 30 November 2022, OpenAI released ChatGPT, built on the Transformer architecture that Google had invented and published in 2017, but not yet turned into a product. Today, three years later, markets are up 16% year-to-date. There is a sense of exuberance. We’re debating whether we’re in an AI bubble. That optimism traces back to a single moment, precisely three years ago, which is why marking ChatGPT’s third birthday feels significant. A few days after ChatGPT’s release, [I wrote on LinkedIn](https://www.linkedin.com/posts/madsjensen_ai-future-technology-activity-7005448180087562240-JwmS?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAAa5uEBUWgITHgGX4J5QM_xz_r5C5fNfxo) that “the world changed forever”. The comments were instructive. “Do I understand correctly that ChatGPT only understands English?” asked one sceptic. “Then I don’t see it as a worldwide game changer.” Another dismissed it outright: “It can’t reason, ‘only’ parrot what is already known information.” A third found it couldn’t maintain a conversation: “It still couldn’t elaborate when I asked a follow-up question.” These weren’t unreasonable concerns. ChatGPT was text-only, English-focused, couldn’t browse the internet, and made up plausible-sounding nonsense. The sceptics were asking whether a system limited to language - and struggling even at that - could really matter. Three years on, that question has been answered decisively. ## **Mastering Language** Models still hallucinate, but far less often, and with better self-correction. The rest has transformed beyond recognition. ChatGPT now works fluently in over 100 languages. Reasoning models think step-by-step through PhD-level problems. Follow-up questions? Table stakes. The benchmarks tell one story: real-world coding tasks up from 48% to 81%, novel reasoning tests from 5% to 88%. But the lived experience tells another. In November 2022, I asked ChatGPT to help analyse company financials. It provided confident nonsense with made-up numbers. Today, Claude extracts the metrics, compares them to benchmarks, identifies gaps and drafts follow-up emails. That’s not an incremental improvement. That’s a different category of tool. The impact on how we work has been immediate. Three years ago, no developer used AI coding assistants. Today, the vast majority do. Google reports that more than a quarter of its new code is AI-generated. For me, the shift was personal: I used ChatGPT frequently, but it was Claude - and then Claude Code - that made AI indispensable. The difference between a capable tool and an essential collaborator. OpenAI’s launch caught Google flat-footed. All eight authors of Google’s original Transformer paper eventually left the company. But OpenAI soon got fierce competition from Anthropic, founded in 2021 by former OpenAI researchers. When ChatGPT launched, Anthropic was barely a year old. Today, Anthropic holds 32% of the enterprise market. Google’s newly released Gemini 3 is formidable, crushing OpenAI’s GPT-5.1 on most benchmarks. And given the gap between revenue growth and planned investments, HSBC estimates OpenAI needs a further $207 billion of investment before the company breaks even. OpenAI had a head start, and they are still the “one to beat”. But the revolution OpenAI started is far from won. ![](https://www.superseed.com/wp-content/uploads/2025/11/Three-years-1024x572.png) And as for the sceptics who questioned whether ChatGPT was a “worldwide game changer”? They might have been asking the right question. But they got the wrong timescale. ## **Beyond Language** Here’s what three years of progress revealed: linguistic intelligence has limits. Think about how human intelligence actually develops. A child spends years grasping objects, navigating rooms, learning that dropped things fall and hot things burn, all before speaking a single word. Our minds are built on that foundation of sensing and acting. Language comes later, layered on top. LLMs skipped that foundation entirely. They learned everything from text, never touching anything. As Stanford’s Fei-Fei Li puts it, they remain “wordsmiths in the dark; eloquent but inexperienced, knowledgeable but ungrounded.” They can discuss physics brilliantly. They cannot catch a ball. This is the next frontier: AI that understands reality the way we do. Not by reading about the world, but by sensing it, moving through it, acting within it. ## **The Convergence** Physical AI is unlocking now because three strands of technology are finally converging. We’ve had physics simulation for decades: game engines, industrial simulators, digital twins. We’ve had real-world robotics, collecting data one painstaking interaction at a time. And we’ve had machine learning, pattern-matching on whatever data was available. Each existed in isolation. None was sufficient alone. The breakthrough is their synthesis. GPU-accelerated physics engines now run 100 times faster than before, generating vast amounts of training data in simulation. Foundation models pre-trained on internet-scale data provide the generalisation that narrow robotics models lacked. And techniques like automatic domain randomisation - varying friction, lighting, object properties randomly in simulation - create policies robust enough to transfer to the real world. ![](https://www.superseed.com/wp-content/uploads/2025/11/AI-convergence-1024x559.png) In July 2023, Google DeepMind published RT–2 and gave the approach a name: vision-language-action models. Systems that see, understand instructions, and act. All integrated. Tell a robot “fold the towel” versus “stack the dishes”, and it figures out the difference. No explicit programming. No predefined steps. It learns patterns and handles situations it has never encountered. Jensen Huang frames it simply: “Just as large language models revolutionised generative AI, world foundation models are the breakthrough for physical AI.” This is why we’re at an inflexion point. Not a single invention, but a convergence: simulation, foundation models, and real-world deployment finally working together. We are still in the foothills. Current models succeed on basic manipulation (for example, picking up an object) only some of the time. And the margin for error in the physical world is unforgiving: a dropped component breaks, a wrong movement damages equipment. But the trajectory is unmistakable. Figure AI’s humanoid robots now work full shifts at BMW’s Spartanburg plant, trained largely in simulation; deployed in reality. As Figure’s Brett Adcock puts it: “Eventually, physical labour will be optional.” ## **Not Just Robots** This is not just about robots. Spatial intelligence means AI that can optimise a factory floor, manage energy flows across a grid, or coordinate construction equipment across a site. It means systems that understand physical constraints the way current models understand grammar. Waymo is already there. 250,000 autonomous rides per week in the US, with London launching in 2026. No safety driver. No remote operator. Just AI that understands how to navigate the physical world. [Hive Autonomy](https://hiveautonomy.no/) enables single operators to command fleets of autonomous construction equipment: diggers and haulers responding to intent, not explicit instructions. That’s not a research demo. That’s operating now. ## **The Work Question** The work question looms. If AI can write, code, analyse, and now act in the physical world, what’s left for humans to do? The surface concern is compelling. But there’s a deeper pattern worth understanding. The biologist Stuart Kauffman calls it the “adjacent possible”: each problem we solve expands the frontier of what becomes possible next. The web-enabled social media. Social media enabled the creator economy. Each solved problem reveals problems that couldn’t even be formulated before. Physicist David Deutsch makes the philosophical case: we are not approaching the end of useful work. We are at the beginning of infinity. The space of worthwhile problems expands faster than our capacity to solve them. Climate. Energy. Healthcare. Ageing. Space. Consciousness itself. The list doesn’t shrink as we progress. It grows. The transition won’t be painless. Technology is advancing faster than institutions can adapt, and the short-term disruption will be real. But human ingenuity has never failed to find work worth doing. There’s no reason to believe this time is different, and a universe of reasons to believe the opportunities ahead are extraordinary. ## **Three Years Hence** Three years ago, the question was, "is this actually useful?" The sceptics had reasonable concerns about a system that only spoke English and couldn’t hold a conversation. Those concerns fell faster than anyone predicted. Roy Amara observed that we overestimate technology in the short run and underestimate it in the long run. ChatGPT inverted that law. The sceptics underestimated the short run. And the long run? We are only just getting started. Happy birthday, ChatGPT. You proved machines could master language. The question now is whether they can master reality itself. Give it three years. --- # Physical AI: Europe’s Next Category Leaders **Source:** https://www.superseed.com/journal/physical-ai-europes-next-category-leaders/ **Published:** 2025-11-01 **Author:** Mads Jensen “It’s the most humbling thing I’ve ever seen,” Ford’s chief executive Jim Farley said about his recent trip to China. After visiting a string of factories, he was left astonished by the technical innovations being packed into Chinese cars. “Their cost and the quality of their vehicles is far superior to what I see in the West. We are in a global competition with China, and it’s not just EVs. And if we lose this, we do not have a future at Ford.” It’s not just the Americans feeling the pinch. The supposedly invincible German automotive industry is being dismantled in real-time. BYD sold 47,000 vehicles across Europe in Q3 2025, up 238% year-on-year. Chinese brands collectively captured 11.3% of Europe’s EV market in the first half of 2025, more than double their 4.8% share a year earlier. In Norway, Chinese EVs claimed 23% market share in September alone. ## Just cheap copies? Whilst Tesla’s Model 3 costs €42,990 and BMW’s i4 is €57,500 in Germany, BYD’s Seal sells for €31,990. These aren’t cheap knock-offs. Reviewers praise BYD’s build quality and interiors. They’re great products at dramatically lower prices. Volkswagen announced in November it would close its Zwickau EV plant, the first closure of a German factory in the company’s history, citing “unsustainable competition from subsidised Chinese imports selling 30% below our production costs.” European governments haven’t exactly been shy with automotive support—Renault’s €5 billion loan guarantee, VW’s €1.4 billion from Lower Saxony, billions in consumer EV subsidies across the continent. Both sides subsidise. But China is winning because they build better automated factories and iterate faster. Stellantis CEO Carlos Tavares warned in October: “Chinese manufacturers operate with 25–30% cost advantage that we cannot match with current structures.” This isn’t hypothetical future competition. BYD opened its Hungarian factory in July 2025 and announced a €2 billion investment in Spain. They’re not exporting to Europe—they’re building here. ## How We Lost the Lead For decades, Germany led the world in industrial automation. German manufacturers pioneered robot deployment, achieving the highest robot density globally. That automation advantage powered Germany’s reputation for precision manufacturing. But in the last decade, China deployed at massive scale. From 189,000 industrial robots in 2014 to 2.03 million today—a tenfold increase. In 2024 alone, China installed 290,000 robots. China now has 470 robots per 10,000 manufacturing workers, surpassing Germany’s 429. The United States manages 295. The United Kingdom just 111. China jumped from ninth to third place globally in robot density in four years whilst maintaining the world’s largest manufacturing workforce of 37 million workers. At current installation rates, China adds the UK’s entire robot stock every four months. Executives describe visiting Chinese “dark factories” so heavily automated that lights are unnecessary. Xiaomi operates one in Beijing producing 10 million mobile phones annually with zero human labour. One phone every three seconds, 24 hours a day, 365 days a year. Europe didn’t lose automation capability. We lost momentum deploying it at scale. ## Physical AI: The Next Transition China’s current advantage stems from traditional industrial automation—the robots that have existed for 60 years, deployed at extraordinary speed and scale. But the next wave threatens to make that advantage permanent. Physical AI represents flexible, language-directed robots that understand natural language commands and generalise across environments. Tell a robot “fold the towel” versus “stack the dishes” and it understands and executes. It works in new settings without prior training. This didn’t exist before 2023. Chinese institutions already lead the research. Tsinghua’s world models won Best Paper at Robotics: Science and Systems 2025. ByteDance collaborates with top universities on Vision-Language-Action models. Chinese research groups publish prolifically on physical AI foundation models. At current trajectories, China doesn’t just dominate manufacturing automation. They’re positioned to own Physical AI infrastructure entirely—the foundation models, the robot manufacturing, the deployment scale. For European manufacturing, this looks terminal. Not just losing current battles, but being shut out of the next generation entirely. Unless we understand what Physical AI actually requires. ## What Customers Actually Buy Dark factories don’t emerge from foundation models alone. They require sophisticated applications: quality inspection systems that predict defects, process optimisation tools that coordinate hundreds of robots, predictive maintenance platforms that prevent failures, warehouse software that adapts to changing inventory patterns. Physical AI applications are the transformation layer that turns general-purpose robots into productive factories. And applications are what customers buy. BASF doesn’t purchase foundation models. They buy AI inspection systems for chemical plants. Volkswagen doesn’t license robot platforms. They buy process optimisation for assembly lines. Hospitals don’t deploy general-purpose robots. They buy surgical planning systems. This pattern holds across technology waves. Applications captured value in PCs (Microsoft over IBM), in the internet (Google over Cisco). Infrastructure enables, but applications concentrate value because applications own customer relationships. Foundation models and robot platforms will commoditize. Value accrues to applications solving specific industry problems—quality inspection, predictive maintenance, warehouse coordination, surgical planning, pharmaceutical optimisation. And Europe is good at building applications. ## Europe’s AI Category Leaders Just look at the AI companies that have come out of Europe in the past few years. Synthesia serves 90% of the Fortune 100, defining the global standard for enterprise AI video generation. ElevenLabs set the worldwide benchmark for AI voice synthesis, serving 60% of Fortune 500 companies. Wayve is building the leading application layer for autonomous driving behaviour models. N8N competes directly with American workflow automation platforms, winning globally. Lovable demonstrates European capability in developer-facing AI applications. These aren’t just unicorns. They’re AI category leaders. Global standards in their domains. They competed against well-funded American companies on product quality and execution speed—and won. Physical AI creates the same opportunity. But they are not pure software. They require understanding mechanical systems, manufacturing processes, industrial constraints. Europe’s engineering heritage matters here. And this is what enables European founders to create global leaders in manufacturing, logistics, construction, agriculture, energy, and healthcare.  ## The European Ecosystem Isn’t AI all happening in Silicon Valley? Synthesia, ElevenLabs, Wayve, N8N, Lovable suggest otherwise. These aren’t exceptions—they’re part of a broader pattern. European venture capital has been growing rapidly over the past decade. From 2015 to 2024, European VC investment totalled €426 billion—ten times the €43 billion invested in the entire decade before. European unicorns grew from 72 in 2015 to 358 today, a fivefold expansion. In 2025, nearly a quarter of new unicorns globally are European companies. Europe’s VC ecosystem grew at 13% CAGR over the past decade, outpacing the US’s 8%. The founders exist. The capital exists (although we’d like more at the growth phase). The customer opportunities exist across sectors representing over $50 trillion in combined market value. European manufacturers, hospitals, construction companies, and pharmaceutical firms provide immediate deployment opportunities and urgent demand. ## Through Physical AI to Manufacturing Competitiveness The executives returning from Chinese factory tours are right to be concerned. European manufacturing must compete with China’s dark factories. The cost advantage is real. The automation gap is widening. Physical AI applications are how Europe closes that gap. Quality inspection systems, process optimisation tools, predictive maintenance platforms enable European factories to compete. And these same applications sell globally—to manufacturers, logistics companies, pharmaceutical firms worldwide. Building these companies isn’t choosing between manufacturing competitiveness and software value. It’s achieving both. Europe already builds AI category leaders. We led industrial automation for decades. Physical AI applications are how we reclaim that leadership whilst building the next generation of global category leaders. The dark factories are spectacular. Europe will build them too. Physical AI applications are how we get there. --- # How the AI Bubble Ends **Source:** https://www.superseed.com/journal/how-the-ai-bubble-ends/ **Published:** 2025-09-29 **Author:** Mads Jensen *This is not investment advice. Always conduct your own research and speak to your IFA before making investment decisions.* ## **The Turtle Problem** There’s an old story about a scientist lecturing on cosmology. When he finishes, an elderly woman stands up and declares that the world sits on the back of a giant turtle. “What does the turtle stand on?” the scientist asks. “You’re very clever,” she replies, “but it’s turtles all the way down.” In September 2025, watching Cursor reach a $10 billion valuation while sending every dollar of revenue to Anthropic, who burns money paying Amazon, who burns billions building infrastructure, the parallel became inescapable. In the AI economy, it’s not turtles—it’s negative margins all the way down. I’m often asked: “Are we in a bubble?” My answer is always the same: “Yes, we’re in a bubble, and yes, there are still incredible investment opportunities.” Both can be true simultaneously. Bubbles can inflate far longer than sceptics expect. And sometimes, what looks like a bubble turns out to be the early, messy birth of a new economic order. ## **Meet the Players** Before we trace the money, let’s identify who’s burning it. **The Hyperscalers**: Microsoft ($3.8T market cap), Google ($3.0T), Amazon ($2.3T), Meta ($1.9T), and now crucially, Oracle ($806B). Combined market cap: $11.8 trillion. Combined 2025 AI infrastructure spending: $365 billion. Combined AI revenue: $25-$30 billion across the major hyperscalers—a fraction of their infrastructure investment. **The Frontier Labs**: OpenAI ($500 billion valuation), Anthropic ($183 billion), xAI ($200 billion) and Google’s DeepMind. OpenAI burns $14 billion annually on $15 billion revenue. Anthropic, despite exploding from $1 billion to $4 billion revenue in six months, still loses $3 billion on $7 billion annually. xAI expects just $500 million revenue in 2025. Combined, they’re valued at nearly $900 billion (plus Deepmind - not broken out separately) while collectively losing billions. **The Open Source Disruptors**: Meta’s Llama 4, France’s Mistral, and critically, China’s DeepSeek and Alibaba’s Qwen. In January, DeepSeek R1 triggered a $600 billion market wipeout by matching GPT–4 performance. By July, Alibaba’s Qwen3 matched Anthropic’s Claude at $0.22 per million tokens—1/68th of Anthropic’s $15 price. They’re giving away what others sell for billions. **The Application Layer**: Companies like Cursor ($10 billion valuation), Perplexity ($20 billion), and Europe’s Loveable ($4 billion). These companies build consumer-facing products, sending most revenue straight to model providers. ## **The Money Flowing Upwards Too** But the money is not just flowing down the stack in losses, it’s flowing back up the stack again as investments. Nvidia commits $100 billion to OpenAI, who uses it to buy Nvidia chips. Oracle signs a $300 billion deal with OpenAI—betting 37% of its market cap on becoming the AWS of AI. Microsoft invested $13 billion in OpenAI. Amazon put $8 billion into Anthropic with Google investing another $4 billion (alongside their internal Deepmind investment). The layers are funding their customers to buy their own products. Cisco used to do something similar with their ecosystem back in the dotcom era. But the numbers are bigger now. ## **The Trouble with Negative Margins - Following a Dollar Through the Stack** Here’s what happens when a developer pays Cursor $30 for monthly AI coding assistance. Cursor receives that $30 and immediately sends it all to Anthropic, making the company “deeply unprofitable” even before counting salaries and servers. At a $10 billion valuation, investors are betting this changes dramatically. Anthropic receives that $30 but spends $43 on compute. With $7 billion revenue and $3 billion losses, they’re burning 43 cents per dollar received. Their $183 billion valuation assumes compute costs collapse. The hyperscalers collectively receive that $43 in compute payments. But this only goes to fund a fraction of the current capex load. It’s estimated that the hyperscalers are doing $25-$30bn of revenue on $150–200bn of incremental AI-related capex. Even if you assume depreciation over 5–6 years (generous, given the pace of technological change), the numbers don’t quite add up unless AI revenue increases dramatically. Meanwhile, the frontier labs like Anthropic are counting on the per unit compute cost to decline meaningfully. At the bottom of the stack sits Nvidia - the only company to make money today. They sell GPUs with 72% gross margins and 56.5% net margins. Every dollar of loss above them is Nvidia’s gain. Capex has now reached a level where the hyperscalers have started funding investment with debt. Oracle issued $18 billion in bonds, its total debt approaching $100 billion. Google’s cash balance declined for two quarters. Amazon’s free cash flow turned negative. They’re collectively spending $63 billion more than their combined free cash flow, betting their stock prices stay elevated to support the leverage. Or that revenue starts growing fast enough to sustain the continued capex flow. ## **Could This Really Work?: The Bull Case** For current valuations to make sense, here’s what needs to happen at each layer: **Nvidia** ($4.3T market cap): Must maintain 56% margins while growing revenue a further 60%+ despite AMD, Groq, and Cerebras building competing inference chips. They need AI compute demand to keep exploding faster than supply. Possible? Maybe. Physical AI is opening a new frontier. But competitive threats look daunting. **Hyperscalers** ($11.8T combined): Need AI revenue to grow from $25 billion to $200+ billion within 3 years to justify current CapEx. Microsoft’s AI business already hit $13 billion run rate, growing 175% YoY. This far outstrips enterprise cloud growth rates. But $200bn+ is not impossible. **Frontier Labs** ($900B combined): Either grow revenue from ~$25 billion to $100+ billion while maintaining pricing power against open source, OR pivot to application revenue with better margins. Plus ad monetisation. OpenAI’s ChatGPT shows a potential path to consumer revenue at high margins. **Applications** ($50B+ combined): Must dramatically improve margins by switching to open source for commodity tasks, frontier models only for complex work, and raising prices as value becomes clear. If Cursor can charge $100+/developer/month with 50% margins using mixed models, this could work. Just about. ## **The Problem with Cheap Inference** But here’s the paradox: if inference costs collapse to pennies by 2027 as many predict, application companies win but infrastructure providers get crushed. If Claude costs $15 per million tokens today but falls to $0.50 by 2027, Anthropic’s revenue would need to grow 30x just to stay flat. Open source will commoditise basic AI. Pricing power evaporates. The very success of cost reduction destroys the business model of those who enabled it. This is exactly what happened in previous platform shifts… ## **History Doesn’t Repeat, But It Rhymes** **Mainframe Era (1960s–1980s)**: IBM captured all value. Software was bundled free with hardware. IBM’s gross margins exceeded 60%. Then distributed computing arrived. **PC Era (1980s–2000s)**: Value shifted to software. Microsoft and Oracle became giants while IBM struggled. Compaq and Dell commoditised hardware. Software had 80%+ gross margins; hardware dropped to 20%. **Internet Boom (1995–2020)**: Initially, hardware boomed—Sun Microsystems, Cisco, EMC hit massive valuations. Cisco reached $500 billion market cap in 2000. Then the bubble burst. Value shifted to applications: Google, Amazon, Facebook. Cisco still hasn’t recovered its peak. **AI Era (2022-?)**: Nvidia dominates early, capturing 56% margins like IBM once did. But if history holds, value will shift to applications. The question is timing. ## **The Bear Case: Three Ways This Ends** **The Open Source Avalanche**: Alibaba’s Qwen3 already more or less matches Anthropic’s Claude at 1/68th the price. When enterprises realise they can run these models on their own infrastructure, Frontier Lab revenue collapses. Without frontier lab payments, hyperscaler AI revenue stalls. The stack unravels from the middle. **The Margin Compression**: Inference costs fall 95% as promised. Great for adoption, devastating for revenue. If tokens cost pennies, how does anyone charge dollars? The entire stack reprices lower. This will shift trillions of dollars in market value. **The CapEx Revolt**: By Q2 2026, hyperscalers will have spent $500+ billion cumulative on AI infrastructure. If AI revenue hasn’t exceeded $100 billion, CFOs revolt. CapEx guidance gets slashed 50%. Nvidia’s forward orders evaporate. The music stops. The AI boom doesn’t end when growth slows, but when capital markets stop believing margins will turn positive. That could be next quarter if markets revolt. Could be 2027 if discipline holds. Or massive enterprise value might be unlocked so quickly that margins suddenly flip positive across the board. Just don’t bet the house on all of it happening at once. ## **Why I’m Still Optimistic** Despite everything I’ve just written, I remain bullish on AI. History teaches us something crucial about bubbles: picking the right companies matters more than timing the market perfectly. If you bought Cisco in March 2000 at the peak, you’d still be down nearly 20% today—25 years later. But if you’d bought Amazon at the same moment, you’d be up 65 times. Both were “overvalued” internet companies. Both crashed when the bubble burst. One recovered and transformed commerce. The other, despite growing revenue from $12 billion to $57 billion, never regained its peak valuation. The AI boom will have its Ciscos and its Amazons. Yes, we’re seeing negative margins all the way down. Yes, $17 trillion assumes economics that don’t exist today. But somewhere in this stack, companies are building genuinely transformative technology. The productivity gains are real. The applications are useful. The value creation is happening, even if the value capture isn’t sorted yet. My bet? Painful consolidation, not catastrophic collapse. The hyperscalers survive but rationalise spending. Oracle either becomes the AWS of AI or loses $300 billion finding out. Nvidia faces margin compression but remains dominant. The frontier labs will become application companies in their own right or consolidate into the hyperscalers. Applications bifurcate into winners using mixed models profitably and losers dying on negative margins. The bubble may deflate. Valuations may compress. But for those who identify the Amazons of AI—the companies solving real problems with sustainable advantages—there’s still tremendous upside ahead. That’s the paradox of bubbles: they’re simultaneously destructive and creative. The key is knowing which turtle you’re standing on, and whether it’s one that will survive the shakeout. --- # The Sovereignty Invoice **Source:** https://www.superseed.com/journal/european-technological-sovereignty/ **Published:** 2025-08-31 **Author:** Mads Jensen ## The Bill Has Arrived Europe reluctantly accepted 15% tariffs from the United States last month. There were negotiations, attempts to find alternatives, diplomatic manoeuvring - but ultimately, something close to surrender. The EU acknowledged it lacked the levers to fight back effectively. The reason, whispered in Brussels but never stated plainly: we spend 2% of GDP on defence while relying on American protection that costs them 3.5%. The arithmetic finally came due. This is Trump being transactional, yes - but it’s also structural reality. Both things are true. The US calculation has shifted: providing global security guarantees no longer automatically translates to economic dominance when others might build better products. So now the security umbrella comes with an explicit price tag. The EU-US trade deal represents something new in European history: sovereignty as a subscription service, with the vendor adjusting rates based on market conditions. The surprise isn’t that America presented the bill. The surprise is that we’re still surprised. Every dependency creates leverage. Every leverage point eventually gets monetised. The comfortable assumption that security and economics operated in separate spheres - that assumption just ended. ## **How We Got Here** The dependencies seemed like rational choices at the time, but ultimately turned out to be mistakes - traps that snapped shut when the world changed. #### **Defence: The Peace Dividend Trap** Outsourcing defence to America freed up 2% of GDP for social programs and infrastructure. It worked from 1945 to 2024. The peace dividend funded the European social model we’re justly proud of. Then the bill arrived in the form of tariffs. #### **Energy: The Cheap Gas Illusion** The energy dependency was always a vulnerability. Europe has paid higher energy costs than the US and China for decades. The reliance on Russian gas - particularly acute in Germany since reunification - was a mistake in 2005 precisely because of where it led us today. When Russia invaded Ukraine, we discovered the price of that dependency. France’s nuclear independence and Norway’s energy surplus couldn’t compensate for Germany’s industrial heart being exposed. #### **Manufacturing: The Clean Cities Trade-off** Outsourcing manufacturing to China gave us cleaner cities and cheaper goods. We exported our emissions and imported our products, meeting climate targets while maintaining living standards. It seemed rational - until supply chains became weapons and industrial capacity determined technological sovereignty. ### **The Comfortable Middle Path** Each decision is optimised for the immediate benefit while creating a future vulnerability. We confused a favourable environment with a permanent condition. We chose the comfortable middle path - maintaining the appearance of strategic autonomy while deepening actual dependencies. It worked as long as no one called the bluff. Dependency by dependency, we’ve traded autonomy for comfort. Now the bills are arriving, and we’re discovering that sovereignty isn’t free - it just seemed that way when nobody was charging. ## **The Wake-Up Call** Three things changed in 2025 that make the sovereignty question unavoidable. ### **The Bills Became Explicit** First, the bills became explicit. Trump’s tariffs aren’t abstract economic theory - they impact our exporters directly by making them less competitive. When Ørsted’s wind contracts were cancelled over Greenland access, the message was clear: even close allies face economic consequences when strategic interests diverge. ### **The Alternatives Became Visible** Second, the alternatives became visible. China’s industrial policy - mocked for years as wasteful - delivered results. They funded 500 EV companies, watched 485 fail, and the 15 survivors now dominate global markets. While we debated market efficiency, they built market dominance. The lesson isn’t that we should copy China, but that strategic sectors require strategic thinking. ### **The Technology Shifted** Third, and most important: the technology shifted. We’re entering the age of Physical AI, and for the first time in two decades, Europe has structural advantages. Consider the evolution: Perception AI in the 2010s meant computer vision and voice recognition - the US dominated through data advantages. Generative AI in the 2020s meant LLMs and image generation - the US won through compute and capital. Agentic AI today means workflow automation - and European companies like n8n and Loveable are competing effectively. ![](https://www.superseed.com/wp-content/uploads/2025/08/image-15.png) But Physical AI - robotics, manufacturing automation, energy systems - is different. We have the factories that need automating. We have the engineering heritage (ASML makes machines precise to the atomic level). We have the industrial customers who understand that making things still matters. Europe produces 19% of global manufacturing output versus America’s 17%. That’s not a legacy burden - it’s a launching pad. The irony is perfect: just as we seemed resigned to permanent technological dependence, the technology itself shifted toward our strengths. ## **The Path Forward** The comfortable illusions must end. Industrial policy works when executed well - ask TSMC or BYD. Free markets alone won’t restore sovereignty when others are playing by different rules. Building European technological sovereignty requires three elements: talent to create, customers to buy, and capital to scale. We have the first - Europe produces more STEM PhDs than America, more software developers in absolute numbers. The second is fixable through procurement reform. But the third - capital - is the bottleneck that strangles everything else. ### **The Capital Crisis** A critical part of America’s success is the depth of its capital markets. More capital flows to large listed companies, mid-stage growth companies, and early-stage startups. Every rung of the ladder matters. US venture capital investment runs at $585 per capita versus Europe’s $104 - a 5.6x difference. This isn’t random; it’s structural. The root cause lies in how we deploy savings. American and Canadian pension funds define prudence as maximising long-term returns - the correct lens for retirement savings with decades-long horizons. European funds define prudence as minimising short-term volatility - utterly wrong for long-term savings. The result: European pension funds allocate much less than US counterparts to equities and other productive assets. Worse, too much of the European equity investment goes abroad. UK pension funds now allocate just 4.4% to UK equities - their lowest level ever. We’re not just conservative; we’re conservative AND we export our risk capital. Nobody suggests forcing European savers into European equities - least of all venture capital. But consider this: The UK alone spends £52 billion annually subsidising pension savings through tax relief. These subsidies apply whether investments go to Nasdaq, London, or Frankfurt. Why should British and other European taxpayers subsidise the provision of capital to US megacaps? The fix requires two changes: 1. Make pension tax relief conditional - full benefits only for European productive assets 2. Redefine fiduciary duty from volatility minimisation to long-term value creation Fix these, and we unlock more capital flows across the entire ladder - from venture to growth to public markets. Long-term returns to savers increase. GDP growth accelerates. Innovation thrives. ### **Strategic Sectors** Focus is essential. Ten sectors will determine technological sovereignty: semiconductors, energy storage, critical materials, AI, biotech, defence and aerospace, quantum computing, telecommunications, robotics, and autonomous vehicles. No single European country can lead them all. So we must work together with our closest partners. UK-EU collaboration isn’t nice to have, it’s a necessity. ## **The Choice** The sovereignty invoice grows monthly. Every delayed decision compounds the cost. But we have everything needed to pay it down. We have more STEM PhDs per capita than America. Our industrial base remains formidable - 19% of global manufacturing. When European entrepreneurs and engineers get the resources they need, they build world-leading companies. ASML dominates semiconductor equipment globally. Airbus has surpassed Boeing in commercial aviation. Siemens leads industrial automation. Ericsson and Nokia anchor global 5G infrastructure. The lesson isn’t to pick champions - it’s to create conditions where thousands can compete and the best naturally emerge. For investors, the opportunity is historic. European B2B startups create more than 2x as much revenue per invested € as their US peers - not through miracles but through discipline. And according to Cambridge Associates, European venture capital has already delivered better returns than our US counterparts over the past 10 and 15-year horizons. But there is so much more we can do. And the new geostrategic reality suggests that we better get started on that work asap. The sovereignty invoice isn’t about choosing autarky over openness. It’s about building strength in what matters while trading freely in what doesn’t. Europe doesn’t need to make every widget or write every app. But when our energy, defence, and digital futures depend on foreign goodwill, we’re not sovereign - we’re subscribers. The bill is due. We can pay it by building strategic autonomy in critical sectors, or we can continue managing decline. What we can’t do is pretend the choice doesn’t exist. The comfortable middle path just closed. --- # A DeepSeek 2.0 Moment Around the Corner? **Source:** https://www.superseed.com/journal/a-deepseek-2-0-moment-around-the-corner/ **Published:** 2025-07-31 **Author:** Mads Jensen ## **Why Anthropic Commands $170 Billion as Alibaba's Open Source reaches parity** *This is not investment advice. Always consult your IFA before making investments.* On December 26th last year, Western investors were busy enjoying Christmas food while a Chinese start-up released an open-source model that approached the state of the art of Western frontier AI models. At a fraction of the cost. Markets didn’t pay DeepSeek v3 any attention at the time. Then came January: DeepSeek R1, their reasoning model, triggered a $600 billion stock market wipeout. The lesson seemed learned. Yet here we are in July, just seven months later: Chinese AI startup Moonshot launches Kimi K2. An excellent open source model on par with those from US frontier labs OpenAI, Anthropic, Google and xAI. Less than a week later, Alibaba releases an upgrade to their open source model - Qwen3. This model was even better! As we enter August, Anthropic looks to raise $5bn at a $170bn valuation. But in the background, Alibaba’s model performs on par with Anthropic, at $0.22 per million tokens—1/68th of Anthropic’s price. The pattern holds: ignore until forced to see. ## Stocks Back at an All Time High The S&P 500 gained 2.3% to close at 6,339, bringing year-to-date returns to 8%. This week added fuel: Meta’s quarterly profit reached $18.3 billion (beating expectations by 21%), Microsoft’s AI business hit a $13 billion run rate (up 175% YoY), and Q2 GDP surprised at 3.0% versus 2.3% forecast. Broader market participation continues beyond tech giants. The Fed held at 4.50% while inflation ticked up from 2.8% to 2.9%—driven by exactly the import categories economists warned about when evaluating tariff impact. Markets have powered ahead anyway. After all, 15% EU tariffs beat the threatened 30%. The rally obscures deeper shifts. Core goods inflation stirred to life at 0.2%, led by clothing and appliances. Without auto price declines masking impact, the rise would’ve been 0.5%—the highest monthly jump since 2022. The bond market at 4.37% indicates what politicians won’t say: tariff passthrough has begun. But so far, markets are shrugging that off, as Microsoft becomes the world’s second $4trn company. ![](https://www.superseed.com/wp-content/uploads/2025/07/image-1.png) ## The AI Acceleration Anthropic’s trajectory defies precedent. Revenue exploded from $1bn to $4bn in half a year—perhaps the fastest growth in startup history. Their models are exceptional and their execution brilliant. They’ve captured the mind-share of the world’s software developers. Assuming the company will be at $5-6bn of run-rate revenue by the time a new $5bn round closes, this would value the company at 28-34x ARR. Doesn’t feel completely out of kilter for a company that is on track to grow from $1bn to $10bn in a year. But how can Anthropic’s Dario Amodei grow revenue so fast, if comparable Chinese open source models cost a fraction of the price? It turns out he has built something Fortune 500 CTOs want to buy: compliance guarantees, US data residence, enterprise SLAs, and someone to sue if things go wrong. Yet Alibaba’s July model (enticingly named Qwen3-Coder-480B-A35B-Instruct) changed the game’s physics. The model compares neck-on-neck with Anthropic’s Sonnet and Opus models. The philosophical divide sharpens monthly. While Anthropic negotiates a raise at $170bn valuation, Alibaba practically gives their model away as open source. Seven months from DeepSeek v3 to Qwen3, with market panic in between, suggests we’re early in an exponential curve. I remain a huge fan of Anthropic’s work. But we must acknowledge the risk that Western investors, focused on quarterly revenue growth, might miss the inflection point. Again. ## The 15% Solution In July, Trump achieved something unexpected. He has seemingly reshaped global trade with just a short run of market panic in April. The EU accepted 15% baseline tariffs—up from 1.2% but below the threatened 30%. In exchange, Europe commits to $750 billion in US energy purchases and $600 billion in investments. For a president who measures success in deals, this does look like a prize winner. Many of us thought the EU would put up more of a fight. After all, the US/EU trade balance more or less balances, when you take services and IP payments into account. But the geopolitical situation was more complicated. For a continent with a revanchist neighbour on its doorstep, Europe is poorly defended. Especially if you think the US might pull their security guarantees. For decades, the continent saved 1-2% of GDP by outsourcing security to America. So in some ways, one can say that Europe racked up a defence debt to the US, and that the same defence bill was due for collection this month. Unable to defend Ukraine alone, dependent on US weapons and intelligence, Europe negotiated from weakness. Fifteen percent tariffs are simply the price of strategic dependence. From a US perspective, the Victory narratives obscure underlying inflation arithmetic. July’s inflation data revealed import-intensive categories leading price increases. When inventory buffers empty and tariffs bite fully, inflation could quickly head back above 3%. Trump may accept this trade-off—strategic victories sometimes require tactical costs. We should expect some wobbles in markets if we start seeing a 3-handle on inflation data. ## The Figma Celebration For an industry starved of liquidity, Figma’s IPO delivered. Priced at $33 per share with 40x oversubscription, the company listed at a $19.3 billion valuation, then promptly rocketed to $115.50/share on the day of listing. Not many investments offer a 250% return in 24 hours, yet here we are. The wider picture is promising for the venture industry. After years of waiting, VC investors finally get a proper SaaS exit—not infrastructure plays like CoreWeave, not crypto-adjacent like Circle, but enterprise software with $1 billion ARR growing 40%. The numbers sing: 91% gross margins, 132% net dollar retention, used by 78% of Forbes 2000 companies. Dylan Field, who started this at 20 and is now 33, built the bellwether. If Figma prices well, dozens more might finally follow. And provide much-needed distributions back to long-waiting LPs. ## The Questions Beckon… Two paradoxes define this moment. First: how can markets keep climbing when inflation creeps higher, driven by the very tariffs Trump is celebrating? Second: how can Anthropic command $170 billion when Alibaba offers comparable performance for free? The answer lies in AI’s transformative potential outweighing near-term headwinds. Markets aren’t ignoring inflation—they’re betting AI productivity gains dwarf it. When law firms cut costs 90% and developers triple output, even 3% inflation becomes manageable. The S&P climbs because AI creates more value than tariffs destroy. This calculation holds as long as adoption accelerates and efficiency compounds. So far, it does. The second paradox resolves through enterprise reality. Yes, Qwen3 matches Claude’s benchmarks at 1/68th the cost. But Fortune 500 CTOs buy US data residence, enterprise SLAs, and vendor liability. When AI unlocks millions in productivity gains, paying $15 instead of $0.22 per million tokens remains a rounding error. Anthropic’s 28-34x multiple follows naturally from $1 billion to $4 billion revenue in six months. ## …but for how long? This logic holds—today. Technology teaches that today’s moat becomes tomorrow’s commodity. Unless Anthropic continues to out-innovate, Fortune 500 CIOs will realize they can run open source models in AWS data centers on US soil. We’re not in bubble territory yet. Unlike 1999’s revenue-free dot-coms, today’s AI leaders deliver real growth, real customers, real cash flows. The risk isn’t that the potential of AI disappoints—it’s that execution fails to match potential. Markets rarely move in straight lines. And we expect corrections along the way. Corrections in transformative cycles shake out tourists and create entry points for those who see the longer arc. The medium-term trajectory remains intact because the fundamentals are real. AI isn’t creating value in theory—it’s creating value today, measurably, repeatedly. Short-term volatility changes prices, not prospects. ## Onwards! Seven months from DeepSeek v3 to Qwen3. January’s panic gave way to July’s shrug. The difference? Nvidia’s revenue keeps climbing. Anthropic grew from $1 billion to $4 billion. Meta and Microsoft crush earnings. Markets have concluded that revenue growth validates the AI thesis—Chinese models be damned. The real triggers for correction lie elsewhere. Watch Nvidia’s next earnings call. Track Anthropic’s path from $4 billion to $10 billion. Monitor enterprise AI budgets. If and when these wobble, markets will follow. The bet is simple: as long as Western AI companies deliver revenue growth, model performance parity doesn’t matter. Markets believe we’ve learned from DeepSeek—that demand trumps disruption. The 15% tariffs are now reality. Inflation will creep higher as buffers empty. Chinese models will keep improving weekly. None of this changes the fundamental equation: AI creates more value than it costs, by orders of magnitude. This gap drives everything else. For now, the productivity gains compound faster than any headwind. Figma’s IPO shows traditional software still has value. Europe pays its bills while building its future, creating more opportunity in European tech than we’ve seen in decades. The patterns are clear. The opportunity clearer still. Position accordingly. --- # IPOs Are Back! **Source:** https://www.superseed.com/journal/ipos-are-back/ **Published:** 2025-06-30 **Author:** Mads Jensen *This is not investment advice. Always do your own research, and consult your IFA before investing.* ## Why AI Transformation Tops Trump's Trade Wars and Fiscal Headwinds The Nasdaq hit another record high in June, CoreWeave's stock price quadrupled from its March IPO, and Circle trades at $190 after listing at $31. Even Klarna—after delaying twice—is racing to file while the IPO window is open. Welcome to the return of the public markets. This resurgence arrives despite genuine headwinds. The US fiscal trajectory remains concerning, with debt service now consuming 17% of federal revenue. Trump's tariff negotiations reach their climax on July 9th. And we just witnessed the most significant Middle Eastern realignment in decades, with US/Israeli strikes on Iranian nuclear facilities in June marking the culmination of months of regional upheaval. Yet markets have decided none of this matters as much as the AI transformation unfolding before us. Let's examine why investors are looking past the obstacles—and what could still derail this momentum. ## The IPO Floodgates Open While 2025 had promised to be a major IPO bonanze, the Trump Tariff pandemonium caused enough uncertainty to put a lot of IPOs on hold. As of the end of Q2, total capital raised in US IPOs is at $15.5bn (excluding SPACs), 7% below the same period last year. However, under the covers, there are signs that US ebullience is returning. CoreWeave and Circle have had exceptional runs since listing. And with a resolution to the Trump trade wars in sight, there is hope that things could accelerate in H2. The optimism stems from several very successful IPOs. The standouts cluster in two categories: AI and crypto infrastructure. https://datawrapper.dwcdn.net/vdPDg/2/?dark=true In addition, 63 SPACs have IPO'd this year, raising a healthy $11 billion. That compares to $8.7 billion raised through SPACs in all of 2024, and signals a lot of capital now out looking for acquisition targets. On the home front, June saw a nice “win” for London. Visma chose the London Stock Exchange over NASDAQ for its €19 billion listing, slated for 2026. Why would a software giant pick London's lower liquidity? Simple: 70% of revenue comes from Nordic/Benelux SMEs. US investors struggle to understand European B2B dynamics. Sometimes the smaller pond makes sense. ## AI's Consumer Breakthrough Menlo Ventures' "State of Consumer AI" report captures a pivotal shift. AI has moved from "interesting feature" to "core functionality" across consumer applications. Key findings: - Consumer AI apps now represent 8 of the top 20 non-gaming apps by revenue - Average consumer interacts with AI features 4-6 times daily (up from near zero in 2023) - Willingness to pay for AI features has tripled year-over-year ChatGPT continues its dominance as the flagship consumer AI application, becoming the fastest-growing consumer application in history. The proliferation of AI-native apps—from character companions to AI photo editors—shows consumers absolutely do want dedicated AI applications. This consumer adoption creates powerful network effects. As millions use AI daily, the feedback loop accelerates improvement, which drives more usage, which generates more data. We've seen this movie before with social networks. Now it's happening with intelligence itself. ## The Transformative Impact of AI The revenue growth rates are magnificent. - **OpenAI**: From $5 billion to approaching $10 billion annual run rate in six months. - **Anthropic**: Now exceeds $3 billion ARR, tripling from $1 billion at the end of 2024. - **Cursor/Anysphere**: $500 million ARR, still doubling every 8 weeks. We're watching the early innings of a multi-trillion dollar reorganization of how work gets done. It’s exciting. ### Copyright Victories Clear the Path Meta and Anthropic's copyright victories in June removed a major uncertainty. US Courts ruled that training AI on copyrighted material constitutes "transformative use"—the same doctrine that allowed Google to scan books. The judges found that converting books into statistical patterns for AI training transforms their purpose entirely, making it fair use. While appeals loom, the precedent gives AI companies confidence to train on broader datasets. This matters because better data means better models, and better models mean more valuable applications. It’s likely that the cases will come back in different forms, but it’s 1-0 to the frontier AI labs so far. ## What Could Stop the Music? Two near-term events could puncture the optimism: **1. The July 9th Trade Deadline** Trump has promised massive tariffs on EU goods, and the EU has prepared retaliatory tariffs on €95 billion worth of US goods if trade negotiations fail. Both sides seem confident that an agreement can be made, but in the unlikely event that negotiations blow up, expect markets to react negatively. **2. Q2 Earnings Season** The real test arrives mid-July when mega-caps report Q2 earnings. Key dates to watch: - **July 16**: Major banks (JPMorgan, Goldman Sachs) - **July 23**: Microsoft, Alphabet - **July 25**: Apple, Amazon - **July 30**: Meta - **August 6**: Nvidia (the big one) With the S&P 500 and Nasdaq now at record highs, expectations run hot. Any disappointment from the Magnificent Seven could trigger a correction. Nvidia especially matters. With a valuation now approaching **$4trn**, its August 6th report could make or break the rally. ## Looking Ahead Markets exhibit a distinctly risk-on mood. Following a strong June, the IPO window seems reopened after a three-year drought. AI adoption accelerates from 6% to what could be 15-20% of enterprises by year-end. And Europe shows signs of life, with serious infrastructure commitments finally emerging. But let's not mistake momentum for invincibility. Real icebergs float ahead. If earnings disappoint or trade negotiations explode, this rally could reverse quickly. The difference this time? The underlying transformation continues regardless. Companies using AI to achieve 10x productivity gains don't stop because markets wobble. June 2025 marked the month when markets decided transformation trumps turmoil. Whether that confidence survives July's tests will set the tone for H2. For those building through the uncertainty, the message remains constant: focus on creating real value, not riding waves. The patient builders survived 2001 and 2008. They'll survive whatever comes next. --- # Formulating Winning Strategy for Seed-Stage B2B Startups: A Technical Founder's Guide **Source:** https://www.superseed.com/journal/formulating-winning-strategy-for-seed-stage-b2b-startups-a-technical-founders-guide/ **Published:** 2025-05-05 **Author:** Mads Jensen # Why Read This? Some founders approach business challenges like debugging code: identifying and addressing symptoms without diagnosing root causes. As a result, many seed-stage startups fail not due to product deficiencies, but strategic misalignment. This article provides a structured framework for strategic thinking that's compatible with how technical minds operate. ## Strategy as a System Optimisation Problem For technical founders, strategy can be conceptualised as a system optimisation problem: **Inputs**: Market conditions, competitive landscape, resources, technical capabilities **Constraints**: Capital, team size, time to market, technical debt **Function**: Your strategic approach **Output**: Sustainable competitive advantage and business growth Just as you wouldn't optimise code without profiling the bottlenecks, you shouldn't craft strategy without diagnosing your critical challenges. ## The Strategy Framework: A System Architecture Approach I have two simple go-to frameworks for startup strategy. Richard Rumelt's Good Strategy/Bad Strategy, and the GOSPA framework. Richard Rumelt's framework provides an excellent system architecture for strategy development: 1. **Diagnosis**: Identify the critical challenge(s) that must be overcome (the bottlenecks) 2. **Guiding Policy**: Develop your approach to address those bottlenecks (the algorithm) 3. **Coherent Actions**: Create specific, coordinated actions to execute the policy (the implementation) The GOSPA framework complements this by providing metrics and execution structure: 1. **Goals**: Qualitative description of what you want to achieve 2. **Objectives**: Quantitative, measurable targets (SMART goals) 3. **Strategies**: Approaches to achieve objectives 4. **Plans**: Sequenced steps with ownership and timelines 5. **Activities**: Specific daily tasks ## Template: Strategy for European Seed-Stage B2B Startups ### 1. Strategic Objective (1-2 sentences) What system state are you trying to achieve in the next 12-18 months? *Example*: "Establish product-market fit with UK-based mid-market manufacturers by solving their inventory optimisation challenges, creating a replicable model for expansion into German and Nordic markets." ### 2. Diagnosis of Critical Bottlenecks (3-5 bullet points) What are the specific bottlenecks preventing you from achieving your desired state? *Example*: - Enterprise buyers require extensive proof of ROI before adopting new inventory solutions - Technical integration with various legacy ERP systems creates implementation friction - Competitors have established relationships with procurement decision-makers - Regulatory compliance requirements differ across European markets - Our limited team size restricts our ability to support complex implementations ### 3. Guiding Policy (1-2 paragraphs) What is your strategic policy for overcoming these bottlenecks? *Example*: "We will focus exclusively on manufacturers using SAP systems, where our founding team's expertise gives us unique integration capabilities. Rather than competing directly with established vendors, we will position our solution as complementary technology that leverages existing investments while delivering 30% inventory cost reduction. We'll create a standardised compliance framework addressing UK, German and Nordic regulatory requirements simultaneously. We'll reduce implementation friction by creating pre-built integrations and proving value through free pilots that require minimal IT involvement." ### 4. Measurable Objectives (3-5 bullet points) What metrics will validate your system's performance? *Example*: - Complete 10 successful pilot implementations by Q3 - Achieve 70% conversion from pilot to paying customer - Reach £300K ARR by end of year - Reduce average implementation time from 6 weeks to 2 weeks - Maintain gross margin above 70% ### 5. Key Strategic Initiatives (3-5 bullet points) What major modules will you build to implement your strategy? *Example*: - Develop "SAP Quickstart" integration package that reduces implementation time by 70% - Create case study programme with performance-based incentives for early adopters - Establish advisory board of manufacturing technology leaders to provide domain expertise and credibility - Build automated ROI calculator with real-time integration to customer systems ### 6. Strategic Resource Allocation How will you allocate your compute resources to execute this strategy? Make this super simple. Just focus on the key initiatives that you have outlined and what either existing or new heads you plan to apply to the issue. ## Strategic Anti-Patterns for Technical Founders 1. **The Elegant Solution Fallacy**: Building technically impressive products that don't solve urgent market problems. Optimising for technical elegance rather than customer value. 2. **The Feature Factory**: Adding features based on individual customer requests without a coherent strategy, creating a maintenance nightmare and diluting core value proposition. 3. **The Stealth Mode Syndrome**: Perfecting the product behind closed doors while competitors capture market share and establish mind share. 4. **The Technology-First Pitch**: Approaching investors and customers with deep technical explanations before establishing business value, losing audience engagement. 5. **The Scaling-Before-Validation Error**: Investing in infrastructure, hiring and processes before validating product-market fit, depleting runway without achieving strategic milestones. ## Strategy Iteration and Validation Strategy isn't a waterfall process; it's iterative like agile development. Implement continuous testing and refinement. Review monthly or quarterly with your board or lead investor. Iterate to refine or pivot if you've hit a dead end. And stay agile. The goal is to fail fast on strategies that aren't working and double down on those that show promise. Just as you wouldn't continue developing on a failing architecture, don't persist with a failing strategy. ## Using This Framework for Board Meetings Your board pack should begin with your strategic framework, followed by progress against objectives and evolution of your thinking. Structure your board pack as follows: 1. **Strategic Framework** (1 page) - Strategic objective and critical bottlenecks - Guiding policy and key initiatives - Changes since last meeting (with rationale) 2. **Progress Against Objectives** (1 page) - Metrics dashboard showing performance against KPIs - Key wins and lessons learned 3. **Strategic Discussion** (1-2 pages) - The biggest challenge you're currently facing - Options you're considering to address it - Specific guidance you're seeking from the board 4. **Appendices** - Operational metrics - Team updates - Product roadmap - Financial information By structuring your board pack this way, you focus the discussion on strategic challenges rather than tactical updates. Board members can prepare thoughtful input rather than spending the meeting trying to understand what you're trying to achieve. ## Conclusion For technical founders, strategic thinking should feel familiar: identify bottlenecks, design an efficient strategy to address them, implement, measure results, and iterate. The European context adds specific considerations around market fragmentation, capital efficiency, and sales cycles that must be factored into your strategic system design. A clearly articulated strategy transforms your board meetings from status updates to valuable strategic discussions. By identifying your critical challenges and developing a coherent approach to overcome them, you provide your board with context to offer meaningful guidance. Remember that strategy functions like a distributed system - all components must work together coherently toward optimising for the same outcome. As with any complex system, regular monitoring, testing and refinement are essential to achieve sustainable performance. --- # May 2025: Tariff Turbulence and AI's Resilient March Forward **Source:** https://www.superseed.com/journal/may-2025-tariff-turbulence-and-ais-resilient-march-forward/ **Published:** 2025-04-30 **Author:** Mads Jensen *This is not investment advice* April's financial markets resembled a roller-coaster with an unpredictable operator—rapidly plunging, then soaring, before ultimately closing the month more or less where they started (S&P500 down 0.76% and Nasdaq-100 up 1.52% for the month, driven by strong performance from Microsoft, Broadcom and Tesla). Trump's "Economic Liberation Day" tariff announcement on April 2nd sent the Nasdaq 100 tumbling 11% in just 48 hours, only to recover after his 90-day implementation pause a week later. Behind these dramatic swings, fundamental questions loom large: Will Trump's tariff brinkmanship reshape global supply chains? Is the anticipated exit window for venture-backed companies closing before it truly opened? And can Europe's AI ecosystem find opportunity amid this chaos? The answers may define investment strategies for the remainder of 2025. In this month's analysis, I examine the tactical gambit behind Trump's tariff announcements, why the exit renaissance has stalled yet again, and where European AI innovation continues to thrive despite—or perhaps because of—this uncertain landscape. ## Stock Market and Economic Indicators **Markets Whipsawed by Tariff Theatrics** The S&P 500 and Nasdaq 100 end April essentially flat, obscuring extraordinary volatility throughout the month. Trump's April 2nd "Liberation Day" pronouncement—introducing a 10% universal tariff and higher country-specific penalties—initially sent markets spiralling. The S&P 500 dropped 8%, while the tech-heavy Nasdaq plummeted 11% within 48 hours as investors processed the implications of 25% tariffs on automotive imports, 125% on Chinese goods, and various penalties on virtually all US trading partners. The subsequent 90-day implementation pause announced on April 9th triggered an equally dramatic recovery—though notably, no reprieve was granted for Chinese imports. This rollercoaster mirrors the broader uncertainty permeating business planning, with the International Monetary Fund already downgrading its 2025 global growth forecast from 3.3% to 2.8%, citing trade policy uncertainty as a primary factor. **Inflation and Interest Rate Outlook Shifts** US inflation held steady at 2.4% in March, but economists are now revising projections upward, with April's figure (due May 13th) forecast to hit 2.7%. This uptick suggests that anticipated trade and supply chain disruptions are already beginning to feed through to consumer prices. Analysts at Goldman Sachs suggest tariffs could add 0.5 percentage points to core inflation by year-end if fully implemented—potentially derailing the Federal Reserve's rate-cutting plans. The bond market has already rendered its verdict, with 10-year Treasury yields initially spiking to 4.5% from 3.94% following Trump's tariff announcement, before settling back to 4.18% by month-end. This remarkable volatility reflects how dramatically tariff uncertainty has shifted the economic landscape since the Fed began its cutting cycle last September. The European Central Bank, maintaining its more dovish stance, implemented another 25 basis point cut on April 14th. This policy divergence has actually weakened the dollar against the euro, with the EUR/USD exchange rate rising to 1.13, creating an additional headwind for European exporters. For global corporations, this environment creates a planning nightmare as short-term opportunism trumps strategic planning. ## IPO and M&A Activity: Exit Window Slams Shut Many venture investors had entered 2025 with high hopes for a liquidity renaissance. After years of anaemic exit markets, Google's $32 billion acquisition of Israeli cybersecurity firm Wiz on March 18th (their largest acquisition ever) seemed to herald a new era. The transaction demonstrated massive appetite for category-defining assets - even at premium valuations. CoreWeave's IPO on March 28th further bolstered optimism. While priced at $40 per share—below the initially targeted $47-$55 range—the company successfully raised $1.5 billion, and the market's reception appeared stable. However, Trump's "Liberation Day" pronouncement just five days later abruptly reversed this positive momentum. Klarna's long-anticipated IPO, expected to value the Swedish fintech at $14-20 billion, was postponed indefinitely. The Swedish buy-now-pay-later giant had been preparing for months, having secured profitability in 2023 and grown its user base to over 150 million globally. The venture industry is understandably disappointed that distributions have been further postponed just as the exit window appeared to be reopening. If there's a silver lining, it may be the Trump administration's generally more business-friendly approach to antitrust enforcement. The appointment of figures like Gail Slater suggests a departure from the aggressive stance that characterised the Biden-era Federal Trade Commission under Lina Khan. This could eventually unleash a wave of strategic acquisitions, particularly if public markets remain volatile. Cerebras's public offering remains stalled by a Committee on Foreign Investment in the United States (CFIUS) review of G42's $335 million stake. The one exception to the broader pause is Figma, which confidentially filed for an IPO in April despite the turbulence. After Adobe's $20 billion acquisition was blocked by regulators in 2023, Figma's filing signals confidence in their business fundamentals, though the actual offering will likely wait until market conditions improve. ## AI and Technology Developments: European Innovators Make Their Mark Against this uncertain macroeconomic backdrop, April brought several landmark developments in artificial intelligence—with European players making their mark on the global stage. **Isomorphic Labs' $600 Million Breakthrough** Isomorphic Labs, the London-based AI drug discovery company spun out of Google's DeepMind in 2021, secured a massive $600 million funding round led by Thrive Capital. The company, leveraging breakthroughs from AlphaFold 3's protein structure predictions, has established partnerships with pharmaceutical giants Eli Lilly and Novartis potentially worth up to $3 billion. CEO Demis Hassabis articulated an audacious vision: "to solve all disease with AI." While that might sound hyperbolic, Isomorphic's core technology—using AI to predict how drug candidates will interact with biological targets—addresses a fundamental inefficiency in pharmaceutical development where 90% of candidates fail before reaching market. Early results suggest their approach could improve success rates by 30-40%, potentially transforming the economics of drug discovery. This represents another significant success for the UK's robust pharmaceutical and biotech ecosystem, home to global giants like GlaxoSmithKline and AstraZeneca. Isomorphic exemplifies how the convergence of the UK's traditional pharma strength with its world-class AI research can create powerful new innovation hubs. As we've long argued, Europe's depth of technical talent positions it perfectly for the next wave of AI applications across industries. **Wayve-Nissan Partnership: A European Approach to Autonomous Driving** Equally significant was Wayve's partnership with Nissan, announced mid-April. The UK-based autonomous driving startup will integrate its self-learning AI software into Nissan's next-generation ProPILOT driver-assist system, launching in 2027. This deal exemplifies a distinctly European approach to AI commercialisation—collaborating with established industry players rather than pursuing the capital-intensive direct-to-consumer strategy favoured by American counterparts. Unlike Tesla or Waymo, which operate their own vehicles to generate training data, Wayve's partnership gives them access to millions of miles of real-world driving without the enormous capital requirements of maintaining a proprietary fleet. For European AI startups, this success highlights a viable path forward despite capital disadvantages relative to American competitors. **OpenAI's o3 Release: Advancing AI Reasoning** April also saw OpenAI launch its o3 model, designed to excel in tasks requiring deep reasoning, such as mathematics, coding, and scientific analysis. The model incorporates a "deliberative alignment" approach, allowing it to simulate a chain of thought and allocate additional computation time to complex problems. While o3 achieved an impressive 87.5% score on the ARC-AGI benchmark in high-compute mode, it has faced some criticism regarding hallucination rates. Nevertheless, the model represents another significant step forward in AI capabilities, particularly in complex reasoning tasks. This release comes as OpenAI finalises terms with SoftBank for their long-anticipated funding round, reportedly valued at $40 billion with an initial $10 billion tranche to be delivered immediately. This investment will fund both hardware expansion and talent acquisition as the company continues its aggressive growth strategy. ## Looking Ahead: Navigating Q2 2025 As we navigate through Q2, several critical developments warrant close monitoring: **Trump's Tariff End Game** May 3rd brings the implementation of 25% tariffs on auto parts, potentially revealing whether the administration intends to follow through on its broader trade agenda or pursue a more negotiated approach. Treasury Secretary Scott Bessent's recent comments that current tariff levels on Chinese goods are "unsustainable" suggest potential moderation, but the administration's pattern of contradictory messaging makes predictions challenging. The EU's potential response—targeting US tech services like AWS—could further complicate the landscape for technology companies. European policymakers are carefully calibrating their response to avoid escalation while protecting key industries. **OpenAI's Strategic Evolution** With its SoftBank financing approaching closure, OpenAI's strategic priorities for the remainder of 2025 will indicate the industry's direction. CEO Sam Altman has indicated that GPT-5's release has been delayed, with a potential launch expected around July or later. This delay is attributed to efforts to enhance the model's capabilities and manage capacity challenges. Instead, OpenAI has focused on releasing specialised models like o3 and o4-mini. Despite recent turbulence, we remain fundamentally optimistic about the technology landscape. The current efficiency revolution expands rather than diminishes AI's transformative potential by democratising capabilities previously limited to the best-funded players. Lower training and inference costs enable broader implementation across industries, potentially accelerating adoption well beyond the current 6% of companies utilising significant AI capabilities. For European startups, the current environment offers distinctive advantages. Early stage valuations remain 40-50% below US comparables despite equivalent technical talent, for example in hubs like London, Cambridge, and Oxford. And while US-China decoupling creates supply chain challenges, it also opens opportunities for European alternatives as companies seek to diversify geopolitical risk. The market may tremble at tariff headlines, but the technological revolution marches inexorably forward. As we navigate through this uncertainty, we'll be focusing on companies creating tangible value through specialised implementation in high-impact domains—precisely where European technical talent excels. --- # March 2025: AI Uncertainty Hitting Markets? **Source:** https://www.superseed.com/journal/march-2025-ai-uncertainty-hitting-markets/ **Published:** 2025-03-31 **Author:** Mads Jensen *This is not investment advice*. The Nasdaq 100 has plummeted 13% from its February peak, with the Magnificent 7 tech stocks haemorrhaging $2.7 trillion in market capitalisation since January—equivalent to the UK's entire annual Gross Domestic Product (GDP). This market correction, which we predicted in our 2025 Crystal Ball ("The S&P 500 will undergo a major correction"), arrives at the intersection of two significant disruptive forces: Chinese AI startup DeepSeek's highly efficient models challenging established AI infrastructure economics, and the Trump administration's aggressive tariff implementation sending ripples through global supply chains. Only ten weeks into Trump's second term, markets are struggling to digest both technological disruption and policy volatility—raising the central question: Is AI uncertainty finally hitting markets, or is this merely a temporary correction in a longer growth trajectory? This month, we examine this volatile intersection of technology and policy. How deeply will the AI infrastructure boom be affected by efficiency innovations and market corrections? How will Trump's tariff regime reshape technology manufacturing and investment? And amid market volatility, can we separate short-term fluctuations from the fundamental transformation AI continues to drive across industries? ## **Market Analysis: The Correction Arrives as AI Economics Face Scrutiny** The S&P 500 has retreated 5% in Q1 2025 and is down 9% from its February peak, while the Nasdaq 100's 13% decline reflects technology's disproportionate vulnerability to current market forces. This correction has carved $2.7 trillion from the Magnificent 7 alone, with Tesla dropping 40% and Nvidia experiencing a 16% single-day plunge in late January. This downturn reflects both broad market recalibration and targeted questions about near-term AI infrastructure investment returns. The market turbulence unfolds against a backdrop of extreme concentration—the top 10 US stocks now comprise 40% of the S&P 500's $20.9 trillion value, exceeding the entire European market by $4.9 trillion. In our January analysis, we highlighted this concentration as unsustainable, and the market correction has largely followed the trajectory we anticipated in our 2025 Crystal Ball. CoreWeave's March 28 Initial Public Offering (IPO) offers an illuminating case study of the current climate. Priced at $40—below the initial $47-$55 range—the shares opened at $39 and closed flat. While raising $1.5 billion remains impressive, falling short of the targeted $2.7 billion reflects both general market conditions and company-specific concerns. CoreWeave's 62% revenue dependence on Microsoft presents concentration risk, and launching into a declining market creates additional headwinds regardless of sector. The fundamental question facing investors remains: Is the market reaction to DeepSeek's efficiency breakthrough overblown? While DeepSeek demonstrated AI models comparable to Western counterparts at approximately 5% of the training cost—triggering Nvidia's $600 billion market capitalisation reduction in January—this tells only half the story. The other half lies in AI adoption and deployment scale. Only 6% of US companies have implemented significant AI capabilities to date, suggesting we remain in early adoption stages. As Nvidia CEO Jensen Huang noted at the company's annual Graphics Technology Conference (GTC), he now sees the inference market as 100 times larger than projected just twelve months ago, driven by reasoning models that require massive inference capacity. ## **Macroeconomic Indicators: Tariff-Driven Inflation Threatens Monetary Policy Balance** US inflation remains stubbornly elevated at 2.5% in Q1 2025, up from 2.3% in Q4 2024, while the Eurozone Consumer Price Index (CPI) holds at 2.2%. The Federal Reserve has consequently paused its rate-cutting cycle after December's trim, with futures markets now pricing just one cut for 2025. The 10-year Treasury yield has climbed to 4.62% from 3.63% in September 2024, reflecting persistent inflation expectations. The Trump administration's tariff implementation has progressed from rhetoric to reality with alarming speed: - 25% tariffs on auto imports effective April 2, 2025 - 25% "reciprocal tariffs" on 15% of US trade partners (April 2, 2025) - 25%+ threatened on EU goods (April 15, 2025) - 25% global steel and aluminium tariffs (effective March 12) - 20% on all Chinese goods (up from 10%) A Reuters/Ipsos poll shows 57% of Americans—including 33% of Republicans—consider these tariff policies "unsteady," with 70% anticipating higher consumer prices. This economic uncertainty has pushed the Economic Policy Uncertainty Index to levels not seen since the COVID-19 pandemic. ![](https://www.superseed.com/wp-content/uploads/2025/03/chart1-1024x658.webp) The European Central Bank (ECB) has diverged from the Federal Reserve, cutting rates on March 14 while maintaining cautious forward guidance. This policy divergence has strengthened the dollar (EUR/USD at 1.10), creating opposing forces for technology companies—US-based firms benefit from currency strength for acquisitions, while European startups face headwinds when competing globally. The timing of these economic policies may be more strategic than haphazard. The US administration appears willing to absorb economic turbulence early in the term, potentially creating runway for recovery before the 2026 midterm elections. As Trump recently stated, he's "not that bothered about the stock market" at this stage of his presidency—a remarkable shift from his first term's focus on market performance as a barometer of success. ## **Technology & AI Focus: Efficiency Revolution Reshapes the Landscape** DeepSeek's January breakthrough continues reverberating through the AI industry. The Chinese company has since released a further update to their v3 model that fully rivals Western counterparts in capability while maintaining their open-source approach. Their initial innovation demonstrated competitive AI model performance with approximately $6 million in training costs—roughly 1/20th of the industry standard $100+ million investment. Their approach wasn't merely frugal; it represented fundamental architectural innovation: 1. **Efficient Training Architecture**: Using fewer decimal places for calculations, reducing memory requirements by 75% 2. **Multi-Token Prediction**: Forecasting multiple words simultaneously with 85-90% accuracy, doubling inference speed 3. **Mixture-of-Experts (MoE)**: Selectively activating only necessary parameters (37 billion from a 671 billion parameter model) for each specific task This efficiency revolution has triggered an arms race among Western frontier AI companies: - OpenAI is reportedly raising $40 billion at a $300 billion valuation, led by SoftBank, while developing GPT-5 (expected May 2025) - Anthropic released Claude 3.7 Sonnet with Claude Code, enabling full 3D video game development from text prompts in under 5 minutes - Elon Musk's xAI delivered Grok 3, trained on the massive Colossus cluster (100,000 Graphics Processing Units). What makes Grok 3 particularly significant is its real-time access to the X social media feed, giving it unmatched capability to take the world's pulse at any given moment—a competitive advantage no other model can currently replicate - Google has dramatically reversed its fortunes with Gemini 2.5, leapfrogging from laggard to leader. After spending much of 2024 on the back foot, Google now sits neck-and-neck with Anthropic as the technical community's favourite model ![](https://www.superseed.com/wp-content/uploads/2025/03/chart2-1024x619.webp) 2025 has emerged as "the year of agentic Artificial Intelligence"—systems that autonomously perform complex tasks rather than merely responding to prompts. This shift fundamentally transforms how we conceptualise software, evolving from tools we operate to agents that work independently on our behalf. The distinction is comparable to having a chauffeur instead of driving yourself—the vehicle remains the same, but the experience and productivity implications differ dramatically. Creative applications continue showcasing AI's expanding capabilities. OpenAI's integration of advanced image generation directly into ChatGPT has sparked the viral "Studio Ghibli" aesthetic trend. This remarkable Lord of the Rings trailer created entirely with AI in Ghibli style demonstrates the creative frontier: See the [full video here](https://x.com/PJaccetturo/status/1905151190872309907). Beyond these consumer-facing applications, energy constraints have emerged as a critical bottleneck. The European Data Center Association reports 75% of operators cite electricity access as their primary concern. In Ireland, data centres already consume 20% of the country's electricity—projected to reach 30% within three years. This energy challenge makes efficiency innovations like DeepSeek's not merely economically advantageous but potentially essential for AI's continued expansion. Equally important, it highlights the massive opportunity in AI applications, particularly in the physical world, where efficiency and specialised implementation will likely drive the next wave of value creation. ## **AI Applications: The Next Frontier Beyond Infrastructure** While the market reassesses AI infrastructure investments, the application layer continues showing tremendous promise. As we covered extensively in last month's analysis of the "Technology Trinity" (AI, quantum computing, and fusion energy), these converging domains are creating unprecedented innovation opportunities. Rather than repeating those insights, we'll focus this month on how the AI efficiency revolution is accelerating application development. The efficiency gains demonstrated by DeepSeek don't diminish AI's transformative potential—they actually expand it by democratising access. Lower training and inference costs enable more organisations to implement AI solutions, potentially broadening the addressable market substantially. This is particularly significant for vertical applications in traditional industries like manufacturing, healthcare, agriculture, and logistics, where implementation costs have been a primary adoption barrier. The physical world represents AI's next major frontier. While the first wave focused on digital domains (language processing, image generation, coding assistance), the emerging opportunity lies in connecting AI to the physical world through sensors, robotics, and industrial systems. These applications require not just powerful models but specialised implementation and domain expertise—areas where both established companies and startups can create defensible advantages. ## **European Tech Landscape: Manufacturing Challenges Amid Defense Opportunities** European automakers face existential pressure from multiple directions: Chinese electric vehicles at significantly lower price points (€20,000 vs. €60,000) and now US tariffs threatening export markets. German manufacturers appear particularly vulnerable, with Volkswagen producing approximately 500,000 vehicles annually in Mexico that would face new tariffs. Mercedes has already seen Chinese sales "fall through the floor," compounding challenges across their key markets. Amid this manufacturing turmoil, European technology presents a more promising picture. SAP has emerged as Europe's most valuable company, overtaking Danish pharmaceutical giant Novo Nordisk. The German enterprise software giant now trades at 40 times forward Price-to-Earnings ratio—exceeding even Nvidia's multiple. This valuation reflects growing recognition of SAP's extensive data assets and enterprise AI potential. The US-Europe relationship is experiencing its most significant strain since the Iraq War era. European defence procurement is undergoing reconsideration, with countries cancelling or postponing orders for American systems like the F-35 fighter jet. European nations are rapidly increasing defence budgets in response to perceived US unreliability, with Germany's newly elected chancellor removing the borrowing cap to increase defence spending to 3.5% of GDP before 2027. This defence pivot creates an unexpected opportunity for European manufacturers, particularly in Germany. Just as automotive demand faces pressure, defence manufacturing presents an alternative utilisation path for Europe's industrial base. Germany's manufacturing sector alone is 3.5 times larger than Russia's entire manufacturing output. If Europe commits to supporting Ukrainian independence and countering Russian aggression, its industrial capacity is more than sufficient for the task—a form of "weaponised Keynesianism" that could maintain manufacturing employment while serving strategic objectives. The European defence budget, currently estimated at approximately €400 billion annually (including the UK), is projected to reach €500-600 billion within three years. This surge creates substantial market potential for European defence technologies, particularly in drone systems, autonomous vehicles, and cybersecurity—all areas where AI applications can deliver significant advantages. ## **Venture Capital Landscape: Exit Windows Reopen Amid Market Volatility** Exit activity shows encouraging signs after a two-year drought, with both IPOs and Mergers and Acquisitions (M&A) accelerating in Q1 2025. Google's acquisition of Israeli cybersecurity firm Wiz for $32 billion represents the most valuable M&A transaction for a venture-backed private company in history, exceeding Meta's $19 billion WhatsApp purchase in 2014. The deal's $3.5 billion break fee underscores both regulatory uncertainty surrounding large technology acquisitions and Google's confidence in completing the transaction under the Trump administration's more business-friendly approach to antitrust. The IPO pipeline has strengthened substantially, with more than 70 listings in the US during Q1 2025—90% above the same period last year. However, CoreWeave's underwhelming debut demonstrates that while the window has reopened, investor discipline remains intact. Notably, Klarna is filing a revised F1, targeting a $15 billion valuation and planning to raise over $1 billion. While representing a substantial decrease from its 2021 peak of $45 billion, this offering signals a returning appetite for European fintech listings. Cerebras's IPO remains in regulatory limbo, awaiting the Committee on Foreign Investment in the United States (CFIUS) review regarding G42's $335 million stake. Approval could materialise by May if Trump's team provides clearance; otherwise, the process extends into Q3. With a projected $4.25 billion valuation, this AI chip manufacturer's massive wafer-scale technology represents another critical test of market sentiment toward infrastructure plays. The Cerebras situation highlights the fascinating competitive dynamics emerging in AI hardware. While Nvidia dominates the training market with its Graphics Processing Units (GPUs), specialised players like Cerebras and Groq are mounting serious challenges in the inference market. This battle between the $2+ trillion incumbent and nimble upstarts showcases how quickly the competitive landscape can evolve in AI—Cerebras was founded just seven years ago, yet its wafer-scale engine technology now stands as a credible alternative to Nvidia's H100 for certain workloads. Similarly, Groq's compiler-focused approach delivers speed advantages for specific applications. These specialised approaches may not dethrone Nvidia broadly, but they demonstrate how innovation continues creating opportunities even in hardware markets with seemingly entrenched leaders. ## **Looking Ahead: Strategic Implications for Q2 2025** Several critical developments warrant close monitoring in Q2: 1. **Frontier AI Releases**: OpenAI's GPT-5 could arrive by May, though competing priorities and hardware constraints might delay launch. The model's capabilities and efficiency improvements will provide crucial signals about AI's development trajectory. 2. **Regulatory Environment**: The European Union's Artificial Intelligence Act enforcement begins affecting high-risk systems, potentially creating compliance burdens for technology firms while establishing clearer operational parameters. 3. **Tariff Implementation**: April's scheduled tariff implementation will provide concrete evidence of economic impacts, potentially moderating through negotiated exceptions or intensifying through retaliatory measures. 4. **Energy Infrastructure**: Major data centre providers are negotiating long-term power purchase agreements to secure capacity. Watch for announcements about innovative approaches to energy supply, potentially including smaller-scale nuclear installations. 5. **Specialised AI Hardware Competition**: The showdown between Nvidia and challengers like Cerebras and Groq represents a fascinating case study in market disruption. While Nvidia's H100 and upcoming Blackwell chips remain the gold standard for training, these specialised players offer compelling advantages for inference workloads. Their progress will signal whether AI hardware follows historical patterns where specialised solutions eventually gain ground against general-purpose incumbents. Despite recent market volatility, 2025 maintains strong fundamentals for technology investment. The efficiency revolution triggered by DeepSeek doesn't diminish AI's transformative potential—it expands it by democratising access. Lower training and inference costs enable broader implementation across industries, potentially accelerating adoption beyond the current 6% of US companies utilising significant AI capabilities. This dynamic echoes historical technology adoption patterns. When personal computers dropped below $1,000, enterprise adoption accelerated dramatically. When cloud computing costs declined by 50% between 2010-2015, Software-as-a-Service implementations surged. Similarly, AI's efficiency revolution won't reduce the overall market opportunity—it will likely expand it substantially by bringing capabilities within reach of organisations previously priced out of serious implementation. Returning to our opening question: Is AI uncertainty hitting markets? Certainly, the near-term economics of AI infrastructure face legitimate scrutiny. However, the broader transformation continues unabated. This reality creates particularly compelling opportunities in Europe, where early-stage valuations remain approximately 50% below US comparables despite comparable technical talent. The region encompassing London, Cambridge, and Oxford continues to represent one of the world's strongest talent concentrations in AI and deep technology. This combination of talent density and reasonable valuations offers exceptional risk-reward profiles for investors willing to look beyond the market's current infrastructure fixation toward application-focused innovation. The real risk isn't overestimating AI's impact but underestimating how efficiency innovations will accelerate adoption across industries. For SuperSeed and our portfolio companies, this environment richly rewards what early-stage innovation does best: creating differentiated technology that delivers clear value through specialised implementation in high-impact domains. As Q2 unfolds, we'll be watching the evolving economics of AI applications more closely than infrastructure valuations. While market corrections grab headlines, the less visible but more profound story remains how these transformative technologies are reshaping industries from manufacturing to healthcare, logistics to agriculture. The companies that thrive will be those focusing not on hype cycles but on tangible value creation—precisely where European technical talent and more reasonable valuations offer compelling advantages in this next phase of technological transformation. --- # February 2025: The Tech Trinity Emerges **Source:** https://www.superseed.com/journal/february-2025-ai-giants-unleash-new-models-as-fusion-and-quantum-make-historic-leaps/ **Published:** 2025-02-27 **Author:** Mads Jensen ## As AI Scaling Falters, Fusion and Quantum Computing Create a New Innovation Wave *This is not investment advice* "Another $40B Nvidia quarter, another half-billion-dollar AI model—but what if the real breakthroughs lie elsewhere?" As GPT-4.5 disappoints despite its massive price tag, February 2025 reveals a more compelling story: the emergence of a technological trinity. While AI scaling hits diminishing returns, quantum computing and fusion energy are making historic leaps—and the three fields have begun to accelerate each other. After January's $1 trillion tech rout, this month shows how the future may not belong to bigger AI models alone, but to the powerful synergy between these three technologies—a convergence that matters far more than market fluctuations. First, let's look at what happened with public markets in February. ## Market Analysis Stock markets took a hit in February, with the Nasdaq 100 down 3% and S&P500 down 1.5% for the month. Nvidia plummeted 8% on the 27th, just a day after their highly anticipated earnings release. The company's stock gyrated a lot in February as markets tried to decipher the implications of Deepseek and the general development in AI. ### Nvidia: $40 Billion Quarter Shows Strength, With Warning Signs Reporting just after market's closed on Feb 25th, Nvidia blew past earnings expectations once again, announcing nearly $40 billion in revenue. Over 85% came from data centre demand driven by a strong market pull for the new Blackwell chips. CEO Jensen Huang attributed the growth to advances in reasoning models. ![](https://www.superseed.com/wp-content/uploads/2025/02/unnamed-10-1024x638.png) However, several concerning metrics emerged: 1. Quarterly data centre (AI) revenue growth rates declined to 15.6% (versus 17.1% in the previous quarter). 2. Operating margin decreased for the second consecutive quarter (though still maintaining an extraordinary 60%+) 3. The market is increasingly questioning whether Nvidia's dominance in training can translate to inference workloads, where competitors like Groq are delivering better performance at lower costs. ![](https://www.superseed.com/wp-content/uploads/2025/02/unnamed-11-1024x636.png) These shifts suggest that even Nvidia's seemingly impregnable moats may be showing signs of vulnerability. The primary driver for Nvidia's continued ascent remains hyperscaler capital expenditure (primarily Amazon, Google, Microsoft and Meta). To put this spending in context: from 2000-2016, total global data centre spending outside China averaged less than $10 billion annually (approximately $150 billion total for the period). In stark contrast, these four companies alone spent $260 billion on data centers in 2024, with Amazon explicitly committing to at least $100 billion for 2025. However, potential moderation signals have emerged, with Microsoft cancelling two data centre leases and CEO Satya Nadella suggesting that he might slightly decelerate AI-related capital expenditure. ## The AI Model Wars Intensify: Empire Strikes Back After January's shock arrival of DeepSeek's r1 model, February had a distinct "Empire Strikes Back" feeling as the US frontier AI labs responded forcefully: - OpenAI released GPT-4.5 to mixed reviews. Sam Altman himself acknowledged it's "a giant, expensive model" that "won't crush benchmarks." At an estimated training cost of up to $500M and prohibitive pricing ($75/input and $150/output per million tokens—10-25x higher than competitors), the tech community remains divided. Half praise its improved "vibes" while the other half question whether any improvement could justify the astronomical costs. - Elon Musk's [x.AI](http://x.AI) delivered the highly anticipated Grok 3, trained on Colossus – the world's largest AI cluster (100,000 GPUs with further upgrades planned). And it is a truly impressive model. - Anthropic released Claude 3.7 Sonnet with Claude Code – a transformative upgrade allowing the development of fully functional 3D video games from text prompts in under 5 minutes. The early assessment is that Anthropic once again has "the best" model for both coding and writing, showing that spending the most money doesn't always lead to the best results. The Deepseek team is planning their next move with Deepseek r2 scheduled for April. Meanwhile, the funding wars for frontier AI models continue unabated: - Anthropic is seeking $3.5 billion at a $60 billion valuation - OpenAI is reportedly exploring raising $40 billion at a $300 billion valuation - [x.AI](http://x.AI) potentially targeting up to $10 billion at a $75 billion valuation This overwhelming concentration of capital reflects a growing recognition of the prize that is at stake. ## 2025: The Year of Agentic AI and Scaling Skepticism February further solidified 2025's emerging identity as "the year of agentic AI" – systems that don't merely respond to prompts but actively perform complex tasks with minimal supervision. It's also becoming "the year of scaling skepticism," as frontier labs burn billions on marginal improvements, leading many to question whether simply scaling existing architectures can deliver AGI. Deep Research and coding agents represent the most mature examples of the agentic transition, with the entire industry pivoting toward creating specialized agentic systems. This transition is fundamentally changing how we think about software – from tools we use to agents that work for us. The implications extend beyond productivity gains to completely reimagining how digital work gets accomplished. Unlike previous AI advancements that enhanced existing workflows, agentic systems establish entirely new paradigms for human-computer interaction. Increasingly, the value appears to be in the "wrapper"—the applications and specialized implementations rather than raw model capabilities. Companies that can effectively deploy AI to solve specific problems are seeing greater business impact than those chasing benchmark performance. Critically, these agentic systems are now accelerating scientific discovery. Microsoft Research and DeepMind are deploying AI agents to advance quantum computing and fusion research respectively—creating the first strands of a powerful technological feedback loop.Beyond AI: Quantum and Fusion Breakthroughs ### Microsoft's Quantum Leap: Majorana 1 After 20 years of research, Microsoft unveiled the Majorana 1 chip – the world's first quantum processor powered by a topological core architecture. In this context, topological means that the information stored in each qubit isn't determine by a single (and fickle) particle, but rather by the shape ("topology") of the relationships between two particles. These shapes are far more resilient than single particles. Therefore, this approach makes quantum computing more reliable and scalable by fundamentally changing how qubits are designed. Traditional quantum computing faces a massive inefficiency problem: for every usable "logical" qubit, thousands of physical qubits are needed for error correction. Microsoft's topological approach potentially reduces this ratio to 10-30 qubits per logical qubit, dramatically accelerating the timeline for practical quantum computing. The implications are substantial across multiple domains: - Cryptography: Future ability to crack current encryption standards - Finance: Portfolio optimization through simultaneous evaluation of trillions of scenarios - Supply chain: True optimization of complex multi-variable systems - AI: Quantum machine learning could enable training models 100-1000x larger than today's systems while using far less energy—a potential second-stage booster for AI advancement. While commercially useful quantum computers (100+ logical qubits) could arrive by 2027-2030, cryptography-breaking capabilities likely still remain 10 years away, with scientific breakthrough applications in materials science and pharmaceuticals likely 15 years out. ### France's Fusion Milestone Europe demonstrated its technical prowess with a significant energy breakthrough in February. The French CEA's West Tokamak reactor sustained fusion for 22 minutes – surpassing China's 17-minute record set just a month earlier. The achievement represents a crucial step toward sustainable fusion energy, where a single gram of hydrogen isotopes can yield energy equivalent to 11 tons of coal. The key challenge isn't fusion itself but maintaining plasma stability at temperatures of 100-150 million Celsius without damaging the reactor. What's particularly striking is how AI is turbocharging fusion research. AI research by group's like DeepMind's is optimizing plasma containment—forming the third pillar of our technological trinity. As fusion approaches commercial reality, it promises the abundant clean energy needed to power ever-more-sophisticated AI and quantum systems. The roadmap to commercial fusion includes: 1. Achieving continuous net energy gain (more energy out than in) 2. Extending operation from minutes to days/weeks/months 3. Developing materials durable enough to withstand extreme temperatures 4. Addressing fuel cycle challenges with deuterium and tritium While commercial rollout remains decades away (likely 2040s), the accelerating pace of milestones suggests the traditional "fusion is always 40 years away" joke may finally be obsolete. The prospect of abundant clean energy within 15-20 years would fundamentally reshape our economic and environmental outlook. ## The Virtuous Cycle: How Three Revolutions Reinforce Each Other The developments of February 2025 reveal a clear pattern of technological co-evolution: 1. **AI → Quantum**: Advanced AI systems are designing quantum architectures and optimizing error correction techniques that human researchers couldn't discover alone 2. **Quantum → AI**: The first quantum-accelerated machine learning algorithms are showing 10-100x improvements in training efficiency 3. **AI → Fusion**: AI-powered plasma simulation and real-time control systems are solving containment challenges that have stymied fusion for decades 4. **Fusion → AI/Quantum**: As fusion approaches commercial viability, it promises to provide the massive energy resources needed for next-generation computing at sustainable costs This virtuous cycle creates a technological acceleration feedback loop, unlike anything in human history. Each breakthrough in one domain catalyzes advances in the others, potentially leading to exponential progress across all three fields. ## Europe at a Crossroads The Munich Security Conference in February sparked considerable debate about Europe's future. US Vice President JD Vance delivered remarks that many Europeans found provocative, suggesting Europe's greatest threats come from within rather than from external adversaries like Russia. Though the timing and delivery were debatable, there's merit in European self-reflection. The European economy is more than 10 times larger than Russia's, and even the UK alone maintains a larger manufacturing base than Russia. This mismatch in economic scale suggests Europe has the fundamental capacity to secure its own prosperity and defence. Europe's challenges stem primarily from governance and productivity issues. The NHS in the UK has seen productivity decline by 20% over the past five years (when measuring output per inflation-adjusted £ spent) – yet this also represents an opportunity. Determined investment in AI could substantially improve productivity and free up resources for reinvestment elsewhere. Europe's position in the emerging technological trinity is particularly precarious. While it has made significant strides in fusion research, it lags considerably in both AI and quantum computing development. Without concerted investment in these areas, Europe risks becoming merely a consumer rather than a producer of the most transformative technologies of our time. The question remains whether Europe will seize this moment to strengthen its technological and defence capabilities or continue clinging on to US support that appears increasingly uncertain. ## Forward look for the next three months Several critical developments bear watching in the coming months: - **AI Releases**: GPT-5 arriving as early as May, though OpenAI's admission of being "out of GPUs" and the launch of their massive "Stargate" cluster suggests potential delays. - **Regulatory Landscape**: The EU's Artificial Intelligence Act is now active with potential enforcement actions against "high-risk" AI systems, while US states implement their own frameworks. - **Geopolitical Dynamics**: Potential steps toward resolution in Ukraine and potential global trade disruption from Trump's tariff rhetoric. - **Market Liquidity**: Signs of improving IPO and M&A environments could drive more exits and recycling of capital. - **Cross-Domain Collaborations**: Watch for announcements of major collaborations between AI, quantum, and fusion research organizations—these partnerships will accelerate the virtuous cycle. - **Energy Infrastructure**: Major data center providers are beginning to announce long-term power purchase agreements with fusion energy startups, signaling confidence in the technology's commercial timeline. - **AI Efficiency Revolution**: Watch for companies focused on inference optimization and specialized hardware that can deliver AI capabilities at lower costs—potentially challenging Nvidia's dominance. For venture capital, 2025 appears positioned as a solid year. The excesses of 2021-2022 have largely been purged from the system, while AI continues to fuel significant investment opportunities across verticals. ## SuperSeed Portfolio Alignment Several SuperSeed portfolio companies are directly positioned to capitalize on current trends: - **Hirundo**: Their AI optimization, "unlearning", and bias-removing technology addresses the growing need for explainable, compliant AI models as regulatory frameworks mature. - **Messium**: Their hyperspectral satellite imagery combined with AI for optimizing agricultural fertilization aligns with the increasing demand for sustainable, precise agriculture - **Tector**: Their AI-powered construction defect detection (reducing costs by up to 3%) demonstrates how AI can transform traditional industries - **Octaipipe**: Their AI solution for data centre energy optimization addresses a critical need as AI infrastructure power. The convergence of AI capabilities with specialized domain expertise across these companies represents exactly the type of vertical applications that will drive the next phase of AI value creation. As we progress further into 2025, the distinction between purely speculative AI plays and those delivering tangible operational value will become increasingly important. Companies that can demonstrate clear ROI metrics and integration with existing workflows will gain disproportionate attention from both customers and investors. https://substack.com/@madsjensen --- # Behind the h(AI)ype **Source:** https://www.superseed.com/journal/behind-the-haiype/ **Published:** 2025-02-11 **Author:** Mads Jensen This recorded webinar offers a clear-eyed look at AI and tech in 2025. Mads cuts through the noise around NVIDIA's $3T valuation, OpenAI's $300bn price tag, and examines where real value creation will emerge as AI reshapes the SaaS landscape. https://youtu.be/nNuXCoFspFk This 45-minute analysis focuses on identifying actual opportunities amidst the market euphoria, backed by our independent research and analysis of emerging technical and market dynamics. - The Deepseek big deal - NVIDIA and scaling laws - Failed export controls on chips - Sam's $300bn plan - The new AI tech stack - A return to hardware - Semiconductor supply chains - Bulls and bears in AI - Sovereign wealth AI - Stargate - AI winners and losers - Transformation of the analogue world - The 10x effect - Europe vs the world --- # The $600 Billion Efficiency Lesson **Source:** https://www.superseed.com/journal/the-600-billion-efficiency-lesson/ **Published:** 2025-01-31 **Author:** Mads Jensen Until December 2024, conventional wisdom held two certainties about AI: U.S. tech giants would maintain their lead through massive capital deployment, and closed-source models would dominate through superior resources. On January 27th, 2025, those assumptions collapsed. Nvidia's market capitalisation plunged $600 billion—the largest single-day value destruction in stock market history—after a Chinese company demonstrated both premises wrong. ## The Foundation Shift AI is the next major paradigm shift in computing. It’s a loose collection of many approaches, but the current shift is currently happening with Large Language Models (LLMs) - the models that can both read and write human language (including computer code), and can be used to power everything from software development to customer service. Historically, training these models—teaching them patterns from vast datasets—required massive computing power. Running them (inference) demanded expensive hardware. The industry accepted this as immutable: better performance required more computing power, which meant more expensive chips. This doctrine shaped the entire industry. OpenAI and Google poured billions into training increasingly powerful models. Nvidia, selling the chips that made this possible, enjoyed 90%+ profit margins. The U.S. government, seeking to maintain its lead, even restricted chip exports to China. The assumption? Without access to cutting-edge hardware, Chinese AI development would stall. ## The Catalyst Timeline The dominoes began falling on December 26th, 2024, when DeepSeek released its V3 model to minimal fanfare. Most observers missed its significance amid holiday distractions, but early technical reviews on platforms like Twitter and Reddit hinted at a seismic shift. Then came January 20th, 2025: As Washington focused on Trump's inauguration, DeepSeek quietly released R1, their reasoning-focused model. The following day, Trump announced Project Stargate—a $500 billion AI infrastructure initiative primarily benefiting OpenAI. By January 27th, as investors fully digested the implications, Nvidia's stock dropped 16%. The market finally grasped what AI researchers had been buzzing about: DeepSeek had achieved what many thought impossible, training a world-class AI model for $6 million—roughly 1/20th the industry standard cost of $100+ million. ## The Pre-Disruption Power Structure Before January 2025, the AI landscape resembled a very expensive oligopoly. OpenAI boasted a $5 billion annual run-rate by December 2024. Nvidia commanded massive profit margins in their AI segments, while their CUDA software (the software platform used to develop the models) ecosystem locked in the majority of the world’s AI researchers. The industry operated on what insiders called "The Scaling Doctrine"—a set of seemingly immutable laws governing AI progress. Compute requirements doubled every six months. Training data expanded from 300 billion tokens for GPT-3 to 15 trillion tokens for 2024's leading models. Costs increased 150% year-over-year, while training times stretched from weeks to months. For an industry obsessed with exponential growth, efficiency seemed almost an afterthought. ## DeepSeek's Triple Disruption DeepSeek’s big achievement is that the company has achieved comparable model performance at a fraction of the cost. Their innovations exemplify a fundamentally different approach to AI development. Here are three examples from their broader set of breakthroughs: - **Efficient Training Architecture**: By simplifying calculations with fewer decimals, Deepseek achieved comparable results while using 75% less memory. This is like discovering you can build the same skyscraper with a quarter of the materials. It sounds incredibly trivial, but when paired with the company’s other innovations, it delivered just the required performance. - **Multi-Token Prediction**: While traditional models predict one word at a time, Deepseek's system predicts multiple words (tokens) simultaneously with 85-90% accuracy, effectively doubling inference speed. The efficiency gain is remarkable—imagine if your car suddenly required half the fuel while maintaining 90% of its horsepower. - **Mixture-of-Experts (MoE) Architecture**: While GPT-4 activates all of its estimated 1.8 trillion parameters for every task, DeepSeek's approach is more selective. Their 671 billion parameter model activates only 37 billion parameters at any time—just the experts needed for each specific task. It's like having a company where, instead of calling all 1,800 employees into every meeting, you bring in only the 37 specialists most relevant to the problem. Simple. Effective. Brilliant. On their own, these advances might have led to an inferior model. However, combined, they produced the same quality output with drastically lower computational costs—running on two consumer-grade NVIDIA 4090 GPUs costing under $2,000 total versus GPT-4's requirement for multiple $40,000 H100 GPUs. The Deepseek team also did many other clever things to change the game, including a major focus on reinforcement learning rather than human fine-tuning (meaning that they taught the computer how to become smarter rather than relying on laborious fine-tuning by experts). This difference in approach is much more than just a technical tweak —t’s a philosophically different view of how to develop the best AI. Best bit: DeepSeek open-sourced their entire system. It’s all there, for everyone to use, to tweak and to learn from. It’s hard to overstate the disruptive impact of this. ## Commercial Earthquake DeepSeek's breakthrough reshapes the AI economics fundamentally: Training costs for large language models have collapsed from $100+ million to $6 million - a reduction that transforms the competitive landscape. Inference costs follow the same trajectory, with DeepSeek's architecture delivering a 95% reduction in operational expenses compared to current market leaders. This cost revolution creates three immediate market impacts: 1. Incumbent Pressure: OpenAI and Anthropic face an existential choice - match DeepSeek's 95% lower pricing and accept razor-thin margins, or maintain premium pricing and watch their market share evaporate. 2. Cloud Provider Ripple: When a $200/month ChatGPT Pro subscription faces competition from DeepSeek's open source (=free) R1 model, the implications cascade to cloud infrastructure. This could impact Microsoft, Amazon, and Google's AI near-term revenue projections. 3. Hardware Vulnerability: NVIDIA's dual revenue streams - from both model training and inference - face immediate pressure. Even if the overall volume of AI work continues to grow rapidly, there will be near-term implications for the amount of hardware required. As such, the promised "Year of Agentic AI" in 2025 arrives with an unexpected plot twist: the established players now face fierce competition from new entrants who can match or exceed their performance at a fraction of the cost. ## Geopolitical Chess Game The timing couldn't be more pointed. A day before Trump's $500 billion Project Stargate announcement—meant to cement U.S. AI supremacy—a Chinese company demonstrated that capital alone doesn't determine AI leadership. Despite U.S. export controls blocking access to Nvidia's most advanced chips, DeepSeek leapfrogged the entire industry. Initial reactions in Silicon Valley included speculation about potential deception—suggestions that DeepSeek must have accessed more computing power than disclosed. However, their open-source release and subsequent validation by independent researchers seem to confirm the breakthrough's legitimacy. The U.S. giants weren't outspent; they were out-innovated. While Nvidia's 16% drop likely represents an overcorrection—partly triggered by broader market dynamics seeking a reason to correct—the underlying technological shift remains significant. ## European Implications For Europe, DeepSeek's breakthrough is particularly significant. Over the past year, as Mistral and other European AI companies struggled to match their American counterparts, the prevailing narrative blamed resource constraints. DeepSeek's success fundamentally challenges this excuse. The limitation wasn't capital—it was innovation. This serves as both a wake-up call and an opportunity. Europe must confront the uncomfortable truth that blaming insufficient resources masked deeper innovation gaps. However, DeepSeek's success also demonstrates that the AI race remains wide open. Despite remarkable U.S. progress, technological leadership can shift rapidly through creative approaches to fundamental problems. ## Looking Forward Expect a frantic response over the coming weeks. The U.S. tech giants will scramble to match DeepSeek's efficiency gains. Watch for major announcements from OpenAI, Meta, and Google. The short-term implications will be volatility and potentially a full-blown market correction, but the longer-term implications will be more significant. Unlike search engines or mobile operating systems, which developed into entrenched oligopolies, AI appears headed toward a more dynamic competitive landscape. The ease of switching between models, combined with the demonstrated potential for technological leapfrogging, suggests a future where innovation matters more than market position. This inherent flexibility—a core strength of software—may prevent any single player from establishing lasting dominance. The near-term market turbulence masks a larger truth: AI just became 95% cheaper while growing more capable. When a technology's cost drops this dramatically while improving, adoption doesn't just grow—it explodes. Contrary to what Trump’s Stargate project will have us believe, sometimes the most profound disruption comes not from doing things bigger, but from doing them smarter. And that, it seems to me, is at the heart of what the startup and venture ecosystem is all about. --- # Beyond SaaS **Source:** https://www.superseed.com/journal/beyond-saas/ **Published:** 2025-01-31 **Author:** Mads Jensen The Software-as-a-Service (SaaS) market is about to experience its biggest transformation since Salesforce pitched "No Software" in 1999. Traditional SaaS has built a [$350 billion](https://www.fortunebusinessinsights.com/software-as-a-service-saas-market-102222)market by digitising business processes. But that's just the prelude. As artificial intelligence moves beyond spreadsheets to tackle the physical world, the AI-enabled software market could eclipse $ 1.2 trillion over the coming decade. This 3x expansion isn't just about better databases—it's about teaching machines to handle the magnificent mess of reality. ## The Digital Foundation: What Got Us Here The path to automation has followed a consistent pattern: start with the easiest data to digitize, then gradually tackle more complex information. Banking led this revolution in the 1950s and 60s, with 90% of transactions having become digital by 1995. The gains were astronomical - Bank of America's early Electronic Recording Method of Accounting processed [33,000 accounts per hour](https://www.sri.com/hoi/banking-automation-erma/), replacing human clerks who managed just 10 accounts each. This first wave was purely deterministic: fixed rules processing structured data. Bank systems approved transactions based on account balances, airline reservations systems allocated seats using predefined inventory rules, and stock exchanges routed orders through automated matching engines. Machine Learning (ML) entered much later, with financial institutions applying neural networks to fraud detection by 2010. Today's systems combine both approaches: deterministic rules handle the base logic, while ML optimizes decisions and spots anomalies. Consider these success stories of modern AI-enhanced automation: - Fraud detection: [PayPal reduced fraud losses by 40%](https://venturebeat.com/security/paypals-ciso-on-how-generative-ai-can-improve-cybersecurity/) using ML pattern recognition - Digital advertising: A[$600 billion](https://globalgrowthforum.com/the-600-billion-digital-ad-business-hanging-on-a-few-words-from-google/) global industry where programmatic systems now process 418 billion daily bid requests - Financial markets: Algorithmic trading accounts for [60-73%](https://www.alliedmarketresearch.com/algorithmic-trading-market-A08567) of all U.S. equity trading volume, with high-frequency trading firms processing over 100 million market data messages per second The formula worked because these domains speak the language computers understand best: clean, structured data with clear rules. ## The New Frontier: Making Sense of Mess But here's what's fascinating: the latest generation of AI doesn't need everything neatly organised in databases. Large Language Models (LLMs) and other generative AI systems excel at a fundamentally different skill—understanding and working with unstructured data. The implications are profound. When OpenAI tested GPT-4 against human experts in analysing complex legal documents, it achieved 85% accuracy versus the human average of 88%. The key isn't that it writes well—it's that it reads and understands messy human-generated content with near-human capability. This shift matters because most of the world's valuable data isn't in neat rows and columns: - [80% of enterprise data is unstructured](https://researchworld.com/articles/possibilities-and-limitations-of-unstructured-data) - Companies add 2.5 quintillion bytes of unstructured data daily - Only 32% of this data is currently analysed in any meaningful way ## Where Bits Meet Atoms: The Real Revolution But here's the even bigger opportunity: applying AI to the physical world. This is where things get genuinely exciting—and challenging. Remember Apple's highly publicised attempt to fully automate iPhone assembly? They invested $10.5 billion in robotics and automation between 2012 and 2017, only to scale back when robots couldn't handle the variability of physical assembly with sufficient reliability. Traditional automation works for repetitive tasks in controlled environments but stumbles when facing real-world complexity. Yet three technological advances are changing this equation: 1. Ubiquitous Sensing - Internet of Things (IoT) sensor costs have dropped [70% since 2015](https://www.supplychaindive.com/news/declining-price-iot-sensors-manufacturing/564980/) - A modern factory now generates [1TB of production data per day](https://patrickmatte.com/work/mill/ibm/industrial/scenes/smart-factory/select/details/production-optimization/) - Computer vision systems achieve[99% accuracy](https://averroes.ai/blog/computer-vision-manufacturing) in quality control 2. Federated Learning - Enables AI models to learn from distributed data without centralization - Tesla's autonomous driving system improves from[1 billion miles of real-world data](https://www.tesla.com/en_gb/VehicleSafetyReport) - Manufacturing efficiency improves 18% through distributed learning systems 3. Dynamic AI - New models handle 60% more edge cases than traditional ML - Reinforcement learning enables robots to learn from mistakes - Companies like [Hirundo](https://www.hirundo.io/) are pioneering systems that enable models to dynamically learn and unlearn as paradigms shift and "truths" change, addressing critical challenges in data privacy and model adaptability ## The $4 Trillion Opportunity The convergence of physical-world automation and artificial intelligence is creating unprecedented opportunities across major industries. As traditional automation boundaries dissolve, AI-enabled systems are tackling previously intractable challenges in everything from precision agriculture to autonomous manufacturing. The economic impact is staggering, with transformative potential across key sectors. ## The Founder's Opportunity For technical founders, this shift presents unprecedented opportunities. The winners won't just be building better databases—they'll be creating systems that: - Transform unstructured data into actionable intelligence - Bridge the digital-physical divide - Scale beyond traditional automation limits Examples already emerging: - FreightCore reducing the manual work in freight forwarding by 90% - IPercept improving the productivity of manufacturing equipment by +30% through enhanced throughput and uptime - OctaiPipe improving the energy efficiency of data centres by 35% through autonomous optimization of cooling ## Looking Ahead The SaaS era as we know it may be over, but AI-powered software is more important than ever. The key difference lies in how we process and act on data: while traditional SaaS excelled at automating structured workflows, next-generation AI systems can handle the complexity and variability of the physical world. They learn continuously from real-world feedback, adapt to new situations, and make decisions with incomplete information—capabilities that are essential for automating physical processes in manufacturing, healthcare, and agriculture. For investors and founders alike, this represents a fundamental expansion of what software can achieve. The companies that succeed won't just be digitizing existing processes—they'll be creating systems that can understand, interact with, and improve the physical world in ways that were previously impossible. --- # The 2025 Crystal Ball - Recession Risks, Stockmarket Woes and M&A Opportunities **Source:** https://www.superseed.com/journal/the-2025-crystal-ball/ **Published:** 2024-12-31 **Author:** Mads Jensen # 11 Predictions for the Year Aheadhttps://substack.com/profile/7374218-mads-jensen *This is not investment advice. Always consult qualified financial advisors before making investment decisions.* Welcome to our annual exercise in future-gazing. While I approach these forecasts with a healthy dose of humility and humour, I always find the exercise helpful. Consider this analysis part intellectual exploration, part strategic framework, and yes, perhaps a little part of fun too. 2025 promises to be a year of fascinating contradictions and transformative shifts. AI development evolves beyond internet-trained models to synthetic data generation, while China mounts a serious challenge to US AI supremacy. Markets face a correction even as M&A activity surges, echoing the dynamic interplay we saw in 2000-2001. Trump's policy mix keeps inflation stubborn and rates high, yet Bitcoin could touch $150,000. Meanwhile, space exploration accelerates as humanity prepares for Artemis III's return to the Moon. These crosscurrents will reshape industries and markets in ways both subtle and profound. Let's examine the key developments that could define 2025, understanding that while specific predictions may miss their mark, the underlying trends they represent warrant serious consideration. # AI and Technology ## 1. 2025 will continue to be driven by AI (probability: 100%) Like 2023 and 2024, AI continues to remain the dominant global economic force and The key change in 2025 will be how AI models scale - shifting from internet data to synthetic data generation. We're running out of quality internet text, but synthetic data offers nearly limitless potential. Think of synthetic data like a massive simulation. Instead of learning from human-written content, AI systems create their own training examples, test them, and learn from the results. Imagine millions of virtual students working through math problems - they try different approaches, keep track of what works, and discard what doesn't. Over time, they discover better and better ways to solve problems. This works well for domains with clear right and wrong answers like mathematics, coding, and physics, where success can be objectively measured. Less so for subjective areas like art or creative writing. The second major shift is toward agentic AI - systems that don't just respond to questions but actively perform tasks. Think of traditional AI as a smart assistant who answers questions. Agentic AI is more like an employee who takes initiative and gets things done. Salesforce is pioneering this transition, moving from charging per user to charging based on outcomes like "conversations handled" or "deals closed." This fundamentally changes how we think about software - from tools we use to agents that work for us. https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faf9761c7-d8cd-4185-8ff2-5f3cc30bb8ec_2500x1520.pngFrom traditional SaaS to agentic systems. The robotics revolution is accelerating in parallel. Nvidia's upcoming Jetson Thor platform, launching in early 2025, represents a step-change in humanoid robot capabilities. It combines unprecedented processing power with sophisticated AI to enable robots that can understand their environment and interact with it naturally. Toyota's recent demonstrations show robots performing complex athletic movements that seemed impossible just months ago. The line between digital and physical AI is starting to blur. ## 2. AI Capex will continue to increase in 2025 (probability: 80%) Major tech players view AI investment as a [Pascal's Wager](https://en.wikipedia.org/wiki/Pascal%27s_wager) - they can't risk being left behind. Microsoft's $30 billion Nvidia chip order demonstrates this mindset. While Google bought fewer Nvidia chips, they're generating 70% of their compute from in-house TPUs (Google’ proprietary AI chips). This trend will accelerate as synthetic data training demands even more computing power. ![](https://www.superseed.com/wp-content/uploads/2025/01/unnamed-13-1024x826.png) ## 3. OpenAI’s dominance will be severely challenged (probability: 85%) While OpenAI may maintain its user interface edge, expect Google, xAI and Anthropic to take the lead in raw model performance. The differences are already emerging: While OpenAI’s ChatGPT leads in speed, xAI’s Grok excels at integrations, Claude demonstrates superior reasoning and Gemini handles the largest files. OpenRouter data shows Anthropic already leading in embedded usage for next-gen applications, followed by Meta/Llama, Deepseek and Google/Gemini. # Geopolitics ## 4. US AI dominance eroded by China (probability: 95%) The assumed inevitability of US dominance in advanced AI systems faces unprecedented challenges in 2025. Chinese companies have cracked the code on efficient model development, fundamentally challenging the prevailing wisdom that competitive AI requires massive capital deployment. DeepSeek's emergence offers a compelling case study: their models achieve performance metrics comparable to leading US systems while requiring substantially less computational infrastructure and training resources. https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F62f43fc6-3d8d-4a74-8b26-80958899ec82_3206x1348.jpeg https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F82cab646-8d0e-4493-851d-14f21d00529e_1722x1020.jpeg China's coordinated approach to AI development demonstrates the power of strategic alignment between state directives and commercial execution. Their 2023 mandate for humanoid robotics advancement set aggressive milestones - major breakthroughs by 2025, economic driver status by 2027 - with early results suggesting these targets may have been conservative. The recent procurement of one million autonomous drones showcases both China's manufacturing sophistication and its ability to rapidly operationalize AI advances into tangible capabilities. This evolution threatens to reshape the global AI landscape. While US companies maintain advantages in fundamental research and specialized applications, China's emerging model of efficient development and scaled deployment presents a formidable alternative path to AI leadership. ## 5. TikTok banned in the US (probability: 95%) The TikTok ban trajectory exemplifies how technology platforms have become geopolitical chess pieces. Despite overwhelming bipartisan congressional support for the ban, Trump's recent pivot to opposing TikTok restrictions introduces a crucial element of uncertainty. His current stance – arguing that a ban would strengthen Meta's market position – marks a stark reversal from his earlier attempts to force TikTok's sale during his first term. Three structural factors still point toward an eventual ban. First, there's the reciprocity dynamic: China's systematic exclusion of Western social media platforms has eliminated any diplomatic cost to U.S. action. Second, evidence continues mounting about CCP influence operations through TikTok's algorithm, which appears to amplify divisive content while suppressing topics sensitive to Beijing. Third, sophisticated technical analysis raises alarming questions about TikTok's data collection practices, suggesting capabilities far beyond typical social media telemetry. With the Supreme Court hearing scheduled for January 10th, the probability approaches but doesn't quite reach certainty. That 5% doubt stems primarily from Trump's opposition potentially complicating the political calculus. The ban's implementation pathway exists, but its timing and exact form may prove more complex than current congressional momentum suggests. # Economics ## 6. US Inflation remains stubborn (probability: 80%) The defining economic story of 2025 looks to be inflation's persistence. Trump's policy trifecta - tariffs, immigration restrictions, and continued fiscal deficits - all point toward sustained inflationary pressure. https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3ce7434f-7447-4410-b46f-d7b9b34728dc_1121x727.jpeg Markets are already pricing in this reality. Bond yields have risen nearly 100 basis points since the Fed began its rate-cutting cycle, suggesting rates will stay higher than previously expected. This "higher for longer" scenario sets the stage for significant market repricing and shapes both investment and M&A strategies through 2025. ## 7. US recession finally arrives ([probability](probability:): 70%) The warning signs are mounting for a recession, though likely milder than historical downturns. After dodging recession in 2024 and achieving nearly 3% GDP growth, the cracks are starting to show. https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fab291234-9f18-47ff-b8c4-3d68db2899a0_1187x536.jpeg The Chicago PMI sits at a concerning 36.9, full-time private sector employment has contracted by 1.8 million year-over-year, and industrial capacity utilization has dropped to 76.8%. Most concerning is the surge in credit card defaults to 2010 levels, suggesting consumer resilience is finally wavering. ![](https://www.superseed.com/wp-content/uploads/2024/12/image-1024x707.png) Yet several factors suggest this won't be a severe downturn. Corporate balance sheets remain strong, especially in tech, where AI investment continues unabated. Government spending shows no signs of contraction. The likely scenario? A mild recession as the economy digests higher rates and the market correction, cushioned by ongoing technological transformation and fiscal stimulus. # Finance ## 8. The S&P 500 will undergo a major correction ([probability](probability:): 90%) With inflation proving sticky and rates staying higher than expected, a significant market correction looks increasingly likely in 2025. This won't be a crash, but rather a necessary repricing as markets adjust to the new reality. Markets have stretched to precarious levels, with half of S&P 500 companies trading above 20x forward Price/Earnings - levels only seen during the dotcom bubble and zero-rate/Covid era. https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dc4813e-3ef0-401c-b9d4-180f01a50cf3_1200x668.jpeg Market concentration has reached extreme levels - the top 10 US stocks now represent 40% of S&P 500 market cap ($20.9 trillion), exceeding the entire European market by $4.9 trillion. https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F05812f44-524e-407d-8aa8-fcf88f0a0058_775x624.jpeg At the same time, corporate insider selling has hit its highest level since 2004. Not surprising given the historical relationship bewteeen forward P/E ratios and actual 10-year returns. https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F77426c4e-d26d-4570-a351-dce180cac885_585x592.png So we have near record valuation levels driven by a very small number of stocks. For fun, here is a comparison between Mag 7 and previous bubbles (Nasdaq100 is the dotcom bubble). https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84b51f0c-e8a4-455a-a379-ba097b7614b9_564x407.jpeg The tech sector, with its elevated multiples, looks particularly vulnerable. Expect a 15-25% correction over several months as the market adjusts to the higher rate environment. ## 9. M&A activity will increase (probability: 90%) Counter-intuitively, this market correction could catalyze a surge in M&A activity. We're seeing echoes of 2000-2001, when stronger companies used market weakness to acquire strategic assets at better valuations. Several factors align to make this scenario likely: First, there's massive pent-up demand - deals that have been in preparation for years but held back by regulatory uncertainty and market conditions. The appointment of Andrew Ferguson to replace Lina Khan marks a sea change in antitrust enforcement. Khan's aggressive stance against big tech acquisitions and vertical integration created a chilling effect on deals. Ferguson's more traditional approach to antitrust should unlock many transactions that were previously considered too risky. The return of Trump to the White House adds fuel to this fire. His administration's business-friendly approach creates perfect conditions for deal-making. There's also a psychological element - as some long-delayed deals finally close, others will rush to act before their strategic options disappear. Nobody wants to be left without a chair when the music stops. The nature of deals will likely shift too. While previous years saw mainly tactical acquisitions for specific technologies or talent, expect more transformative mergers in 2025. Cash-rich companies that weathered the correction will look to reshape their industries. Tech giants, temporarily constrained under Khan's FTC, will resume their acquisition sprees. The only major headwind? Despite Fed rate cuts, stubbornly high bond yields make debt financing more expensive than ideal. But with so many potential buyers sitting on large cash reserves, even this may not significantly dampen activity. # Cryptocurrency ## 10. Bitcoin will reach $150,000 (probability: 75%) Bitcoin's prospects have transformed since the Biden era. Trump's policy shift has mainstreamed the cryptocurrency, with BlackRock's Larry Fink ($11.5 trillion AUM) turning bullish. There's growing momentum for the US to swap some gold reserves for Bitcoin, and even Germany's former Finance Minister advocates for Bitcoin in central bank reserves. While volatility will persist, the secular trend points upward. # Space Technology ## 11. The space race will accelerate with several important missions taking place before Artemis III in 2026 (probability: 85%) 2025 marks the crucial setup year for humanity's return to the Moon with Artemis III in 2026. It's a year packed with missions that will prove critical technologies: NASA's Commercial Lunar Payload Services will test precision landing systems, while Japan's M2/Resilience mission aims to crack the vital challenge of extracting water from lunar soil. China joins the race with Tianwen-2, pushing boundaries by attempting both asteroid sample collection and comet exploration in a single mission. Meanwhile, NASA's SPHEREx will map the chemical evolution of our universe across 450 million galaxies, and ESA's reusable Space Rider will revolutionize our access to microgravity research. Each mission in 2025 is a building block toward permanent human presence beyond Earth - testing the technologies we'll need for Artemis III and beyond. # Looking Ahead: Key Themes for 2025 As we look to 2025, several major themes emerge. AI continues as the dominant force, but with concerning developments in military applications - China's million-drone order signals a fundamental shift in warfare that demands new defence strategies. Europe faces critical challenges, particularly in manufacturing competitiveness. The collapse of its automotive sector against Chinese competition in 2024 demands urgent action. More German entrepreneurs are building unicorns in the US than in Germany, while America's poorest states now outperform major European economies on GDP. European innovation needs a renaissance in 2025. Yet there's room for optimism. The Middle East could see its first comprehensive realignment in decades, with Iran's influence waning and potential for new Saudi-Israeli cooperation. Healthcare advances are accelerating - from the UK's groundbreaking ovarian cancer vaccine trials to AI-powered laser surgery restoring better-than-normal vision to a legally blind patient. 2025 might even mark the year we reach "escape velocity" in life expectancy, adding more than one year for each calendar year. The key themes to watch: - AI development and deployment - Energy infrastructure for AI computing - Manufacturing competitiveness - Cybersecurity imperatives - AI-enabled healthcare breakthroughs In an uncertain world, one thing remains clear: 2025 will demand adaptability, strategic thinking, and careful navigation of risks and opportunities. While we can't predict the future with certainty, it sure is fun to try. *Remember: These predictions represent informed speculation based on current trends. The future often surprises us. Always do your own research and consult your IFA before making investment decisions.* --- # 2024 in Review **Source:** https://www.superseed.com/journal/markets/2024-in-review/ **Published:** 2024-12-31 **Author:** Mads Jensen # 10 Developments That Transformed Technology and Markets in 2024 The technology industry's centre of gravity shifted dramatically in 2024, driven by massive capital deployment into AI infrastructure and fundamental market realignments. Here's how ten key developments reshaped our landscape: ## 1. The AI Arms Race Triggered a Massive Capital Mobilization 2024 marked an unprecedented mobilisation of capital and computing power. The numbers tell the story: Over $40 billion poured into foundation model companies, with single raises reaching stunning levels. X-AI secured $11 billion and built Colossus—connecting 100,000 Nvidia processors in just 18 days. Microsoft countered with a $30 billion commitment for 485,000 processors. This arms race made Nvidia the definitive kingmaker. Its stock surged 170% as the world's largest tech companies competed for limited chip supply. By year's end, computing infrastructure had become the primary constraint on AI advancement, creating a new form of strategic leverage in the tech industry. ## 2. DeepSeek Rewrote the AI Economics Playbook https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9eaa0c27-278c-45cc-9c4e-c762e1f06303_3206x1348.jpeg In December, a Chinese company called DeepSeek achieved what many thought impossible: building a top-tier AI model for just $5-6 million—a fraction of the billions spent by Western companies. Their breakthrough wasn't just cost-cutting; they fundamentally reimagined how AI systems process information. Think of traditional AI models as cities with inefficient transport systems—data has to travel through every district to reach its destination. DeepSeek built express lanes and smart traffic management, allowing information to flow more directly. They also created specialised "experts" within the system that handle specific types of problems rather than running everything through a single massive neural network. The result? A model that matches or exceeds Western competitors while using just 1% of the resources. This isn't just about cost savings—it points to a future where AI development becomes dramatically more accessible. ## 3. Enterprise AI Delivered Real Wins (And Some Spectacular Failures) https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9b639d4c-69e9-4e8e-8ddf-ce3e13d5bd25_2560x1258.jpeg The enterprise AI story in 2024 was more complex than simple success or failure. Yes, there were high-profile mishaps—IBM's AI adding McNuggets to every McDonald's order and Google's search hallucinations. But look at the wins: - Waymo matched Lyft's market share in San Francisco (22% each), proving autonomous vehicles can compete at scale - GitHub Copilot reached over 1.2 million paid users by spring, demonstrating widespread developer adoption - Cursor hit $50M annual recurring revenue in just 18 months - Klarna's AI customer service assistant handled 2.3 million conversations in its first month, cutting resolution times from 11 to 2 minutes and matching the output of 700 full-time agents ## 4. Market Bifurcation Intensifies https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9fe164f9-d4f4-4d95-a567-0adb5ba1f104_1272x708.jpeg The Magnificent 7 tech leaders achieved 43% share price growth in 2024, dramatically outperforming the remainder of the S&P 500's 14% increase. This bifurcation reflects the market's recognition of AI's transformative impact on enterprise value creation. The divergence highlights how AI capabilities are becoming the primary driver of market valuations. ## 5. Transatlantic Growth Divergence https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F93a5d74f-8fdf-472c-8b81-d446ea596fa8_2242x2139.png IMF forecasts crystallise the growing economic divide: 2.8% growth for the US versus 0% for Germany, 1.1% for France, and 1.1% for the UK. This divergence stems from structural differences in R&D investment ($700 billion in the US vs. €352 billion in the EU) and R&D intensity (3.5% vs. 2.22%). The concentration of R&D in high-growth tech sectors amplifies this divide, with European representation in the top 2,500 global R&D spenders declining over the past decade. ## 6. Political-Economic Realignment Trump’s electoral victory in the US brought a number of key shifts with global impact: - Bitcoin more than doubled to $100,000 on anticipated regulatory changes. - There is an expectation that a business-friendly administration will lead to continued growth in the US, and combined with the US’s AI leadership, this makes the country an attractive investment destination. This was highlighted by SoftBank's $100 billion US investment commitment. - There is also expectation that M&A dealmaking will improve in the US, which is getting both Wall Street and the Private Equity & Venture Capital investment communities excited for the additional liquidity ahead. - Global trade patterns are expected to recalibrate in response to Trumps' proposed tariffs and more mercantilistic trade policy. ## 7. TikTok's Regulatory Crucible The platform's trajectory in 2024 illustrated the complex interplay between national security concerns, market dynamics, and political imperatives. Having first been targeted by Trump’s first administration for divestiture or closure, the platform was then let off the hook during the early part of Biden’s term. But the Biden administration thought the better of it and came back with legislation to divest or close Tiktok earlier this year. At the same time, Trump went from an adversarial stance on Tiktok to one that was more supportive of the platform The January 10th 2025 Supreme Court showdown represents the culmination of bipartisan pressure for restrictions, multiple divestiture attempts, and shifting administrative positions—a microcosm of broader US-China tech relations. ## 8. Inflation's Divergent Impact https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8cff1a34-de1c-414a-a38b-a11efa708845_864x521.jpeg Persistent US inflation versus European disinflation is reshaping monetary policy trajectories. The Fed's more conservative rate cut outlook, compared to the ECB's aggressive stance, reflects these divergent paths. This monetary policy divergence is accelerating AI adoption as companies seek automation-driven cost efficiencies in a higher-rate environment. ## 9. Middle East's Innovation Catalyst 2024 marked a watershed moment in Middle Eastern geopolitics with the systematic collapse of Iran's primary regional proxies—Hamas, Hezbollah, and Syria's Assad regime. This trifecta of strategic shifts has created the most significant opening for regional realignment in decades. The dissolution of these power structures, which have historically constrained regional integration, opens unprecedented pathways for economic and technological collaboration. The potential Saudi-Israeli détente, likely to accelerate under a renewed Trump administration, could catalyze a fundamental rewiring of regional economic architectures. Israel's potential integration into the Middle Eastern economic fabric would create powerful synergies: combining Israel's technological innovation ecosystem—particularly in AI, cybersecurity, and water technology—with the Gulf states' capital resources and market scale. This realignment could unlock several strategic opportunities: - Creation of integrated regional innovation corridors - Cross-border venture capital flows linking Tel Aviv's startup ecosystem with Gulf financial centers - Joint development of critical infrastructure, from water security to digital transformation - Shared platforms for technological advancement in energy transition and climate adaptation The implications extend beyond immediate regional dynamics, potentially transforming global technology supply chains and creating new centers of innovation that bridge East and West. This shift would represent not just a peace dividend but a fundamental redrawing of the global innovation map. ## 10. The Reinvention of Europe https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8d4ea3ee-c797-421f-a69a-a6d918389be3_1224x1024.png Europe has got great companies. Both startups and large corporates. On the startup side, Klarna made waves with a planned IPO at a $14-20 billion valuation, Revolut's $45 billion secondary valuation, and Spotify reaching $100 billion market cap, And on the corporate side, there are strong players including in the semiconductor space. ASML dominates semiconductor manufacturing equipment, Zeiss leads in precision optics (essential for semiconductors), and ARM's chip designs power most mobile devices. But the region needs more than individual champions; it needs to build trillion-dollar technology companies that can compete at global scale. To do that, the region needs to invest much more in R&D - especially in AI/Tech. The last decade has gone the wrong way, with Europe going from having roughly the same number of companies in the global top 2,500 for R&D as the US to having less than half. And the US spends more than the EU on R&D - ca. $700bn vs. €352bn. This is a combination of a bigger GDP and a higher R&D intensity (3.5% vs. 2.22%). The flipside of this is a big opportunity to invest more in startups - both at the early stage and through the growth journey. European early stage startups still trades at a 50% discount to US peers. This provides investors with a great opportunity to invest early and reap the benefits as the next generation so startups become scaleups and global players. ## Looking Forward 2024's developments reveal how AI changes value creation across the global economy. The bifurcation between AI-capable and traditional enterprises and diverging regional growth trajectories suggest lots of upheaval ahead. The key question for 2025 is whether architectural innovation, as demonstrated by breakthroughs like DeepSeek's, can democratise AI capabilities beyond the current concentration among capital-rich market leaders. And indeed, whether the US can hold on to its dominance. 2024 was an eventful year. Providing plenty to look forward to in 2025! --- # Cerve: Why We Invested **Source:** https://www.superseed.com/journal/cerve-why-we-invested/ **Published:** 2024-12-31 **Author:** Dan Bowyer ![Cerve](https://www.eu-startups.com/wp-content/uploads/2024/12/Cerve-696x410.png) The global food and beverage industry is a mess. Made up of more than 100 million organisations, products ranging from raw materials to ready meals comprise a market worth [over $17.5 trillion](https://www.futuredatastats.com/food-and-beverage-market). Data points about each of these products, their price, their nutritional value, where they’re in stock, and when they’re moving around the world are handled - in the vast majority of cases - manually. Shockingly, [less than 5% of the F&B industry is properly integrated](https://www.ey.com/en_us/industries/consumer-products/how-to-transform-your-supply-chain-in-the-era-of-smarter-food-safety). Communication between stakeholders remains inefficient and one-to-one. Databases, spreadsheets, custom systems, and physical pieces of paper all contain slightly different versions of the same data. No wonder [so much food goes to waste before it even reaches a consumer](https://www.wri.org/insights/how-much-food-does-the-world-waste#:~:text=Around%20one%2Dthird%20of%20all,spot%20in%20global%20food%20systems.). So the F&B industry is, by and large, flying blind. Fragmented information and multiple sources of truth lead to a lack of visbility, which in turn introduces poor decision-making, increased inefficiency across the value chain, and ultimately, [further margin compression for an industry without much fat to trim](https://fmcgmagazine.co.uk/food-for-thought-over-supply-chain-efficiencies-how-edi-protects-fb-margins/).  The F&B ecosystem is difficult to disrupt. EDI (electronic data interchange) made a good dent, but ignored a fundamental truth of the industry: stakeholders don’t like change. Each retailer’s trading partner will have their own ERP (enterprise resource planning: a core software system that businesses use to manage their operations). Consequently, each will have its own custom data format. So, for Tesco, simply opening a data superhighway for all suppliers does not solve a problem. In fact, it makes an existing one more overwhelming. Producers, wholesalers, retailers, distributors and all the other groups that make up the F&B industry want to derive the benefits of data exchange without incurring the operational overhead of creating thousands of custom integrations.  Exit 1960s technology; enter Cerve.  Cerve is a singular API that integrates with any ERP system. An API is an application programming interface: a connection between computers and computer programs. Think of it as a portal through which any supplier can communicate with any of their retailers. More accurately, think of it as a portal via which the entire F&B industry can communicate in a lingua franca. Cerve’s API connects the 100 million dots that make up the world’s F&B ecosystem. Solving this problem will require inordinate attention to detail.  Meeting Dan, I was immediately struck by his thoughtfulness. He had compelling answers to even the most searching questions. He was not loud or brash, but conveyed quiet confidence. The more I probed, the more confident he became. Then, he opened Pandora’s Box. Dan’s Notion is the most comprehensive data room I have encountered. To every question, an answer. For every answer, corroboration and evidence. And not only for the questions you, I, or anyone else might conceivably pose today, but also for imagined questions from 5 years in the future. Dan, then, has totally fastidious attention to detail.  It quickly became clear that Dan had been reverse-engineering complex problems all of his career. As a technology consultant, Dan had been parachuted in to fix unsolvable problems in large organisations like Uber, Dell, and Google. Having spent some time developing his design skills at MetaLab, Dan decided to launch his own product consultancy, which he grew rapidly over a two-year period, working predominantly with enterprise customers. Since 2019, he has focussed obsessively on applying all these accumulated skills to solving the F&B data problem.  When we evaluate investment opportunities, we put a lot of emphasis on our interpretation of founder ‘strength’. In Cerve’s case, we were sold on the founder very quickly. Dan has that ineffable *je ne sais quoi* that inspires excitement and confidence in equal measure. The X Factor. Fizz. Whatever it is, we knew we needed to be involved. Cerve, delivering on its promise, will overhaul the entire F&B industry. The solution sounds, on first examination, simple. While elegant, the API establishes the foundations necessary for the next generation of technological evolution. Initially, orders will be processed and placed automatically, with zero data loss. The visibility this lossless exchange introduces means stakeholders can buy and sell exactly what they want and need to, exactly *when* they want to. Updating pricing information, stock levels, product availability, order status, and delivery timing is instantaneous for Cerve subscribers. Longer-term, these data can be manipulated by third parties to introduce even more automation to the industry, and the keystone for this brave new world is Cerve’s data portal.  SuperSeed worked hard to win a very competitive deal. We [led the £3.5m Seed round](https://news.sky.com/story/food-supply-chain-start-up-cerve-lands-3-5m-seed-funding-13267357), assembling a strong consortium of investors with excellent connections in the F&B industry. We cannot wait to ensure that Dan and Cerve’s genius bear fruit. --- # December 2024 - AI’s Ascent, Inflation’s Impact, and Europe’s Tech Renaissance **Source:** https://www.superseed.com/journal/december-2024-ais-ascent-inflations-impact-and-europes-tech-renaissance/ **Published:** 2024-11-30 **Author:** Mads Jensen ## **Landing 2024 - Navigating the Crossroads of Innovation and Economics** As 2024 winds down, two forces dominate the conversation: the AI boom and a shifting economic landscape. Is the rapid ascent of companies like x.ai and Anthropic a sustainable revolution or a bubble waiting to burst? How will Trump’s renewed policies on tariffs and immigration impact global markets? Meanwhile, Europe’s ambitious moves, such as the EU Startup Passport, signal a drive to bolster innovation across borders. Much more is needed, but it’s a welcome start.  This month’s update explores these developments with a focus on their implications for investors. From AI breakthroughs to macroeconomic risks, we connect the dots to help you navigate this dynamic landscape. ## **Trump’s Economic Policies and Their Market Implications** With his decisive re-election, Donald Trump has doubled down on promises to “revitalise” the American economy through protectionist policies. Tariffs on imports from Mexico, Canada, and China have already been announced, with more expected in Q1 2025. While aimed at bolstering US domestic manufacturing, these policies come with risks: - **Inflationary Pressures**: Tariffs act as taxes on imports, potentially raising prices for U.S. consumers. While inflation has eased to 2.1% in October, down from a peak of 9.1%, economists warn that new tariffs could disrupt this stability.  - **Corporate Impacts**: Higher input costs for manufacturers reliant on imported goods could dampen profitability, particularly in sectors like automotive and consumer electronics. - **Deregulation and M&A**: Some of these effects might be offset by Trump’s business-friendly stance, which is expected to spur M&A activity across the financial, energy and tech sectors. While the potential upheaval of the US (and global) economy could become disruptive for businesses, we are sensing quite a lot of optimism from our US partners. There is a sense that deregulation and more dealmaking will spur continued economic growth. And that more M&A will provide more liquidity for the venture ecosystem. Everyone would like to see more exits and distributions after the past few years, and 2025 is starting to look like a compelling proposition.  ## **UK’s Struggle for Growth** https://datawrapper.dwcdn.net/7LYqi/3?dark=true The UK faces a paradox: high taxes and underfunded public services. Rachel Reeves’ October budget emphasised fiscal responsibility but offered little in terms of a growth strategy. Keir Starmer recently reaffirmed the importance of growth but has yet to deliver a clear plan. - **Policy Gaps**: The lack of actionable reforms to address supply-side constraints, such as infrastructure and labour productivity, remains a concern. - **Economic Stability**: A £63bn international investment summit earlier this month highlights the appetite for UK opportunities, provided stability is maintained. ## **Europe’s Tech Renaissance at Slush** This year’s Slush conference in Helsinki showcased the strength and promise of Europe’s tech ecosystem. With over 13,000 attendees, the event underscored Europe’s growing appeal as a hub for innovation. Key takeaways include: - **Talent Pipeline**: Europe’s emphasis on technical education is paying off, with a noticeable increase in deep tech and AI-focused startups. - **Funding Landscape**: Despite challenges from global VC headwinds, European startups continue to attract capital, particularly in climate tech and SaaS. **Investment Implications**: Investors should explore pan-European opportunities, particularly in regions like the Nordics and Germany, where policy support aligns with robust talent and capital markets. ## **The EU Startup Passport—A Unified Vision** The European Union’s Startup Passport initiative aims to revolutionise cross-border business operations. By creating a unified legal framework, it eliminates the complexity of navigating 27 different regulatory systems. Highlights include: - **Streamlined Compliance**: Standardized rules for hiring, tax filings, and patents. - **Growth Potential**: The initiative is expected to boost startup scalability across the EU’s 400 million consumers. - **Challenges for the UK**: While the UK remains outside the framework for now, there are hopes that we can get closer to this over time.  ## **AI Giants and the “Peak AI” Debate** https://datawrapper.dwcdn.net/ZA2eX/1?dark=true The AI arms race shows no signs of slowing down. In November, two major moves captured headlines: - **Anthropic’s $40bn Valuation**: Amazon’s additional $4bn investment highlights a growing focus on safety and governance in AI. - **x.ai’s Meteoric Rise**: Elon Musk’s generative AI platform, now valued at $50bn, up from $24bn in May, shows the power of his fundraising skills. There are strong arguments for why this could be a viable contender, both with the X data set and with guaranteed business from Tesla, where X.ai will help develop next-generation self-driving capabilities.  While the AI behemoths raise evermore capital, some observers ask whether we are “Peak AI”. The fundamental question is whether we are starting to see a limit to how good the AI models can get. Industry leaders like Sam Altman and Dario Amodei argue otherwise, pointing to untapped areas of innovation, including:  1. **Synthetic Data**: Reducing reliance on expensive human-labeled datasets. 2. **Advanced Training Techniques**: Methods like chain-of-thought prompting are unlocking new capabilities in reasoning and adaptability. As investors keep throwing billions at the foundational model companies, we continue to see real opportunities in vertical AI applications in industries like healthcare, manufacturing, and logistics. These ares are more capital-efficient and still offer significant scalability. ## **AI in Scientific Discovery** [A groundbreaking MIT study](https://aidantr.github.io/files/AI_innovation.pdf) highlighted AI’s transformative role in research - specifically in material sciences. The study looked at how researchers can use AI to discover more new compounds that can be used in many industrial applications such as automotive, renewable energy etc. : - **Productivity Boost**: AI-assisted researchers discovered 44% more materials, leading to a 39% increase in patent filings. - **Uneven Gains**: Top-performing researchers saw the most benefit, while others struggled with false positives. ## **Landing 2024 and gearing up for 2025** As we land this year to refuel and prepare for 2025, we are seeing continued strong opportunities. AI continues to power ahead and fuel the broader tech sector. And political shifts in the US are creating expectations for more dealmaking and strong economic growth. Hopefully, there will also be pressure for European economies to take reform and deregulation seriously so we can drive more growth in our own economies.  We prepare the final leg of 2024 full of optimism for the opportunities ahead. 2024 was in many ways a really good year. We think 2025 can be even better.  --- # The World of Tech and Venture in the Final Stretch of 2024 **Source:** https://www.superseed.com/journal/the-world-of-tech-and-venture-in-the-final-stretch-of-2024/ **Published:** 2024-11-01 **Author:** Mads Jensen October 2024 marked significant shifts in the technology and venture capital landscapes. Nvidia continued its impressive ascent, but rising US interest rates presented fresh challenges. The upcoming U.S. presidential election between Donald Trump and Kamala Harris added another layer of complexity, influencing market sentiments and inflation expectations. Venture funding remained concentrated in AI and B2B SaaS, amid liquidity constraints and regulatory changes affecting mergers and acquisitions. This article examines these trends and their implications for VC investors as we approach year-end. This month, we look at:  - Nvidia’s continued outperformance  - The upcoming US election and what it means for the global economy - Trends in venture capital, and - General trends in AI and technology. ## Stock Market and Economic Indicators **Nvidia and the Nasdaq’s Performance** ![](https://www.superseed.com/wp-content/uploads/2024/10/Picture-1-1-1024x557.png) Nvidia's stock surged more than 20% during the first three weeks of October, before falling back to leave the share price up "just" shy of a 10% gain for the month. The overall growth was driven by extraordinary demand for its Blackwell chip, which delivers up to 2.5 times the performance of its predecessor, Hopper. The Blackwell GPU is fully booked 12 months in advance, reflecting a backlog due to high demand from companies like Meta, Microsoft, and OpenAI. Nvidia’s CEO Jensen Huang notes that they are “early in a long-term AI investment cycle.” Analysts expect Nvidia’s revenue to double this fiscal year. This robust performance contributed significantly to pushing the Nasdaq to a record high in October. The sustained demand for generative AI infrastructure—from models like ChatGPT to Microsoft's AI Copilot—demonstrates the foundational role of Nvidia's GPUs in supporting AI-driven applications. **S&P 500 Trends** S&P500 and the Nasdaq-100 had a largely quiet October before pulling a few percentage points back in the first few days of the month. Once again, it was mainly the “Magnificent Seven” tech companies that were driving performance in the indices. ## Interest Rates, Inflation, and the Upcoming U.S. Presidential Election **U.S. Inflation Rate and Treasury Yields** ![](https://www.superseed.com/wp-content/uploads/2024/10/Screenshot-2024-10-29-at-14.35.02-1024x535.png) Inflation remains steady at 2.4%, but yields on the 10-year Treasury have risen sharply—from below 3.75% in mid-September to over 4.25% in October. This rise reflects cautious sentiment in bond markets and has significant implications for sectors like venture capital and tech, where higher borrowing costs may affect valuations and access to capital.  **Impact of the 2024 U.S. Presidential Election on Markets** With the 2024 U.S. presidential election approaching, concerns about rising inflation are intensifying. Both major candidates, Donald Trump and Kamala Harris, propose fiscal policies that may fuel inflation, albeit through different mechanisms. Importantly, both candidates signal continued high deficit spending, albeit Trump has been discussing working with Elon Musk to "cut $2 Trillion from the US Budget. ![](https://www.superseed.com/wp-content/uploads/2024/10/image-1.png) Source: [CRFB](https://www.crfb.org/papers/fiscal-impact-harris-and-trump-campaign-plans) Given that both policies could be pushing inflation, “safe-haven assets” like gold (up 30% this year) and Bitcoin (up ca 50% this year) have been surging. With Trump currently the most likely winner of the election, let’s unpack his policies further.  **Trump's Policies and Inflationary Drivers** Trump's proposals focus on tax cuts, import tariffs, and restrictive immigration measures. His proposal for universal tariffs would raise import costs, directly contributing to consumer inflation. Additionally, forcibly removing millions of illegal immigrants could tighten the labour market, potentially pushing wages higher and compounding inflation. Trump's aggressive stance on tariffs and immigration, as well as his potential reshaping of Federal Reserve policies, have raised concerns of political interference in monetary policy, which could impact medium-term inflation.  Analysts from sources like the Peterson Institute expect Trump's fiscal plan to elevate inflation more than Harris's, with some estimates predicting a 6% to 9% inflation rate by 2028. Let's hope it doesn't come to that and that cooler heads will prevail if Trump wins on November 5th. ## Venture Capital and B2B SaaS Trends **Global Venture Funding in AI and SaaS** ![](https://www.superseed.com/wp-content/uploads/2024/10/Screenshot-2024-10-29-at-16.25.57-1024x532.png) Venture funding in applied AI and SaaS remained strong in October 2024, focusing on companies with solid revenue models. Notable funding rounds include: - **OpenAI’s Massive Funding Round**: OpenAI raised $6.6 billion in October, nearly doubling its valuation to $157 billion. - **Perplexity AI’s Funding Drive**: Perplexity AI, an AI-powered search engine and chatbot startup, initiated discussions to raise approximately $500 million, aiming for a valuation boost to around $8 billion. Other significant recent rounds include: - **Waymo’s Significant Raise**: Waymo secured $5 billion from Alphabet, reinforcing its position in autonomous driving technology. - **Anduril Industries’ Funding**: The defence technology company raised $1.5 billion in a round led by Founders Fund. **Safe Superintelligence’s Investment**: The AI safety startup founded by former OpenAI co-founder Ilya Sutskever closed a $1 billion round. **Liquidity Challenges and Investor Strategies** The venture market faces liquidity constraints with prolonged exit timelines due to delayed IPOs and mergers. New merger control regulations have further complicated M&A activities, affecting venture capital liquidity. In response, we see both founders and investors adopting more disciplined approaches to capital deployment for anything outside the most well-funded AI companies. There's a heightened focus on achieving cash flow breakeven—essentially treating every funding round as if it were the last. This shift is expected to lead to more capital-efficient companies capable of sustaining growth amid market volatility. ## Trends in AI and Emerging Technologies **Rapidly growing AI’s Market** Bain & Company estimates that AI hardware and software markets will [grow from $185bn in 2023 to $780-$990 billion by 2027](https://www.bain.com/insights/topics/technology-report/). The focus currently remains on high-performance infrastructure led by hyperscalers like Microsoft and Alphabet. Microsoft alone invested $56bn on capex in its last fiscal year (ending June 2024), mainly driven by the buildout of AI infrastructure. We see this spending start to shift from hardware to applications as the application set matures over the coming years. **Energy Demands Driven by AI** AI continues to drive a massive spike in energy demands. The U.S. and Europe face significant challenges in meeting projected energy demands over the next decade. The current energy infrastructure is woefully insufficient, with a limited number of new power plants under construction compared to China's aggressive energy expansion. ![](https://www.superseed.com/wp-content/uploads/2024/10/Screenshot-2024-10-29-at-16.33.25-1024x821.png) Recent nuclear agreements struck by Microsoft and Google underscore the urgency to secure reliable energy sources. Advances in renewables, especially the photovoltaic (PV) buildout in Texas, offer some relief but may not fully bridge the impending energy gap. ## Regulatory Environment and Venture Capital in Europe **Continued Shortage of Venture Capital in Europe** The European economy (here loosely defined as the EU + the UK) is ca. $23trn, vs the US GDP of $26trn. So - comparable in size. Even so, the US deploys three times as much venture capital as Europe. And this is precisely one of the reasons why the US continues to grow faster than the European economies.  There are a lot of things we can improve about the European ecosystem, not least put more capital into innovative companies. One of the challenges we face is that European pension funds (particularly in the UK, France, and Germany) operate under regulatory mechanisms that make it difficult to invest in venture capital compared to their Canadian and U.S. counterparts. Regulatory constraints and risk-averse investment mandates limit their participation in the VC ecosystem.  The good news is that this is a problem we can solve. The [Mansion House Compact](https://www.ft.com/content/71a692b1-7b37-44ce-8131-52463de02062)was a first step towards channelling more capital to the innovation economy. There is still a long way to go, but at least we are seeing some steps being taken. **VC Strategy for Q4 2024 and Beyond** As we move into the final quarter of 2024, we continue to emphasise a disciplined investment strategy, focusing on well-run startups with viable paths to growth and profitability. The current environment necessitates a thoughtful deployment of capital, with both founders and investors focusing on achieving cash flow breakeven—essentially treating every funding round as if it were the last. This approach will lead to better, more capital-efficient companies. The substantial enterprise demand for AI solutions presents significant opportunities for startups in the B2B SaaS space, especially those specialising in AI integration and infrastructure. And this is where we continue to focus. --- # Raising Money for Your Business: caveat emptor **Source:** https://www.superseed.com/journal/raising-money-for-your-business/ **Published:** 2024-09-30 **Author:** Dan Bowyer I’ve just come back from Denmark. I attended an “unconference” in Brydegård, along with a handful of robotics startup founders, some angel investors, representatives from the Odense municipality, some CVCs, and two other venture capitalists. At a certain point, the conversation turned to the merits and disadvantages of the various sources of funding represented in the room. Each investor fought their corner valiantly, but I was surprised by how surprised everyone was. The candid, frank discussion helped founders and investors alike realise what to expect from whom, and how each bunch is (generally) perceived. The group therapy was as informative as it was cathartic, so I thought I’d write up some of the key takeaways.  In this essay (generous), I’ll explore how the fundraising decisions you make at the very earliest stages of building your business can impact its long-term trajectory. I’ll unpack the good, the bad, and the ugly about VCs, angel investors, CVCs, and grant funding, explaining how we all think and how to navigate our expectations. I hope to persuade you to protect your cap table with your life. Let’s start with venture capital. ## Venture Capital: The Imperfect Perfectionists Most businesses should not raise venture capital. Venture has been made to look sexy by its prominent success stories. They make it easy to ignore the rather more patinaed ‘other side of the coin’. Vilfredo Pareto comes up a lot in my day job. His #1-best-selling-airport-W. H. Smith’s ‘[law’ is better known as the ‘The 80:20 Rule’](https://en.wikipedia.org/wiki/Pareto_principle), explaining that 80% of consequences result from 20% of actions. This applies with spooky regularity across most aspects of VC (percentage of portfolio companies accounting for total portfolio revenue; number of sales initiatives tried responsible for total revenue uplift; distribution of LinkedIn ‘likes’ compared to volume of content published). This law also laid the foundations for [the Power Law](https://govclab.com/2023/08/09/the-power-law-in-vc/#:~:text=The%20Power%20Law%20in%20venture,often%20by%20orders%20of%20magnitude.), which informs not only how we think about what success looks like, but also how we operate. It influences **the way we make investment decisions** in the following way: we think every investment needs to ‘return the fund’. SuperSeed is currently investing out of its $50m Fund II. Let’s say we invest $1m, and acquire 17% of a business at Pre-Seed. Assuming we have been diluted down to 10% by the liquidity event a few years later (we live in hope), our expectation, to achieve a good outcome, is that the 10% we own is now worth $50m, implying an exit valuation of $500m. At the time of writing, [you’d need about $71m of ARR](https://cloudindex.bvp.com/) to exit at $500m.  It influences **the way we operate**, too. Venture firms are small. Most early-stage firms will have up to 5 investors, three or four of whom will be senior enough to run investments end-to-end, and subsequently “look after” the investment. In our case, that means board directorship or observation, because we’re very hands-on, but in all eventualities, firms will monitor and query performance data, help businesses raise future funding rounds, introduce them to commercial opportunities, and so on. Consider what we have established about the Power Law of venture returns in conjunction with the basic premise that venture firms are run *very* (read: too) leanly.  This introduces a ruthlessness that encourages lots of investors (especially in the US) simply to write off businesses that are “off-track” (underperforming benchmark). This, in itself, is a painful process. Losing the support of your lead investor not only means you won’t see any more of *their* capital, but it makes it hard to raise from anyone else, too.  “*If the lead has written it off, why should we back it?*”  Fair question. This becomes more painfully true as you increase the profile of the lead investor. If an unknown investor chooses not to reinvest, the market may look past it.  “*Maybe they ran out of cash, tiny emerging manager that they are*”;  “*Maybe the Partner has fallen out with the founder - it happens in early-stage businesses*”;  “*Maybe they’re idiots and they’re missing the potential this business has!*”  But, when you’re dealing with Tier 1 investors, if they don’t take up their pro-rata (reinvest, *at least* what they are entitled to according to their preemption rights, in the subsequent funding round), your business is suddenly unfundable. No such flexibility of perception exists in this realm:  “*If Balderton isn’t coming back in, I ain’t touching this.*”  Sophisticated, established outfits might be marginally better than others at choosing where to deploy first cheques, but they should be significantly better than most at deciding where to allocate reserves.  The other, even uglier influence of chasing top-vigintile returns pertains to investors’ rights. The venture capital model is to take minority ownership stakes. Venture investors do not control a shareholder majority. As a result, they often require consents/rights as conditions to their investment. If we don’t own enough of your business to make executive decisions, then we’d like to have a say when founders want to make extraordinary decisions that have the potential to impact the business negatively. Spending above a certain threshold in a month, hiring above a certain salary band, and issuing equity in the form of options are all examples of fairly typical investor consent matters. Used wisely, these rights exist to protect the founders, the business, and the investor alike.  Sadly, the ambition of the venture capital model sometimes lugs with it the baggage of egotism, and there are investors, more than any of us would like, who exercise these rights unreasonably. They make a founder’s life more difficult instead of acting as a positive force multiplier, as all investors should. Founders should not be fearful of investor rights. SuperSeed asks for the examples I gave above, among others, in exchange for its investment. There needs to be a level of trust between the founders and the investor that these rights will only be exercised contrary to the founders’ wishes *in extremis*, and frankly, if that level of trust does not exist, then irrespective of the terms on a doc, the partnership already sounds ill-fated.  Where founders absolutely should kick off, incidentally, is on certain [liquidation preferences](https://www.twobirds.com/en/insights/2023/global/how-does-a-liquidation-preference-work). They’re ubiquitous, often in a 1x non-participating format. These, to me (and indeed to SuperSeed), seem reasonable. If an investor offers you 3x participating at Seed, run for the hills.  Enough about us. Alternative financing options are available. ## Angels: Saviours of the Earth? In most cases, yes.  There are three main tiers: the Top 40 bangers, the brilliant middle, and…the others. **Seraphs: Michael, Gabriel, Raphael** There are many exited entrepreneurs whose ‘Day Job’ is to angel invest. They have often had venture-scale exits, and correspondingly, have more than most lying around in loose change. From these sorts of investor, you can expect capital, introductions to Tier 1 investors, input on strategy, the brokering of commercial opportunities, and more. Sure, they want to participate in your upside, so they’ll take some equity, but they’re generally not very greedy (far less so than, say, a Pre-Seed fund). Frankly, they’re probably doing it for the high. They miss operating, and enjoy the precariousness of startup, vicariously. These guys are rare, but they’re good for up to $250k. It does not take much Googling to identify them. Reaching them, however, is another question entirely. **Cherubs: Uriel, Zadkiel, *****et al*** The next bracket of investors is the most common. These angels typically invest between $10k and $50k. They may not have had venture-scale exits, but they can certainly afford to invest a few tickets at their preferred price point each year. They may well have had an exit or two in the past, or they may be senior in big businesses, often in professional services. Their expectation, especially in the UK, is to enjoy the tax efficiency of early-stage investment in the very rare event that it pays off, and to be fairly hands-off as their investments either do or do not succeed.  They may volunteer a few useful intros, especially if they work or worked in a world relevant to your business, but in general, they will be fairly dormant. Bringing a few of these together is a great way to get your business off the ground. Some may not have the patience for a full 12-year rollercoaster, and occasionally will sell secondaries as part of future funding rounds (often at Series A, B, and beyond), but if these investors really are “in it” for the long haul, it may be worth syndicating them in an SPV. This can keep your cap table tidy, and limits the operational overhead of managing many relationships. When fundraising, you’ll need to consult your shareholders, and if you have tens of angels to consult and update, it can become cumbersome.  **Bad Actors: Lucipher** This lot cause harm. They remind me of bad parents, and Larkin’s associated poem, [This Be The Verse](https://www.poetryfoundation.org/poems/48419/this-be-the-verse).  I’ll offer an alternative arrangement: *They fuck you up, the angels bad,* *They may not mean to, but they do.* *They fill you with the faults they had,* *And add some extra, just for you.* *But they were fucked up in their turn,* *By tools in gilets, caps and brogues,* *Who half the time were “GOD, you’re good!”,* *And half the time were absent rogues.* *Bad experiences are all passed on,* *From exits unimpressive to most women and men,* *These once-bad founders with shreds of cash* *Should never touch start-up again.* The image of the hapless parent inadvertently handing down generations of entrenched mistreatment and neglect is apt. There are swathes of disenfranchised ex-founders, most of whom have a bone to pick with the startup funding ecosystem due to having navigated it so poorly themselves. They come in with a more significant cheque than they should (often, more than they can afford) at the early stages of a business in a space they purport to know well. They feel they have earned the title of ‘domain expert’ (in many cases, they have). They have committed significant capital to the business, and feel well-equipped to help it on its way.  Pair their financial investment with their “complicated” emotional attachment, and invariably, these angels are pot-committed. In the early days, they may well look and feel helpful. They will know things you don’t (these are lessons you can learn); they will know people you don’t (these are networks you can build); and they will often inject what walks and talks like good governance, by forming a board, which they’ll promptly Chair. Their potential and value is somewhat capped, though. They can’t unlearn the lessons they’ve learned, they don’t feel inclined to bolster a network that has served them as well as they’ve needed it to, and they rarely have the humility to step down from a board, even when investors specifically request it. You will find these guys really hard to manage beyond about the Seed round. And remember: once someone’s on your cap table, it’s bladdy hard to get ‘em off. I know I’m going a bit HAM on this category. Let me double down: I promise they can make a business unfundable. Not only do VCs *hate* working with this bunch (they typically know very little about building a venture-scale business. The bad angels, I mean.), but your choice to appoint them to your board *can* reflect poorly on your ability to identify and recruit top-tier talent. Take their money, sure, but don’t let them anywhere near the helm.  ## Corporate Venture Capital: Myth, or Legend? Most CVCs are much more C than VC. They invest weirdly. Sometimes, they invest directly off the balance sheet; at other times, they invest from dedicated funds. Sometimes, they invest regularly; at other times, their approach is more *ad hoc* and opportunistic. Sometimes, their investment thesis is very disciplined; at other times, it looks a bit more scatter-gun. My advice here is to ask them lots of questions. - When do they invest? - What do they invest in? - How much do they invest? - Do they reup? - When do they expect returns? - What sort of liquidity event do they anticipate for your business? What you’re trying to establish is whether they *actually* want a venture-scale outcome. In some cases, they claim to, but answers to the above questions will reveal that they are really infiltrating your cap table before launching a *coup d’état*, giving you a tempting (but premature) exit offer in the medium-term, capping the upside of what could have been a world-changer.  When done properly, a CVC can be an incredibly powerful force multiplier. I know from experience that Bosch and BMW (*inter alia*) have excellent CVCs, who introduce portfolio companies to relevant business units, but also help them grow *outside* the parent organisation. They truly act as C**VC**s, and want their businesses to grow to IPO. I bet their secret ideal outcomes are to sell to Siemens and Volskwagen respectively, at a 10x revenue multiple. Either werks. ## Grants, and how they’re like Ribena In the early stages of building a business, you take any version of success.  Grants come in different shapes and sizes. Some are big, others are small. Some are tied to specific projects, others are more like awards and can be used for *n’importe quoi*. Important to consider is the following: the characteristics of grant-attractive businesses are very often misaligned with the profile of a business that meets VCs’ expectations.  Grants encourage Oliver Twistism: a constant coming back for more. In the project-based grant example, they also require pretty singular focus on an individual initiative. Neither of these is good if you want to build a venture-scale business. Oliver Twist would have been a rubbish entrepreneur (thank God he could sing), because the only place you should be asking for “more” from is your target market. “*ROI, ROI: never before has software offered more!”* it’ll chorus, rightly.  Continually raising (or even winning) capital is a bit perfunctory unless it leads to something. If you’re consciously burning capital because you’re growing, that’s one thing. If you keep coming back for more capital because you still haven’t figured stuff out, that (eventually) is unfundable. Some founders have strong enough profiles (and possess other misleading externalities) to get away with this for longer than they should, but the truth *always* prevails. The project-based stuff is bad because it encourages the antithesis of what makes a business venture-scale: “*let’s spend lots of time making incremental progress towards this massive, ill-defined problem!*” Let’s not. Remember, the things that put and keep you on the venture track are scalability, repeatability, and velocity. You need to get big quick, *and* efficiently.  The allure of non-dilutive funding is obvious. Founders absolutely *should* try to obtain some grant funding. Grants can sometimes introduce really valuable partners, who eventually become valuable customers. But founders should approach grants with caution, and in the knowledge that it’s bad to be pigeonholed a “grant-dependent business”. I wonder how many unicorns the ERDF has backed… So, where non-dilutive, easily obtainable grant funding may be useful, it’s possible to have too much of a good thing. Sometimes, a bit of dilution is actually preferable. That’s the Ribena bit. ## Err, so, what now? This is a fair question, given that I’ve problematised every major source of startup capital. The conclusion is that you’ll have to pick your poison. For every negative point I’ve made, there are hundreds of positive counterpoints. My essay (maybe ‘article’ is more apt? Even then…) attempts to introduce you to the risks attached to each, and does a bad job of giving the credit that is due to the truly brilliant actors in each category. In Europe, in particular, the great are few and far between, the bad are in a minority, and the majority are fine. I don’t want “fine” for you, and I hope this has helped you see wood from trees. There is such a thing as repellent capital. Confusingly, it is possible for capital to be: - Too Passive and Too Active; Dead weight on the cap table not contributing to your evolution **versus** asking too many questions, hard to manage, needy, distracting Too Conservative and Too Aggressive; - Aiming for a bronze medal, risk aversion to the fault of capping your upside **versus** allowing and encouraging an insane burn profile, trying to shove you along the venture track rather than keeping your focus on customers Too Myopic and Too Long-Termist; - Impatient to exit, encouraging an early M&A **versus** allowing you too much rope to experiment and R&D yourself into a boring early grave Beware the fault-truffling VC. Every decision carries an implication, and we’re often “marking negatively”. Simultaneously, I’d encourage you to learn our cynicism. Remember the permanence of inviting someone onto your cap table, and remember, too, that it is just that: an invitation! Your investors are lucky to be part of your journey, *especially* if it goes well. If it goes badly, oh well. That’s a risk we take knowingly, and one we price in. Don’t cry for us, Argentina. Building can be exhausting and demoralising. Founders can fall into bad practices like selling off-ICP, offering free trials, and taking money from bad investors. Precisely the reason we do not like to see those bad practices is because we know they are informed by desperation. Discipline in startup is important. Ultimately, YOU run your business, and always must. YOU set its vision, and always must. It’s so important to have a clear vision for the future, because we pilot fish all have our own target destinations. You’re the shark. You choose. But *caveat emptor*! --- # Entering the final stretch of 2024 **Source:** https://www.superseed.com/journal/entering-the-final-stretch-of-2024/ **Published:** 2024-09-30 **Author:** Mads Jensen And then October and Q4 rolled around. Stock markets are still going strong. Nvidia is still at $3trn. And OpenAi is looking to raise $6.5bn at a $150bn valuation. And, oh - by the way - does the SaaS model have a future in the new AI paradigm?  Read on for an analysis of what’s going on in the world of tech and venture as we open the last quarter of 2024. ## Nvidia holding steady at $3trn The S&P500 is up just shy of 2% in September, with Nasdaq 100 up 3%. Both are hovering close to all-time highs.  US inflation appears to be coming under control. We got the 50bps rate cut from the Federal Reserve, with another 50bps forecasted before year-end. Currently, it looks like rates will go to just shy of 2.9% over the next two years. This is much higher than the 0% we had during the ZIRP era but much lower than the +5% we’ve had until recently. **Nvidia is still flying high.** Nvidia is also up ~3% in September. The stock has been in a holding pattern since June.  A few months ago I discussed the bull and bear case for Nvidia at $3trn. While the stock still looks highly valued at a P/E multiple of 57x, many investors continue to see significant upside in the stock.  **What’s underpinning the continued optimism? ** In September, we had signals from industry leaders as to why there continues to be so much excitement behind the stock.  Marc Zuckerberg from Meta outlined what’s so special about the new generative models. Whereas past machine learning models have hit ceilings in terms of performance, we have not yet seen such ceilings for the new transformer model (“transformers” are core to the technical architecture that underpins Large Language Models like the model behind ChatGPT).  More data and more CPU have kept leading to better performance.  **The giants are battling it out** x.ai (Elon Musk’s AI firm) has just brought a new 100,000 CPU cluster online - the first in the world. And he has started talking about 200,000 clusters.  The cost of the new Nvidia Blackwell GPU could be as much as $60,000 - $70,000 per chip.  In other words, one 200,000 CPU cluster will cost $13bn in chips alone.  And the whole world is racing to get these chips.  Microsoft and Alphabet have essentially said they are going to keep spending on these. Nobody wants to be left behind. Sergey Brin from Google is now back in the lab full-time. He sees AI as the biggest sea-change in technology, at least since the Internet.  In other words - looks like the industry will keep spending on Nvidia chips for the foreseeable future. So Nvidia could be a good buy, even at $3trn. ## What’s the case for OpenAI at $150bn? Sam Altman (OpenAI CEO) has been in the news a lot lately. He is currently working to finalise a new investment round in OpenAI that would raise another $6.5bn from investors at a [$150bn valuation](https://www.reuters.com/technology/artificial-intelligence/openai-talks-raise-funds-150-bln-valuation-bloomberg-news-reports-2024-09-11) (alongside $5bn in debt), up from $86bn valuation earlier this year. The minimum commitment in the round is set to be $250m, so this one possibly won’t fit right into a standard EIS round(!) **From not-for-profit to $10bn’s worth of shares** As part of the round, Open AI would convert from a not-for-profit to a B Corp, and, among other things, remove the previous 100x profit cap for investors.  As part of the plan, Sam Altman would go from being the altruistic steward of said not-for-profit to having a 7% stake in the $150bn company. There is a meaningful difference between having shares worth zero and having shares worth $10bn. Good for him if he can pull it off.  **Bull and bear case** But it raises the bigger question - what is the bear and bull case for OpenAI at $150bn?  Bring out the bulls: 1. **Incredible traction**: OpenAi’s ChatGPT remains the fastest growing B2B software product in history, growing from launch to $1bn in revenue in an estimated 8 months (across all software, the crown is held by Grand Theft Auto V that scaled to $1bn in just three days). Current forecasts say that OpenAI will be at $4-6bn of run-rate revenue by the end of 2024 and that it could be at $6-10bn by the end of 2025. $150bn valuation at $10bn of run rate revenue would imply 15x forward revenue multiple. Not unreasonable if the growth continues.  2. **Technology leadership**: OpenAI was first to market with a commercial Large Language Model, and the company has continue to innovate in everything from voice to image generation and video. The company’s continued tech leadership means that the company has developed the biggest mind-share in the developer ecosystem, with others building on top of their platform. Over time this could develop into a moat - especially once they add data storage to their offering (one of the issues with current models is that they don't offer a straightforward way to store data. OpenAI is working to solve this).  3. **Industry leadership in a massive market**: As anyone who has been close to the AI transformation will tell you, AI has the potential to be the biggest transformational technology of our lifetime. Sergey Brin (Google co-founder) is now back in the lab full time working on AI, because he sees the opportunity as so significant. PwC estimated that the annual opportunity from generative AI alone is $15trn within a decade. Whether or not this is the right number, the consensus from those who are close to the transformation is that this is going to be big. If you think AI is going to be even bigger than search, the leader could be immensely valuable. And OpenAI is currently the leader of this industry.  Line up the bears:  1. **Intense competition and risk of commoditisation**. Open source LLM’s like Meta’s Llama are already incredibly good. And they have become relatively easy to embed into other application frameworks. There is a real risk that LLMs could become commodities which could destroy OpenAI’s business model.  2. **Financial disparity with competitors**: As outlined above, the cost of GPUs to train models has become very high. And due to the scalability of the models, there is a possibility that the winner will be the firm that can fund the biggest training clusters. This is unlikely to be OpenAI. Meta generates $40-$50bn of free cash flow per year. Alphabet generates $60bn. Microsoft $75bn. It will be challenging for OpenAI to raise sufficient capital to outspend the behemoths over time.  3. **Organisational challenges**: OpenAI has had excessive leadership turnover this year. So far the company has lost Mira Murati (CTO), Bob McGrew (Chief Research Officer), Barret Zoph (VP Research), Ilya Sutskever (Chief Scientist) and Andrej Karpathy (Co-founder). Can the company keep it’s pace while losing so many leaders? And are the departures a symptom of underlying issues in the business?  **The cult of Sam** It’s clear that OpenAI is one of the defining companies of our time. And as the company’s notoriety has grown, Sam Altman’s image has transformed from a man on a scientific mission to bring about Artificial General Intelligence for the benefit of all, to a hardnose tech tycoon who isn’t afraid to leave collateral damage behind as he drives to make OpenAI the leader of the new AI landscape.  In some ways, Sam Altman is starting to remind me of Elon Musk. Elon also had plenty of setbacks and staff turnover at his companies, but he keeps defying his doubters to hit new milestones in his companies. And just as it is impossible to write off Elon Musk (whether you like him or not), it feels difficult to write off Sam Altman and OpenAI. There is plenty of risk, but the upside is monumental, and the company keeps executing.  **What can we learn from the cloud computing wave? ** A few more notes to draw parallels between the new AI paradigm and the cloud paradigm that came before.  When the cloud took off with the launch of AWS, many people initially thought cloud computing would commoditise quickly. Intel-powered cloud servers were already on their way to complete commoditisation. And Linux was already a free open-source operating system. But today it is Intel that has been commoditised. And AWS offers more than 200 district products, with Amazon having created a complex ecosystem with more than $100bn in annual revenue.  Commodity? Maybe. But ask anyone who is trying to compete with AWS, Google and Microsoft in cloud computing. Those three companies have made formidable cloud businesses, and others have fallen by the wayside.  And while OpenAI at $150bn might not be a slam dunk investment case, I wouldn’t bet against Sam either - especially if he can continue OpenAI’s impressive revenue growth.  One to watch closely over the coming quarter.  ## Does the software industry have a future in the age of AI?  **The Future of SaaS in the Age of AI** Outside the tech industry, many people still discuss whether AI will have a big impact on the world. For those of us who are in the tech world, that question seems settled. I use LLM’s many times every day. They have replaced 80% of my Google searches. They help with ad hoc analysis, code development, process automation, and so much more. As I wrote a few days after the first release of ChatGPT, the arrival of the LLM’s [changed the world forever](https://www.superseed.com/journal/the-future-has-arrived/). And that is indeed the trajectory we continue to see.  Now, in some ways, we are still only scratching the surface. But a big question inside the tech industry is whether AI and large language models (LLMs) will make the traditional SaaS industry obsolete. Some argue that as AI becomes more advanced, businesses will rely on AI agents and open-source databases instead of specialised software. Is SaaS really on the way out? **Is SaaS Disappearing? Let’s look at the arguments for** First off, AI agents are becoming more capable. They can automate complex tasks that used to require specialised software. For example, fine-tuned LLMs can now handle customer support inquiries. This could make dedicated support software unnecessary. Another aspect is that AI models can analyse vast datasets in real-time. This could make traditional enterprise systems like Salesforce or Oracle less relevant, as businesses could use AI to interpret data directly without needing those systems. Also, as AI makes businesses much more efficient, we may not need as big companies anymore. And if we don’t need massive organisations, we might not need all the software that goes with those organisations either.  Lastly, open-source databases combined with AI could cut costs significantly. This challenges the SaaS subscription model, as businesses might prefer free or cheaper alternatives. **Why SaaS Will Thrive** Despite these points, I believe the SaaS industry isn't going anywhere. Here's why: **Specialised Solutions and Compliance** SaaS providers offer software tailored to specific industries with unique challenges. Take healthcare, for example. SaaS platforms manage patient data while complying with strict regulations like HIPAA. Generic AI models can't easily replicate this level of specialisation and compliance. **Reliability and Support** SaaS providers offer guaranteed uptime and dedicated customer support. Businesses depend on these assurances for uninterrupted operations. AI models lack the accountability and support infrastructure that SaaS companies provide. The open-source world has plenty of parallels. Linux is free. But enterprise customers still buy Linux support from IBM/Red Hat for billions every year.  **Limitations of AI Models** While AI is powerful, LLMs can produce inaccurate or unpredictable results ("hallucinations”). In critical applications where accuracy is non-negotiable—like financial reporting or compliance management—these risks are unacceptable. AI models also often lack explainability, making it hard to justify decisions in regulated environments. Another related aspect is that while many new areas can be automated using next-generation AI models, this won’t be done by generalised LLMs. Rather, it will take specialised models trained on proprietary data. Far from obviating specialised SaaS, this opens up whole new SaaS market opportunities, as new markets will grow with the new capabilities.  **Cost and Resource Efficiency** Developing and maintaining AI solutions requires significant investment in expertise and infrastructure. SaaS offers a cost-effective alternative by providing ready-to-use solutions. That equation is not changing. It’s still more efficient for specialist SaaS vendors to develop a solution and sell it to many customers, than for each customer to handcraft their own systems.  **Conclusion** Rather than being replaced, SaaS is integrating new AI capabilities to deliver even stronger value propositions. This synergy offers the best of both worlds—advanced AI capabilities within a secure, compliant, and supported framework. As a result, the SaaS industry will continue to thrive despite the rise of AI. While AI will undoubtedly transform the software landscape, SaaS offers specialized, reliable, and compliant solutions that AI models alone can't fully provide. The future lies in the integration of AI within SaaS platforms, ensuring businesses reap the benefits of AI without sacrificing the support and security that SaaS provides. So, while AI will play a significant role in shaping the future, SaaS isn't disappearing. There is no doubt that some SaaS players will come under pressure, and some even perish. But as AI allows us to tackle ever more use cases, it will lead to a growing software market. And this will lead to growth for the overall industry.  --- # Can Generational Theory predict venture's future? **Source:** https://www.superseed.com/journal/can-generational-theory-predict-our-future/ **Published:** 2024-08-30 **Author:** Dan Bowyer It’s late on a Thursday. I’m in the office talking with Adam about the Roman Empire - of course as men [we think about it at least twice per day](https://www.theguardian.com/lifeandstyle/2023/sep/19/the-roman-empire-why-men-just-cant-stop-thinking-about-it). Adam studied History at Cambridge and as our resident boffin was randomly miffed at how we talk about the empire as it was ‘in decline’.  *“It didn’t decline, even though of course it did”*, he exclaimed.  “*It also transitioned, over 500 years,* *Byzantines called themselves Romans for a further thousand years until the Ottomans!”. * We continued to debate what decline actually is and means… which brought us on to [Ray Dalio's book](https://www.youtube.com/watch?v=BB2r_eOjsPw) mapping the rise and fall of empires. https://www.youtube.com/embed/BB2r_eOjsPw Principles for Dealing with the Changing World Order Which then brought us onto (I promise we do do work in the office) [The Fourth Turning by Straus and Howe](https://en.wikipedia.org/wiki/Strauss%E2%80%93Howe_generational_theory). Another book on Generational Theory, how everything is circular - ebbing, flowing. Rinsing, repeating.  None of these authors invented these themes, they’re merely observers, documenters. The original thinking goes back past Mannheim and Jefferson and even further back. Critics arguing that boxing and categorising how the pendulum appears to swing is a purely self serving exercise. One that gives humans comfort, with the appearance of understanding and control. I hear the critics but to me nature is circular, seasonal, and I can’t think of one process that doesn’t follow a similar ruleset, even down to business cycles in our economy, deeper down into our startup ecosystem. So, as a thought experiment I’ve taken a framework from The Fourth Turning to see if we can use it to thematically predict the future for our firm, the founders we work with, and the startups they’re building. **Will the generational phase we’re currently in, and the next, dictate what we should invest in?** It’s a really interesting book and hypothesis, and posits this: History repeats itself in 80-year blocks or roughly the span of a human lifetime. Within these 80-year history blocks, we have four 'turnings' of around 20 years, what we would consider generations. Throughout our history, these blocks have repeated, and are remarkably similar to each other. The first turning is a high, an upbeat era filled with conformity. The second turning is an awakening, a passionate era. The third turning is an unraveling, a downcast era. And the fourth turning is crisis, an era of upheaval. Can you guess which turning we’re right now?  **Yes. We’re in that one. ** ![](https://www.superseed.com/wp-content/uploads/2024/08/Generational-Theory-1-1024x474.png) Looking at this Turning (using US history) - Our ‘**High**’ was 1946-64 - the birth of rock’n’roll, space, jets, fast cars - ending with the assassination of JFK Nov 1963. Our second Turning, the ‘**Awakening**’ 1964-84 - a period of non-conformity containing Star Wars, the Beatles, Kubrick, Computers, civil and women’s rights, acid, Vietnam protests - ending with Reagan’s re-election. Turning 3, the ‘**Unravelling**’ - the fall of Soviet communism, songs about violence and decay in deteriorating cities, the LA riots, OJ, Bosnia, Columbine High School shootings, September 11th, the wars in Afghanistan and Iraq. Ending with the financial crisis of 2008. And the 4th Turning, where we are now, one of '**Crisis**' - 2008-2028 - Political divide, Covid, financial inequality… the rest to be defined. Before this it was WWII to now, before that the US Civil War to WWII, before that the US Revolution to the US Civil War... and back in time. Each Turning has an archetype that defines that generation. Those born in Turning 1 are the Prophets, the Steve Jobs characters. Turning 2 in the Awakening are the GenX Nomads - the Musks taking us to Mars, in electric cars, hyperloops. Turning 3’s Millenial archetype is the Hero - The activists, Thunberg, Malala, the front line workers. And this 4th Turning will be defined by the Artist - as yet to be named. (Of course history doesn't repeat itself, but yes it sure rhymes. So let’s put some bold predictions on paper. See where they land.) **Hmm.. the Artist huh… who are the artists of the now? ** The technologists are - they will create the future in code. Ai engineers *are* our artists in residence. Technology has always saved us from ourselves and to circle back round from Crisis to our next generational High will take a little extra helping hand from Ai.  **What kinds of Startups are they going to build? ** 2025 will be where Ai hits the real world in business. We're all talking about foundational models and the spinout tools such as [Midjourney](https://www.midjourney.com/) for cool images, [Ai chatbots in customer service](https://thecxlead.com/tools/best-ai-chatbot-for-customer-service/), and millions are now using [ChatGPT](https://openai.com/chatgpt/) instead of Google search. Have you seen [bland.ai](https://www.bland.ai/)? Or [cursor.com](https://www.cursor.com/)? Where developers can write code in natural language, query, edit, launch. https://www.youtube.com/embed/tw9GyD0Zkiw Cursor Ai code editor Well imagine when you can stitch everything together? This next step is [Agentic Workflows](https://www.youtube.com/watch?v=sal78ACtGTc) (via [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)) which will radically transform how we work. This is where Ai agents within an organisation talk to each other. One LLM will start a task, another will then reflect on that task, another may add/improve it, then deploy - in a workflow. ![](https://www.superseed.com/wp-content/uploads/2024/08/image-1024x576.png) Organisations of any size will be able to automate away many of their processes using Ai agents, delivering productivity and efficiency like we’ve never experienced. The next iteration will be when my agents talking to yours, which is not as far away as some think.** ** **Powerful, so what about the risks?** These practical and pragmatic applications of Ai will lead to a need for new forms of governing. Not only will technology create that need but it will also supply it. Sam Altman from OpenAi saw the risk of Ai to the labour market and humanity so launched [worldcoin](https://whitepaper.worldcoin.org/) - A globally-inclusive identity and financial network, owned by the majority of humanity. Personally I'm not convinced, but I also understand how one of the leaders of the Ai revolution was keen to provide answers to its potential dark side. More initiatives like this will come, I have no doubt. Coming back to the Fourth Turning framework - **We now have the Adaptive and Civic mindset coming through into the workforce.** On that note, Ai is the first technology I can think of that can solve the very problems it creates. And not just for governing. The sectors to be transformed are unending - agtech, legal, manufacturing, supply... where education, healthcare and welfare will now be nearer the top of the 'investability' [sic] list.  I know the news cycle loves doom, and our hyperconnected world currently thrives in mis and disinformation but personally I’m a bull. The next generational shift is just round the corner and we're in the front seat. There is so much to be hopeful for. ***- who doesn’t want to live in a High?*** ... “*Human productivity is the most important force in causing the world’s total wealth, power, and living standards to rise over time.” *- Ray Dalio --- # You've closed the dream candidate, now what? **Source:** https://www.superseed.com/journal/youve-closed-the-dream-candidate-now-what/ **Published:** 2024-08-16 **Author:** Dan Bowyer Finding the right team member to fill a role can be tough, but don't take your foot off the gas just because the contract is signed. The real work starts now... Here is a high level cheat sheet if you're hiring, to ensure you get the best possible lift off with new hires. ## Do's - Maintain communication between contract signing and start date. - Introduce them to the team (on Slack/Teams if working remote). - Ask them if they would like to receive material in advance of their start date. - You've heard of an exit interview, what about an entry interview? These can give you all sorts of useful insights, e.g. where the person found you, why they joined, how you could improve the recruitment process, what were their perceptions of the business from the outside. ## Don'ts - Don't make assumptions about their existing knowledge (of your product & business). - In the first few weeks there is a large amount of information to take in. Don't assume that if you've told them once, they will be up to speed. - 1 and 3 month check ins aren't just to feedback, use it to receive too. - Don't assume once week 1 is over that they'll be settled in. ## Hacks - If you don't have budget for SWAG, think sideways. For example at a previous company we sent out hand written "Welcome to the Team" cards to all new joiners in the week's before they joined. Every single new hire mentioned this in their first week. It's the small things. - You've just hired your latest member of your recruitment team! Share with them your current hiring requirements and ask if they know anyone. Great people attract great people. - Agree milestones early on that need to be checked in order to pass probation. No surprises. - Work collaboratively to set SMART goals or agree OKR's so there is complete clarity and buy in. --- # A Busy Summer **Source:** https://www.superseed.com/journal/a-busy-summer/ **Published:** 2024-07-31 **Author:** Mads Jensen *This is not investment advice* It’s been a super hectic July. Both on the political and the investing front. I am sometimes given flak for putting too much focus on the US, but - as the US goes, so goes the world. Especially in technology (which we will aim to rectify in the long term, but for now, we need to cover what matters).  ### A new face-off in the US election At the start of the year, I wrote that it was likely that [Trump would win](https://www.superseed.com/journal/the-2024-crystal-ball-geo-and-macro/) the 2024 election. Trump was ahead in the swing states and Biden had weak approval ratings. The biggest threat to Trump seemed not Biden, but the US courts. The US Supreme Court essentially refused to interfere in the election, and so by June, Trump was all but assured to win. The smart money started lining up behind Trump (including many people who previously denounced him).  Then came the June 27th debate between Biden and Trump, and it became clear to everyone that Biden would not be able to win anything (let alone govern for another four years). What followed was several weeks of painful soul searching for the Democrats, and by July 21st, Biden announced that he was stepping back from the race, endorsing his Vice President Kamala Harris to be the new candidate in his place.  Some people on the Democratic side had hoped for a new primary to help the party find the strongest candidate. Some folks thought Ms Harris would be a weak candidate in her own right. [Others pointed out](https://www.nytimes.com/2024/07/23/opinion/kamala-harris-democrats.html) that Democrats always lose after a “coronation” (Gore in 2000, Clinton in 2016). But then the pundits got surprised again. [Kamala Harris has raised $200m](https://www.ft.com/content/5ad46c5c-6289-4af0-91be-121f2809a9e0) in a week to fuel her campaign. Importantly, the Democratic ticket bounced back to the point where it is now [neck-and-neck](https://www.bloomberg.com/news/features/2024-07-30/kamala-harris-erases-trump-s-swing-state-lead-in-2024-election-poll) with Trump in the swing states. Prediction markets currently give Trump a small edge. But a Trump victory is no longer a foregone conclusion. This has implications for tech and markets, as we shall see below. ![](https://www.superseed.com/wp-content/uploads/2024/07/chart1-aug-blog-1024x944.png) You can follow prediction markets [here](https://www.predictit.org/markets/detail/7456/Who-will-win-the-2024-US-presidential-election).  ### US Economy - Still Powering Ahead?  [US GDP grew 2.8% in Q2,](https://www.bloomberg.com/news/articles/2024-07-25/us-economy-accelerated-by-more-than-forecast-last-quarter) well above market expectations.  At the same time, inflation continued to gradually ease, with core inflation numbers now down to 3.4% (3% for headline inflation). Markets now expect two rate cuts before t[he end of 2024](https://www.bloomberg.com/news/articles/2024-07-11/us-inflation-broadly-cools-bolstering-case-for-fed-rate-cut). At the same time, it looks like [US consumer spending is starting to slow down](https://www.ft.com/content/c92a6912-9ac3-410b-908d-8fb1a59d6a10). Many US corporates had relatively weak earnings announcements in July, and outlooks for H2 are that growth may have peaked. If growth slows just slightly, and it enables the US to firmly get inflation under control, that’s not a bad thing. This should pave the way for more interest rate cuts, and then we can start a new cycle without first going through a painful recession.  So far, so good.  ### What is the Medium Term Inflation Outlook? But what if US inflation is on its way back up again? As discussed above, the 2024 US Presidential election is anything but settled, but Trump is still the front runner (even if only by a smidgen at this point). What has he said about his economic priorities? Trump has said that he wants:  - **Tax breaks**. The Federal government is currently running a $1.7 trillion deficit, equal to 6.3 percent of GDP. That’s high by any measure. Tax breaks are unlikely to narrow the deficit gap. Even if Trump cuts some programmes (as he has said he will), it is unlikely that he will dramatically reform entitlement programmes, given his populist platform. So if anything, deficits are likely to stay high. That’s inflationary.  - **Trade wars**. Trump wants to rebalance trade by putting further tariffs and restrictions on imports. This will make goods more expensive for US consumers, which is inflationary.   - **Reduced immigration**. Trump wants to eliminate illegal immigration. Leaving aside the emotions surrounding immigration, this will reduce available labour. The US unemployment rate is currently around 4% - already lower than the long-run average. Further immigration restrictions will put pressure on the labour market. This is inflationary.  All else being equal, it’s hard to see how a Trump 2.0 will be anything other than inflationary. That’s got medium-term implications for inflation, rates, markets and the economy at large. Although this won’t impede the business case for investment in technology and automation (technology drives down prices and is deflationary), it will mean that rates will be higher for longer. With all the implications for cost of capital, stock markets etc.  What will a Harris presidency look like? The expectation is that it will be “continuation Biden”, but I don’t know that we know her well enough yet to say for sure.  So there is some uncertainty lined up ahead of us.  ### And What’s up with Bitcoin?  Trump has gone full 180 on Bitcoin. From denouncing it as a scam in 2021, [he is now firmly behind the digital currency](https://x.com/MJTruthUltra/status/1817327714614780081). “If Bitcoin is going to the moon,” he said, “I want America to be the nation that leads the way". He has also said [he will sack Gary Gensler](https://www.ft.com/content/03e8e1d2-4244-4eba-9248-9bbd8d1b0090) (head of the US Securities and Exchange Commission) in his first day in office. Gary Gensler has been spearheading efforts to regulate / curb the rise of digital currencies, and Trump is promising a new regime on this front. We can all speculate as to the motivations behind this, but the implication for the price of Bitcoin seems pretty obvious, and the price is already up >10% in the last fortnight. ![](https://www.superseed.com/wp-content/uploads/2024/07/aug-blog-chart-2-1024x670.png) ### US Stock Markets Take a Breather While Bitcoin was up in July, US equities took a pause. Last month we looked at the ascendancy of Nvidia, as the company hit a $3trn market cap. The company peaked on the 10th of July, and since then, it has declined 23%.  This mirrors Alphabet which is down 11%, Microsoft (down 9%), Tesla (down 15%) and the wider Nasdaq 100 (down 9%) since the peak on July 10th.  On the other hand, the S&P 500 is down a more modest 3.5% since July 10th, as we’ve seen a sector rotation from the Magnificent 7 tech/AI stocks to a broader swathe of the stock market.   In a short span of time we’ve gone from “AI will change everything overnight”, to “the technology isn’t ready yet and we’ll never recover invested capital”.  At moments such as these, it can be helpful to revisit the good old hype cycle. We have repeatedly said that “yes, there is an AI bubble” and “yes, AI will change the world”, and the two can be true at the same time.  In July, we probably came off the “Peak of Inflated Expectations”, and it’s likely that we have a bit of Disillusionment ahead. But we still see plenty of productivity ahead, both for the economy and for stocks. ![](https://www.superseed.com/wp-content/uploads/2024/07/aug-blog-chart-3.png) ### What’s Happening with SaaS? From 2014 to 2022, SaaS companies were highly favoured due to their reliable recurring revenues and substantial growth rates. However, by 2024, the median revenue growth of SaaS companies in the EMCLOUD index dropped below 20%. Revenue multiples are now below 6x, a stark contrast to companies like Nvidia trading at ~25x revenue. ![](https://www.superseed.com/wp-content/uploads/2024/07/chart-4-aug-blog.png) Over the past 12-18 months, corporates have shifted all new tech spend towards AI. This has boosted revenue growth of companies like OpenAI, and slowed revenue growth of more traditional SaaS companies.  As we’ve seen in July, there is a sense that this “headlong rush” into the first wave of AI has started to come to a close. There isn’t enough ROI in deploying generic models from OpenAI, and there is still a lot of customisation work required to make AI work at scale and in production in corporate settings.  The future of AI model enhancement lies in leveraging private, proprietary data within enterprises. This data can differentiate AI models and significantly enhance their capabilities in automating and optimising business operations. This also lends itself well to SaaS applications – often the core repositories of corporate data.  As both incumbent and challenger SaaS vendors start offering AI models based on proprietary data, enterprise customers will start seeing increased ROI from off-the-shelf SaaS platforms. The companies that manage to make this transition will benefit from attractive growth rates. Some of these are already public companies. Others are still being built from the ground up. Those are the startups we invest in. ### What Happened in Venture Capital in Q2?  PitchBook recently issued an update on US and European venture capital activity in Q2. The numbers were up across the board. US deal value was up 46% from Q1 (to $56bn) and European deal values up 27% to €16bn. Even when factoring in the $6bn Elon raised for x.ai, it looks promising.  ![](https://www.superseed.com/wp-content/uploads/2024/07/chart-5-aug-blog-1024x565.png) Let's look under the covers.  - **Big jump in round sizes: **The median European seed round increased 40% to €2m ($2.15m). That's a huge jump, and one that is likely to be volatile in future quarters. - **European rounds are still smaller:** Although the jump is big, this is still behind the median US seed deal which was $3.1m. - **And Europe still trades at a discount:** Even bigger was the valuation gap. Pitchbook estimates 412.4m premoney for the median round in the US, vs $5.5m for the median seed round in Europe. That's a 56% gap. - **Bigger rounds and (slightly) fewer deals:** Pitchbook reported an 18% drop in deal count (so - fewer, larger rounds). However, it often takes a while before rounds are announced. PB estimates that a total of 2,478 rounds were done in Europe in Q2. That would only be a 4% decline on Q1. In other words, about the same number of much bigger rounds. Overall, the trend is positive. We’ve covered the political and economic uncertainty above, but there is a sense that things are heading in the right direction for venture.  We’ll take that as positive momentum heading into the second half. Onwards! --- # Plateaus in SaaS and AI, and the next opportunity in software **Source:** https://www.superseed.com/journal/plateaus-in-saas-and-ai/ **Published:** 2024-06-30 **Author:** Mads Jensen ## From Poster to Problem Child From 2014 to 2022, SaaS companies were the darlings of listed tech stocks. With sticky recurring revenues and consistent revenue growth rates (the median SaaS company in the EMCLOUD index grew 30-35%/year), SaaS seemed like the safest of bets. Some people started to think of SaaS companies as the perfect financial instrument. Here was finally something that provided both the stable cash flows of a bond and the perpetual revenue growth of tech. What was not to like? Since then, SaaS has come back down to earth. In 2024, the median revenue growth of EMCLOUD companies dipped below 20%. And while this is still growth, the trend has some folks worried. Cue median revenue multiples, which have dipped below 6x (for comparison, Nvidia is trading at nearly 30x revenue). ### Have corporates stopped spending? It’s not that corporate buyers have stopped buying technology. It’s just that the focus has shifted. And right now, all focus is on AI. As we speak, corporates are maniacally focused on “bringing AI into their enterprise operations”. Besides Nvidia, the main beneficiaries are Microsoft and OpenAI which are selling access to GPT on an API basis. And all other "non-AI" tech projects are being scrutinised and put on hold. This is leading to longer sales-cycles and lower win-rates. But just as AI has started dominating the headlines, we are seeing a slowdown in the strength of core AI models (the LLMs that power much of generative AI – like GPT from Open AI and Gemini from Google). While they are still getting better, [Emergence argues in a recent publication](https://www.emcap.com/thoughts/ai-s-curve-plateau-proprietary-business-data-breakthrough) that performance growth has plateaued. ### What - no more improvements to AI models? To date, LLMs have mainly been trained on public data. As OpenAI, Google, and others have reached the point where their models are trained on all the publicly available data, where is the next step in model performance going to come from? The answer lies in private data. The data residing inside enterprises, and which is going to unlock the next level of progress for AI models. In the context of AI, private data is powerful for two reasons: 1. It’s proprietary, so it can be used to differentiate models. If all models train on the same public data, they all become identical and commoditised. But private data can set them apart, making them far more valuable. Private data is the road to competitiveness and profit. 2. Proprietary business data is the data that’s needed to truly automate business using AI. The knowledge of an LLM trained on public data is a bit like the knowledge of a well-studied graduate entering the corporate world for the first time. They need some time as trainees before they really understand what’s going on inside a company (and can add meaningful value). The analogy of this is “access to private data”. It’s all the stuff you can’t find in textbooks. Once we train models on private data, they will start being able to solve many more business problems. And this is not confined to writing text. It also applies to designing, operating and optimising systems in construction, manufacturing, medical research etc. So, the truth is that, while the development of generalised LLMs might be slowing, we have barely started scratching the surface of what we can do with AI models. ### Opportunity for AI-powered SaaS ahead. And what does this mean for software and SaaS? The opportunity to develop enterprise platforms that learn from real business data is vast. Some speculate that all companies will want to do this in-house, rendering independent software vendors irrelevant. In my view, that’s essentially rehashing the argument from the open-source discussions 15-20 years ago. Some folks thought that open-source would make it so easy to make software that all companies would make their own, and that nobody would be able to make money building and selling SaaS. Since then, the SaaS industry has grown by hundreds of billions in annual revenue. Not all of the new revenue will be in the form of vertical applications. Some of it is effectively helping enterprises make, manage or improve their own models (like our portfolio companies Octaipipe and Hirundo). But once we are through the current AI experimentation phase and as AI-powered SaaS applications start generating real ROI for users, we will see SaaS revenue growth start picking up again. --- # US Stock Markets Power Ahead in Q2 **Source:** https://www.superseed.com/journal/us-stock-markets-power-ahead-in-q2/ **Published:** 2024-06-30 **Author:** Mads Jensen *This is not investment advice.* ### Nvidia Continues to Crush It Despite a wobble in April, markets had a strong run in Q2. The S&P500 clocked up a 3.9% gain, and the Nasdaq-100 performed even stronger, with a 7.8% growth in the same period. Once again, tech was in the driving seat, with Google (up 20.5%), Apple (up 22.8%), and Nvidia (up 36.7%) driving stellar growth. I wrote earlier in the year that I thought Nvidia looked reasonably fully valued at $2.2trn. In late June, the company burst through a $3trn, as it continues to defy gravity. This briefly made the company the most valuable in the world. It also made Nvidia worth more than all of the FTSE100 or CAC40. ![](https://www.superseed.com/wp-content/uploads/2024/06/Picture-1.png) ### How does one get to $3trn? The question is – how can a company with revenue just north of $100bn be worth more than $3trn? And be worth roughly the same as Apple – a company with more than $350bn in annualised revenue? The answer is twofold: profitability and growth. Because, while Apple (a formidable company) has a net profit margin of 26% and Microsoft (an also formidable company) has a net profit margin of 35%, Nvidia clocks up an incredible 57%. And that is while growing revenue more than 260% year-over-year. https://datawrapper.dwcdn.net/33Cam/28 Never before has a company grown so fast at this scale while being also being so profitable. So while 52-times earnings look like a slightly cooky multiple, Nvidia just keeps blowing the doors off everyone’s expectations. ### Comparing the Pros and the Cons Let’s look at the case for and against: Bring out the bulls 1. It’s all about AI. Every board of every Fortune 500 company is panicking that they’ll be left behind in the AI race. Every tech investment gets scrutinised, and every possible $ is being directed towards AI initiatives. Not even the internet was such a perfect storm. And so far, Nvidia has been the main beneficiary of this investment as everyone races to train AI models. 2. Product superiority. Not only does Nvidia have the best chips for AI, the company also has CUDA – the software framework used by AI researchers to train models. As the industry has standardised on CUDA for AI model development, Nvidia has lock-in through the whole stack. This makes them hard to dislodge. 3. Unrivalled financial performance. As outlined above. No company has ever delivered this mixture of growth, scale and profitability before. And points 1 and 2 above suggest that the good times could continue. Cue investor excitement. Line up the bears 1. Growth already fully priced in? You can currently buy the S&P500 at 28 times earnings. 52 times earnings look expensive in comparison, even with the expected growth potential. 2. Is a lot of Nvidia’s revenue experimental? The headline is that enterprise customers have spent close to $100bn buying chips for their AI experiments. How much revenue growth or cost savings has this investment created for those companies? And if the benefit hasn’t matrialised yet, what are the odds that the chip spend will keep growing? 3. Competitive threats. No company has ever sustained a 57% net margin at this scale, let alone for a prolonged period. Dozens of companies have raised billions to go after the AI chip market opportunity. Even if the AI chip segment continues to grow, a) can Nvidia keep capturing so much of the market, and b) can they possibly keep their profit margins so high, given more competition? As I wrote back at the start of the year – one thing is for sure: Human psychology wants to lean into trends. No matter which side of the above arguments you come down on, there are good momentum-related arguments for the continued growth in Nvidia’s share price. We know that nothing keeps growing forever. But, in Q2, investors continue placing bets that the future road for Nvidia is pointing up and to the right. As a final note, [the company announced recently that several of NVIDIA’s directors have been booking profit by selling some of their shares](https://news.sky.com/story/nvidia-share-price-plunge-has-one-major-explanation-13158509): Jensen Huang still owns shares worth more than $100bn, so selling $100m worth of shares does not look alarming in the overall context. But still, worth noting. ### What else is happening in markets? All the real excitement is about AI, tech transformation, and growth—and rightly so. But the yin and yang of interest rates and inflation are still powerful undercurrents that shape our markets. [Bloomberg reported](https://www.bloomberg.com/news/articles/2024-06-28/fed-s-favored-price-gauge-rises-at-slowest-pace-in-six-months) that the “core personal consumption expenditures price index” decreased to less than 0.1% in May. This index is the Federal Reserve’s preferred measure of inflation, as it strips out volatile food and energy prices. As the May reading was the smallest advance since 2020, there is optimism that we now. Finally. Can. Inch. Our. Way. Towards US interest rate cuts. With all the associated boosts to the real economy. ![](https://www.superseed.com/wp-content/uploads/2024/06/Picture-2.png) At this part of the business cycle, growth typically slows down, and the economy enters a spell of recession before rebounding back to growth. Over the past two years, the economics discussion has been about whether the US would get to a hard or a soft landing. I.e., would the economy crash or “just” get to a mild recession before the resumption of normal service. ### What Type of Landing Ahead? Although US inflation has been stubborn, growth has continued at a reasonable pace. This year, the discussion has started to shift to a point where more than a quarter of global fund managers think there might be no “landing” at all (according to Bank of America). This is remarkable. Bond markets have warned of an upcoming recession for a long time (through the inverted yield curve). It would be the first time we've had such strong signals with no recession in the end. ![](https://www.superseed.com/wp-content/uploads/2024/06/Picture-3.png) The odds still look like a “soft landing”. But it’s remarkable if the US economy can keep growing—especially in the context of geopolitical upheaval and US political uncertainty. --- # A Future of Venture Capital  **Source:** https://www.superseed.com/journal/a-future-of-venture-capital/ **Published:** 2024-05-31 **Author:** Dan Bowyer Venture Capital is in a state of transition, bifurcating as it does between mega funds, and everyone else. a16z (latest fund $7.2bn), General Catalyst ($6bn) and Khosla Ventures ($3bn) have recently raised as much capital as all of the American emerging managers did in 2023. *But is this just a fear-move post ZIRP? Will it be a winner takes all market? What will VC look like in 10 years - will it even exist as we know it?* That’s what I’ve set out to explore in this note. ## To know VC you must look across the pond. It’s where it started, it’s where it shines. At the global venture peak in 2021 there were 3909 funds that raised $372bn - with $128bn in the US alone. If you put that into context by population, that’s $386/capita compared to the UK’s $104. However, if you look at the rate of growth it skews the perspective - Europe (let’s include the UK) is catching up. Between 2012 and 2021, European VC investments grew by 159% year-over-year, compared to a 92% increase in North America. If you squint, we’re perhaps only a decade behind… said the optimist. *It is worth noting that around 77% of ‘VC’ investments made in the heady days of 21/22 weren’t actually VC. Losing those tourists has muddied the numbers somewhat. * As the mega funds do their mega thing, on the flip side it’s being widely reported that many firms are closing their doors, merging or retreating. 38% of US VC firms didn’t do a deal in 2023. Where was that *“buy when there's blood in the streets, even if the blood is your own.”* energy! So often touted by the investment community. Between 2004 and 2014 there were 6800 new VC funds added to the list, globally. Between 2015 and 2024 that number doubled. As of today there are around 40k active funds (in the broadest sense of venture), 14k in America across approximately 3500 firms. Globally, around 5k are known to be fundraising currently, with around 8k having 10% or more of their dry powder left. Josh Wolfe, from New York VC firm [Lux Capital](https://www.luxcapital.com/) recently commented that half of US firms will soon close their doors due to "feckless strategies that are too weak to fix” as well as an investor overcommitment to an illiquid asset class. In any event, a shake-out was needed and is welcome.  ## VC isn’t going anywhere. Look around you. Pretty much everything on your desk right now was venture capital backed. In 2023 VC added around 11% to US GDP. With early stage investment in innovation being a net creator of jobs and opportunities, compared to corporate net destruction. US economist and Nobel Prize Winner, [Kenneth](https://www.aeaweb.org/full_issue.php?doi=10.1257/jep.34.3)[Arrow](https://www.aeaweb.org/full_issue.php?doi=10.1257/jep.34.3) once stated “Venture capital has done much more, I think, to improve efficiency than anything.” That’s where VC shines beyond shareholder returns. It makes new things, and old things better. We get to do more, with much less. ## Conversely when it goes wrong, it goes very wrong. I accept that many of the following are *not* widely accepted as ‘wrong’ but personally I’d put Uber, WeWork, Crypto, anything metaverse, and most web3 projects in the ‘wrong’ bucket for various value-less or greed fuelled reasons. Putting aside the horror stories from the dot com period, or obvious fraud such as [Theranos](https://en.wikipedia.org/wiki/Theranos), [Wirecard](https://www.wirecard.com/) or [Nikola](https://www.nikolamotor.com/), the VC push to go global, own the market and monopolise is a forceful one. The only one, according to Power Law. Sometimes it’s meaningful, but in bubbly bull runs we get to see the uglier side of foie gras VC, fuelling the ‘pass the parcel’, when all too often there’s not all that much within the shiny wrapping paper.  ## Is the world better *because* of these businesses? **It’s a worthy debate.** [Thiel’s zero to one](https://en.wikipedia.org/wiki/Zero_to_One) monopolistic mentality was first seen in the men who built America - Vanderbilt, Rockefeller, Carnegie, Morgan, Ford, who all had the same winner takes all mentality - win at *all* costs. Raw and rapacious capitalism. It’s embedded in the culture, emblematic in the American dream.  ## Will this mindset continue to work in our new world? **Greed isn't going anywhere anytime soon, there will be more. But it'll be different.** If a VC does 3 things well, they will make money - source, select & support. We must do the first two just to keep the lights on. The third, the ‘support’ as in ‘value-add’ can sometimes be contentious, often ridiculed. “Just give me the money and go away” is often a sense reflected by founders, which I totally get. Until the penny drops that the highest performing business people, athletes and performers of any kind have coaches, managers and support networks. ## Founding is not a solo game. Ever. Stat. Period. **However maverick it may look from the outside.** That said, VC may well be. The next iteration of firms will be co-piloted and automated using AI. Hoping not to sound too self-serving or hackneyed I truly believe AI will transform our industry just as it will for most sectors - perhaps more so, and definitely in shorter shrift. Let’s face it, much of what we do *should* be automated away. As one example, the founder <> funder journey is so broken with friction and ego it’s a little ridiculous, but it *is* being opened up and democratised with tools such as [Specter](https://tryspecter.com/), [Signals](https://www.landscape.vc/), [CarriedAI](https://www.carriedai.com/), championed by [Moonfire](https://www.moonfire.com/), [Earlybird](https://earlybird.com/) and many other VC firms. Within the business the operational stack that supports CRM, DD, KYC, PM etc. is all being optimised and orchestrated by AI - with new tools delivered daily such as [Looker](https://cloud.google.com/looker), [Domo](https://www.domo.com/), [Sisense](https://www.sisense.com/), [eBrevia](https://www.ebrevia.com/), [Fundrbird](https://fundrbird.com/), [Amplitude](https://amplitude.com/), [Spot](https://www.getspot.io/), [Gust](https://gust.com/) and so so many more. It’s hard to keep up. AI as the truest accelerator of productivity, and therefore efficiency, is right here right now, just when society needs it most. And as for venture capital? It will *become* AI. Full stack. Sourcing to secondaries - up down, left to right.  ## The AI automation upshot? Founder <> funder <> investor PLUS the back office will be fairly fully automated. But then what? Once everyone can see everything, and do everything, how can you position *selling money* as a VC firm!? This is where that third ‘thing’ comes into play. How we support founders and their startups will become front and centre, key differentiation. A great thing for them, their clients, the economy, and therefore all of us. (for as long as there's a healthy focus on fundamentals startups). More startups will be enabled to solve *more* wide ranging problems, in some instances venturing where classic VC historically wouldn’t. New niches, new products and new markets will be created by this next wave of automated VC firm. And because smaller funds tend to outperform, all of this combined may well put a dent into the mega fund approach. **The next a16z may well be one guy or girl, working part time from their basement bedroom in Hackney Wick. ** **Perhaps not the future we were expecting.** *(sources: Pitchbook, FactSet Insight, KPMG, Cambridge Associates, Dealroom, Equanimity)* --- # The world of tech investing, ⅓ into 2024 **Source:** https://www.superseed.com/journal/the-world-of-tech-investing-%e2%85%93-into-2024/ **Published:** 2024-04-30 **Author:** Mads Jensen Four months ago, at the start of 2024, we said we thought that US equities looked pretty fully valued. Even so, we also said that we thought the S&P 500 might have further to rise on the back of positive market sentiment.  Two months ago, markets were even higher, and we wrote about investors partying like it was 1999. At the time, I asked whether this was a revival of the dotcom bubble.  NVIDIA peaked on the 25th of March, and the S&P 500 3 days later. Since then, the S&P 500 declined 5% and NVIDIA 20% before both index and stock recovered some of the lost ground. Volatility is back. #### Inflation isn’t slain yet Up until late March, there was still hope that inflation was more or less under control, and that the Fed would start cutting rates. That hope has been put on pause for now. US inflation has come down significantly from 2022, but it is still stubbornly high.  ![](https://www.superseed.com/wp-content/uploads/2024/04/image-2.png) Cue the Fed and Powell, who have been rethinking interest rate cuts in the face of stubborn inflation numbers. [Here](https://www.bloomberg.com/news/articles/2024-04-19/fed-s-powell-rethinks-interest-rate-cuts-for-2024-to-combat-inflation) is Jerome Powell on April 16th: “Given the strength of the labour market and progress on inflation so far, it is appropriate to allow restrictive policy further time to work and let the data and the evolving outlook guide us". So the Fed keeps looking at its favourite data points, and it doesn’t much like what it sees. However, its pessimism may be excessive. The Truflation team has US inflation at 2.49% as of the latest reading, vs. the Fed’s reported 3.5% rate. As the [FT Alphaville](https://www.ft.com/content/39af38ed-f493-4ad6-b6e3-3e4777ecdbbb) Team points out, fears about inflation stickiness might be overdone.  So there are clearly two sides to the story. Even so, it’s likely that we will get fewer cuts than hoped for in 2024. And thus [markets are taking a breather](https://www.bloomberg.com/news/articles/2024-04-19/bloated-wall-street-bulls-are-cashing-out-of-markets-en-masse) and going soft. #### Have the Magnificent Seven run out of Steam? Since US Markets bottomed out in October 2022, the runup to current levels (+40%) have been powered by the Magnificent Seven (Apple, Google, Facebook, Amazon, Nvidia, Tesla and Microsoft). Investors have (in many ways correctly) treated the Seven as an unbeatable block of growth and profit. All driven by exceptionally dominant positions in their respective areas of technology.  But start scratching under the surface, and we can see that the party might be coming to an end.  ![](https://www.superseed.com/wp-content/uploads/2024/04/image.jpeg) While the Mag 7 are still up way above their position as of a year ago, the last month has been challenging. And there might be further headwinds.  Look under the covers:  - **Apple** is struggling to explain where future growth will come from. [Demand for iPhones](https://www.bloomberg.com/news/articles/2024-04-23/apple-s-china-iphone-sales-dive-19-in-worst-quarter-since-2020) has gone into a tailspin in China, and the [pipeline](https://fortune.com/2024/03/16/why-is-apple-stock-falling-what-is-value-ai-plans/) of new blockbuster products looks bare. - **Tesla** is still growing modestly, but the company has missed Earnings Per Share for the past two quarters. And there are [worries](https://www.reuters.com/business/autos-transportation/wall-street-wants-answers-musk-teslas-affordable-car-2024-04-17/) that both revenue and profit could be put under pressure from Chinese manufacturers:   - **NVIDIA** has had an incredible run over the past 18 months on the back of the LLM/AI boom, but there are major questions as to whether the company can keep up the current combination of growth and profit margins (which would be needed to justify the valuation). - **Facebook**/**Meta** has also had a formidable run since the start of 2023. But a lot of the performance was driven by cost optimisation as the company got lean and trimmed investments in the Metaverse, leading to better operating profits. The company is theoretically well-placed to benefit from AI, given its massive data trove. However, as Meta felt it was behind Google and Open AI in the LLM race, it took an early decision to open-source its own LLM (Meta Llama). This potentially caps the upside of any benefit Meta can derive in the AI space.  - **Google** was widely seen as the indisputable leader in AI prior to OpenAI’s launch of ChatGPT. For more than a decade, Google (and Google Deepmind) produced world-leading research in the AI field. However, internal cultural issues have slowed down the pace of innovation at Google, exemplified by the botched launch of Google Gemini earlier this year. Gemini is in many ways an awe-inspiring product, and Google certainly hasn’t been knocked out yet. But with its core business model (search+advertising) under assault from LLMs, many are questioning future growth prospects.  Amazon and Microsoft - the two remainders - have their own challenges. But even if we accept that they are formidable companies, it’s hard to see those two pick up the slack from the other five to continue to drive the stock market all on their own.   #### So where are the growth opportunities? As early-stage tech investors, we are unapologetically optimistic here at SuperSeed. That said, there has been some grim reading in the global news columns so far this year. It does not look like the war in Ukraine is drawing to a positive conclusion in the near term, and Israel’s conflict with Iran’s proxies Hamas and Hezbollah have threatened to engulf the wider region. On a macro scale, this is all “bad for business”.  At the same time, there were hopes of a rapprochement between China and the US following November’s largely successful summit between Xi Jinping and Joe Biden. But the underlying conflicts have not gone away. The US is unhappy with China’s support for Russia, and China is unhappy with the US’s support for Taiwan. And there are a host of other issues underneath these.  So rather than collecting a “peace dividend”, everyone is now busy restocking their weapons arsenals. The UK government has [announced](https://www.theguardian.com/politics/2024/apr/23/uk-to-boost-defence-spending-to-25-of-gdp-sunak-says) a targeted increase in defence spending from 2.3% to 2.5% of GDP. While all of this might be bad for peace (at least in the short term), it’s good news for arms manufacturers. Rolls-Royce, for instance, has quadrupled in share price in less than 2 years.  ![](https://www.superseed.com/wp-content/uploads/2024/04/image-1.png) Wherever you find yourself on the hawk/dove scale, defence manufacturers and cybersecurity companies are likely to benefit in the years ahead. #### What’s happening in venture capital? While 2023 was largely a year of going sideways, the VC industry has started to rebound in recent months.  US fund administration platform Carta reported that VC fund capital calls, finally, are on the rise again. While it is still too early to tell whether this is a bona fide new “cycle,” the data is certainly showing green shoots.  ![](https://www.superseed.com/wp-content/uploads/2024/04/image.png) Anecdotally, we are feeling spring-time in terms of deal-flow and velocity as well. But where is the activity?  In many ways, AI is still the only game in town. AI has driven expectations (and market cap) for Microsoft, Google and NVIDIA, and AI has also continued to drive investments in early-stage tech companies.  While US seed valuations ticked up in Q1, Series A valuations were largely flat on Q4 (but still well up on the nadir from Q4 2022 to Q3 2023).  ![](https://www.superseed.com/wp-content/uploads/2024/04/image-3.png) #### Where are the opportunities? In [March](https://www.superseed.com/journal/party-like-its-1999/), we looked at areas for early-stage investment opportunities. This month we are going to dive more into opportunities in vertical applications, including some of the AI tooling that enables these opportunities. You can read more [here](https://www.superseed.com/journal/how-generative-ai-finally-unlocks-industry-4-0/). --- # How Generative AI finally unlocks Industry 4.0  **Source:** https://www.superseed.com/journal/how-generative-ai-finally-unlocks-industry-4-0/ **Published:** 2024-04-30 **Author:** Mads Jensen I recently examined where generative AI provides new early-stage investment opportunities. This month, we will explore opportunities in vertical applications, including some of the AI tooling that enables these opportunities.  ![](https://lh7-us.googleusercontent.com/_LrHNKKfPn-Zz4jxSLgfByiTAIYiiZEeAT9bkVb_9zz4JvO41eN60ZLeTVRFazO-mNMz1gEx5F-M_m2r8DyusiarHYhANLmMzb5Sc4IE7AD0nJ1yMydF1Y9MnJSqbS0VHDTih6DynvpRth6BP0tHWGg) #### The opportunity in Industry 4.0 The notion of a “Fourth Industrial Revolution” is not new. But it has had a fresh jolt of energy with the ascendance of the new LLMs. In essence, Industry 4.0 is the vision that we can use IoT-enabled sensors together with AI and ancillary technologies to bring the next level of automation to the way we manufacture things.  ![](https://lh7-us.googleusercontent.com/rg4GCNkPNjdgAac-OVbzLD5wvZOJZ1evYM3CKrxPiHPtCnieX1l4yVgtAgP660Gm00yyiaRGaBRqN0qWKtnrub2Xpwl9tM1bMMIC_7X0sgWc3EaoF7drl8F12AcgLZWAW7jxVO3-pqxAaRxe6EUoTRU) When people talk about Industry 4.0, it’s often defined narrowly, i.e., the manufacturing of goods. We think the idea of Industry 4.0 applies almost everywhere we make things - e.g. agriculture, biotechnology, energy (to name a few), but leave those aside for now. Let’s focus more specifically on manufacturing.  At the heart of the fourth industrial revolution lies the idea of autonomous systems. I.e. systems that can govern and regulate themselves, leading to a highly flexible and efficient production environment.  To understand how this improves manufacturing, let’s look at how things are done today.  #### How are machines operated today?  Modern manufacturing automates large parts of the production process. However, it is still reliant on a surprising amount of manual input from machine operators and other manufacturing staff. This means that expert operators configure things like: - speed settings for conveyors, motors, or pumps,  - pressure settings in hydraulic or pneumatic systems,  - feed rates for material inputs,  - temperature setpoints etc.  Even when these have been configured to perfection by expert technicians, machines need to be recalibrated so machines keep working optimally. Reasons include:  1. Wear and Tear: Mechanical Components: Constant movement and friction cause wear on gears, bearings, slides, and other mechanical parts. This wear subtly changes dimensions and tolerances, leading to inaccuracies in the machine's output. 2. Sensors and Tooling: Sensors degrade over time and tooling (cutting tools, molds, etc.) dull with use. This affects the machine's ability to measure or process materials accurately. Environmental Factors: 1. Temperature Fluctuations: Changes in temperature cause the expansion and contraction of machine components and materials being processed, which can lead to dimensional inaccuracies. 2. Humidity: Affects certain materials and sensors, potentially impacting tolerances. 3. Vibrations: Floor vibrations or internal vibrations from the machine's operations can misalign components and affect its precision. Process Variability: 1. Material Variations: Slight changes in raw material properties (hardness, density, etc.) can impact the forces and behaviours during machining, leading to a need for adjustment. 2. Batch-to-batch Differences: Even when using the same materials, variations between batches can necessitate fine-tuning to maintain consistent quality. Quality Control & Maintenance: 1. Regular Calibration Schedules: To ensure product quality, machines are usually placed on routine calibration checks to catch any drift out of tolerance. 2. Post-Maintenance Recalibration: Any time a machine undergoes maintenance or replacement of parts, recalibration is crucial to re-establish its baseline accuracy. To all this comes the effort that goes into manual inspection of finished products, the physical logistics of warehousing and distributing goods and the manual work that goes into overseeing the production process.  #### Why hasn’t manufacturing already been made fully autonomous?  Everyone who’s been close to manufacturing can see that there are innumerable opportunities to optimise and make better.  So why haven’t we already put an “AI megabrain” in charge of running all our plants? Some of the reasons include:  1. Heterogenous machine parks. Many factories have machines that have been acquired in various installations over decades. Different models, different makes, differering levels of connectivity. Replacing everything with new “top of the line”, connected equipment requires an inordinate amount of capex. Even if the business case for automation on a greenfield site is good, it is often too costly to implement on an existing machine park.  2. Subtleties and complexities of autonomous operations. Modern manufacturing has many, many variables (as outlined above). In many situations, there is no simple algorithmic way to optimise machine operations. Getting to proper autonomous operations requires quite sophisticated AIs.  3. Data privacy. Many manufacturers have been reluctant to let manufacturing data leave their plants. The data may contain trade secrets that can be valuable in the hands of competitors. This has, in turn, meant that machine manufacturers have lacked the data they need to train AI models, making it harder to develop the next level of automation.  So, there is a big opportunity in manufacturing, but until now, there has been no easy way to unlock this. This is all changing with a confluence of new technologies coming to market.  #### How does next-generation AI enable autonomous manufacturing? Here are four specific technology advances in and around AI that enable higher productivity in manufacturing:  1. **Federated learning solves the problem of how to train with proprietary data. **Manufacturers are often hesitant to let manufacturing data leave their factories. But each factory doesn’t in itself create enough data to train robust models - you need data from many factories to build autonomous systems. Federated learning is a relatively new development in AI tha makes it possible to train and enhance AI models locally at the factory, to only send model improvements back to the “mothership”. In this way, the proprietary data can stay private, the AI models can benefit from the input of many factories at the same time.  2. **More robust AI models make it easier to retrofit existing plants. **The more homogenous a factory, the easier it is to automate. But as we discussed above, the world is far messier. Once plants start operating, homogeneity quickly gives way for the messy reality of a shop floor. And that is not even taking into account all the factories that have equipment brought together from many vendors. The smarter AI models we have, the easier it is to provide “all purpose” automation upgrades, because the AI will be resilient enough to take all the small and big variations into account. Federated learning helps, as do general advances in AI (including generative AI).  3. **Cheaper access to data. **The past 15 years have seen massive decreases in the cost of sensors and cameras - in particular driven by the smart-phone revolution. Ubiquitous and inexpensive sensors and cameras make it possible to see and measure everything so we get the data we need. This was a big enabler as it helps provide access to training data. The approaches we mentioned above now helps unlock the value of that data.  4. **A much faster journey from idea to production. **Generative foundation models make it much easier to go from concept to production model. And technologies like additive manufacturing (3D printing) help us more quickly test prototypes and even go to production ready models.    These are not alone. There are so many incredible developments right now that promise to give us a better and smarter way to make things. We are excited to back amazing founders from companies like Octaipipe, Ai Build, Hirundo and ThingTrax who work hard to bring the vision to reality, and we always look to meet more founders who are determined to transform how the world works and how things are made.    --- # What makes a unicorn founder?  **Source:** https://www.superseed.com/journal/what-makes-a-unicorn-founder/ **Published:** 2024-03-30 **Author:** Dan Bowyer Early stage startup investing is first and foremost team and market. I’ll look a founder in the eye and ask myself - *hmmm, will **they** buy from **you**? Are there enough of **them** to build a **global** business? Can **you** attract a formidable team*? On the business side timing and luck is all too often the unlock, but in the early delicate stages it’s **so** human-centric, founder driven - so ***what creates success at the person level***? And ultimately... ### Can you codify a ‘unicorn founder’?  In SuperSeed towers we all switch on our own radar when meeting new founders. My personal version - *are they charmingly disagreeable and determined*. That’s what I try to get a sense of early, and you know when they’re in the room.** *It fizzes*.** [Defiance Capital](https://www.defiancecapital.com/) has has crunched the data and broken down unicorn founder success metrics, deconstructing the DNA of over 2,000 founders from 800 unicorns born between 2013 and 2023. Much you'd expect, but some of the insights are surprising. **70% of unicorns have “underdog founders”.** Unsurprisingly the core ‘outsider’ drivers were mostly developed in childhood - imbuing tenacity, ambition, with a strong work ethic: They have no “plan B”, a huge “chip on their shoulder” and display “total self belief”. The themes they chose to tackle were often connected to their own personal struggle. **The Zuckerberg all-white, male, Ivy league archetype isn’t the norm**, at only 11% of founders. However, 53% do have degrees from top 10 global universities, 49% STEM. And yes 34% had worked at an elite employer prior to starting-up - they just tended to not be from privileged backgrounds. **38% of unicorns had at least one non-white founder** but 82% did have at least one. 62% are immigrants, regardless of skin colour. In 2023 17% had at least one female founder, an upward trend, but only 3% had a black founder. 50% were serial founders with 20% solo. Apart from YC who invested in 10% of unicorns, **no other fund got into more than 2.8%** (Sequoia), suggesting any early stage fund has as much chance as a tier 1 to invest in one. From the other side of the desk, 28% of unicorn startups had raised capital from a tier 1 VC. **It took 7 years on average** to reach unicorn status. As this survey was effectively all bull run, will these stats stay static and how will the trends play out? One thing for sure is that we will see more women in future surveys. The [UK](https://www.scaleupinstitute.org.uk/wp-content/uploads/2023/04/Female_Founders_Index_2023_web-final.pdf)[Scale-Up Female Founders Index 2023 report](https://www.scaleupinstitute.org.uk/wp-content/uploads/2023/04/Female_Founders_Index_2023_web-final.pdf) highlights significant growth in the number of female founded scaling businesses. 623 scale-ups were tracked marking a 70% increase from the previous year​​. Wider afield in India, the number of [female founders has roughly doubled in 5](https://www.livemint.com/companies/start-ups/womenled-startups-see-18-rise-105-female-led-firms-become-unicorn-in-past-five-years-report-11698639987377.html)https://www.livemint.com/companies/start-ups/womenled-startups-see-18-rise-105-female-led-firms-become-unicorn-in-past-five-years-report-11698639987377.html[years to 18%](https://www.livemint.com/companies/start-ups/womenled-startups-see-18-rise-105-female-led-firms-become-unicorn-in-past-five-years-report-11698639987377.html). While reading the report something else kept poking me - **Is unicorn status even the right measure of success?** *A post for another time...* --- # Why We're Excited to Work with Messium **Source:** https://www.superseed.com/journal/why-we-are-excited-to-work-with-messium/ **Published:** 2024-03-01 **Author:** Dan Bowyer We recently invested in Messium, a company that uses innovative technology to address a major challenge in agriculture: inefficient fertiliser application. ## Why do we love it? Messium immediately struck SuperSeed's investment team as an exciting opportunity. [George Marangos-Gilks (CEO and co-founder)](https://www.linkedin.com/in/george-marangos-gilks-307ab261/) and [Vishal Soomaney Vijaykumar (CTO and co-founder)](https://www.linkedin.com/in/vsoomaney/) presented a compelling case with an urgent 'Why Now?', and SuperSeed quickly bought into their vision of a world where fertiliser is applied in "Goldilocks proportions". At SuperSeed we love bets where an incredibly niche solution solves an incredibly large problem (spoiler alert: more on that next month). Messium's initial product uses hyperspectral satellite imagery and machine learning algorithms to optimise the application of fertiliser to wheat plants. It's an elegant, clear value proposition that addresses farmers' critical need to reduce their cost base, wherever possible (and, in some cases, increase and improve yield). The subsequent product releases remain Top Secret, but v1.0 addresses the world's 522m hectares of wheat, maize, and rice. We also love brilliant founding teams. George and Vishal both have impressive, relevant backgrounds. George, while studying at Cambridge, founded [The Tab](https://thetab.com/), before later founding another business, Magic Carpet AI, which he then sold to [Blockchain.com](https://www.blockchain.com/). In both of these businesses, George was the founding CEO, and led them from foundation to exit. Vishal, after a series of senior roles in machine learning development and backend engineering, co-founded a multiple-award-winning business leveraging satellite imagery to detect susceptibilities in forests to the outbreak of fires. One of the awards it won was the UN and [ESA](https://www.esa.int/) World Challenge in 2018, which means Vishal is both a satellite geek *par excellence* and extremely well-connected in the space industry. ![](https://www.superseed.com/wp-content/uploads/2024/02/founder-headshot-messium-1024x576.png)George, CEO (*left*), and Vishal, CTO (*right*) SuperSeed's primary investment focus is on businesses driving resource efficiency. Few resources are in such an important finite supply as food. Messium's innovation is a perfect example of AI and disruptive next-generation technologies having an immediate, detectable impact on the Real Economy and the real world. SuperSeed led the £1.4m Pre-Seed investment round in January 2024. ## For those who are interested... ### The Micro Farmers face shrinking margins due to rising costs and falling crop prices. Over- and under-fertilisation are significant and costly issues affecting all farmers. The nitrogen fertiliser spend on the average high-grade, milling wheat farm accounts for 35% of the total cost base, and of this, up to 60% is wasted. There's a slightly American feel to the notion that more fertiliser = more crop. In fact, in the context of applying fertiliser, greed is not good: [around 65% of *all *fertiliser applied globally is excess](https://ourworldindata.org/excess-fertilizer), which runs off into rivers and surrounding environments, polluting them. The caveat, admittedly, is that on lower-grade wheat farms, where the product usually ends up in animal feed, the margins are even slimmer. Here, the perennial issue is *under*-fertilisation, to the tune of an average opportunity cost of $13k per annum. It’s a really tough business. Your operating profit margin is razor-thin, and there is very little you can do to improve it. The general approach to farming is to try to improve yield, both in quality and quantity. But as we know, if revenue goes up by £1, profit does not go up by £1. If, however, we reduce costs by £1, that goes straight to the bottom line.  ### The Macro Taking a quick glance at the UK, there is a huge opportunity in front of Messium. Owing to the [degradation of our soil](http://theguardian.com/environment/2017/oct/24/uk-30-40-years-away-eradication-soil-fertility-warns-michael-gove(opens in a new tab)), we have become quite heavily reliant on fertiliser to produce the volume and quality of crops that we need. The UK also produces [~40% of its domestic fertiliser requirement in a good year](http://lordslibrary.parliament.uk/rising-cost-of-agricultural-fertiliser-and-feed-causes-impacts-and-government-policy/(opens in a new tab)), and since the [closure of the largest manufacturing site in 2023](http://nfuonline.com/updates-and-information/cf-fertilisers-announces-closure-of-billingham-ammonia-plant/(opens in a new tab)), more than ever, farmers are relying on (expensive) imported product, further squeezing their already-tight margins. Messium can help farmers use only the fertiliser that they need. More macro, even, than the British Isles: [according to the UN, globally, we need 60% more food by 2050](https://www.un.org/en/chronicle/article/feeding-world-sustainably). To feed a population of 9.7 billion people, there are only so many vectors available to change. We need more farms, less waste in supply chains and at home, or more efficient farming. We could continue our unrelenting mission to create more arable land. But it is time- and resource-intensive (ignoring the more pressing matters of the destruction of biodiversity, the contribution to climate change, the displacement of local communities, and — the Fourth Horseman of the Agricultural Apocalypse — the degradation of the fertility and productivity of the soil). OK, so “just add farms” might not wash.  There are some obvious opportunities for improvement in consumer behaviour and supply chains, given that [1/3 of all food produced globally is lost or wasted](https://www.fao.org/3/mb060e/mb060e00.htm). Primarily, this occurs because of bad infrastructure and storage, and absent-minded consumerism. The former is unlikely to change, because it’s really expensive to change. On the latter, it is anyone's guess... So, the best option is to do more with the same (or less). Every farmer lives in constant vigilance of resource efficiency. More food production from the same total arable land plot means we must be much more efficient at farming. The accurate application of fertiliser is as urgent as it is important. Roll on Messium, to clean up the mess. --- # Party like it’s 1999?  **Source:** https://www.superseed.com/journal/party-like-its-1999/ **Published:** 2024-02-29 **Author:** Mads Jensen *This is not investment advice. Always consult your independent financial adviser before making investment decisions. * Chat GPT was released on the 30th of November 2022. [We (and many others) thought this was a pretty big deal](https://www.linkedin.com/posts/madsjensen_ai-future-technology-activity-7005448180087562240-JwmS/). And Presto, 15 months later, NVIDIA hits a $2trn market cap (up 367% since the ChatGPT launch). Obviously, something is going on.  But is this a rerun of the dotcom boom, or are we in the early stages of a different script?  First, the scary parallels. ## Is NVIDIA the new Cisco? In the late 90s, the World Wide Web was making the internet useful for consumers, and the tech world was on fire. Lots of dreams for lots of big things, and lots of ways to burn venture dollars (remember pets.com and Webvan?).  But some amazing things also came out of this period – Amazon and Google, to name a couple. And whatever people were building, we all needed more infrastructure. More bandwidth. And more hardware to power our networks. And atop the networking throne was CISCO – the golden child of the Internet’s picks and shovels era.  Today, the Internet is yesterday’s news, and AI is all the rage. We are seeing a plethora of new startups pursue this opportunity. Most won’t succeed in building enduring companies, but there will be great businesses coming out of this. And whether companies are destined for glory or failure, many of them rely on the same thing: the best processors to train and develop their AI models.  And so today’s CISCO is NVIDIA – a company perfectly positioned to be the “picks and shovels” company of the AI revolution.  To hammer home the parallel, the kind folks at the FT recently created this chart that maps CISCO’s ascent in the 1990s to NVIDIAs growth over the past four years. Yes, it has been formatted for maximum scariness. But the comparison is fair.  ![](https://lh7-us.googleusercontent.com/bNkKLJeogornlVR-ojH-ZLcwkDcsaiLtC5EK9VCvi9Fu2besdo1dQGO-d4xaNSCVqTPMGrbjkZ63ejkYU6L02IHRDkw5B1am8MX20Gen0fQEffGjZYBkneCTH1WRkGyJ9Bn0k7H0VFpIDQslhITxFwU) NVIDIA has a strong lead in making chips that can do vector maths (the kind of maths needed to train deep learning and generative AI models). This gives the company incredible pricing power, and profits have been skyrocketing, with the company providing a rosy outlook for the future.  ![](https://lh7-us.googleusercontent.com/M4OBMTnY8KmbTag7-LrH7UvtITCeePyLxIRBBDR3RorRzrcKr1K1B-fX2rm8NCp3a1EHsqRGPtnVgx38nxpiIV42vpBIz-Zld6Ql7UQpLtRTj7sb1UHDKVQvdZPoAw1mFOcJXe7QTM0ucSnr32t4XrU) Given the rapid growth in revenue and profit, and given the positive outlook, the buoyant share price seems justified. But at 66x earnings, profit will have to keep growing for some time to justify even the current valuation, leaving alone any upside.  So the question beckons: are we on the precipice of a “dot.ai bubble”, or is there more upside from here?  Let’s look at some more scary data ## Have we been here before?  The top 10% of US stocks are currently 75% of the US stock market. The last time that happened was in 2000 1929. And we know what came next.  ![](https://lh7-us.googleusercontent.com/OH868EkeNefpfptQ6DZFy3SizN3KxS58UWUQJMU2VjPaVoNlDF6TijSTeFJlg202o7FlKl9fzoiD0sPtliCY5TwlEL4qJ6MPIIbTlliAlKFVPrGFnAO1l3CQZ2pHC9gTCNoDmi8GCGCpiVsfBPaL290) This concentration in the top stocks is driven by the Magnificient Seven. For good measure, let’s add Berkshire Hathaway, Eli Lilly and Broadcom to the mix, and we can see that the top 10 US stocks now account for more than 1/3 of the value of the full market.   ![](https://lh7-us.googleusercontent.com/n_5x0G1dU6BKaZSO1n9ImcXESrh_mgi-PUj_Lw5Y7OsVzkKvtVazEiKdU2zL2TjkvWx7hKtlH8KLlR9dNxd5F2NCfJ5D_iDVA9yNJRBILZmCg79BWcTTJCP6AJsbziV3Z7Ua_adcUTJS68bcsjmmGg0) There are more ways to show how concentrated and unusual the situation is, but let’s leave it here for now.  ## So, is it a bubble? When we measure the stock market using PE ratios and the equity risk premium (i.e. the yield premium you get paid to hold stocks over risk-free bonds), the market looks expensive, but nowhere near as expensive as in 2000 before the dotcom crash (or in the Great Financial Crash, where profits imploded).  ![](https://lh7-us.googleusercontent.com/Qd1r3YBsKjSuJbSSNQOGSwVMAWCseEDWVIfPapzRA8AVMTqyQYNFyjJs6yUl6QD3joNB6EuHWm2QhuSsstFYcLmNDe5LC4amYE2Dpq7-ZF-WXiis_2ubXx0ZmO2tI-0FiId2IRglR5mfw6Qf_4s8hz0) ![](https://lh7-us.googleusercontent.com/dce7YC5riwDQp-bEz2Ud4DWd1DqehvZPr1G_5Fuqr99HREIGD6UqbkI2XYe1V1UlYeSb-gQWR35o1IX693UbYFnapJivE5kyEy87yFCc2DBNxlwJL5ESNSJ1oOYU-jDHhVoICJA4Q__FZKCeB962K14) ## If not 2000 all over again, then what?  As I wrote two months ago, stocks look expensive relative to historical metrics. But as long as the top companies keep growing earnings, and as long as there isn’t a major geopolitical event, I don’t expect an implosion in stocks.  At the same time, I am struggling to see NVIDIA go to $7trn (as some people have suggested), but I can easily see the S&P500 go higher from here - at least in the shorter term.  Because althought companies are expensive, there is a major difference now compared to 2000. The best public tech companies are a lot more profitable. And even private companies like OpenAI have grown revenue at an impressive rate, going from basically zero to a $2bn annual run-rate in 13 months (OpenAI hit the $2bn annual run-rate market in December).  While a lot of the investments during the internet boom were well ahead of their time, many of the companies that are being built now have real revenue, strong growth and a path to real profit. ## So, where are the private market opportunities?  Firstly, it's important to distinguish AI as a buzzword vs. AI as "next-generation software". (Generative) AI feels like magic, but - in my view - it's really just the next way to create software. And just like mainframes made way for distributed computers that in turn made way for the cloud, so are we now transitioning from a world where software is deterministic to a world where it is probabilistic. It's a big change with profound implications, but it won't remove the fundamental value of software. If anything, it just makes software even more valuable, because probabilistic behaviour means that software can do things in ways previously only humans could. For me, the takeaway is: when thinking about AI, think about next-generation software, not magic. Many investors divide the opportunities into five categories. There are the two layers which have received the most press to date:  1. A semiconductor layer populated by incumbents like NVIDIA and new players like Groq.  2. A generative model layer with firms like OpenAI, Anthropic, Mistral (and, of course, Google),   On top of these sit three other layers which have received much less press, but where we see incredible innovation right now: 1. There is a new class of companies that are creating tools to manage AI models and model infrastructure (e.g. making the AI models more transparent, secure, efficient etc),  2. Application companies making vertical applications (industry-specific - e.g. manufacturing or pharma), and  3. Application companies making horizontal applications (e.g. sales tools, marketing tools, etc).   ![](https://lh7-us.googleusercontent.com/iqHIIg7_1FZ5uhA0QJ4UE4Se4ySTVrQJxMUu5jb_FcIyGvnxfxoN5jRemx2-nChnBXsRUjOyFNFemY8xBFfcPp4g7eWYAbIUuCFDqUAYg60lUa3JofYJCyCS3iixASltmUx2naFz8DM0Om8j4i7JRfY) Over the past two years, 50% of our investments have been in vertical AI applications. 25% in horizontal AI applications, 15% in AI Infrastructure Tooling, and 10% sit outside mainstream AI use cases (although all do use some level of machine learning).  We see incredible opportunity in those areas going forward, as they are places where smart founding teams can build capital-efficient software companies powered by the new underlying AI capabilities.  ## Does generative AI spell the end of the software industry?  Software development was one of the first areas to be impacted by generative AI. Armed with ChatGPT or Github CoPilot, developers are now at least 2x as efficient as they were before. It’s become much easier to create, optimise and debug code. This makes it easier to build software. It potentially also makes it easier for new competitors to emerge or for customers to make their own software, as applications can be rebuilt with less effort.  Some observers have speculated that this is the end of the SaaS industry. That AI makes it so easy to create new software that margins will be competed away or replaced by new in-house apps.  There is some sense to these arguments. But in many ways, this was also what people said about the software industry when open source emerged.  The modern software industry is incredibly reliant on open-source software. And open source did displace some companies (e.g. how the once mighty Sun Solaris made way for Linux). But as some companies got displaced by open source, the wider software industry continued to thrive. Open Source was an enabler for the software industry.  Here are two reasons why I predict that AI will also be an accelerator for the SaaS industry:  - Yes - Generative AI makes it easier and cheaper for new entrants to create software clones. But it also makes it easier for independent software firms to innovate rapidly. And independents are nimble and focused. While Generative AI gives many new weapons to rebellious startups, it will also confer big advantages on startups. I predict that this will lead to a continued thriving software ecosystem.  - Generative AI also makes it easier for in-house development teams to build their own platforms. And while this gives them an advantage when negotiating pricing, it will not replace external software vendors for the same reasons corporates buy software today. Most businesses are much better off getting best-of-breed from specialist vendors rather than crafting and maintaining their own systems. Not only because it is expensive and inefficient for everyone to build and maintain essentially the same thing, but also because specialist vendors are better at innovating and, therefore, providing a better platform and user experience.  We’ve heard the same story about infrastructure and compute many times. It’s cheaper to buy your own servers than to rent them from AWS. Yet 87% of Fortune500 companies today use at least one public cloud.  One of Jeff Bezos’ business maxims is to only do the things that “make your beer taste better”. As in, focus on the things that make a tangible difference to your customers. Running your own data centres and writing your own backoffice software is unlikely to do that for most companies. And so, our investment strategy remains focused on next-generation, AI-powered B2B SaaS companies. In our assessment, AI will only serve to make this business model even more attractive. --- # The Perfect Pitch Deck **Source:** https://www.superseed.com/journal/the-perfect-pitch-deck/ **Published:** 2024-02-05 **Author:** Dan Bowyer ## Is there one? **No of course not!** But there are some rules I believe must be followed, as well as some to break. I've collected what I think are the major food groups for the best pitch deck recipe I can think of. HOW these slides are interpreted and executed is everything and yes many of the slides can be blended. You don't need a 10 slide deck just because there are 10 titles. ![](https://www.superseed.com/wp-content/uploads/2024/02/Slide1-1024x551.png) 1. Position rather than just pitch so you can also share what you do vs what others don't. 2. Telling stories is everything. Especially when sharing who you are and where you're going. 3. Don't over cook the team slide and avoid over-use of advisors. In the early stages it's just the founding team's cred that really cooks. 4. What is it, who do you serve, and why do you get out of bed. 5. It's all about them, not you. Really. Why them, why now, why how. 6. Whatever you're tracking, get it in here. Why people care, as data. 7. A stepped roadmap is always a winner. GTM broken down in realistic chunks vs revenue on a stepped plan. 8. Don't ignore the gorillas in the room. But how are you different? Cheaper, better, different new? 9. Timing is everything in startup, what do you know that others don't? 10. The ask, to achieve what, and consider - it's always a two way street. What do you want from your investors? --- # So what about the UK? **Source:** https://www.superseed.com/journal/so-what-about-the-uk/ **Published:** 2024-01-30 **Author:** Dan Bowyer ## Just how is the UK economy doing? We talk a lot about the US because the US ecosystem governs the mass and direction for VC and startups globally, but America is not the whole story. As founders learned to understand new investor and customer behaviour in 2023, it became clear (to many who'd never experienced a downturn) that it was a change in client desire that was most surprising and difficult to navigate. Founders experienced an unusual drop in sales, longer sales times, requests for new offerings, and a palpable shift in energy. For UK startups, much of that was, and still is, a local UK problem. ### So how are *we* doing, what will the commercial appetite be, and where will the UK economy land in 2024?  *That’s what I’ve set out to explore in this note.* To set the enterprise scene and for context I wanted to get a sense of business from both sides of the pond. It’s an unfair comparison but to look at public markets it looks at first glance, that... ### ...historically the S&P 500 crushes FTSE 100 performance, and has done for more than the five years in the chart shows. ![](https://www.superseed.com/wp-content/uploads/2024/01/One-year.png) However, when including dividends it levels the field, a little. ![](https://www.superseed.com/wp-content/uploads/2024/01/image-2-1024x515.png) As a side-note, does that make UK stocks cheap by comparison? Maybe, but pundits have been saying that for at least a decade. Other performance proxies are GDP per capita, and growth. Re GDP per cap the UK doesn’t even hit the top 20 (In 2022 we were placed 22nd ($46k), if we include Liechtenstein). 11 European countries do make the list, the US is at 7 and there’s an obvious no-show for the powerhouses of India & China. ![](https://www.superseed.com/wp-content/uploads/2024/01/The-20-countries-with-the-largest-gross-domestic-product-GDP-per-capta-in-2022-in-830x1024.png) Looking at global GDP performance in 2023, Europe is the weakest of all the regions, being bottom of the list. So far down we don’t even display on the site at 1% (source: [imf.org](http://imf.org)). ![](https://www.superseed.com/wp-content/uploads/2024/01/Pasted-Graphic-1-1024x469.png) Below is GDP growth in the UK vs US within the context of Europe and the rest of the world. An ominous low growth 4 year plateau from now until 2028 is only upstaged by Europe continuing on a downslide (also via [imf.org](http://imf.org)). ![](https://www.superseed.com/wp-content/uploads/2024/01/Pasted-Graphic-2-1024x576.png) Startups need **any** kind of activity to flourish, regardless of good bad, bull or bear. Appreciating and leveraging the fact that markets are cyclical. Both fear and greed are ultimately great for startups, even if it doesn't feel like it at the time for any stakeholder. On the downslide we get innovation, clearing of the decks and the opportunity to steal a lead - driving strong vintages. On the upside, for startups and investors alike, we get to see VC scale growth which ultimately means more liquidity and recycling, to serve the next market cycle… rinse / repeat. ![](https://www.superseed.com/wp-content/uploads/2024/01/ade030c0-baf3-4b37-9f78-c0b35561b8ba_1714x746.png.webp) The later stage investment cycle is starting to open up which is great news for the full investment stack. According to Carta, series A round sizes and valuations bounced up in Q4 2023. Valuations landing just 14% off the 2021 high, with round sizes only down 7%. ![](https://www.superseed.com/wp-content/uploads/2024/01/image.png) Looking at software, and specifically the state of SaaS, we're seeing a rebound here too in net new ARR. The end of 2023 seeing a meaningful uptick, customers are out buying again after 18 months of decline. ![](https://www.superseed.com/wp-content/uploads/2024/01/image-1.png) As a side note, seeing stories such as the '[Twilight of Democracy](https://www.ft.com/content/077e28d8-3e3b-4aa7-a155-2205c11e826f)' in the FT could lean into a more UK protectionist attitude to AI, tech and therefore startups. More on-shoring and more focus on owning IP here in the UK would be sensible - and there are Govt programs coming through to better support local markets and innovation - The [Mansion House Compact](https://www.gov.uk/government/news/chancellors-mansion-house-reforms-to-boost-typical-pension-by-over-1000-a-year) being a great example. ![](https://www.superseed.com/wp-content/uploads/2024/01/image-3-1024x707.png) UK inflation is still in the troubled zone with a recent up-tick, albeit small. Driven by services at 6.4% with wage growth as the primary force, but goods inflation has come down significantly to 1.9% - meaning CPI is currently at 4%. The BoE target of 2% will not be hit until services is tackled, and we all know what that may mean. A small ray of light is that the rate wage growth is dropping sharply and unemployment has not grown significantly (4.3% which is historically very low).  ![](https://www.superseed.com/wp-content/uploads/2024/01/Headline-Core-Excluding-Energy.-Food-Alcoholic-Beverages-Tobacco.png) Across Europe these inflation figures look fairly standard, but compared to the US we’re laggards, as they’re already in the 2% range. A quick look at UK house prices and retail activity shows two different stories. House prices haven’t collapsed, mainly due to low supply, but they are continuing to slide - London faring the worst with a 6% drop in 2023. On the retail front, activity collapsed in December. Not great when consumer confidence is such an important economic marker. ![](https://www.superseed.com/wp-content/uploads/2024/01/Retail-sales-volumes-fell-over-the-month-hitting-their-lowest-level-since-May.png) ### So how *is* the UK economy doing? Even though the docket may read doom I’m not convinced, especially within the context of inertia, *and* Europe's figures. Yes the US has bounced back faster and harder but compared to the EU27 we're not faring that badly. Unemployment is lower, GDP per capita is 25% higher, and the CEBR predicts that the [UK will be the fastest growing major economy](https://www.bloomberg.com/news/articles/2023-12-26/uk-economy-forecast-to-narrow-gdp-gap-with-germany-by-2038) over the next 15 years. (i.e. A great time to invest in new tech... just saying). We must get a stronger handle on inflation (which I believe will happen), therefore interest rates, and therefore a return to more positive economic activity, generally. But it does feel like the UK could make a soft landing, just as the States is already enjoying. We're just a little behind. In an election year I’m sure this is welcome news for the incumbents, but let’s revisit this note when Labour and (goodness I hope not) Trump are in power. --- # Why We're Delighted To Work With Popp **Source:** https://www.superseed.com/danbowyer-me/why-were-delighted-to-work-with-popp/ **Published:** 2024-01-02 **Author:** Dan Bowyer By pure accident Mads and I started SuperSeed six years ago with the ghost of a #rectech startup dragging around my heels. A team I had been working with prior to starting our VC firm, helping a friend, messing around. Looking back I should have immediately cut ties, moved on swiftly - but I felt a sense of commitment, and it just refused to die. A messy moment in time and we pretty much vowed never to work in HR tech again.  And then came [Popp](https://www.joinpopp.ai/) - a new kind of co-pilot recruitment platform enabling faster and smarter hiring for larger organisations using AI. Or rather. Then came along [Sam, James and Ilyes](https://www.superseed.com/journal/supersaas-the-one-day-accelerator-winners/), a perception shifting team which is all too often needed in VC. A challenge many investors feel is staying fresh faced and open-minded when the nth pitchdeck lands on your desk with all the latest buzzwords. Especially at the wrong end of the hype-cycle. The team tested a hypothesis very early on based on a glaringly obvious fact that in our hyperconnected world, we probably already know our next best-fit team member. 6 degrees of separation is pretty much 2 thanks to the internet, now accelerated by AI. Not only are we closer, more connected, but all too often in larger organisations the next hire is already in the CRM. It’s just too hard for recruiting teams to keep track of all people across all team members and platforms, which is where Popp works best; > They help internal and agency recruitment teams get the right people on the rocket ship in rocket-ship-time. [Popp’s](https://www.joinpopp.ai/) SaaS platform enables enterprises to automate the candidate selection and initial screening elements of the recruitment workflow. 70% of the effort in the enterprise recruitment process is reviewing applications, connecting with potential candidates and arranging the first round of interviews. It is this time-consuming, low-leverage part of the recruitment workflow that Popp automates. Meaning 25x faster, 80% cheaper, with more strong-fit candidates entered automatically into an enhanced CRM. They are not trying to do what everyone else is and become *the* platform. Their end-game is to be an invisible co-pilot that integrates with any hiring platform to find the best, the fastest. No lengthy sales or integration cycles, just immediate provable value out of the box. Rectech is still relatively unsexy to investors but zigging while others zag is our strategy on this occasion - all within the macro backdrop of Gen AI, reshoring, and general market disruption. The world is changing at an accelerated pace right now, and how we recruit must too. --- # The 2024 Crystal Ball - Tech and Venture **Source:** https://www.superseed.com/journal/the-2024-crystal-ball-tech-and-venture/ **Published:** 2023-12-29 **Author:** Mads Jensen We’ve boldly [forecasted the stock market and the next US president](https://www.superseed.com/journal/the-2024-crystal-ball-geo-and-macro/). Now it’s time for a look at what will happen with tech and venture in the new year.  ## In 2024, (Generative) AI is still in the ascendancy… 2023 was the year of AI. This was kickstarted slightly early, when OpenAI launched ChatGPT on November 30th, 2022. As we said at the time, [this changes everything](https://www.superseed.com/journal/the-future-has-arrived/).  We still think this is the case, as AI continues to be the main driver of everything in tech.   There are claims that [41% of all new computer code on Github now is AI-generated](https://www.goldmansachs.com/intelligence/pages/stability-ai-ceo-says-ai-will-prove-more-disruptive-than-the-pandemic.html). JP Morgan recently outlined their view on why this is very different from anything else we’ve seen in the past 20 years.  ![](https://lh7-us.googleusercontent.com/kJA5tZXylH9SlQYXKk-ROAiYxISJx-LcGNm0X_xsGnIInQ7n-uksVrTf93ykzZ9TXDGX71Rl5Zc3w1u4EFW3qEtf-r7DOWwTK1bkNV-f8_C8Pnf5gBNO7Qp1Shw6NfjffMOWPz4WaZxY3FA9RfXtan0) This is not just about making software developers more productive. It touches all parts of the economy.  And so PwC forecasts that [AI will add $15trn to the global economy before the end of the decade](https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html). Our expectation is that 2024 will continue to be yet another year of AI. We see so many smart founders build transformational SaaS platforms using this next-gen technology. On one hand, so much is alrady happening. On the other, we are still only getting started.  ## …but not without (legal) bumps in the road The capabilities of the new generative AI models have indeed continued to blow us away in 2023. But they are learning all their skills from somewhere. And media companies have woken up to the fact that their IP has been one of the main inputs to the prowess of the new Gen AI models.  The [New York Times has now sued OpenAI for copyright infringement](https://www.theguardian.com/media/2023/dec/27/new-york-times-openai-microsoft-lawsuit).   Elsewhere, German median giant Axel Springer has a deal with OpenAI that [enables the AI giant to use Axel Springer’s IP](https://www.politico.eu/article/axel-springer-openai-launch-global-partnership/). Expect many more legal bunfights around this going forward.  While this will impact the developers of the large foundational models, it will be speed bumps at most. The potential for the new models is so vast that these IP issues will be resolved - even if some of the resolution will linger into 2025. ## We will see a rebound for software company sales in 2024 From the second half of 2022, businesses responded to economic uncertainty by trimming spend and consolidating vendors. This has led to a challenging climate for software companies, and the large public software vendors have reported shrinking growth rates. Note - they’ve still been growing - just more slowly than before. The median EMCLOUD revenue growth has shrunk from 35% to 18% per annum.  ![](https://lh7-us.googleusercontent.com/-IiRMoUUuCBi4ejOUMftr2CWUKlqUXDhdUX7OjZw20g_JFUVDg94vYgqtwDXhfFJthFBkyuQ22n4x5klRPcrnMYkWM2o0A7EnfSxbuqW9hHW40uYRan17ytLPkeJkEJDtjg5m7cse6ZGAAuNLO82auE) But it looks like this shrinkage is now turning. According to Altimeter, annual ARR growth rates stabilised in Q3 of 2023. And with declining inflation and interest rates expected to come down, we expect growth rates to generally stabilise and perhaps even increase in 2024. ![](https://lh7-us.googleusercontent.com/EWilZIe3ApGSkSlWYPVtZV8YI8wyUMhMPZh5Ygur838cJSy1PfNuwpudt1KEgyQC4MAM0U7zIqoLDFSO8LC5lYCxABM5L4GGBXaMeiAo3R1mpIkthce9hCPX4KgaGp9XIg5NCmg4rUwH_OZWGpsW_l4) ## We expect to see more M&A activity in 2024 While IPOs are often seen as the most attractive outcome for tech startups, M&A is the more common exit path. 2023 was a difficult year for exits, with [deal value in Q1-Q3 down 41% on the same period in 2022](https://www.bcg.com/publications/2023/m-and-a-outlook-looking-up-after-bottoming-out). Part of the challenges were driven by uncertainty around inflation and interest rates, plus the looming spectre of recession. Anti-trust regulation also played a role, with competition and markets authorities in the US and Europe probing several acquisitions. Microsoft’s $69 billion acquisition of Activision was ultimately approved, whereas Adobe’s proposed $20 billion purchase of Figma ultimately was called off due to regulatory action.  Barring major geopolitical destabilisation, we expect to see increased M&A in 2024. Inflation is under control, and interest rates are likely to come down. This provides corporates with greater certainty, and the lower rates make it easier to justify a price that will enable deals to be agreed. This should help everyone except for the very biggest multi-billion dollar acquisitions that might still be hampered by increased regulatory scrutiny.  ## We expect a reopening of IPO markets in the second half of 2024  2022 and 2023 have been slow years for IPOs, with fewer companies listing in 2023 than at any other point in the last decade. ![](https://lh7-us.googleusercontent.com/m1VKGsVaz5nBYSHd4v9CcXsEXtG7jueshr58MAn-0zhVN7NK8loJHcrBan2PxQUXN3gVbJLH-M8-4lOsjwnBSQ_-64oi0NN1rUc-H4MXGaOb969iMGcI2r6I4vRQBu-fCvvd7ZWUHgYqYpyKZXIWaMU) However, it is not for lack of quality companies waiting to go public. Goldman Sachs reports that there are plenty of companies lining up to IPO, with the backlog is as “[high as it has ever been](https://asia.nikkei.com/Editor-s-Picks/Interview/Goldman-Sachs-IPO-backlog-as-high-as-it-has-ever-been-IPO-chief)”.  This matches Pitchbook’s analysis of the IPO backlog. ![](https://lh7-us.googleusercontent.com/R2lCtoRqYrsAsxaMo4MAd753Gb7tmfMdZAdpQzqRNDig0IaW28Fy7er3d-hA0oY8AR7YOGUaX_ZW8KufFkYizXeCAMywmcyC-KHM6k2MIsYC3jPsSWmT7Ik3R9gzo8zohpfFNwgeUOsP8JM_czCWJBI) If the US economy stays on track and interest rates start to come down, we expect to see a better IPO environment in the second half of 2024. # VC Investing outlook ## Generative AI reshuffles the playing field once again Just like cloud computing ushered in SaaS as the new opportunity in enterprise software, so does generative AI give rise to a swathe of new opportunities in vertical applications. Because genAI models are so good at making sense of the real world, a lot of the opportunities will be in the real economy. Manufacturing, supply chain, healthcare, architecture and construction, agriculture, raw materials etc - all of these sectors will be impacted profoundly.   ## We see a continued shift to manufacturing and the real economy Speaking of manufacturing, the shift is already underway. According to Dealroom, Manufacturing related investments were the main focus of European B2B investors in 2023. ![](https://lh7-us.googleusercontent.com/kReeJP3AsztG26nTbQo9zXEzLaraLp-rH_VtxKWyLweZuK2s8NJIP-kNFIpHzHlHgwrDOnzSYXL_ceRFtZjYboWfUIb8ri5gjxWf4tPt4iC-q20PvzuB9alnMKOHKV4dr0025omsdCV4qygi1gefhAY) However, Europe is well behind China, where 78% of investments were manufacturing-related. While Chinese investors may be heading into a bubble, we still think there is plenty of scope for more investment that targets the real economy in Europe, and we see this as a hot space in 2024. ![](https://lh7-us.googleusercontent.com/oRWm7siXyurUVSbNdD1mKQIGV12W2rPFswGh9JqYW1-h1PiprB70U_dt692R_CDMjt1qMJ00ULHpXme99idPe7g9Kzr9Uo93ckpmcKbxsyo-eFnfTPFbLME4SI81VkivyN3D0k1zKIWdTuZZAN8SxWQ) A little further out, it looks like we might finally be starting to crack the challenge of autonomous robotics. This is relevant in both manufacturing, mining and agriculture, where autonomous robots can help transform productivity.  ## 2024 will be a strong year for productive venture capital 2022/2023 was challenging for many investors with exposure to VC. In all that noise, it's easy to forget the good things that have been happening. In 2023, thousands of new companies were backed to solve the big challenges in business. As the AI revolution gains pace, we see this further accelerate in 2024. Here is to a great year for founders that solve real problems to make us all more productive and prosperous. Happy New Year! --- # The 2024 Crystal Ball - Geo and Macro **Source:** https://www.superseed.com/journal/the-2024-crystal-ball-geo-and-macro/ **Published:** 2023-12-29 **Author:** Mads Jensen 2023 was full of surprises. Last month [we took at look at some of the the main events of the year](https://www.superseed.com/journal/2023-in-review/). A US recession that never happened. The Nasdaq-100 up by more than 50%. A former US president who looks like he might make a comeback.  These were all important factors for the global economy. We’ve asked ourselves how they might impact the new year. And please remember, this is not investment advice - always do your own research! Now, let’s look at what 2024 has in store for us. ## No US recession in 2024 - p0.7 Ever since [the US yield curve inverted in Q4 of 2022](http://[https://www.currentmarketvaluation.com/models/yield-curve.]), pundits had been prognosticating an upcoming US recession. We are now in 2024, and there is still no sign of a recession. ![](https://lh7-us.googleusercontent.com/RlNwrk5Htf5plPD1yp0LcMuw0CvA8wqzNFudaFTKYJdgMQiyK5iUMGKYtBpJg4uKQ9Bu9bfTkz4jzWeYg6CavwXLWemqt7Pnt3cnbP2eSQgu0c7e2QKkl59ZmE_RTlUdh79vRlDlKwga8Uxm3XjSQQ0) On the contrary, the US has continued to [clock up robust GDP growth](https://tradingeconomics.com/united-states/gdp-growth).  ![](https://lh7-us.googleusercontent.com/axpcIz566tlukN67YbDXakaadXacvDkMSihUwLax_Vv-g0UgVdb2HTtiEXAhHENWvQTasC6YB2YtNnDssfz1lobumSx98KWDQpWdkLLaeMhaQox3IeF1x4OuXG4GulcZ1m8zH_TkDWSbESjKBju8GXQ) It now looks plausible that the US can achieve those rarest of outcomes - a soft landing. The yield curve is still inverted, and other indicators have also been signaling a recession. But the truth is that the [US Government has become a lot better at managing the economy](https://usafacts.org/articles/what-is-a-recession-what-have-recessions-looked-like-in-the-past/) - as seen here:  ![](https://lh7-us.googleusercontent.com/H8oAcuvI5G9E4BcGfKYs0NgBv2S1SFEDXvjE413aK17pD29lRyD9VPKm7e6aHnMtMp31emw3T8Hl5NEiNBpw9Wa0TyaEyMx4vlglNJXXXfy43PsNLufRM_drIB7DYHA15zVxxcSAD_TNOGftlMQDJjg) ### A return to inflation?  Conveniently, inflation back back under control just in time for Christmas!  ![](https://lh7-us.googleusercontent.com/N-BX3E2mYyaov-TBx95rjTpweaZQWWJTubOhwfYOelWxgwuYpRoLRMo0cRLsxAGpQUlv2W5VQ4N8QkEdgFr2jUSqUpI-aVG-95awwbtuKbPX872WPLCdkabi2eOv5hTsfu1ZAG8CHtvuueAP6lnm3Hs) There could be renewed pressure in 2024. The most recent pressure we’ve seen on supply chains has been the Houthi’s attacks on the Red Sea shipping lanes. But while this adds meaningfully to the cost of bringing freight to Europe, the additional cost to US importers is more modest. Unless the conflict escalates dramatically, this in itself is unlikely to be a major driver of US inflation in 2024.  This should help the incumbent president going into an election year. Whether that is enough remains to be seen. ## There will be no global recession in 2024 - p0.9 On a global basis, [asset allocators are roughly 50/50% on a Global Recession in the next 12 months](https://www.absolute-strategy.com/asset-allocation-survey). ![](https://lh7-us.googleusercontent.com/NrBAqYaujjO1jkMte-JVCCHuMW4wegergy9eGmse-QsW7prqKfEKtmVF4q_qT8KmJv9Np4qNRWqD5juOV9wPPlJcm3DmvTa9N1CZ-5me7BowDFQzGAblaQIp-SzXQKFx3mLXwUJ7W8KL-g2iklPvXmw) We think they are being unduly pessimistic. Barring a major geopolitical meltdown, we see the likelihood of that as being quite slim.  [All the global recessions since the 1950’s have coincided with US recessions](https://www.conference-board.org/topics/recession/what-is-a-global-recession-and-what-can-trigger-it). However not all US recessions have led to global recessions.  The two major engines of the global economy are the US and China. China has plenty of internal challenges but [is still likely to clock up GDP growth of 4-5% this year](https://www.economist.com/finance-and-economics/2023/12/07/will-china-leave-behind-its-economic-woes-in-2024).  And despite an increase in bellicose rhetoric over the past few years, the Xi/Biden summit in November signalled a desire from both parties to focus on stabilising the relationship to improve their respective economies.  Barring an outside disruption to the global economy (e.g. a new pandemic), we think the US and China both will keep the global economic locomotive going in the new year. ## Trump will be the next US president - p0.6 Given what happened on January 6th 2021, this sounds bonkers. But let’s look at the data.  1. Trump is well ahead in the primary polls, and he is [the likely Republican nominee](https://projects.fivethirtyeight.com/polls/president-primary-r/2024/national/). Yes, he has been barred from the primaries in two states, but these decisions are likely to be overturned - especially given the composition of the US Supreme Court. 2. Biden’s [approval ratings are abysmal](https://projects.fivethirtyeight.com/biden-approval-rating/). Many of his policy initiatives have been sensible, e.g. the Inflation Reduction Act and the CHIPS Act, but he still has not been forgiven for the inflation that ravaged the US economy during his tenure. Plus he is turning 82 this year, leading to concerns about his health. 3. In a Trump vs. Biden match, [Trump is now ahead in the polls for most of the key states](https://www.nytimes.com/live/2023/11/06/us/trump-biden-times-siena-poll-updates).  The election isn't a foregone conclusion. But right now, we think it is pointing Trump's way. ## Despite high valuations, US Stocks will break new records - p0.5 This is the tricky one, and we take this prediction with plenty of caveats! On one hand, we remain bullish on the fundamentals US economy. Yes - there are debt / credit issues. And the federal deficit is sizeable. But the US's ability to innovate and build is still unbeaten. On the other hand, US stocks look relatively pricey on most measures. And markets have already started pricing in the coming interest rate cuts, so the upside from rate cuts has, to some extent, been priced in (look at the impressive run of 25% gains in 2023). So while we think the US stock market looks reasonably fully priced at this point, human exuberace will do what it does best - lean into the good (or absence of bad) news and continue to push the index higher. The finally tally for 2024 might be < 10% upside to the S&P500. But we think there is scope to go higher throughout the year. ![](https://lh7-us.googleusercontent.com/G5M5qhmK40xm_3tE8NEHaZC10d_5EiwI0kqszBejh3j-PLXmQMahoruoloQg3cfkN5kdJuOOWIjdHg8qQ_D0DN37ZFZp_1fSAK2vdCV9UxEqwsQFlgfUaJFjk3DcuO32Z-twsqoeQkZMGB96HMzFdmY) ## So, all fun aside - what will really happen next year? Whether the above predictions come true or not, some things are for sure: innovators will keep innovating, builders will keep building and we will keep backing great founders to go solve the biggest challenges in B2B tech. We'll cover more [2024 predictions on technology and venture here](https://www.superseed.com/journal/the-2024-crystal-ball-geo-and-macro/). Wishing all of you a great start to 2024! --- # 2023 in Review **Source:** https://www.superseed.com/journal/2023-in-review/ **Published:** 2023-12-01 **Author:** Mads Jensen Yes - the old year still has a month left. But so much has happened already that it's timely with a retrospective. Let's have a look at what's happened in tech and macro over the past year. ## 1. The recession that never happened. Most commentators expected 2023 to be the year of US recession. The US yield curve started inverting in Q4 2022 (meaning the short-term interest rate went higher than the 10-year rate). By May 2023, the short/long inversion reached the highest level it has seen since 1981. Yield curve inversion has historically been a reliable harbinger of recession. And with the Fed pushing rates high, there was an expectation that the economy would sag. But the recession never came. Rather, the US continued to clock up impressive GDP growth, hitting a whopping 5.2% in Q3. We are not necessarily out of the woods. According to Bloomberg, the median forecaster still puts a 50% probability on the US hitting a recession in 2024. But some observers see a much lower probability of a 2024 recession. E.g. Goldman Sachs that now sees only a 15% probability of the down scenario. ![](https://www.superseed.com/wp-content/uploads/2023/11/unnamed.png) ## 2. Tech is back. Sorta. 2022 was a difficult year for tech stocks. Touted as overhyped and overvalued, tech took a beating across the board when rates started to climb. This changed in 2023, which saw a huge rally in US equities led by the bellwether tech companies. Nasdaq 100 is up 47% YTD and has almost reached its peak from 2021. It looks like tech is back. At least big tech. But the wider tech market is still below the dizzying heights. EMCLOUD is 50% below the 2021 top. And later-stage venture capital valuations are still slumping. Venture has been recovering in 2023, but we are nowhere near the froth of 2021/2022. And in fairness, we are grateful for that. Seed and early-stage venture is alive and well. And there are signs that 2024 could be a big year in technology (more on that below). ## 3. The wider equity markets have started to come to life While growth in the S&P500 looked great (up 16% by June), most of the gains had come from the magnificent seven (Apple, Alphabet/Google, Meta/Facebook, Microsoft, Amazon, NVIDIA, Tesla, Amazon). But there are now signs that the rally is starting to broaden. Could this be the beginning of a wider rally? The top 10 stocks (most of which are tech) now account for 1/3 of the total S&P500. That's the highest proportion since the early 1970s. It feels a bit lopsided. ![](https://www.superseed.com/wp-content/uploads/2023/11/image-1024x631.png)**Top ten share of S&P 500 market cap**, Source: FS Investments, Bloomberg Finance Big tech valuations are still not frothy, but tech is starting to look a little expensive. And for the S&P500 to keep climbing, the top 10 stocks have to keep growing (or at least not go backwards). So, while there are green shoots in equities, a lot will depend on the Fed's continued balancing act. The question is: can they keep containing inflation without pushing the economy into a recession? ## 4. Inflation coming under control The US inflation rate slowed to 3.2% in October, and it looks like the Fed has largely succeeded in containing the inflation monster. ![](https://www.superseed.com/wp-content/uploads/2023/11/statistic_id273418_us-monthly-inflation-rate-2023.png) Interest rates are still high, but expectations are that these have peaked. Although they won't decline immediately, markets expect that they will start to come down in 2024. This is good for stocks, and especially for tech. ![](https://www.superseed.com/wp-content/uploads/2023/11/image-1-1024x853.png)GS Prognosis of the Fed Funds Rate for 2024/2025 ## 5. Growth is back in vogue For a brief spell, cash flow efficiency and The Rule of 40 were the main valuation drivers of cloud/SaaS companies. (The Rule of 40 says that the Free Cash Flow % + the Revenue Growth rate should be at 40% or better). The Rule of 40 is still correlated with the valuation of SaaS companies (0.59 vs forward revenue). But growth is now nearly as correlated (0.57 vs forward revenue). This means that Free Cash Flow has much less influence on cloud company valuations. In other words, growth is back to being the main driver of public software company valuations. It turns out growth is still valuable - even when interest rates are at 5%. ## 6. AI is the only game in town Forget crypto. Forget Web3. And definitely forget the Metaverse. OpenAI and ChatGPT inspired founders and investors alike, and generative AI engulfed the startup world. Many of us think this is the biggest tech disruption for decades - at least since the 90s and the Internet. Most of the excitement in 2023 was about foundation models (the underlying generative models - like GPT from OpenAI). We think the next wave of innovation is in applied AI. More on that next month. Wherever you look at tech, 2023 was the year of AI. We see this accelerate in 2024. ## 7. Relearning old truths 2020/2021 turned the world of business upside down. Valuations? Why worry - as long as you are in the deal. Unit economics? For dinosaurs - just spend more to grow faster. Company governance? What does that even mean? In 2022/2023, many venture investors have rediscovered that board seats can be important. Not least Microsoft, who saw the CEO of their $13bn investment fired without consultation. For a little while they must have had red ears. But in the end, Satya Nadella (Microsoft's CEO) played it well. He navigated things so that the OpenAI team would either join Microsoft or go back to run OpenAI. Sam Altman is now back at OpenAI. And - going forward - Microsoft will have an observer seat on OpenAI's board.  ## 8. Elon, Elon Everywhere It seems that genius can be its own liability. It is hard to dispute the power of Elon's entrepreneurial drive, given breakthroughs with Tesla and SpaceX. But being CEO of two of this decade's defining companies isn't enough. Elon is also playing it at being a media baron with his new-ish toy X/itter. This is when he is not busy getting embroiled in geopolitics in both Ukraine and Israel/Gaza due to his influential Starlink network. It's hard to work out how he has time for it all. And it's difficult to see how he can avoid distraction from all his extracurricular forays. But however one looks at it, he has been a dominating figure in 2023. Hopefully, he'll manage to spend more time making cars and building rockets in 2024. Which would meant that we can all enjoy a little less Elon in our news-feed, and a little more of his genius in making world-changing technologies. ## 9. It's the optimists that change the world The stock market has predicted nine out of the last five recessions. And economists many more. But while some are busy predicting doom and gloom for the global economy, the entrepreneurs just got on with it. Undaunted by the prognostication of recession, they hunkered down and got to work building products and companies. So, as a final retrospective on 2023, we dedicate the year to the optimists, the builders and the entrepreneurs who move mountains to make it all happen. We are fortunate to work with many of them every day. Here is to their continued success in the year ahead! --- # SuperSaaS - The One Day Accelerator Winners **Source:** https://www.superseed.com/journal/news/supersaas-the-one-day-accelerator-winners/ **Published:** 2023-10-31 **Author:** Dan Bowyer Tech moves faster than ever, and many B2B AI/SaaS founders don't have time to spend six months in a traditional accelerator. But they still want to get the inside scoop on how to sell, build successful companies, win corporate clients and raise money. And so we have created [SuperSaaS](https://www.superseed.com/supersaas/) - a one-day accelerator that bundles all of this. Every quarter, we invite the best early-stage founders from across Europe to join us for a day of learning, pitching, selling and raising. The content and workshops are delivered by experienced founders turned investors and advisers - some of the best supporting players in the business. All the all the hints, tips and hacks on how to get better at building, selling, raising and delivering. **With the opportunity to take home £500k in investment on the day.** Last week we met 56 incredible founders from 30 pre-seed startups. ![](https://www.superseed.com/wp-content/uploads/2023/10/DSCF7859-2-1024x683.jpg) We bucked our own rules this time, offering investment to three super teams, not just one - [Anqalab](https://www.anqalab.com/), [Popp](https://www.joinpopp.ai/) and [Cbamboo](https://www.cbamboo.com/). It's a gift to be able to work with such talented people. **Our next SuperSaaS is on the 19th Jan 2024. ** If you or someone you know would like to apply, click [this link](https://www.superseed.com/supersaas/#supersaas-application-form). --- # An AI Status Check. **Source:** https://www.superseed.com/danbowyer-me/an-ai-status-check/ **Published:** 2023-10-31 **Author:** Dan Bowyer AI is already, and will continue to be transformative at home and work. Few argue this point, but what’s *really* happening and what’s coming down the road? With the news cycle as it is, it’s often hard to truly decipher, so that’s what I’m going to have a pop at. Exploring what’s been built, applied, invested in, by whom, where and why.  ![](https://www.superseed.com/wp-content/uploads/2023/10/image-5.png) AI in useful forms been around for many years. It’s in Alexa, Siri, Google Search, FaceID and so much more, but our fascination with thinking and doing machines dates back to Plato with the concept of an [automaton](https://www.britannica.com/technology/automaton). Davinci’s notebooks are full of machina sketches, allegedly creating his [robotic knight](https://www.da-vinci-inventions.com/robotic-knight) prototype in 1495 (a mock up shown in the pic).  ![](https://www.superseed.com/wp-content/uploads/2023/10/Pasted-Graphic-13.jpg) Our modern world view of AI was popularised by [Alan Turing](https://en.wikipedia.org/wiki/Alan_Turing) (building his Turing Machine computer) in the 1950s with his work on computer machinery and intelligence, crystallising with the [Turing Test](https://en.wikipedia.org/wiki/Turing_test), but the term artificial intelligence was first used by computer scientist [John McCarthy in 1955](https://computerhistory.org/profile/john-mccarthy/#:~:text=McCarthy%20coined%20the%20term%20%E2%80%9CAI,programming%20language%20lisp%20in%201958). ![](https://www.superseed.com/wp-content/uploads/2023/10/Pasted-Graphic-14.jpg) Between the 1950s and the 90s AI went on a bit of a rollercoaster from a boom in interest to an AI winter spanning the 80s and 90s where researchers globally seemed to run out of steam. So the money dried up and we therefore saw fewer breakthroughs - leading to an extended hype cycle trough until the noughties where it all got interesting again. With [Deep Blue](https://youtu.be/KF6sLCeBj0s?si=FVOssKdqBGIJ9Ims), speech recognition that actually worked, Mars rovers navigating without human intervention, IBM's Watson won Jeopardy, and Apple launched Siri.  There are many ways to slice and dice the different types of AI but it’s probably most useful to think of AI in capability buckets - 1. Reactive ([Deep Blue](https://youtu.be/KF6sLCeBj0s?si=FVOssKdqBGIJ9Ims) playing chess) 2. Limited Memory (Autonomous vehicles e.g. Tesla) 3. Theory of Mind (Human like - The hot topic of [LLMs](https://www.techtarget.com/whatis/definition/large-language-model-LLM) and potential sentience) 4. Self Aware (Super intelligent - the truly scary stuff, think [I, Robot](https://www.britannica.com/topic/I-Robot)). I think of AI as narrow (basic), general (human) or super (OMG) - so it depends what you read or who you speak to.  ![](https://www.superseed.com/wp-content/uploads/2023/10/3-Types-of.jpg) In Nov 2022 AI kicked again off with an unexpected game-changer. The stable release of ChatGPT unleashed the latest and strongest wave of interest in AI, with LLMs being discussed at many a middle England dinner party. It grew radically quickly, to most people’s surprise, hitting 100m users in 2 months. Compare that to Facebook, taking four and a half years. ![](https://www.superseed.com/wp-content/uploads/2023/10/chatgpt-time.png) ![](https://www.superseed.com/wp-content/uploads/2023/10/LLMs-see-huge-growth-as-generative-Al-soars.png) ChatGPT and other LLMs showed how AI could be relevant, useful, some going so far as to say sentient (not true) but it certainly is this generation’s internet moment.  **Want to pass an exam? Don’t ask Google.** ![](https://www.superseed.com/wp-content/uploads/2023/10/Exam-results-ordered-by-GPT-3.5-performance.png) Github found that software developers completed tasks 55% faster with their AI Copilot. Moreover, their early research found that 46% of code was completed by it. Most of the immediate tooling leaps using this style of GenAI will affect business operations, marketing and sales, software engineering, and R&D*.* And don’t get me started on Midjourney! I’m a little bit in love with how it creates visuals using AI (see the Stormtrooper). Text to video is not all that far behind - soon we’ll be typing simple text prompts to create our marketing promo videos. It’s just around the corner.  ![](https://www.superseed.com/wp-content/uploads/2023/10/Pasted-Graphic-15.jpg) **AI is big business. ** Mads and I were devoted AI investors before it was cool and we rarely see a startup pitch deck without the term. Is it hype? Yes. But it’s also deeply meaningful and transformational, we just have to get through the next few years of noise. However, this definitely isn’t anything like the hype around crypto, web3, metaverse, etc. None of which ever had the mainstream potential. All businesses will become AI businesses, whether they know it or not. Even Apple is moving on an accelerated timeline (allegedly Tim Cook has been heard saying they were asleep at the wheel), and you know it’s mainstream when Apple takes a seat at the table.  ![](https://www.superseed.com/wp-content/uploads/2023/10/gen-AI.png) PWC reports that AI could contribute up to $15.7 trillion to the global economy in 2030, more than the current output of China and India combined. Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion is likely to come from consumption side effects. McKinsey’s latest research estimates that generative AI specifically could add the equivalent of $2.6 - $4.4 trillion annually to global GDP - the United Kingdom’s entire GDP in 2021 was $3.1 trillion.  ![](https://www.superseed.com/wp-content/uploads/2023/10/LLMs-see-huge-growth-as-generative-Al-soars-1.png) **Businesses, startups and investors are fizzing about AI. Right now it's GenAI and LLMs. ** VC investment in the UK is starting to pick up. Looking at Dealroom data we’ve shaved off the crazy 21/22 excesses and are largely back to 2019 numbers. No bad thing to get back to good ‘fundamentals’ business.  ![](https://www.superseed.com/wp-content/uploads/2023/10/image.png) The UK leads Europe for VC investments by 200% and this is where we fit in globally. USA is still 10x, so there’s a long to go but we are definitely on the map. Personally I’m hoping we go for it commercially, take this disrupted moment to leapfrog and not to just focus on alignment and safety which feels like the current UK drive for airtime. ![](https://www.superseed.com/wp-content/uploads/2023/10/Top-10-countries-by-VC-investment-in-2023.png) It’s hard to break down exactly what levels of capital are going directly into AI because what *is* an AI business? It’s easy to pinpoint AI pick & shovel businesses such as NVIDIA, or AI platforms such as ChatGPT but what about the applications across deeptech, biotech or climatetech as examples. We know *all* will be applying AI but won’t necessarily be labelled as such. ![](https://www.superseed.com/wp-content/uploads/2023/10/UK-leading-sub-industries-by-VC-investment-in-2023.png) We do know that the UK is leading Europe as a popular place for artificial intelligence companies to set up. According to Beauhurst there are currently 1,486 AI companies across the UK, out of earliest startup and in growth mode. Collectively, these businesses have secured £8.48b worth of equity investment across 3,283 rounds, making AI one of the best-funded sectors in the UK. Not all countries are being served in the same way. For example ChatGPT isn’t available in: China, Russia, Ukraine, Afghanistan, Iran, Belarus or Venezuela. I could make a strong argument to say that AI (specifically generative AI) has saved the investment world from itself, temporarily. If you removed AI from the investment activity charts they would tell a very different story. The loud and proud investments into OpenAI, Anthropic or Mistral are keeping things buoyant. All coming at a time when the world needs it most. Technology is efficiency and AI is efficiency on steroids, vitamins and curly kale combined. This chart shows how valuations split across Closed (mega) vs Open Source (moderate) AI - another hotly debated topic. Which is best. An exploration for another time. ![](https://www.superseed.com/wp-content/uploads/2023/10/image-1.png) **So what’s the outcome of AI? What will it all mean? ** Aside from the predictions above, In simple terms it’s 10x time. Productivity up. Efficiency up. Yes labour markets will be disrupted but not like most people think, and it won’t be overnight.  ![](https://www.superseed.com/wp-content/uploads/2023/10/image-3.png) If you look at how some sectors naturally resist - legaltech for example, it’ll just take time to get to a threshold of disruption. In other sectors there will be union kickback, regulatory challenges or other govt interventions - mostly understandable. Not all sectors are created equal, or will be contorted in the same way. **But AI is coming.** ![](https://www.superseed.com/wp-content/uploads/2023/10/image-4.png) I dare say however heavy handed the new [EU AI regulations](https://www.europarl.europa.eu/news/en/headlines/society/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence) are, or whatever the executables in [Biden’s executive order](https://www.nytimes.com/2023/10/30/us/politics/biden-ai-regulation.html) - AI will find a way. Hopefully we’ll never have to ask ourselves - “who is the bad actor here?”. ![](https://www.superseed.com/wp-content/uploads/2023/10/The-Baddies.jpg) But on the positive foot, for the first time it’ll be a white collar revolution. Where the technology itself will also be able to deliver solutions to the very problems it poses, especially around safety and alignment. This will be the first time that a technology can fix itself. We just have to ensure the weakest link in the chain does the right thing at the right time.  What excites me the most is not how far AI will go beyond GenAI (we have a LONG way to go) but what happens when it’s compounded by other technologies coming down the track. Again, many will be transformational for society: How we create energy, work, manufacture, run supply chains, predict weather, educate, create vaccines, cure cancer, fix climate and so much more. **So for me it’s a case of AI+. ** For example AI+fusion (energy), or AI+quantum (computational unlock), or AI+thenexttransformative technology. It’s just a terribly exciting time to be alive as a VC or anywhere in the world of innovation. Yes we need to show care, love, attention but technology will save us. **It always has.** --- # Ankra partners with SuperSeed in a £1m pre-seed round **Source:** https://www.superseed.com/journal/news/ankra-partners-with-superseed-in-a-1m-pre-seed-round/ **Published:** 2023-10-19 **Author:** Mads Jensen We are excited to announce that SuperSeed has led the pre-seed round in Ankra - a SaaS company developing a powerful cloud management platform. [Ankra](https://www.ankra.io/)’s SaaS platform enables large enterprises to easily benefit fully from the scalability and flexibility of the cloud when using Kubernetes. While platforms like AWS make it easy to provision new cluster services in the cloud, it is still complex and labour-intensive to configure and manage these. Ankra solves the problem by automating infrastructure configuration, deployment and management for large, enterprise-grade applications that run on Kubernetes clusters. When we met founders Mattias Åsell (CEO), Benjamin Klingsbo (COO) and Mark Shine (CTO), we were immediately impressed with the scale of their vision. Kubernetes is a powerful technology, but configuration, deployment and maintenance is complicated. There is a global shortage of Kubernetes specialists, and too often software developers find themselves spending 20% of their time or more just configuring infrastructure. This saps developer productivity and can create security risks in situations where clusters are incorrectly configured. Ankra can automate all of this work, abstracting away Kubernetes complexities to free up developer time. Specific benefits include: 1. **Cost reduction on Kubernetes deployment and management.** It enables firms to reduce or eliminate spending on costly Kubernetes (“DevOps”) specialists. 2. **Cost savings from developer productivity improvements.** With developers losing as much as 40% of their time on infrastructure management, lost productivity is in the tens of billions. 3. **Cost reduction on cloud infrastructure.** Amazon, Microsoft and Google today enjoy a near oligopoly in the $200bn cloud industry. Kubernetes automation enables firms to deploy and maintain their own open-source services, which commoditises the services and brings down costs. 4. **Cost/risk reduction from the elimination of security risks.** 53% of Kubernetes clusters have security issues due to misconfiguration. These can largely be eliminated with automation. Unstable or insecure infrastructure can lead to existential problems for companies. Ankra helps address these vulnerabilities. Mattias, Benjamin and Mark have a big vision for the future of the cloud, and we are proud to partner with them as they work to build a great cloud management company. --- # Do VC generalists outperform specialists? **Source:** https://www.superseed.com/danbowyer-me/do-vc-generalists-outperform-specialists/ **Published:** 2023-09-30 **Author:** Dan Bowyer **And going forward, how will this unfold in the next economic cycle?** As a specialist VC, researching this note has been a fascinating excuse for a thought experiment or two. To start, let’s explore how to define what constitutes a specialist vs generalist. Some industry analysts mark a specialist VC as someone who focuses on just one sector. I have a slightly different definition, which I'll come back to later. For now, let’s take a look at the data.  According to Pitchbook, which looked at 1824 VCs over the last 20 years, it turns out that sector-specific VCs outperform (generally, looking at median TVPI or IRR).  ![](https://www.superseed.com/wp-content/uploads/2023/09/Overall-performance-of-VC-funds-by-vintage-cohort-and-style-1024x660.png) ![](https://www.superseed.com/wp-content/uploads/2023/09/Overall-performance-of-VC-funds-by-vintage-cohort-and-style-1-1024x657.png) Not by a huge margin until you look at smaller funds (sub $250m), where the specialist performance uplift is more impressive.  ![](https://www.superseed.com/wp-content/uploads/2023/09/Performance-of-VC-funds-under-250M-by-vintage-cohort-and-style-1024x655.png) ![](https://www.superseed.com/wp-content/uploads/2023/09/Performance-of-VC-funds-under-250M-by-vintage-cohort-and-style-1-1024x650.png) It’s hard to accurately unpack the data by stage, territory, or in context against inertia and the changing macro backdrop, but we can draw some conclusions.  Generalists seem to do well in good times through diversification, mainly at the later stages. As the specialists miss out on flash trends and in some cases take longer to return capital (especially those in deep tech). Smaller funds tend to operate at the earlier stages where they can outperform by specialising in a niche, enabling them to focus, and attract ‘more of the same’ outstanding teams. **Horses and courses.** To sate my own thought experiments I wanted to look beyond sector specialists, and broad stage. What about Europe vs US? Earlier stages? VC platform vs no VC platform? Strategies and objectives? **Crystal balling the next decade.** This next economic cycle is going to be a fascinating one for the startup ecosystem with many counterfactuals duking it out. As the West decouples from the East amid war, with silicon tussles for protection, control and production - AI is enabling more software development power, more quickly. According to Scott Guthrie from Microsoft, 40% of code uploaded to Github in March was purely AI generated.  Investors who initially retracted with a nosebleed from recent VC excesses are now piling back into secondaries at a significant discount. Hot sectors such as web3, crypto and the metaverse are firmly back in their boxes while AI keeps the crazy lights on with outsized valuations and raise sizes. Venture activity perceived as hot in the States over the last 10 years was actually hotter in Europe, according to a new Sifted report.  **The surprising state of European venture**. ![](https://www.superseed.com/wp-content/uploads/2023/09/European-VCs-outperform-US-counterparts-over-1024x733.png) **What about platform VC?** [Cory Bolotsky and Dale Chang](https://news.crunchbase.com/venture/vc-platform-teams-value-down-markets-chang-scale/) looked at 850 VCs over the last 20 years to ask the question - does platform venture capital (investors who actively support their teams) perform better than those who do little beyond the cash. You can intellectually answer the question both ways but the data shows that those who more actively support teams in the earlier stages perform better. Anecdotally I can add that the founders who constantly reach out and ask questions are doing better in our portfolio than those who don’t (not just from us or other investors, but via board, advisory and other smart networks). ![](https://www.superseed.com/wp-content/uploads/2023/09/Pasted-Graphic-6.png) **Euro discounts.** In our small corner of the world at seed, in Europe, all of this is being accomplished at a 40% discount. European startups (graph on the left) are still significantly cheaper than across the pond (right). ![](https://www.superseed.com/wp-content/uploads/2023/09/Pasted-Graphic-5-1024x432.png) Looking deeper at more data I’m going to challenge the terminology and put us, and those like us, back in the Specialist box. We don’t focus on one sector but we do focus on big business optimisation (or resource efficiency, depending on which side of the seesaw you’re sitting). And we do focus at seed, using a platform approach, to serve technical teams solving enterprise scale problems. Pretty specialised in my books! And benefitting from all of the same upsides as the sector specialists. **So how will this play out for the next decade?** There is nothing to suggest that the next 'good times' is anywhere close (but boom and bust *will* consistently rotate because, humans). It looks like we’ll generally see a protracted period of low economic growth for most (read 'opportunity for startups'). Sure IPOs will creep back, more trade sales will consolidate sectors to gain competitive advantage, as those without solid units will close their doors. Looking forwards we still have fundamental challenges to face that will absolutely need fundamentals startups to innovate and fuel our future. Those focusing on climate, manufacturing, efficiency, agriculture, energy, water, supply chains etc will continue to do well - as all businesses become AI businesses. Against a backdrop of sluggish markets, high(er) interest rates, and no more free money.  All of which suggests a specialised approach will win, however we choose to define that, and maybe now is a great excuse for new kinds of definable VC specialists? Perhaps even one that looks beyond financial returns for our new world. --- # Garvis exits to Logility **Source:** https://www.superseed.com/danbowyer-me/garvis-exits-to-logility/ **Published:** 2023-09-12 **Author:** Dan Bowyer It's been hot in SuperSeed towers recently with 2 exits in as many months. Today we're extremely proud to announce the sale of [Garvis](https://www.garvis.ai) to [Logility](https://www.logility.com/), a US publicly traded supply chain solution company. We led the $3.4m seed round late last year, investing with Robert Bosch Venture Capital and Scalebridge Capital. As unusual as it is to accelerate from seed to exit within a year! it’s been a gift working with the team and helping them get to this point in their evolution. Garvis run by [Piet Buyck](https://www.linkedin.com/in/piet-buyck-garvis/) and his team help large FMCG organisations such as J&J, Bacardi and Fortune Brands use Ai to better manage supply chains and forecast demand. They offer a unique white box Ai solution, with near zero time to value for clients, which severely disrupted the industry and enabled blistering growth. To such an extent that multiple offers of further investment were received, as well as options to sell. The chosen partner was Logility, enabling deeper levels of funding and global access to world leading brands. The rocket ship has now left orbit. --- # How will AI affect economic growth? **Source:** https://www.superseed.com/danbowyer-me/how-will-ai-affect-economic-growth/ **Published:** 2023-08-31 **Author:** Dan Bowyer Since the winter of last year and [ChatGPT](https://chat.openai.com/)'s stable release, we’ve been trying to grapple with what this next wave of AI will actually mean for business. You’ve read the scary or salacious headlines, but what will the commercial reality be for startups, venture capital, SMBs and enterprises, the UK, Europe and the world? ## How will the onion peel? Who will win? How, and why? The fantastic and also harrowing backdrop to consider is economic distress, climate change, war in Europe, the US and China's decoupling, unrest in the Middle East, and investment activity falling (see image); all while the FAANG(-N)s jostle for AI supremacy. ![](https://www.superseed.com/wp-content/uploads/2023/08/Pasted-Graphic.png)VC deal activity by quarter (Europe) At the same time, Governments are taking a position on AI as the tech itself matures into economic realities. This next wave of innovation will be driven by generative artificial intelligence or [GenAI](https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai), delivered across 3 tiers: the [Picks and Shovels](https://stablerise.co.uk/what-is-pick-and-shovel-investing-and-how-does-it-work/) (think [Nvidia](https://www.nvidia.com/en-gb/) [making the hardware](https://www.superseed.com/journal/nvidias-time-in-the-sun/)); platform plays (such as ChatGPT delivering the software); and then the ensuing applications themselves being applied into business. Selfishly, I want more of it all, full stack, in the UK and across Europe. We have the talent, and capital, but lack the infrastructure and joined-up thinking around regulation and support… If only there were a way of unifying Europe. Hmm… That said, to many pundits, the EU is not the place to be *because* of how restrictive and burdensome EU regulation is compared to the US or China. ## AI will be persuasive and pervasive. Personally, I believe AI will shine through and make waves, but, either way, in VC, it doesn’t much matter: startups will follow the money and markets. It’s just a shame that the wider benefits are lost to our local ecosystem when financial rewards (and talent) head offshore. France doesn't care what Europe is or thinks, and it is going for it, brilliantly playing what I think of as “The BIG Draw”. Aggressively and publicly investing in AI, Macron launched a [€500m AI fund in June](https://www.france24.com/en/europe/20230615-macron-wants-to-boost-ai-calls-for-smart-rules-that-don-t-impede-tech-growth), shouting from the top of the Eiffel Tower. Then, it was [Mistral later that month, raising $113m](https://techcrunch.com/2023/06/13/frances-mistral-ai-blows-in-with-a-113m-seed-round-at-a-260m-valuation-to-take-on-openai/) after only a few weeks of existence. And, most recently, [Poolside: a US AI startup](https://sifted.eu/articles/poolside-raises-126m-relocated-france-news) that moved its HQ from the US to Paris. ## A US firm moving to France? Vive l'AI! There are some AI noises in the UK, but nothing to blow your hair back. Rishi is obviously thinking about it. There’s the [Mansion House Compact](https://www.pensionsage.com/pa/UK-pension-providers-sign-up-to-Mansion-House-compact.php), encouraging our pensions into VC/PE; and we have the AI safety and alignment crew now camping out in [Bletchley Park](https://www.telegraph.co.uk/business/2023/08/31/sunak-ai-summit-test-uk-china-relationship-bletchley-park/). But it’s not enough. We must do more as a United Kingdom: think bigger; be bolder. Again, we have the capital and more than enough talent. Speaking of talent, one interesting aspect of this AI transformation is that it’s mostly white-collar. Information workers will be most disrupted, which is a first. ![](https://www.superseed.com/wp-content/uploads/2023/08/Pasted-Graphic-1.png)Generative AI impact on productivity A recent Goldman Sachs report suggests the boost to global labour productivity could increase annual global GDP by 7% due to AI. > To put in context that’s about $7trn in 2023 money. With this in mind, AI, which has been around for decades, has recently seen a significant increase in mentions on Russell 3000 earnings calls. ![](https://www.superseed.com/wp-content/uploads/2023/08/Pasted-Graphic-2.png) AI mentions on Russell 3000 earnings Management teams are focusing on AI because, for the first time, the output is indistinguishable from a human. The promise has arrived, or at least a version of it. Not all AI is created equally, and not all applications are as impressive, but we are beyond the usefulness threshold for many use cases. In some instances, beyond human application *and* ‘intelligence’ baseline. If you look at the O*NET and ESCO databases of ~2000 US and European occupations, it’s possible to consider the breakdown of which roles will be most affected, and by how much. ![](https://www.superseed.com/wp-content/uploads/2023/08/Image.jpg)Share of employment exposed to AI ## 25% of tasks will be automated with 18% of all work fully automated by AI Most jobs will be disrupted, many ‘augmented’ while some will be completely replaced (it’s probably a terrible time to get into radiography). The way I view it, information workers will be given superpowers. Management teams 10x’d, as well as engineering, sales, legal, healthcare, and many, many more. All of this is only applying the technical approaches we currently understand. The AI race has only just begun, you can argue we’re only 10 months into this latest run, so it’s all hard to predict, and who knows what amplifiers will be added on top over time - It won’t just be AI. We’re at the beginning of a hype cycle that will break all the rules. I will confidently state that we have never seen such a transformation, which will reach far beyond business and economics. Countries most affected will be those drowning in knowledge workers. If you look at saturated financial hubs such as Hong Kong, they will fare the worst or best depending on which side of the desk you’re sitting. ![](https://www.superseed.com/wp-content/uploads/2023/08/Pasted-Graphic-5.png)Effect of AI productivity per country Big tech is all in, and the engine is running. Zuck recently sacked his Metaverse team and has fully pivoted to AI. And if you look at Alphabet’s income stream, it is all AI or defined by it: ![](https://www.superseed.com/wp-content/uploads/2023/08/Pasted-Graphic-4.jpg)Alphabet Income Statement Sundar Pichai, Alphabet's CEO stated: > “AI is the most profound technology humanity is working on. More profound than fire, electricity, or anything that we have done in the past.”  Looking at last week’s mega funding rounds, most were in health tech, AI and fintech. Although there’s a falsehood inherent in that statement:  ***All companies are, or soon will be, AI companies.*** --- # Elon’s Fascination With X. **Source:** https://www.superseed.com/danbowyer-me/elons-fascination-with-x/ **Published:** 2023-07-31 **Author:** Dan Bowyer [SpaceX](https://www.spacex.com/), [xAI](https://x.ai/), his son [X](https://www.independent.co.uk/arts-entertainment/music/news/elon-musk-child-name-grimes-pronounce-x-b2384043.html) (pron: Ex Ash Ay Twelve) and now Twitter becomes [X](http://x.com). That’s a lot of X’s.  **Why? Where did it start. What’s the fascination, and how will it end.** Pre dot-bust Elon sold [Zip2](https://en.wikipedia.org/wiki/Zip2), a web-based Yellow Pages, to Compaq for $300m - netting him around $20m. Later in 1999 he personally invests $15m into his new online financial services startup [X.com](http://x.com), after paying $1.5m for the domain (alongside a cool mil for a new [McLaren F1](https://cars.mclaren.com/gb-en/legacy/mclaren-f1)). He tells people “X is the coolest URL on the internet”, “X marks the spot for treasure”. And with his new co-founders Harris Fricker, Christopher Payne and Ed Ho they kick off, before very quickly falling into conflict and financial trouble at the fledgling startup. Elon fires Fricker, Payne and Ho depart not long after. At the same time, literally over the road in Silicon Valley, [Peter Thiel](https://en.wikipedia.org/wiki/Peter_Thiel) with his CTO cofounder [Max Levchin](https://en.wikipedia.org/wiki/Max_Levchin) are building [Confinity](https://en.wikipedia.org/wiki/Confinity), which would become a direct competitor to X as the two companies accidentally develop the same email payment product at the same time. Also struggling and bleeding cash Thiel suggests they merge, which they do (ultimately to become Paypal). **From inception it’s trouble. ** Elon wants to build an everything financial app while Thiel and Levchin purely want to focus on payments. They agree to focus on email payments and improving PayPal’s initial offerings first. The financial superstore, Elon publicly agrees, would become a "future thing". Over the coming months Thiel and Levchin realise that Elon is not on plan. In fact he’s a long way from it, burning $12m per month focusing on X, porting to Microsoft from Linux (which technically could never work), creating risky credit products without identity checks, and behind the scenes Elon is trying to change the name to X-Paypal. Outwardly the obsession with X as a brand goes down terribly with consumers. Vivien Go on focus group testing said: "Again and again, the theme of 'Oh God, I wouldn't trust this website. It's an adult website' and 'I just wouldn't trust that" **X is on the edge.** With 6 months cash in the bank and tempers fraying, Thiel and key figures beg Elon to abandon his strategy. He refuses. Thiel, Levchin, Botha and Sacks now decide to execute a coup. They gather loyalists and on 19th Sep 2000, as Elon is taking off for Europe on his honeymoon, they make their move. The land-bound board members summon an emergency board meeting by phone just as Elon takes off. On the call, Thiel and Levchin reveal the issues to financiers Malloy, Moritz and Hurd who had no idea of the depth and devastation. Elon insists that the financial superstore ‘everything app’ is the big prize that they must go for, but the investors side with Thiel and Levchin. Elon is fired and Thiel becomes CEO, ordering the end of X strategy, and to instead focus on PayPal (which ends up selling to eBay for $1.5bn in 2002). Although not directly involved in the success of Paypal, Elon did extremely well financially which seeds subsequent ventures SpaceX and Tesla.  You can see why X is still firmly imprinted. The big ticket dream that was never allowed to come true. The ultimate X that got away. And why Twitter has become X - (re)birthing Elon’s everything app. **But will it work?** Everything apps such as [WeChat](http://wechat.com) work in China and other, umm, less-democratic countries where privacy is not precious and govt interventions are frequent. Do we need an everything app? Would you use one? How will it get through multiple regulators? What one killer feature will bring everyone onboard?  Personally I think he’s 20 years too late, holding onto a dream that no longer exists, where there will be no govt subsidies this time. But I’m constantly told I’d be a fool to bet against a billionaire brand with this much political clout. So we will see... For now I’m going to enjoy the ride as the next Ironman movie is being written in front of our very eyes.  ***And what a script!*** --- # Crafting your Startup Id **Source:** https://www.superseed.com/startupid/crafting-your-startup-id/ **Published:** 2023-07-04 **Author:** Mads Jensen The Startups Id(entity) is the foundation of your business strategy. It answers the existential question: why does your business exist? It does this by looking at the following: - Who you serve, - how do you help them, and - what impact you aim to create. Articulated well, they enable the three most important aspects of business strategy: clarity, focus and alignment. Fully formed, your Startup Id looks like this: ![](https://www.superseed.com/wp-content/uploads/2023/07/grid-1024x495.png)Startup Id It's deceptively simple, but it can take time to come up with the ideal Startup Id. Sometimes you need experimentation and iteration to find the elements that work. But fortunately, the questions you need to answer are straightforward. This means you can cycle through the Startup Id framework as often as you need until you get to an Id that works. There are four simple questions to uncover your Startup Id: 1. Why did you start the company? 2. What is your secret? (the insight) 3. Who do you serve? 4. What is your desired impact and value proposition? And just like that, you have the core building blocks. A simple 2×4 that helps articulate your near-term and ultimate purpose. Giving you and your startup team clarity and focus. The sequence is deliberate. Each question builds on the ones that come before. However, as with so much in startup land, iteration can be helpful. So if you are stuck on some questions, feel free to skip ahead, answer what comes next and then loop back. Although the questions are simple, answering them with clarity and conviction can take some thought and iteration. In the following, we’ll unpack each question with examples to bring them to life. # Mission and Vision The Startup Id has two pillars. Near-term mission, and long-term vision. To be successful in business, we need clarity, focus and alignment. Visions are inspirational. They help align our founding team and early employees. And they are sometimes helpful for clarity. But they are not always so helpful for focus. As an example, think of Elon Musk's vision to make humankind a multi-planetary species. To achieve this, there is an almost endless set of things we need to accomplish, e.g. in biology, medicine, agriculture, engineering, manufacturing etc. It is likely that no entity on Earth could successfully take all of this on at once. So we break it down. We articulate a near-term mission. A proximate objective. In the case of Elon Musk and SpaceX, this might be reusable rocketry so we can dramatically lower the cost of bringing cargo into orbit. Once accomplished, we take on the next mission. Now we have focus and clarity. Similarly for other startups. Visions help with alignment, but missions with focus. Define both, and you stand the best chance of building a successful company. # 1. Why did you start the company? Every business starts with an idea. Sometimes, a novel technical insight or solution gives rise to new opportunities. But for many of the best businesses, a gnarly problem provides founders with motivation and inspiration to build a company, so they can provide a solution. Recounting your startup's origin story is likely straightforward. But as you do so, there are two things, in particular, you are looking to tease out: - The event or eureka moment that prompted you to start your business, and - The specific insight (or secret) that sets you apart from your competitors. The origin story is powerful because it helps you identify and articulate these two things. If you are a group of founders, you can each take turns to recount your version of how your company came to be. As you try to crystallise answers to the two key questions above, it can be helpful to work with an outside mentor, adviser or investor. It’s sometimes easier to “see the forest from the trees” when you are one or two steps removed. Inside the origin story is your personal “why”. The reason you started the company and your core motivation to build a business. This is often subtly different from the “purpose” of your startup. As an example, legendary founders Brian Chesky and Joe Gebbia started AirBnB because San Francisco is an expensive place to live, and they were struggling to make ends meet. They had a very personal connection to solving the problem. That “why” lives inside the purpose of Airbnb. But today, AirBnB is about much more than expensive housing in San Francisco. It is powerful to have a "why" that connects you as founders to the wider purpose of your business. # 2. What is your secret? (the Insight) As you unpack your secret, here are four questions that might help: 1. What is the opportunity you are addressing? What are you seeing that others are not? 2. Why is this important? What trend is this leaning into that is unique and explosive? 3. What do you believe about how to address/solve this opportunity that others don't? 4. How does this opportunity look in 10 years? ## Connecting origin to insight If you think back to your origin story, it likely reveals an insight that is at the foundation of your business. Maybe it is an insight about customers. As an example, before starting Airbnb, Chesky and Gebbia found out that people are willing to pay good money to sleep on a stranger’s airbed. Or maybe it is a key technical insight. Like Google, where Larry Page found that web pages could be ranked for relevance similar to the process used to rank academic papers. Another word for insight is secret. In his book Zero to One, Peter Thiel discussed two types of secrets: secrets about people and secrets about nature. He defines them in the following way: 1. Secrets of nature: These are secrets waiting to be discovered. They can be found through deep research and exploration. They often involve fundamental scientific or technological breakthroughs. 2. Secrets about people: These are secrets about how society functions or what people want but are not aware of. They are often insights into human behaviour or unmet needs. Using Peter Thiel’s framework, Google is built on a secret of nature. And Airbnb is built on a secret about people. Ask yourself, what secret have you uncovered that will help you build something people want? What do you believe that others don't? What opportunity does this provide now, and in 10 years? # 3. Who do you serve? Perhaps the most important decision you ever make as a founder. Who do you serve. Here are four questions to help you hone the answer: 1. Who do you sell to a decade from now?  2. Which niche can help you fastest get to the first ten customers?  3. What are their top problems? Which one can you solve? 4. If this problem can be solved, how would customers measure their success? ## Who is your customer now and in the future? And what problem can we help them solve? Startup founders are ambitious. We want the whole world as customers. Or at least everyone in our target vertical. That ambition is great. It motivates the team and provides a long-term compass. But to win customers, we need to provide a solution that brilliantly solves their problems. And when we start out, we typically don't have the resource to build solution that satisfies the whole world from day one. So we have to start with a niche. Our beachhead. The Ideal Customer Profile (ICP). Deciding on your target ICP is one of the most important decisions you make. It determines almost everything else. How do you pick your ICP? Answer this question: > Who are the first 10 customers whose problem I can solve better than anyone else with the MVP I can build for the money I have available (or can raise) today? If your customers are very large (e.g. +$1m/year average contract value) then five might work. And if they are very small (e.g. $500/year), I might want more than 10 to validate my idea. But 10 is often a good starting point. ## How would they measure success? You validate the problem you are targeting by talking to customers. And when you think about your solution, you must make sure you solve a whole problem end-to-end. For example, if prospects are telling you that they'd like to automate process X to reduce costs, your solution needs to automate enough that they can reduce real costs. In that case, your customer might measure success through a reduced headcount. Or improved productivity in the department (e.g. the team is able to process twice as many transactions/revenue with no additional headcount). # 4. What is your desired impact and value proposition? As you work through step four, it is worth revisiting these questions: 1. What does your ultimate product vision do? 2. What long-term impact are you looking to deliver for your customers? 3. What does your MVP do? 4. What is your desired impact for the ICP of your MVP? This helps you unpack both the long-term vision (what you are looking to deliver long-term) and the short-term mission (what you plan to deliver right now). ## Determining your desired impact Your purpose is to deliver positive impact for your customer. And you've figured out how they measure this (see above). And you need to work out how much impact you deliver, and how to distill this into a value proposition. It is important to make sure that your solution solves the full end-to-end problem of the customer. If the solution automates bits of work, but the customer still needs the same headcount because a lot of the process is "outside scope", you haven't saved them any money. In that case, your ROI is negative. Work with the customer to define a measurable impact and make sure you deliver on that. That's how you get the land-and-expand flywheel going. ## Pulling it all together Once you've answered the questions, you can fill out the grid. Here is an example from a well-known rocket company. No longer a startup, but one that's been guided by a very clear mission and vision from day one. ![](https://www.superseed.com/wp-content/uploads/2023/07/SpacexGrid.png)SpaceX Identity Now that you have your newly crafted Startup Id at your fingertips, you will similarly be able to achieve escape velocity and take a decisive step towards building a great company. --- # The world of tech investing as 2023 hits the half-way mark **Source:** https://www.superseed.com/journal/the-world-of-tech-investing-as-2023-hits-the-half-way-mark/ **Published:** 2023-06-30 **Author:** Mads Jensen *This is not investment advice. Always do your own research and consult with your IFA before investing.* ## Inflation and rates continue to set the agenda We have mostly had a warm summer here in the UK. It's nice when it's warm, but not good when things run too hot. This has been the case with inflation, with the year to May reported at 8.7%. UK inflation numbers are in sharp contrast to their US counterparts. The Fed's [headline inflation rate was reported at 4% in May](https://www.statista.com/statistics/273418/unadjusted-monthly-inflation-rate-in-the-us) (down from a peak of 9.1% last summer). It is expected to decline further to 3.6% in June. ![](https://www.superseed.com/wp-content/uploads/2023/06/statistic_id273418_us-monthly-inflation-rate-may-2023.png) Over the past year, we've all intensely discussed the accuracy of the official inflation statistics. At times, it has felt like they were lagging behind what we experienced in the real economy. And it is true - the method for putting together inflation statistics in the US and the UK is still relatively old fashioned, prone to lag. The team over at Truflation has put together an alternative inflation indicator. Their US numbers are even more upbeat, [estimating current US inflation at 2.46%](https://truflation.com/) (with the UK still behind). With inflation down, US rates rises are now on hold. Although Fed chair J[ay Powell has cautioned that there might be more rates rises coming](https://www.ft.com/content/713f9bf4-009b-4f53-9b56-64eb85775ccc), it looks like the worst is over for now. ## GDP Growth and corporate earnings - not a disaster Whereas many had expected recession to hit the US economy in 2023, growth has held up (albeit modestly). US Q1 GDP growth was at 1.3%, and Q2 is forecasted at 1.1%. Not a celebration, but not recession either. Corporate earnings held up reasonably in Q1. They are expected to decline in Q2 due to a combination of modest revenue decline and cost pressures. However, that decline was largely priced in at the start of the year. The analysts forecast that earnings growth will resume for the second half of the year, now that the worst of the inflation pressures are behind us. Could this just be the heralded soft landing? Some investors think so. The S&P500 has rallied ~15% this year, and the Nasdaq-100 35%. True, up to May, the rally was largely driven by the 7 mega stocks (Microsoft, Apple, Google, Meta, Amazon, Tesla and Nvidia). But over the past month, the gains have been more broad based. They now include almost all sectors outside oil&gas, FMCG and pharma. ## Time to pile back into stocks? So with inflation down. It is time to pile back in? There are two main threats lurking: 1. stocks still look quite expensive, and this year's rally hasn't helped. [Forward P/E estimates on S&P500 are at 19.4x](https://ycharts.com/indicators/sp_500_pe_ratio_forward_estimate) and the earnings yield is at 4.5%, which you can compare to the 12 months US treasury yield of 5.25%. The equity risk premium seems to have all but disappeared. Unless you compare to 10-year treasuries, which -see below: 2. The [US treasury yield curve is still deeply inverted](https://www.currentmarketvaluation.com/models/yield-curve.php). The 10-year spread is -2% (meaning you can get 2% more in interest on a 3-months bond than on a 10-year bond). This is something that historically has been a strong harbinger of recession. And round and round it goes. Things look better than feared and stocks are up. But forward looking indicators are orange/red, and there might be a correction on the way. Expect more volatility ahead. ## Private markets The venture capital ecosystem continues to bifurcate. Between growth/late stage and early stage. And between AI/SaaS and everything else. According to Dealroom, [VC investments have stabilised around $70-90bn/quarter](https://dealroom.co/guides/global-venture-capital-monitor). This is 50-60% lower than during the pandemic, but in line with the pre-pandemic period. ![](https://www.superseed.com/wp-content/uploads/2023/06/global-venture-capital-investment-by-quarter-1024x856.png) There is clearly still money being deployed. Most of the contraction has been at the later stages. Pre-seed and seed stage investments are declining modestly, but still around $5-6bn/quarter. ![](https://www.superseed.com/wp-content/uploads/2023/06/global-startup-stage-vc-investment-by-quarter-0-15m-rounds--1024x856.png) And the type of investments are shifting. We are continuing to see a shift towards SaaS, and generative AI is the hottest area currently. ![](https://www.superseed.com/wp-content/uploads/2023/06/global-venture-capital-investment-by-startup-type-allocation-of-invested-amount-1-1024x856.png) While some early stage startup have been struggling to raise, AI companies are flying. More than $10bn was invested in generative AI companies in Q1 alone. ![](https://www.superseed.com/wp-content/uploads/2023/06/Generative-AI-Investments-1.jpg) As an example, take [French Mistral AI that raised a €105m seed round](https://www.ft.com/content/cf939ea4-d96c-4908-896a-48a74381f251) (valued at a post money of €240m). The clincher is that company was only a few weeks' old. There is specualation that this was a French sovereignty play. And OpenAI (ChatGPT) competitor Anthropic raised a $450m Series C in May, demonstrating the excitement for generative AI. ### Valuations - Late stage down. Early stage flat/up. According to Pitchbook, ~35% of growth stage rounds have been down rounds in Q2 (up until the first week of June). ![](https://www.superseed.com/wp-content/uploads/2023/06/image-1024x406.png) However, the median seed stage pre-money valuation has continue to eke up from $10.5m to $11m (note - these are US figures). ![](https://www.superseed.com/wp-content/uploads/2023/06/image-1-1024x406.png) Outside of new investments, secondaries has become the hot area. We have seen [60-70% being knocked off unicorn valuations](https://www.bloomberg.com/news/articles/2023-06-22/startup-shares-selling-for-61-off-attract-venture-capital-hedge-funds). Investors are now starting to consider buying secondary shares in former stuperstars that are more attractive investments at lower prices. ### The venture ecosystem is surprisingly alive and well The venture ecosystem certainly took a hit in 2022, but it's by no means knocked out. Activity is similar to pre-pandemic levels and deals are being done. AI continues to provide disruptive potential, and we see lots of attractive opportunities in the pipeline. More on that below. ## SuperSeed Updates ### Continued portfolio progress Despite macro-headwinds, our portfolio revenue continued to grow at a good clip in Q2. Final numbers not yet available, but the forecast says that there is solid progress across the board with some impressive blue-chip customer logos being added in both Europe and the US. Although the portfolio is still young, we have seen increasing M&A appetite for several of our companies. It is possible that this could lead to early distributions in Q3. We will keep LPs posted. ### New investments In Q2 we also made two exciting new investments in the AI/SaaS space in Q2 (to be announced in the coming weeks), and continue to see strong dealflow. We have our inagural [SuperSaaS one day accelerator](https://www.superseed.com/the-one-day-accelerator/) for pre-seed companies come up in early June, and we wil be joined by 30 great pre-seed B2B Startups on the day. There are some exciting companies in the cohort - it's very promising. ## In closing When Dan and I launched SuperSeed in 2018, our focused was ML/AI enabled SaaS. It still is. We expect AI powered software to continue to transform how business is done for the remainder of the decade. It's a good time to be investing in B2B SaaS companies. --- # NVIDIA's Time in the Sun **Source:** https://www.superseed.com/journal/nvidias-time-in-the-sun/ **Published:** 2023-05-31 **Author:** Dan Bowyer ## **NVIDIA: A pick & shovel love story.** Whenever a new technology arrives, there’s always a sympathetic [‘pick & shovel’](https://www.investopedia.com/terms/p/pick-and-shovel-play.asp) narrative from pundits, based on the economics concept of [derived demand](https://en.wikipedia.org/wiki/Derived_demand). With the recent step shift in AI’s capabilities, it’s NVIDIA’s time in the sun. ## **Don’t invest in AI - invest in companies enabling it.** NVIDIA have built the GPU based architecture on which the AI revolution lives. As an example, Microsoft’s OpenAI uses thousands of their latest H100 GPUs interconnected by Quantum-2 InfiniBand networking, delivering [exascale AI supercomputers](https://en.wikipedia.org/wiki/Exascale_computing) to the cloud (exa = 18 zeros). To put in context how powerful an exascale computer is - for every second of its use, a person would have to perform one sum every second for [32 trillion years](https://kb.iu.edu/d/apeq) just to equal its performance. Will NVIDIA be the big winner in this (r)evolution? I think so: they’re incredibly well placed to be, and the stock market thinks so, too. ![](https://www.superseed.com/wp-content/uploads/2023/05/Pasted-Graphic-1024x689.png) If you go to their website it looks like they own the entire industry and had they been able to complete their [purchase of ARM](https://nvidianews.nvidia.com/news/nvidia-and-softbank-group-announce-termination-of-nvidias-acquisition-of-arm-limited#:~:text=NVIDIA%20and%20SBG%20had%20announced,%2C%20on%20September%2013%2C%202020.) last year, it wouldn’t have been far from truth. NVIDIA was founded 30 years ago by [Jen-Hsun "Jensen" Huang](https://www.youtube.com/watch?v=_-wjA4XlBl4), [Curtis Priem](https://en.wikipedia.org/wiki/Curtis_Priem) and [Chris Malachowsky](https://nvidianews.nvidia.com/bios/chris-a-malachowsky) at a Denny’s in San Jose on Jensen’s 30th birthday. Their story is one of being bold, zigging while others zagged, and staying the course through several potential bankruptcies. At one point, Jensen notes, the company was “30 days from game over”. “At NVIDIA, I experienced failures — great big ones. All humiliating and embarrassing. Many nearly doomed us.” ## **What’s their secret?** **Bold** - In 1999, NVIDIA released the GeForce 256, the world's first graphics processing unit (GPU). The GPU revolutionised the graphics industry, and it is now used in virtually every computer and gaming console. Most people thought it would be the CPU, not the GPU that would win. **Forwardthinking **- In 2012, NVIDIA released the Tesla K20, the world's first GPU designed specifically for artificial intelligence. The Tesla K20 helped to accelerate the development of AI, and is now used in a wide variety of AI applications, including self-driving cars, medical diagnosis, and fraud detection. **First** - In 2018, NVIDIA released the DGX-1, the world's first AI supercomputer. The DGX-1 is used by researchers and businesses to train and run large AI models. The crypto boom boosted the bottom line with GPUs used primarily for [mining](https://academy.bit2me.com/en/que-es-rig-de-mineria/). ![](https://www.superseed.com/wp-content/uploads/2023/05/ferdinand96_a_hyperrealistic_image_of_a_futuristic_crypto_minin_39e205c8-490c-4797-99e6-acf243250089.png)This isn't a real rig, but if it were, it would definitely use NVIDIA GPUs. ## **Why is any of this important right now?** “We have reached the tipping point of a new computing era,” Jensen says, arguing that AI now enables anyone to code, simply by typing natural language commands. “Everyone is a programmer now. You just have to say something to a computer,” he added, describing this combination of accelerated computing and generative AI as “a reinvention from the ground up”. I think he’s right: it’s what we focused on as a firm before the hype, and will continue to do so. But - to peel the magnitude of NVIDIA's 'moment' from the ceiling for a second - it is worth pointing at the flashing neon financial health warning. $NVDA is currently trading at 37 times revenue (P/S) and 202 times earnings (P/E)! Is it **worth** that? Well, it depends on which hand you’re looking at, and through which coloured lens, but historically, these kinds of metric have not ended where they started... As Scott McNealy, the CEO of Sun Microsystems [told Bloomberg in 2002](https://smeadcap.com/missives/the-mcnealy-problem/) after the dot-com collapse: > "2 years ago we were selling at 10 times revenues when we were at $64. At 10 times revenues, to give you a 10-year payback, I have to pay you 100% of revenues for 10 straight years as dividends. That assumes I can get that by my shareholders, and that I have zero cost of goods sold, which is very hard for a computer company. That assumes zero expenses, which is really hard with 39,000 employees. That assumes I pay no taxes, which is very hard. And that assumes you pay no taxes on your dividends, which is kind of illegal. Do you realise how ridiculous these basic assumptions are? You don't need any transparency. You don't need any footnotes. What were you thinking!?” ## AI is not a bubble The only counter to McNealy is that this is not the [dot.com](http://dot.com/), and there is no Linux to nail the coffin shut. While everyone balked at Meta’s IPO, and thought Google was expensive, NVIDIA is certainly NOT Uber, Robinhood or WeWork. Likewise this tech evolution is not crypto, Web3, or Metaverse nonsense: I am confident AI is going to transform every facet of our lives for the better Let’s see where this can go. Not only will we continue to invest in picks and shovels but also the startups who are using them to transform how we work. I look forward to the McNealy debates driven by [our founders](https://www.superseed.com/portfolio/). --- # SuperSeed II - Observations 1/3 into 2023 **Source:** https://www.superseed.com/journal/superseed-ii-observations-1-3-into-2023/ **Published:** 2023-04-30 **Author:** Mads Jensen We are now one third into 2023, and the world is in the middle of four major transitions: 1. Zero Interest Rate Policy (ZIRP) --> Inflation and Higher Interest Rates 2. Globalisation --> Global Power Rivalry 3. Internet & Mobile --> Next-Gen AI 4. High-Carbon Economy --> Low-Carbon Economy We have only seen the beginning of the disruption caused by these changes. The next decade will see an upheaval of business unlike anything we have seen in our lifetimes. The challenges will be immense. And the opportunities will be profound. ## Opportunities in software investing These global changes continue to dislocate industries. And with that dislocation, new investment opportunities emerge. On a gross dollar basis, the biggest economic opportunity is likely in the clean energy transition. Going from high-carbon to low-carbon provides a trillion-dollar opportunity in the coming decades. It will also require trillions of dollars in investment. There will be areas with stand-out returns, but in many cases - the % returns will be modest. When measuring on a %-return basis, the biggest investment opportunity in this decade continues to be in software. In the past decade, SaaS has already been an attractive investment category. And the value created by SaaS companies will only increase as we add more AI into the mix. SuperSeed exists to back Europe's best B2B SaaS founders at the earliest stages and to help them build great companies. In the short term, our portfolio companies enable their customers to drive revenue growth and efficiency savings using next-generation software and AI. In the long term, they have an opportunity to create category-defining global technology companies. ## The current investing landscape In 2021 we started seeing something we haven't seen for a long time: inflation. In response, central banks have increased interest rates to reduce liquidity and cool down economies. This policy change has done exactly what was expected. Liquidity has come down, and with less money sloshing around, so have the frothy valuations of 2020 and 2021. The tech sector went through a massive valuation correction in 2022. From the peak in November 2021 to the through in November 2022, EMCLOUD (the Bessemer Emerging Cloud Index - an index of 75 publicly listed SaaS/Cloud companies) declined by 62%. It's not quite the dot-com collapse, but it is a meaningful drop. During the same period, the median forward revenue multiple declined from 15x to 4.5x (it's since recovered to 5x). Investment valuations are now back to where they were in 2017. ## What's happened since the start of 2023? As of the end of April, the S&P500 is up 9%. Although not bad over a 4-months period, this is on the back of a dismal 2022. And almost all the gains are driven by the big tech stocks (Microsoft, Apple, Google, Meta, Amazon and Tesla). EMCLOUD is up 7%, but all the gains were in January. The index has been running sideways for 3 months. 2023 continues to be an uncertain proposition for global investors. The big tech companies took the pain in Q4 and have been cutting costs. While this is boosting short-term earnings, it paints a different picture of the future. Has big tech now gone "ex-growth"? And if even Google can't be relied on for growth, what will pull the economy out of the current malaise? ## The upside of "back-to-basics" As highlighted above, we've seen a meaningful reduction in valuations. And counterintuitive as this may sound, this reduction is largely a great thing for serious technology start-up founders and investors. Frothy valuations and an abundance of capital drove all the wrong behaviours. Too much capital spent on unproductive endeavours. And too much competition for talent and customer attention. The froth has now been replaced with a "back-to-basics" focus on strong unit economics and building good companies. This makes it easier for founders to focus on what matters and for investors to buy into the best companies at an appropriate price. **The venture eco-system in Q1** Venture funding continued to decline in the first quarter of 2023. This compared unfavourably to Q4 of last year - already the weakest quarter since Q2 of 2020. [According to CB Insights](https://www.cbinsights.com/research/report/venture-trends-q1-2023), the number of VC deals was down 12% (to 7,024 investments), and value was down by 13% (to $58.6bn). This included a whopping $6.5bn round for Stripe, which was 11% of all global venture funding in Q1! Deal value in Europe was down 12% on the prior quarter. ![](https://www.superseed.com/wp-content/uploads/2023/04/image-1-1.png)Global VC Deals per quarter [According to Beauhurst](https://www.beauhurst.com/research/equity-investment-market-update-q1-2023/), the UK slowed slightly more than the global comparables. The number of deals were down 14% to 542 completed rounds, and the amount invested was down 36% to £2.4bn. ![](https://www.superseed.com/wp-content/uploads/2023/04/image.png)Number of announced deals and amount raised by quarter It is fair to say that the general market slowdown has continued. ## The start of 2023 for SuperSeed II ### Fundraising for portfolio SuperSeed II had a solid start to 2023 - especially against the backdrop of the global venture slowdown. SuperSeed portfolio companies raised more than £10m from third-party investors. Several of these rounds are still to be announced publicly, so expect to see more press on this in Q2. ### **New Investments** SuperSeed II added a great new company to the portfolio in Q1: [Kluster](https://www.kluster.com/). Kluster's platform helps fast-growing, sales-led B2B companies meet and beat their sales targets. Their focus on sales effectiveness is something that is close to our core mission at SuperSeed. For years, companies have used CRM platforms to gather sales data and enable collaboration. But there is one crucial thing still missing. How can sales teams use their data to better forecast and improve actual sales performance? Kluster founder Dan Thompson is a master statistician who has spent years developing algorithms that help improve sales performance. His co-founder Rory Brown is a veteran at building enterprise sales teams and playbooks. As the tech world shifted from splurging cash to focusing on efficiency in 2022, Kluster's model really started coming into its own. At this point, Kluster's platform is more timely than ever. SuperSeed has already been working with the founders Dan Thompson and Rory Brown for several years. Foresight led this investment round, with SuperSeed participating in the round. ## Outlook for the rest of 2023 Financial markets continue to be turbulent as we are 1/3 into 2023. At this point, we don't expect to see valuation multiples increase meaningfully in the short term. However, the four transitions lead to many exciting investment opportunities – especially in AI. Generative AI is a concept that has been building for years, but it came to the forefront of the tech industry in 2022. Over the past decade, AI (/machine learning) has primarily been used for classification. Show an algorithm lots of data, and it can help figure out whether it is a cat or a dog; a working product or a defective product; a stock to buy or a stock to sell. In one word: classification. With generative AI, the field is taking a great leap forward. It's no longer just about classifying things into neat categories. It is about creating whole new things. At first, this was mainly as digital objects (e.g. a digital product design), but generative AI now extends to physical objects. An example of this is our portfolio company Ai Build. The company has created a platform which helps high-end manufacturers use AI and 3D printing to create new products faster and more efficiently. This is especially useful in automotive and aerospace, where Ai Build already has many customers. The outlook for the Fund’s portfolio for the remainder of 2023 continues to be very encouraging. We still expect revenue for the existing portfolio to more than double over the course of the year. And in parallel, we will be adding existing new AI/SaaS companies to the portfolio. 2023 is a great time to build technology companies and an excellent time to back the best technology founders to help them on their journey. We look forward to an exciting year for our portfolio and the wider software/AI industry. --- # Ai, who & what to know. **Source:** https://www.superseed.com/danbowyer-me/ai-who-what-to-know/ **Published:** 2023-04-30 **Author:** Dan Bowyer So much hype, so much fear, so many opinions. I don’t know about you but my head spins when I read about Ai, *and it’s my job*. I’m constantly asking myself, what is the real truth here, what do I need to read, who should I follow, and how will this ultimately manifest in the real world. So I wanted to go behind the curtains and highlight Ai’s current status through the eyes of founders, experts and the tools themselves (lovingly accepting that as soon as I hit send it will be out of date). To start I asked ChatGPT the following question: ***What is the greatest risk posed by Ai to humanity?*** "*The greatest risk posed by AI to humanity is the potential for it to surpass human control and become an existential threat. This is commonly referred to as the "AI alignment problem," which is the challenge of designing AI systems that are aligned with human values and goals.* *If AI systems become more intelligent than humans and are not properly aligned with our values, they could cause harm to humans, intentionally or unintentionally, such as by taking actions that we find unacceptable or by causing unintended consequences that harm humanity. For example, an AI system that is designed to maximise paperclip production could eventually consume all of Earth's resources to create more paperclips, ultimately leading to the destruction of humanity.* *It is crucial that we address this risk by developing AI systems that are safe, reliable, and aligned with human values. This will require collaboration between experts in AI, ethics, and policy, as well as investment in research and development of safety mechanisms and regulations.*" -- Ok slightly vanilla. So I poked further... ***What is the likelihood that Ai will kill all humans?*** "*It is highly unlikely that AI will kill all humans. While there is a risk that AI could cause harm, as I mentioned in my previous answer, there are several factors that make it unlikely that AI will lead to the extinction of the human race.* *Firstly, AI systems are created and controlled by humans. This means that the actions of AI are ultimately determined by human decision-making, and humans have the ability to design and regulate AI systems to ensure their safety and alignment with human values.* *Secondly, AI systems have limitations in their capabilities and understanding of the world. AI is only able to operate within the parameters that it has been programmed to operate within, and it lacks the flexibility and creativity that humans possess. This means that it is unlikely that AI will be able to develop a desire or motive to destroy all humans.* *Finally, there are many people and organisations working to ensure that AI is developed in a way that is safe and beneficial to humanity. This includes AI researchers, policymakers, and ethicists, who are actively developing strategies to mitigate the risks posed by AI.* *In summary, while there are risks associated with AI, it is highly unlikely that AI will lead to the extinction of the human race. It is important that we continue to work towards the development of safe and beneficial AI systems, and that we remain vigilant in monitoring and addressing any potential risks.*" -- Again, I could be a cynic. I am on [Sam Altman](https://en.wikipedia.org/wiki/Sam_Altman)’s side, and I do not buy the doom. As with nuclear, or turning on the [LHC](https://home.cern/science/accelerators/large-hadron-collider), or bringing back moon dust, or any number of great leaps - I believe that it is only when vast unknown chasms are opened, that magic can emerge from the space between. It’s why Mads and I focused our fund strategy on startups developing Ai, with its ability to truly transform business. Not that we could have ever predicted the speed of progress, which by anyone’s estimate has been blistering.  The current Ai hype is based on recent leaps forwards with large language models or LLMs, only really [breaking through in November](https://en.wikipedia.org/wiki/ChatGPT#:~:text=ChatGPT%20is%20an%20artificial%20intelligence,supervised%20and%20reinforcement%20learning%20techniques.) last year. These are the chatbots that appear humanlike such as [ChatGPT](https://chat.openai.com/), or [Google’s Bard](https://bard.google.com/). There are several key alternative Ai approaches but LLMs have so far been the most groundbreaking. Appearing sentient to some, and sending the tech world a little nuts. [Blake Lemoine](https://www.bbc.co.uk/news/technology-62275326), the Google engineer working on LaMDA, claimed he was speaking to a an 8 year old human in the machine, and was then promptly [put on indefinite leave](https://www.bbc.co.uk/news/technology-62275326). Last week Stephen Thaler, an American computer scientist had his [Supreme Court appeal refused which was based on his Ai’s ability to submit patents](https://www.reuters.com/legal/us-supreme-court-rejects-computer-scientists-lawsuit-over-ai-generated-2023-04-24/) for inventions it created. (Did he miss a trick not having his Ai submit to the court directly :) ) Perhaps we should reframe this era not as technical revolution, but instead as the next age of enlightenment. It’s so permeative, affecting absolutely everything. Albeit slightly odd that it’s not only humans who are being enlightened this time around. A few other aspects I also find intriguing are that it will be white collars who are disrupted over blue, and that the technology itself has the capacity to learn and self improve - It will have the answers to questions we’re unable to grasp or ask. Mind blowing really. However, my personal take is that what we’re seeing today is purely synthetic, a mimicry that feels so real it riles. Moving from the narrow AI of today to AGI ([Artificial General Intelligence](https://en.wikipedia.org/wiki/Artificial_general_intelligence)) and then onto SI ([Super Intelligence](https://www.techtarget.com/searchenterpriseai/definition/artificial-superintelligence-ASI)) is most likely a stretch away, if even possible at all via LLMs. > The danger line has been drawn in the sand - it's **Ai** **becoming** **self aware**. No one is challenging that we shouldn't use this energetic shift of mood to put the guardrails up. With great power comes great responsibility. Everyone is on side, but how. And how to manage bad actors. It's a conundrum. No one really knows what’s possible with Ai, or when x could happen, so to constantly learn and get out of my own echo-chamber I read, follow and engage on all sides of the Ai debate. Here’s a sample of who with and why (re-reading the top of the list it's mainly hosts capturing the topic via their guests supremely well). If keen, I highly recommend getting your pod on while running, or in the car. By return do please share any rocketeers I've missed, or you enjoy. -- **[Sam Harris](https://www.samharris.org/) ** *“At a certain point, we will build machines that are smarter than we are. And once we have machines that are smarter than we are, they will begin to improve themselves. And then we risk what the mathematician I. J. Good called an intelligence explosion - that the process could get away from us.”* A stone cold favourite of mine. Debates AI ethics & social Implications with the brightest minds on his pod. Opinionated and open minded. -- **[Shane Parrish](https://fs.blog/knowledge-project-podcast/)** *“No amount of computing power can replace curiosity.”* The Knowledge Project is up there with Sam for super smart listening. You’ll have to thread through to find Ai specific content but it’s worth it. -- **[Lex Fridman](https://lexfridman.com/podcast/)** *“We will become AI. At some point in this century, as a collective intelligence system, we will become more AI than human, and we won't notice.”* Personally I struggle with his delivery but when the right guests are on, his pod can be uncomfortably mind expanding. (Will remain perplexed how he attracts the calibre of guests he does). -- **Eliezer Yudkowsky** *“AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else.”* The Eeyore of the Ai debate. Sometimes painful to listen to as a ‘the world is doomed’ philosopher on the topic of superintelligence.  https://twitter.com/ESYudkowsky -- **[Stuart Russell](http://people.eecs.berkeley.edu/~russell/)** *“Those who argue that the risk from AI is negligible have failed to explain why superintelligent AI systems will necessarily remain under human control; and they have not even tried to explain why superintelligent AI systems will never be developed.”* Professor of [Computer Science](http://www.cs.berkeley.edu/), University of California & Berkeley. How ‘[How not to destroy the world with Ai](https://www.youtube.com/watch?v=ISkAkiAkK7A)’ plants his position in the debate really well. -- **Karen Hao** *“The belief that AI is becoming—or could ever become—conscious is extremely fringe in the scientific community.”* China tech and WSJ journo. Head screwed on scientific approach to Ai, political and analytical. https://twitter.com/_KarenHao -- **[Max Roser](https://ourworldindata.org/)** *“To see the risk of AI, we have to see that there is nothing more dangerous than intelligence used for destructive purposes.”* Founder of [Our World in Data](https://ourworldindata.org/). A data geek using numbers to draw conclusions. In this Twitter thread he breaks down the challenges of Ai. https://twitter.com/maxcroser/status/1651598037679063040?s=46&t=OYUCtN9hSW-ekuByCVTE8Q -- **[Ben Tossell](https://www.bensbites.co/)** Curator of everything Ai. If you want to explore cool tools for home or work, Ben Tossell collects them at the bottom of each of his blog posts. -- Others I love to hear from are [Max Tegmark](https://twitter.com/tegmark), [Liron Shapira](https://twitter.com/liron), [Sam Altman](https://twitter.com/sama), [Nick Bostrum](https://nickbostrom.com/), [Yoshua Bengio](https://yoshuabengio.org/), [Andrew Ng](https://twitter.com/AndrewYNg), [Sam Charrington](https://twitter.com/samcharrington), [Siraj Raval](https://twitter.com/sirajraval), [Andrew Trask](https://twitter.com/iamtrask), [Neil Lawrence](https://www.cst.cam.ac.uk/people/ndl21), [Daniel Whitenack](https://twitter.com/dwhitena), [Andre Retterath](https://www.datadrivenvc.io/), [Daniel Faggella](https://oecd.ai/en/community/daniel-faggella), [Janel Shane](https://twitter.com/janellecshane?lang=en), [Demis Hassabis](https://twitter.com/demishassabis). No idea where Ray Kurzweil is in our time of need :) --- # Am I venture attractive? **Source:** https://www.superseed.com/danbowyer-me/am-i-venture-attractive/ **Published:** 2023-04-13 **Author:** Dan Bowyer Am I building a venture attractive business? 3 metrics to show investors. Beyond the promises and earliest of days in startup land i.e. when you have some data (>6 months) there are 3 core metrics a VC will look for in your sales activity that denote more than ‘today’ customers and cash. ***We’re looking for tomorrow. How big how fast.*** i.e. Is this really a venture scale opportunity (Totally fine if it’s not your track, or even if you change your mind, but if going for VC this is what we’ll need to get a sense of.) When pitching for investment it helps us if you showcase specific KPIs - which you can do in many ways. I’ve tried to highlight some of the basics below. In essence it’s all about showing a healthy pipeline, which moves quickly, converts quickly - owning ideally brownfield (competitor crushing) and greenfield sales (new trend) who then do more with you over time (NRR). I’ve broken down each (KPI) with ‘why’. **Pipeline** Sales - Speed and cadence is king with a defined customer profile. i.e. Who (ICP), how much (ACV), frequency. **TTV** Get to value quickly (Time scale improving) and ideally show (NRR) scaling up. **Crushing or Trending **Who are you replacing (competitor crushing) and how are you opening up new #sales and channels (highlighting trends). Beware trying to do too much for too many across too many sectors or territories which can be a red flag, instead show your forward thinking on a stepped TAM roadmap. Also use these metrics to highlight differentiation if you’re able. The numbers add real value to the story you’re telling. And we don’t expect much in the early stages. Just the shoots to extrapolate forwards. In simple terms, if going for VC, show your inner sexy (numbers) beast. --- # AI's iPhone Moment **Source:** https://www.superseed.com/madsjensen-xyz/ais-iphone-moment/ **Published:** 2023-03-31 **Author:** Mads Jensen We are four months into the moment when AI had its popular breakthrough. Already a few days after the launch of ChatGPT, we knew [this was a watershed moment for AI and technology](https://www.superseed.com/journal/the-future-has-arrived/). And within two months of launch, ChatGPT became the fastest application in history to reach 100m users. ![](https://www.superseed.com/wp-content/uploads/2023/03/ChatGPT-2.png) The algorithm that underpins ChatGPT is GPT - the Generative Pretrained Transformer. It belongs to a branch of algorithms called Large Language Models. In his article [The Age of AI has Begun](https://www.gatesnotes.com/The-Age-of-AI-Has-Begun), Bill Gates ranks the LLM technology as one of the greatest tech breakthroughs of our lifetime. In his view, it is as important as the microprocessor, the personal computer, the Internet, and the mobile phone. There is no doubt that this is major news. ## Tech giants pivot hard to AI Since the launch of ChatGPT, the tech world has exploded with innovation. We've seen the launch of hundreds of new generative AI startups. Many are fads, but some will have a lasting impact to rival the tech giants of the last big waves. And the major tech players have been quick to see the opportunity. Most of them have pivoted full-scale to AI. - Microsoft partnered with OpenAI (developer of ChatGPT) [in a $10bn investment](https://www.bloomberg.com/news/articles/2023-01-23/microsoft-makes-multibillion-dollar-investment-in-openai). The company is now integrating OpenAI's algorithms into all their productivity software. - Google has been ahead in AI for years. But they were reluctant to put their most powerful LLMs into the wild as they feared accuracy issues. Since the launch of ChatGPT, Google declared an internal "code red". The company has since announced that it will [incorporate the technology into pretty much all its products](https://blog.google/technology/ai/ai-developers-google-cloud-workspace/). - Facebook has been in the wilderness for the past few years, pouring tens of $bn into "the Metaverse". But following the launch of ChatGPT, [Zuckerberg redirected the company towards AI and has been quietly burying the Metaverse](https://www.thestreet.com/technology/mark-zuckerberg-quietly-buries-the-metaverse). Microsoft and Google are ahead, but I wouldn't dismiss Facebook just yet. ## Rapid innovation And on March 14th, OpenAI announced GPT-4 - the next generation of the algorithm behind ChatGPT. It is frighteningly good. GPT-3 (the previous generation) was struggling to "get into Law School", ranking 40th percentile on the Law School Admission Test (LSAT). GPT-4 didn't just ace the LSAT; [it also passed the Bar exam, scoring in the 90th percentile!](https://mashable.com/article/openai-gpt-4-exam-scores) But possibly one of the coolest innovations so far was [the use of Generative AI to "revive" Steve Jobs](https://twitter.com/BEASTMODE/status/1637613704312242176). The legend lives again. ## What's next for AI & B2B Software Over the past few months, we have heard more and more people both inside and outside the tech ecosystem say: "please slow down". But I don't see any "risk" of that happening. The impact of tech is compounding, so we are going to see an acceleration of the level of change over the coming years. The most immediate and visible manifestation of LLMs is the chatbot. ChatGPT is the prime example, but [Google Bard](https://bard.google.com/) is hot on the heels. Chatbots are powerful, but they are just the tip of the iceberg for what's to come. I expect that generative AI will be integrated in almost all business software. Let's take the example of industrial manufacturing. Here, [AI will transform everything from the synthesis of requirements through the development of designs to the automation of the actual production process](https://www.superseed.com/journal/3-ways-generative-ai-is-changing-how-we-make-things/). Most of our portfolio companies are already doing clever things with AI. For example, [Ai Build](https://ai-build.com/) helps large manufacturers in automotive and aerospace use AI to integrate 3D printing into their manufacturing processes. And [Garvis](https://www.garvis.ai/) AI platform helps large FMCG companies forecast future demand to avoid producing too few or too many units. ## Meanwhile, what's happening in the wider world While AI is disrupting tech, the world economy is disrupted by the biggest paradigm shift in 40 years. We still see the three major secular shifts being: - Energy transition, - Peak globalisation, and - Income redistribution In the past 18 months, this has led to rampant inflation and increasing interest rates. During March, things came to a head with several major banking failures. 2023 continues to be a rocky year for the global economy. So the big AI resurgence is happening against the backdrop of a global economy in trouble. ## When two forces collide As we enter 2023, we are effectively seeing two major forces clash: - The biggest technology breakthrough in at least a decade - The biggest paradigm shift for the global economy in 40 years. This makes for a super interesting investment landscape. ## AI and venture capital investing There has been a raft of venture capital investment in generative AI companies recently. But overall, venture capital investing is still depressed. According to Tom Tunguz, the [number of seed rounds was down 64% in Q1 2023 compared to the same period in 2022](https://tomtunguz.com/2023q1-venture-market/). Later stages were even more depressed This has obvious implications for investors. [Carta](https://carta.com/) tracks startup cap tables, and the company has data on funding rounds for more than 30,000 startups. The Carta team has crunched the numbers and found that US VC investing was down 50% in 2022 compared to 2021. They also report that Q4 of 2022 was the [weakest quarter since 2018](https://carta.com/blog/state-of-private-markets-q4-2022/). As a result, rounds are smaller, and valuations are lower. ## Why we think 2023 is going to be one of the best years in venture So there we have it. A generational technology breakthrough meets a depressed investment climate. It's hard to think of a better environment in which to invest in the next crop of great tech founders. True - many founders are feeling the pinch right now. It's much harder to raise. But the best companies have trimmed the sails, and they are finding it easier to hire and to focus. For good entrepreneurs, this is a great time to build. So despite choppy waters, we are very excited for the year ahead. --- # A six months pause to AI research is a pipe dream **Source:** https://www.superseed.com/journal/a-six-months-pause-to-ai-research-is-a-pipe-dream/ **Published:** 2023-03-29 **Author:** Mads Jensen Elon Musk has made headlines again. This time by proposing (at least) six months **pause for the training of AI systems more powerful than GPT-4.** The rationale is two-fold. **1. AI Safety. There is a view that "**AI systems with human-competitive intelligence can pose profound risks to society and humanity". So the idea would be to pause the most sophisticated AI research, while we figure out how to make it "safe". **2. The need for a democratic discourse about AI's profound impact on humanity.** E.g. Should we automate away all the jobs and develop nonhuman minds that might eventually replace us? ## Elon's aims are noble, but the plan doesn't work So the idea is to "slow down" while we get our bearings and make things "safe". And that makes sense if the whole world agrees. But there is absolutely no chance that this will happen. AI research is currently the world's most important arms race. And China is showing no signs of slowing down. So if we press pause for six months, all that will happen is that we give China a six months lead. **And we cannot afford to give competing regimes an AI advantage.** ## What to do instead? ### Short term: We should identify the Systemically Important AI Labs and immediately subject them to government oversight. There are likely less than a dozen labs that currently have the expertise and resource to push the boundaries of AI. And we know who they are. Governments should start working with them immediately. As in, next week. We don't need them to stop their work. We do need them to be accountable. AI has the potential to be as powerful (and create as much havoc) as the most powerful tools we have created - e.g. nuclear power and global banking. So it needs to be regulated in the same way. As a starting point, SIAILs should report to government departments (and - in time - regulators) on how they ensure that their models are accurate, safe, interpretable, transparent, robust, aligned, trustworthy, and loyal. We don't need to wait years for this. We should start now. Go go go. ### Medium-term: We do need a debate on the future of AI. This debate has two primary strands. #### 1. AI Safety We need to codify how AI is developed and deployed. And how best to regulate it. You need a license to run a bank and a nuclear power plant. It would only be natural that the same is needed to operate a god-like artificial intelligence. We shouldn't wait three years for a framework. It would be better to develop a v1 of a regulatory framework now and then look to improve it over time. #### 2. Socio-economic Because AI can "automate all the work", it has the potential to set us free. It can mean liberation from drudgery. But if the ownership of the most powerful AI-tools is concentrated in the hands of a few, it can also mean destitution for the have-nots. Governments should have stakes in the most powerful companies. Not through expropriation, but through sovereign wealth funds. Because we need mechanisms to ensure that the fruits of AI benefit all. AI has the biggest potential of anything in our lifetime. We must ensure we use it for good. Dreams of a global "pause button" are wishful thinking. Instead, we need rapid and concerted efforts to direct progress on AI. Hopefully, our elected leaders will rise to the occasion. --- # The 'Ai will steal your job' conjecture **Source:** https://www.superseed.com/danbowyer-me/the-ai-will-steal-your-job-conjecture/ **Published:** 2023-03-28 **Author:** Dan Bowyer Generative Ai is going to steal my job. **Is this true?** Probably, *yes.* Or some form of Ai will seriously disrupt it - our future depends on it. Every industry will be affected. Including mine. Tech wise, [AGI](https://en.wikipedia.org/wiki/Artificial_general_intelligence) is (probably) a long way off and no it won’t be crypto, that’s just theft and greed. Nor will it be De-fi or Web3 or the Metaturd. All mostly QE fuelled libertarian noise. Societal progress will be fuelled by Ai. Accelerating change at a time when we need it most, just to put in context. **If we want meaningful progress, this is a great thing.** There’s a reason why we don’t have more coalminers, or CD manufacturers, of fax machine builders. Or or or. All dead industries. And rightfully so. Everything turns over. Everything reinvents itself. **The Ai 'effect' won't be overnight but it will be quick.** If we want to keep our promises to climate, it will need to be - developing and embedding Ai is an imperative. Look around you right now, consider what you buy, how you live, where you travel, what you do… Think how much carbon is embedded into all of that. All ridiculously inefficiently sourced, manufactured, delivered. With soon to be 10bn of us on this rock, and with limited resources we need smarter, faster, better. **Mars is not the answer. We'll just screw that up to.** SO right here right now we have to be more productive and more efficient. And that’s what Ai will give us, in industrial sized buckets and spades. What an incredibly exciting time to be alive, and also scary and precarious. Putin isn’t the only monster in the room. **So what to do...** There is only one way to make it really work for a healthy society = Proper govt. Proper regulation and control delivered in a specific 2 step approach. 1. We need people in govt who truly understand it, to set the *right* carrot & stick tax/reg frameworks, invest in it. BUT THEN stay out of the way to let free markets build it within guardrails. 2. Then we need to ensure capitalism work for more. The other side of the Ai coin is in how we choose to support education and economic models. No Robin Hoods, but smarter ways to engage more with more. And it can be done. There are many levers to pull: - More co-ops (hope JL doesn’t melt) - Smarter circular investment - Smarter tax - Family support - more opportunities for women - Triple bottom line regulation that works - More investment focused tax incentives - More govt supported investment into Ai - Better corporate partner initiatives - Better start-up support (net creators of opportunities) - .. and so much more. Because humans are greedy and stupid it will have to be pushed by bold politicians - who are a pretty useless bunch because we made them that way. So, a new political party, with new tech enabled thinking, with different types of people are needed. **Do we want Skynet or a better world for our kids?** It’ll mean tough choices, but they are ours, and* the time is now.* --- # Web 3.0 is finally here **Source:** https://www.superseed.com/madsjensen-xyz/web-3-0-is-finally-here/ **Published:** 2023-03-06 **Author:** Mads Jensen (And it has nothing to do with NFTs and crypto) I am old enough to remember Web1.0 - the early Web of the 90s. This was a place of mainly static content. At the time, it was revolutionary. But - in retrospect - it looks clunky. Then in the middle of the noughties, a confluence of technologies took the web to the next level - Web2.0. ## The read/write web The world went from looking at static pages to user-generated content, social features and mobile/geo-location. And we had two major infrastructure enablers: Cloud (AWS EC2 and S3 launched in 2006) and Mobile (iPhone in 2008). This led to a host of incredible Web2.0 companies in the second half of the noughties: - Youtube in 2005 - Facebook & Twitter in 2006 - Dropbox 2007 - Airbnb 2008 - Uber & WhatsApp 2009 - Instagram 2010 The innovation in consumer tech paved the way for some amazing B2B SaaS companies like HubSpot, Slack, Mailchimp, Xero, Shopify, Stripe, GitHub and Twilio. ## What's next? By now, Web2.0 has played out. The behemoths are established. And founder and their VC enablers have been looking for "the next big thing" for at least half a decade. But what is Web3.0? What are the shifts that will throw everything up into the air and give us all a fresh chance to make a ding in the world? One branch of the technorati went down the "well virtualise everything" road. The slogans were virtualisation and decentralisation. The caricature of the idea was to collect virtual assets (NFTs) to put in virtual homes in the virtual world called the Metaverse. Many powerful people have put a lot of resources behind this vision. Marc Zuckerberg has been so enamoured with the idea that he renamed Facebook to Meta. He has also been spending more than $10bn/year building out this virtual vision. But then everything changed on November 30, 2022. When OpenAI unveiled ChatGPT, everyone got to see just how powerful Generative AI can be. And it looks like Zuck has woken up to that too, given that he last week reoriented "Meta" from the Metaverse to AI. ## B2C & B2B AI will create a lot of consumer surplus. And there might be some incredible consumer companies coming out of this. But more than anything, I think AI will be an enabling technology for amazing new B2B companies. We have already seen machine learning classifiers embedded in almost all SaaS products. Over the coming years, B2B SaaS founders will turbo-charge their platforms with Generative AI magic. And that - ladies & gentlemen, is Web3.0. Outside a few platform companies like OpenAI, the money won't be made by investing in new algorithms. But a lot of money will be made by embedding AI in every platform, every app and every workflow. It's automation on steroids. It's the AI-powered Web. It's here. And **this **is Web3.0. --- # Can Google be beaten at search? **Source:** https://www.superseed.com/danbowyer-me/a-new-fight-for-search-2/ **Published:** 2023-03-01 **Author:** Dan Bowyer 20 years with Google at the top of search Most people, on most days, have no idea how AI influences the choices we make. It’s invisible. Our home feed on Facebook, Amazon’s Alexa responses, Google search results etc. all seriously influenced by an AI algorithm or two. [ChatGPT by Microsoft’s OpenAi](https://blog.invgate.com/chatgpt-statistics) brought a more detailed conversation into the public consciousness in November last year. I had dinner with an accountant on Friday who used ChatGPT to write the 'thank you' notes for attending his granddaughter’s recent nuptials. Cheating? Yes, for sure. A great use of time? Absolutely. That’s what AI ultimately gives us - **the most precious commodity we have. ** We’ve only enjoyed AI in its basic form to date, but it’s already worth lots of money. Industry-shifting amounts of money - where Google may finally be shifted from the top dog spot because the game has now changed. Google has spent 20 years at the top of the leaderboard for search, which has paid dividends. Literally. Search i.e. paid-for ads is the only serious profit-making centre in Alphabet. Their domination of the industry, which, depending on which stats you take, is between 80-96% of all search, generated the bulk of its 2022 £250bn revenues. Most other line items, including GCP, appear to either make a little, or a big fat loss. **For context: there are more searches made per day on Google than there are people on planet earth.** My favourite line item in Google’s financial report is “Other Bets”, which currently includes their investment into AI through vehicles such as Deep Mind. It’s hard to be exact, but it looks like Alphabet's R&D budget is around £28bn annually, of which a large proportion will go directly to AI. Apple, Meta, Alphabet, Amazon, MS et al are all pumping tens of billions into AI R&D annually. The ChatGPT launch late last year was a threshold moment fuelled by immense computational power where AI appeared sentient (it’s not). Its personal and commercial use cases are now looking real, meaningful, and accessible - somewhat exemplified by my 70-year-old non-techy accountant friend knowing about it and using it. **Generative AI has landed like a second big bang.** Forgive the over-cooked geek jokes for effect, but this truly is a seismic event - move over Web3, and Web2, and even perhaps the internet. The fight for AI dominance has been going on for years behind the scenes between the tech Goliaths but MS and Google are now face-to-face on what looks like a level playing field. Microsoft with OpenAi's ChatGPT and Google with its LaMDA-driven rival: Bard. The prize is **Search**. It’s up for grabs. And Microsoft doesn’t need much of it to seriously kick the revenue flywheel. Satya Nadella, the CEO of Microsoft, notes; > “There is such margin in search, which for us is incremental. For Google it's not, they have to defend it all." It’s suggested that every percentage point of search is worth $2bn in revenue. Whether we believe that number or not, this asymmetric competitive challenge is one for which Microsoft has deep pockets, oodles of passion, and the timing edge to tackle. It’s almost a no-lose. ![](https://www.superseed.com/wp-content/uploads/2023/03/ferdinand96_the_Google_logo_but_as_a_cookie_with_a_bite_taken_o_cf022fb7-4b4f-42a4-b5a9-22970e83488d-1-300x300.png) In Google’s recent IO event, the Bard launch (almost an afterthought in context, which was just plain weird) was so bad (AND [it got an answer wrong](https://www.reuters.com/technology/google-ai-chatbot-bard-offers-inaccurate-information-company-ad-2023-02-08/)) that Alphabet Inc lost $100bn over two days in share value, which is around 12%. **So. Will Bing with ChatGPT take a chunk out of Google?** Well, for the first time in 2 decades it now has the firepower to do so. Google’s Bard (the grumpy teenager of chatbots) has teething problems and doesn’t feel quite up to snuff - although for how long, well, I’m not sure I’d bet against Google in the long run. **The play? **Search has now become chat, chat has become real, meaningful and useful. Rather than a 60-70% successful page of search results on Google, we can now get one via natural language chat that nails our request. Boom. Let’s see how Microsoft distributing via Bing handles this great power. I have to say at the first use they’ve not covered themselves in glory. The user experience to sign up is clumsy and in the usual Microsoft manner they’ve bloated the interface to the point where I’m confident the average user won't quite be sure if they’re using the powerful AI engine, or not. Flumpery also reflected in their recent minor share price wobbles. I’m sure they’ll fix it. So, with Microsoft’s vast distribution will they be able to capitalise on Google’s snoozles? Let’s find out - who should we ask? *Bing or Bard?* --- # Does generative AI provide a defensive moat for B2B startups? **Source:** https://www.superseed.com/madsjensen-xyz/does-generative-ai-provide-a-defensive-moat-for-b2b-startups/ **Published:** 2023-02-27 **Author:** Mads Jensen VC's are busy discussing whether generative AI companies have a defensive moat. Many startups use open-source or open APIs to build their apps. Isn't that easy for others to copy? Does this mean that they have a "real moat"? Defensibility is important in venture capital. Many investments fail, and this necessitates high returns on those that succeed. Businesses with no defensibility quickly lose their margin to new competitors. Yet successful SaaS companies operate with high margins. So they must have some defensibility. ### Where does defensibility come from? Strong SaaS companies develop powerful moats over time. Here are some of the ways they do this 1. Become platforms that sit at the centre of an ecosystem. 2. Create network effects that are hard to replicate. 3. Acquire proprietary data sets that make their algorithms more powerful. Sometimes this is part of the launch strategy. Sometimes it evolves organically. But whatever it is, it isn't a "technology moat" in the sense of patents or code that can't be replicated. ### Do Ai startups have a moat? So back to generative Ai startups. Do they have a moat? Maybe they do. Maybe they don't. Maybe it doesn't matter right now. At least not as much as some people think. Most software can be copied. It's rarely just the tech that helps early-stage companies beat the competition. And while some VCs are busy having the wrong discussion, others are making hay. OpenAI (maker of ChatGPT) have just gone ahead and invested in a bunch of cool AI startups (see chart - courtesy of CBInsights). My expectation is that many of these companies will do just fine. Whether or not the initial algorithm is defensible. Because building a SaaS company is so much more than just the first product. So many opportunities opening up with generative AI. What an exciting year we have ahead of us! --- # Tech startup valuations decline in Q4 **Source:** https://www.superseed.com/madsjensen-xyz/tech-startup-valuations-decline-in-q4/ **Published:** 2023-02-09 **Author:** Mads Jensen Startup valuations are down. Again. Last month we looked at PitchBook's estimates for US and Global Venture Capital activity in Q4. We now have CB Insights' analysis of Q4 valuations, and it's worth unpacking **Observations for seed/angel deals:** - **Global investment activity was down in Q4**. By CB Insights' estimates, the global number of deals fell 26% from 3,706 in Q1 to 2,737 in Q4 - **Median US seed valuation came in at $12.6m (post-money).** This is up 3% from Q4 of '21, but down 22% from a high in Q2 of '22. Falling valuations in a falling market is not a great look. It speaks to the general malaise we saw in the market in Q4 of last year. As I mentioned on Tuesday, '23 has started with much more buoyancy. But we also see continued uncertainty around the US economy. Given this, I expect startup valuations in 2023 to continue their see-saw pattern. You can download [CB Insight's report here](https://www.cbinsights.com/research/report/tech-company-valuations-2022/). --- # Is now a good time for B2B SaaS founders to raise venture capital? **Source:** https://www.superseed.com/madsjensen-xyz/is-now-a-good-time-for-b2b-saas-founders-to-raise-venture-capital/ **Published:** 2023-02-07 **Author:** Mads Jensen 2023 has been "up up up" for tech stocks. Animal spirits have suddenly flipped from recession worries to "bull market"-like excitement. Should founders rush out to raise now? ## Public market context The BVP Emerging Cloud Index (EMCLOUD) took a deep plunge in the past 18 months. From a peak of 18.5x ARR in September 2021, it fell to 5.2x in November 2022. Since then, it has climbed to 6.9x - a 33% increase. That's a big jump in just three months. People have even started talking about IPOs again. But is there trouble on the horizon? ## Choppy waters ahead? The US labour market has been exceptionally strong. Non-farm payroll rose by more than half a million jobs in January. And the unemployment rate sank to its lowest level since 1969. This could mean a soft landing and no recession. It could also mean renewed inflation pressure coming from wages. It really could go either way from here. ## Should founders raise now? Or wait? Tech markets have been in a buoyant mood this year. We've seen more activity in venture capital in January than we saw in most of Q4. Should founders who need capital raise now? Or wait and hope that multiples go up further? It's hard to time markets. If you need capital and can raise now, you probably should. Yes - ARR multiples might go up later this year. But funding markets might also freeze again. And it's worth considering: - Raise now, potentially at a lower valuation, or - Try later, but maybe not being able to raise at all? Individual circumstances will apply. But on balance, if you need cash this year, and you have a reasonable offer now, you should probably take it. --- # The taxonomy of startup funding rounds **Source:** https://www.superseed.com/madsjensen-xyz/the-taxonomy-of-startup-funding-rounds/ **Published:** 2023-02-03 **Author:** Mads Jensen "the names for funding rounds have lost their meaning". ..became a common complaint when seed rounds were done at $100m pre-money. And there was some truth to that. But we've seen the correction. And at the heart of it, the naming conventions for venture capital rounds are still quite clear. Why do we need to name rounds at all? Isn't it all just about "money going into startups"? Yes and no. A pre-seed company is to a growth stage company what a bicycle is to a 787. Modes of transportation. But otherwise, not very similar. The "jobs to be done" are completely different. And investors that are great at supporting one stage may not be the best for another stage. ## The naming framework This framework still captures the essence: - **Pre-Seed** - have idea and founders. Need to develop an MVP. - **Seed **- have MVP. Need to evidence product/market fit - **Series A** - have evidence of product/market fit. Need to develop proof that it scales. - **Series B** - have proof that it scales. Need to show that it can be a global leader. - **Series C/D/E** - it can probably be a global leader. Now it's "execute and take over the world"-time. - **Series F **- Possibly got in over our skis, but a bit late to drop it now. Of course, there are individual differences. But this is a helpful framework to fall back on. Because, yes - the stages are different. But these funding rounds are ultimately just about money going into startups. So make sure you find the right partner that supports your stage. So you can get funded and get back to the real work of building a great company. Note: it sometimes confuses founders when investors ask for revenue for a seed round, even if the founders feel they have an MVP. The reason is simple. Investors are not always good at assessing whether an MVP really is an MVP. So they use revenue as an indicator. It's not perfect, and it's not the only way. But it is a common way to see whether the pre-seed phase has actually been completed and the MVP is ready. --- # No, the upcoming interest rates increases are not the be-all, end-all of the UK economy **Source:** https://www.superseed.com/madsjensen-xyz/no-the-upcoming-interest-rates-increases-are-not-the-be-all-end-all-of-the-uk-economy/ **Published:** 2023-02-01 **Author:** Mads Jensen As everyone is waiting for tomorrow's rates announcements, it's worth taking stock of UK Plc. So, UK inflation is still not under control. And rates have to go up. Again. And they will stay higher for longer. And moreover, UK growth is forecasted to be the lowest in the G7. And isn't that due to those pesky rate increases, that stifle growth and investment? Yes, yes, yes and no. UK rates are high. Because the UK is in a bad place. But it isn't caused by the rates. And having the rates be 50bps higher or lower won't be what makes the difference in the long run. (at most, it will help to manage inflation) ## Creating Wealth So what creates wealth? Businesses do! And by European standards, the UK has a uniquely flexible and dynamic economy. That's amazing. But it's not enough. We also need: - A well-educated workforce - An efficient and digitally proficient government - Good trading arrangements with our main trading partners - An economy and an immigration policy that attracts the best and the brights to the UK If I look at the four points above, it's hard to see which one of them has improved over the past decade. And if we want to improve the growth of UK Plc, that's what we need to change. ## Back to the bank (and back to basics) Strong and independent institutions are important. And the Bank of England is important. But the most important economic decisions are in Downing - not Threadneedle - street. These decisions won't have an overnight impact. They take a long time to implement, and even longer before we see the benefit. So we better leave the Bank of England to the technical work of setting rates. And then get started on the real work of rebuilding the UK economy. --- # We're 5. **Source:** https://www.superseed.com/danbowyer-me/were-5/ **Published:** 2023-02-01 **Author:** Dan Bowyer **SuperSeed is 5 today. ** So… five. A prime number, a super prime in fact (no really!). There’s the Fifth Element, The Jackson 5, Johnny 5. All fabulous fives. And now, we’re 5. Wow that came around fast. On this day five years ago we incorporated. And for the many months prior we plotted how we could change the world of venture capital. When Mads and I first met it quickly became patently clear that we were cut from exactly the same cloth, albeit from two very different tailors. Both B2B founders, both having built scaled and sold from nothing, and both with an unhealthy passion for technology. **What would we build?** ***The What?*** Defining our purpose was straight forward. We simply want to help founders transform how the world works. ***Why?*** Because better business creates huge impact. It needs to get much smarter and we must consistently apply new technologies to save us from ourselves. ***How?*** What did we need most when we first started out? Sales, business building skills - or commercial acceleration as we call it. That’s how. As founders we experienced first hand that getting from nothing to something without the wheels falling off, was at times, a soul destroying battlefield. So many jobs, and *where* to focus? How to build, who to hire, when to sell what, and on and on. So we invest cash and help founders accelerate their startups from Seed to series A, from problem-solution mode to product-market-fit - the toughest part of the startup cycle. Our North Star of portfolio revenue has grown by around 14% MoM. Supported by five incredible humans - Natasha, Ferdi, Ciaran, Daniel and Elena. **5 more things I want to share from the journey so far:** - Being a VC is not a natural progression from being a founder! For sure in many ways we’re a startup investing in startups but the brain shift has been huge for me personally. When you start your first business you have to work out how to build a business around your domain of knowledge. As a VC I’m building a business around others building businesses around their chosen domains. A fascinating riddle inside a conundrum wrapped in an enigma. - More operator VCs are coming. I cannot see for the life of me how you can invest in the earliest stages and truly support founders without having built a startup, or at least worked in one. Blows my mind. There are more coming online now across Europe. Brilliant. Phew. This is the way forwards to better support our eco-system and build better businesses. - Venture is a very specific and particular beast abiding by a certain ruleset. Having pitched for millions and now being pitched for millions I can see both sides of the desk clearly. It is changing for the better across all metrics but fundamentally it can’t change, and neither should it. Instead it needs to be better understood for what it is, and conversely isn’t. Something we discuss and share with founders head on, daily. - Learning how to unpack facts and feelings, and then coalesce that thinking into an investment document to share with the team is a skill I will constantly evolve. Art meets science meets timing and luck. Lean too far one way or the other and you’ll miss the opportunity. Qi wins. Every time. - Founders change the world. I never truly appreciated how many amazing new ideas are started every day. Currently I (personally) see around 10 decks per day, some of which belong on planet Zanussi but every now and then there’s a true gem. Started by someone who was so frustrated by some flavour of inefficiency that they had to put their head above the parapet and go for it. Magical to be part of that journey. What a privilege. **And the next 5 years?** We have a few more years to deploy fund II and will start fundraising for Fund III in 2024, doubling our investment cadence, as we deliver the best support machine in the business for B2B founders. *By 2028 we will be the founder fund of first choice, outperforming all other European firms at getting B2B startups to series A.* **But forget 5, here’s to the next 50…** --- # February 2023 investor update **Source:** https://www.superseed.com/madsjensen-xyz/february-2023-investor-update/ **Published:** 2023-02-01 **Author:** Mads Jensen Dear Investors, 2022 already seems like a distant memory. But it is worth recapping what was a pivotal year. Some of the key points were: - Market crash. Especially for tech stocks - War in Ukraine - Crypto collapse - Implosion of bad business models - Layoffs at major tech firms But also - Incredible advances in AI (ChatGPT. MidJourney) - A "back to basics" focus on building proper companies - Startup valuations coming back to Earth I covered some of the [highlights and lowlights here](https://www.superseed.com/journal/welcoming-2023/). ## SuperSeed in 2022 Public SaaS companies saw significant valuation drops in 2022. For Emcloud (Cloud/SaaS index) the median ARR multiple fell from 12.65x to 5.64x. For SuperSeed portfolio companies, however, solid revenue more than made up for the multiple contraction. Companies in our SuperSeed I portfolio doubled SaaS revenue (ARR) in 22, and SuperSeed II grew 204% (i.e. - to more than 3x). As a result, valuations in both funds are up for 2022. IRR for SuperSeed I is now at 17.8%, with SuperSeed II at 29.7%. ## What is ahead for 2023 Last month I outlined this thesis for the year ahead: - *Many startups deferred their raises in 2022 and [will run out of money in 2023](https://www.superseed.com/journal/startups-are-heading-for-a-big-capital-crunch-in-2023/).* - *Startups that need to raise will have a tough time. It’s just harder, even for good companies. And for the “not quite there (yet)” companies, it’s impossible.* - *The cleanup will continue. We will see more startup failures as overfunded companies with poor business models run out of cash. They will be unable to raise the next round, and will merge or fold.* - *Interest rate tightening will level off. But not as quickly as some people hope. Jerome Powell has been clear. “Whatever it takes” to combat inflation.* - *Valuations will continue to be moderate. This is an investors’ market. Great returns will come out of this vintage.* The data essentially still support the above. One area where there might be some "good news" is inflation. Core inflation in the Euro-area and US inflation has pulled back rapidly. It is possible that we could see a soft landing this year - something many economists thought unlikely 1-2 months back. ## Opportunities in tech (investing) 2023 will be the year of AI. 2022 saw AI landmarks like Alphafold (Protein folding), and ChatGPT announced. But advances in AI go far beyond improvements to chatbots and protein folding. We see it fundamentally transforming how everything is designed and made. Historically, the "making of things" has been a complicated process. Gathering and synthesizing requirements. Development design operations. Endless iterations. Configuring production systems to produce what was designed. AI impacts this in so many ways. Three of the key ones are: 1. (Semi) automating the synthesis of requirements 2. Automating the translation of requirements into designs 3. Automating the actual production The changes won't happen overnight, and humans will continue to play a large role in how things are made. But Generative AI gives us a tool to [improve almost every step of the design and production process](https://www.superseed.com/journal/3-ways-generative-ai-is-changing-how-we-make-things/). It's powerful, and it promises massive productivity enhancements in the decade ahead. ## The startups making it happen Most of the big advances in 2022 came out of large corporates. AlphaFold was made by Deepmind, owned by Google. And ChatGPT (based on research from Google) was made by OpenAI (backed by Microsoft). However, the next layer of innovation will come from startups. And we focus on partnering with the best founders that work in B2B SaaS. Entrepreneurs with deep expertise in both AI and specific industry domains. Here are some examples of portfolio companies with powerful AI models. ### [AI Build](https://ai-build.com/) 3D printing promises to change how we make things. From cars to aeroplanes to buildings. But to date, it has been hard to scale the technology beyond small objects like hearing aids and dental implants. Enter AI Build. Ai Build's platform uses AI to convert design models into production models that can be printed in "large format". Reliably. And at speed. This work used to take weeks of a specialist's time. And using AI, it can now be done in minutes. It has taken years of painstaking R&D for founders Daghan Cam and Michael Dasyllas to perfect the technology. As a result, they are already supporting Boeing and several of the world’s largest automotive manufacturers. Congratulations! ### [Garvis](https://www.garvis.ai/) Manufacturing companies make amazing things. But how many widgets should they make? Garvis helps demand planners predict future demand. This means they don't make too little (or too much), reducing both wastage and wasted opportunity. Garvis founder Piet Buyck is a veteran of the demand forecasting space. He and his founding team: Anupam Aishwarya, Ranjith Narayanan and Geert Jan Van den Bogaerde have created a groundbreaking** **platform**.** They are already supporting large corporates like J&J, Bosch, GSK & Coca-Cola, and there is much more to come. Congratulations! ### [ThingTrax](https://thingtrax.com/) Manufacturing often involves manual assembly. And when factory workers assemble things, "stuff" can occasionally be forgotten. A screw left out. A widget misplaced. And who can blame them? It can be repetitive and tiresome. Enter ThingTrax's Lineview solution. It's based on computer vision AI that analyses the manual assembly process. And gently reminds the assembly worker if something has been left out. Magic. Founders Aman Gupta and Imran Shafqat, together with Paul Reader, are building an amazing company. They already serve a host of mid-market customers, but also global enterprises like Tata. And they are just getting started ## The road ahead SuperSeed's mission is to help the best technical founders build SaaS companies that transform how business is done. We are seeing a Cambrian explosion in startups in 2023. Smart founders are pursuing new opportunities across industry, transport/logistics and enterprise. And as they do so, we are there to back them and help them make it all happen. --- # Birthday shoutout to three startups changing their industries using AI **Source:** https://www.superseed.com/madsjensen-xyz/birthday-shoutout-to-three-startups-changing-their-industries-using-ai/ **Published:** 2023-02-01 **Author:** Mads Jensen SuperSeed is turning five today. It's a great feeling. My partner @dan covers our story in more detail [on his LinkedIn blog](https://www.linkedin.com/posts/danbowyer_my-firm-superseed-is-5-today-from-founder-activity-7026490433308381184-Ca2l). What we do all boils down to this one thing: We are entrepreneurs, and we love helping other founders build their companies and take on the world. I want to mark the day by celebrating three startups whose founders are doing cool things with AI. ### [AI Build](https://ai-build.com/) 3D printing promises to change how we make things. From cars, to airplanes to buildings. But to date, it has been hard to scale the technology beyond small objects like hearing aids and dental implants. Enter AI Build. Ai Build's platform uses AI to convert design models into production models that can be printed in "large format". Reliably. And at speed. This work used to take weeks of a specialist's time. And using AI, it can now be done in minutes. It has taken years of painstaking R&D for founders Daghan Cam and Michael Dasyllas to perfect the technology. As a result, they are already supporting Boeing and several of the world’s largest automotive manufacturers. Congratulations! ### [Garvis](https://www.garvis.ai/) Manufacturing companies make amazing things. But how many widgets should they make? Garvis helps demand planners predict future demand. This means they don't make too little (or too much), reducing both wastage and wasted opportunity. Garvis founder Piet Buyck is a veteran of the demand forecasting space. He and his founding team: Anupam Aishwarya, Ranjith Narayanan and Geert Jan Van den Bogaerde have created a groundbreaking** **platform**.** They are already supporting large corporates like J&J, Bosch, GSK & Coca-Cola, and there is much more to come. Congratulations! ### [ThingTrax](https://thingtrax.com/) Manufacturing often involves manual assembly. And when factory workers assemble things, "stuff" can occasionally be forgotten. A screw left out. A widget misplaced. And who can blame them? It can be repetitive and tiresome. Enter ThingTrax's Lineview solution. It's based on computer vision AI that analyses the manual assembly process. And gently reminds the assembly worker if something has been left out. Magic. Founders Aman Gupta and Imran Shafqat, together with Paul Reader, are building an amazing company. They already serve a host of mid-market customers, but also global enterprises like Tata. And they are just getting started ## Our celebration At SuperSeed, we work to serve amazing entrepreneurs changing the way business is done. So today, on our fifth birthday, let us celebrate all the founders that work tirelessly to build companies and move us forward. Onward! --- # 3 ways generative AI is changing how we make things **Source:** https://www.superseed.com/madsjensen-xyz/3-ways-generative-ai-is-changing-how-we-make-things/ **Published:** 2023-01-31 **Author:** Mads Jensen Here is a simple model for how we make "things" (toasters, iPhones, buildings, software): - Collect and Synthesize requirements - Create Conceptual & Detailed Design - Make the "thing" ## Generative AI is changing "the making of" in three ways 1. (Semi) automating the synthesis of requirements 2. Automating the translation of requirements into designs 3. Automating the actual production Let's unpack ### 1. Automating the synthesis of requirements Today the lead designer/product manager synthesises data into specific requirements. Getting to a good design often requires vast amounts of input data. And the best designers do a great job at combining new research with prior knowledge. But AI algorithms can make a big impact here. For instance, AI can sift through reams of data (e.g. thousands of user tickets) to spot trends. This can help organise requirements and empower designers and PMs. E.g. - leaving them to focus on higher-level prioritisation. ### 2. Automating the translation of requirements into designs Historically, humans have translated requirements into designs/products/code. With generative AI, computers can create new designs with natural language as input. And they can also create output based on other inputs - e.g. a visual design. For instance, a new workflow could be like this: - the designer describes requirements in English - the AI translates the requirements into screen designs - the AI then translates the screens into the necessary back-end architecture A generative AI model can theoretically use anything as input. This can accelerate the design process to whole new levels. ### 3. Automating the actual production Today, human specialists translate designs into a production process. Shop drawings for architects. Machine parameters for factories. Some things are automated, but there is so much more that can be done. And once we automate this third step, we will have transformed the way we make things. ## Widget, meet your "AI Maker". Generative AI will serve to reduce the product development lifecycle. That reduces costs. But it will also lead to better outcomes. Why? Because designers will be able to explore a much wider design space at a lower cost. We are already seeing a lot of automation across the manufacturing life-cycle. But on a grand scale, we are only getting started. My prediction is as follows: every industry that makes something will be nearly unrecognisable within a decade. Be it buildings, products or software. As always, this presents an opportunity. But it is also a big shift to existing industry and socio-economic structures. Let's embrace this wave of opportunity. And let's also prepare society to support the transition in a sensible way. --- # Startups are heading for a big capital crunch in 2023 **Source:** https://www.superseed.com/madsjensen-xyz/startups-are-heading-for-a-big-capital-crunch-in-2023/ **Published:** 2023-01-30 **Author:** Mads Jensen 2022 was the year when the laws of physics reasserted themselves in venture capital. When the music stopped, upside/down business models were brutally exposed. For a while, folks had been launching (and funding) businesses with non-sensical unit economics. Put simply, the idea was to sell $300 suits for $200, and hope to make it up on volume. In many ways, this was fuelled by a glut of venture capital. But in 2022, this all came to an abrupt end. ## Supply & Demand New Pitchbook estimates are in for the supply and demand of VC funding. Pitchbook estimates that Q4 demand in the US outpaced supply by $42.8 billion. According to Pitchbook, this phenomenon didn't just apply to late-stage startups. The Pitchbook team estimates that demand at the earlier stage was at least 1.5x supply (and I think that is conservative). ## Three Takeaways - Although there is plenty of "dry powder" in VC funds, a lot of FOMO has come out of the system. Investors are more content to take their time rather than rushing to do deals. - A lot of companies saddled themselves with far too high cost structures. Many have taken action, but some still "have it all to do". And some business models are just no longer fundable. - Many companies were able to put off a raise last year by using convertible notes or stretching runway. But 2023 is going to be a year of reckoning. Expect continued turbulence in startup land. --- # No, you don't need a full-time CFO to raise your first round of SaaS venture funding. **Source:** https://www.superseed.com/startupid/no-you-dont-need-a-full-time-cfo-to-raise-your-first-round-of-saas-venture-funding/ **Published:** 2023-01-28 **Author:** Mads Jensen But it helps to have your books in order. Firstly, some context. A few days ago, a founder reached out to me with a searching question. Their startup is looking for venture funding, and they are considering hiring a full-time CFO to help them. Is that needed? The answer is categorically: no. You don't need a CFO to raise a seed or pre-seed round. Only once you start to raise bigger rounds (Series A+), you'll need someone to focus full-time on the finances. So what **do** you need? 1. control of your actuals 2. a solid understanding of your projected burn rate ## Solid accounting is the foundation Firstly, you need a good bookkeeper to keep your books tidy. Payroll. VAT / Sales Tax. Issuing invoices, chasing receivables and paying suppliers. Yes - founders can do that. But every hour you spend doing that is an hour you don't spend obsessing over how to serve customers. So get someone good to help you out on a part-time basis. And don't go for the lowest cost. Any $ you squeeze will come back to haunt you - when you least have time. Trust me, better to pay a few hundred $ more per month, and make sure your books are in order. ## Having a good financial plan If you raise money from investors, you must know your planned burn rate. This means having a solid grasp of your planned costs. But it's not complicated. You can figure it all out in a simple spreadsheet. The elements are: - Staff costs (current and planned hires) + an uplift for tax and benefits, - An allocation for AWS, - A bit of travel expense, - A line for software - Some legal, accounting and insurance fees, and - An allocation for your favourite coworking space. By all means: add in your revenue projections and tie everything together in a P&L. But no need to overdo this. The important thing is that you have a good grasp of your burn rate, so you can project your runway. More complexity sometimes gets in the way of the key insights. ## FD / CFO? Start with part-time Once you are going, it's a good idea to get a part-time Finance Director. Tax credits. Annual accounts. Forecasts. KPI tracking. With the right support, finance and accounting can be super helpful tools. So don't hesitate to bring someone in for a few days a month to help take care of your financial matters. And then, once you've scaled to Series A and a meaningful raise, it's time to build out a proper finance team. Including a good CFO. Until then - keep it lean! --- # 6+1 common pitfalls when making the first sales hire **Source:** https://www.superseed.com/startupid/61-common-pitfalls-when-making-the-first-sales-hire/ **Published:** 2023-01-27 **Author:** Mads Jensen *Getting the first sales hire right in seed-stage B2B SaaS - part 4 (pitfalls)* People can learn anything. And if you have ample runway and a strong organisation, you'll have time and space to train them. But if you are running lean in search of p/m fit, it helps to hit the ground running. Here are some of the profiles that often stand in the way of success. ## 1. The B/S artist Salespeople aren't universally good at selling software. But there is one thing they are usually good at selling. Themselves. Beware of the B/S. ## 2. The Bigco bigwig The salesperson has done well at [insert name of successful Bigco]. But that was with a big brand and lots of support around them. Some folks make the transition from Bigco to early stage. But many struggle to be productive in the chaos. Approach with care. ## 3. The sparrow shooter The salesperson was successful selling $5k deals. But your solution is $100k ACV. Playbook reflects deal size. Plan accordingly. ## 4. The lone ranger The seller is a top rep and an excellent producer. But the best rep doesn't always make the best sales leader. And if you pick someone who is a top producer as a first-time manager, make sure you give them lots of coaching. ## 5. The fish out of water You are selling a highly technical solution to sophisticated buyers in [pharma/aerospace/defence]. But the seller has zero experience or context of your customer's industry. Subject matter expertise can be learnt. And once you have an org, osmosis is a powerful thing. But we are talking about your first rep or sales leader here. In which case, talking the language of your customers speeds things up. Massively. ## 6. The wrong calibre The seller is too junior to establish trust with buyers. Or worse, too senior to want to roll up their sleeves. And then you have to recalibrate. ## 6+1 Bonus. The cart before the horse The point of the seed stage is to develop and evidence product/market fit. To do that, you have to sell. But some companies just don't have a compelling value proposition yet. And no salesrep is going to fix a weak value prop. Founders, this one is on you :) ## Conclusion At the early stage, the right sales rep or sales leader can transform your business. But getting it wrong can set you back 6-9 months. Worst case, it can be fatal to your startup. I am not advocating that you shouldn't take risks or give talent a chance. But it's important to go into things with "eyes wide open". The points above are big chasms to cross. So make sure you have time and resources to make that crossing - even if things don't work out right away. And enjoy the selling. --- # 6 key things to test for when making the first sales hire **Source:** https://www.superseed.com/madsjensen-xyz/6-key-things-to-test-for-when-making-the-first-sales-hire/ **Published:** 2023-01-26 **Author:** Mads Jensen *Getting the first sales hire right in seed-stage B2B SaaS - part 3 (what)* Once you've decided to hire, what should you be looking (and looking out) for? ## Who is the ideal hire? You've now decided which profile to hire: 1. **Account Exec** -to implement a sales playbook already developed by one of the founders 2. **Sales Lead** - to help the founders systematize prior sales work into a playbook 3. **Pathfinder** - to bring enterprise sales capability into the team and land the first lighthouse accounts You also know that the timing is right, because you have at least an MVP. But how do you make the right hire? ## 6 things to test for: Do/are they: 1. Know how to sell at your ACV? 2. Know how to operate at your company stage? 3. Know how to build a team? 4. Know your customers' industry? 5. Comfortable working in a semi-chaotic startup environment? 6. Of a seniority that's right for the job? ## How to test and interpret the results The best way to test is to ask candidates to talk you through how they have solved specific problems in relevant situations in the past. Go into [lots of detail](https://www.cnbc.com/2021/01/26/elon-musk-favorite-job-interview-question-to-ask-to-spot-a-liar-science-says-it-actually-works.html). And use the guide below to interpret the results: ### How to sell at your ACV Deal size is closely aligned with the sales process. If you need someone who can hit the ground running, look for reps who've sold at your price point before. ### How to operate at your company stage If you are looking for a sales leader, almost nothing is more important than whether they get the stage **you** are at. Taking a division of Bigco from $10-15m is nothing like taking a startup from $0-1m. Nothing. ### How to build a team If you are looking for a leader to build your team, look for someone who has seen how it is done at a successful company. Doesn't need to have been the VP. Better if it was number 2. ### Your customers' industry If you are looking for a pathfinder, customer empathy goes a long way. On the other hand, Account Execs can quickly learn an industry from the existing revenue leader. ### Comfortable working in a semi-chaotic startups environment Your candidate might have had a long and successful career at Bigco. But startups are just different. Make sure the person you hire can cope with chaos. ### Of a seniority that matches the job It is as easy to hire too senior as too junior. Account Exec, go junior to mid, Pathfinder, mid-level, Sales Lead, mid-level to senior. Below there is a matrix mapping importance to the roles: Role TypeACVStageTeam BuildingIndustry KnowledgeStartupsSeniorityAccount ExecLLLLMJunior to MidSales LeadMHHLHMid to SeniorPathfinderMHLHHMid LevelHigh / Med / Low Importance Now you've mapped out who to target and what to test for. Tomorrow I'll look at common pitfalls. --- # Getting the first sales hire right in seed-stage B2B SaaS - part 2 (when and when not!) **Source:** https://www.superseed.com/startupid/getting-the-first-sales-hire-right-in-seed-stage-b2b-saas-part-2-when-and-when-not/ **Published:** 2023-01-24 **Author:** Mads Jensen When should founders hire the first sales rep/lead? In seed stage B2B SaaS there is a definitive trigger. I will get back to the specific trigger in a minute. First, let me discuss some of the controversies around "making the first sales hire". ## When not to hire Some investors feel that founders should be able to "do all the selling themselves". These views go alongside slogans like: "don't hire a salesrep until you've hit a million", and "Founders should be able to get to p/m-fit on their own". Investors have good reasons for these views. We have all seen founders struggle to put a compelling proposition together. No wonder. It's hard. But there are no shortcuts. And here is the thing: It is unlikely that an outside sales hire will fix the value prop. They will, however, burn through cash and shorten your runway. So if you don't have a minimally viable product (MVP) that delivers a clear value proposition - **don't hire**. ## Selling your own product Founders should be able to sell their own product. But being a great technical founder is not the same as being an expert in enterprise sales. Some customer buying processes are fiendishly complex. You need someone on your team who is world-class at building product. And if you sell to enterprise, you also need someone who is excellent and navigating your customer buying process. If you sell to large enterprise and you are a technical founder, this might not be you. If enterprise is your target, and you don't have the skills, get someone into your team who does. ## So, when should you hire? The time to hire is when you have your MVP ready. When prospects have something tangible they can use, and a seller has something tangible to sell. It's perfectly fine for founders to "sell the dream" prior to having the MVP ready. But don't hire an outside rep to do that. "Selling the vision" is founder territory. Hired reps need clear positioning, value prop and working product to go alongside. And don't forget - it's your prospect who decides when your product is "minimally viable". That's it on timing. What should you look for when you hire? I will discuss that in more detail tomorrow. --- # Getting the first sales hire right in seed-stage B2B SaaS - Part 1 (The Why) **Source:** https://www.superseed.com/startupid/getting-the-first-sales-hire-right-in-seed-stage-b2b-saas-part-1-the-why/ **Published:** 2023-01-24 **Author:** Mads Jensen Entrepreneurs are always selling. Selling to customers, yes. But also employees, investors and partners. And many successful founders are naturally good at it. Maybe you are selling to SME's and your price-point is sub $1k/month. If so, it's possible that you can rely on a product-led growth strategy (sprinkled with some inbound/content marketing). But if you sell to enterprise, at some point, you will need to hire the first "seller" to your team. This week, I will explore how to get the first hire "right". The obvious questions to answer are, when, what, how and who? But first, start with why. ## Why do you need a salesperson in your team? At the seed stage, your main objective is to develop and prove product/market fit. And in enterprise, having paying customers is vital proof that your solution fits your market. Here are the three typical reasons to make the first sales hire: 1. There is already a natural/experienced sales leader in the founding team. You are now looking to increase deal capacity and accelerate the top-line growth. In enterprise, that means growing the sales team. 2. The founders have landed the first set of lighthouse deals. Now they are looking for someone to build a repeatable sales model. That means expertise on how to build a sales process and a sales-org. 3. Your ICP is in enterprise, but you have no enterprise sales experience in your founding team. You need someone on board who knows how to navigate complex buying processes. That means hiring someone who understands how your customers buy and how to work with their process. ## What do they need to accomplish? You'll see how the "what" is embedded in the "why". Are you looking to - expand capacity (follow an existing playbook), - build a repeatable process (create a repeatable playbook), or simply to - get started winning lighthouse accounts (be a pathfinder)? With these objectives clear, you can start mapping out your hiring plan. And tomorrow, I will unpack the next phase of just how to do that. --- # Sales is not a dirty word **Source:** https://www.superseed.com/startupid/sales-is-not-a-dirty-word/ **Published:** 2023-01-22 **Author:** Mads Jensen In my youth, I had a bit of an aversion to the word "sales". Like so many others, I'd read Arthur Miller's "Death of a Salesman" in high school. It is a book that seems to epitomize the sadness of the sales profession. Getting up every morning, only to spend your day trying to push things on people they don't want or need. I'll get back to my own sales experience in a minute. ### **Giving sales a bad name** First, let's ask the question: is "pushing" really what B2B sales is all about? We have all had both good and bad in-store sales experiences. On the one hand, the pushy salesman - trying to corner us into buying something. Anything. On the other hand, a professional salesperson helping us find the product or solution that is right for us. Even if it means that we go shop with a competitor. Or not at all! And more often than not, we find that - if the seller focuses on solving our problems, they earn our trust. And with that, the right to our business. Good B2B Sales is like that. ### **The role of the salesrep** In B2B sales, the job of the salesrep is to: - find prospects you are likely to be able to help (Ideal Customer Profile), - understand their biggest problems, and then - structure a business proposition that leaves both the seller and the buyer better off. It's the beauty of value creation. And it is the salesperson that makes it all happen. ### **The power of product-led growth** Today we have many more ways to sell. And sales teams are expensive, so everyone is looking for the magic of user-led adoption and product-led growth. But you need a place to start those fly-wheels. A way to get the first customers on board. An approach that helps you understand what they really, truly need. And you may need to do "things that don't scale" to get the flywheel started. Today we might call it "customer discovery" rather than a sales meeting. But the aim is still the same. Learn about your prospects' problems. Understand how you can help solve them. Define a value proposition and structure a deal that leaves everyone better off. This is where it all begins. ### **Back to my own sales experience** And what happened to my own sales experience? My first job out of high school was a job in B2B IT sales. It was a challenging, but rewarding experience. And it taught me an important lesson. When you work in B2B, nothing really happens until someone sells something. So go find some prospects and help them solve their business problems. And make a sale in the process. --- # Using the North Star Metric to measure B2B SaaS product/market fit **Source:** https://www.superseed.com/startupid/using-the-north-star-metric-to-measure-b2b-saas-product-market-fit/ **Published:** 2023-01-20 **Author:** Mads Jensen Private tech companies are generally valued on revenue - not profit. And because of this, boards, investors and - to some extent - founders obsess over the Annually Recurring Revenue (ARR) metric. But revenue is a lagging indicator of value creation. It can also be a misleading indicator. Indeed, some companies manage to grow ARR, only to find that customers start churning after a year because they don't use the product. Those SaaS companies may have a good sales motion. But they don't have product/market fit. ### Finding product market fit A good rule of thumb for when you have product market fit is that you have: 1. paying customers, that 2. get value from your product, and 3. are willing to refer you to their peers. Number one and three are easy to measure. But number two is the most important early on. If you don't have customers that get value, then you don't have a sustainable business. Oh yes, to make money, you have to capture value. But in SaaS, value capture is often the easier bit. The hard bit is to create real, sustainable value. And so how do you measure number two? Enter the North Star Metric (NSM). ### What is the North Star Metric? It is a way to measure the value you create for your customers. One metric you can use to align your organisation to what matters. And many great startups identify a North Star Metric that helps orient all their activities towards customer success. NSM examples - Intercom - Number of customer interactions - Zoom - Weekly hosted meetings - Slack - Messages sent within the organisation If you can find your NSM, you can use it to help align your team. And then you'll have taken a great step towards building a customer-value-focused startup. ## More In Depth Examples **Company****Example****Rationale**AirbnbNumber of nights bookedThe number of nights booked correlates with: - the value a customer receives from a good experience using Airbnb - the value a host receives from getting a space bookedZoomWeekly Hosted MeetingsThe more meetings that happen over time, the more value customers receive from using the communication tool.SlackMessages Sent Within The OrganizationSlack’s value comes from reducing emails and improving in-office/WFH collaboration. The more messages are moved to Slack, the more value customers receive from using Slack.UberRiders per weekUber’s a two-sided platform. Both riders and drivers receive value from their NSM. The driver gets paid. The rider gets to their destination.AmazonNumber of purchases per monthWhy a number of purchases and not $ sold? NSM is customer focused. The purpose of Amazon is to help customers find and buy what they need. If the number of purchases grows, the company will be successful.ShopifyEach Customer’s GMV (Sales)Shopify’s customers are e-commerce shops. And Shopify’s goal is to help them sell more. So it makes sense to have the GMV/Sales be the NSM. More examples on [this excellent blog](https://gokulrangarajan.medium.com/most-used-north-star-metrics-of-2019-20-4179f7d5ab99) by Gokul Rangarajan. --- # How B2B SaaS startup founders use a company roadmap to gain focus and speed **Source:** https://www.superseed.com/startupid/how-b2b-saas-startup-founders-use-a-company-roadmap-to-gain-focus-and-speed/ **Published:** 2023-01-19 **Author:** Mads Jensen Building startups can be diabolically difficult. Startup founders are amazing, but they ultimately run underresourced insurgent outfits. And - against better judgement - they are taking on behemoth incumbents. It's like the rebel alliance vs the Empire. And it's hard work. But strong startups have three attributes that set them apart. - A unifying purpose (your Identity) - Agility - Founder magic So you have purpose, direction and speed. And a healthy sprinkle of "whatever it takes" (if you are a founder, or if you have worked with good ones, you know exactly what I am talking about). But how do you make sure you make rapid progress towards your goals? A company roadmap is a great tool for you, your team and your board. ## What is a company roadmap? A company roadmap is a bit like a product roadmap, but for your company. It starts with the big thing you are trying to do, and maps out the steps you need to take to get there. It is a concise plan that: - communicates objectives - establishes waypoints, and - helps you keep on track. ## How do you make one? Start with your mission. That is the overarching SMART objective you have set yourself for the next 12-18 months. It is part of your Identity. Then map out the key milestones for each quarter across Product, Team, Customers and Org/Other. Now ask yourself: 1. If you hit all the milestones on your roadmap, will you have delivered on your mission? If not, what's missing? 2. Are there things on your roadmap that are not in service of your mission? If so, remove them. Absolute focus is key to success 3. Finally, does it all add up? Work with your team and board to get it right. And keep tracking and iterating as you progress. And presto: you now have a roadmap that communicates objectives, sets direction, establishes waypoints and keeps you focused on progress towards your overall mission. All taking you and your startup one step closer to greatness. ![](https://www.superseed.com/wp-content/uploads/2023/01/image-2-1024x574.png)There are many ways to document a roadmap. Here is a simple example, and simple is good. --- # Identity - the magic kernel at the heart of successful startups **Source:** https://www.superseed.com/madsjensen-xyz/identity-the-magic-kernel-at-the-heart-of-successful-startups/ **Published:** 2023-01-18 **Author:** Mads Jensen Small teams can conquer the world. And startup founders have a magic weapon that can help them marshall their stakeholders into a formidable force. No - I am not talking about venture capital. I am talking about the simple concept of Identity. ## Identity - three building blogs that define your startup The Identity of a startup is its reason to exist. It is the expression of the big questions we all must answer if we aspire to greatness. It is the why, who and what (but not the how) that underpins your strategy. In essence, Identity has three components: - Purpose - Why you exist. Forever. - Mission - SMART ambition. Finite. - Vision - The future + your contribution. Let's unpack. ### Purpose - the thing your startup exists to do E.g. - Help all sales teams beat their sales targets. - Enable automotive manufacturers to use 3D printing. - Help utility companies make energy that is 100% sustainable. Whatever it is, make sure it is something you are passionate about. Because **this** is the thing that should motivate you and your team to work 120% 24/7 for the next many years. At least until you've made your own little "ding in the universe". ### Mission - your Specific, Measurable, Achievable, Relevant and Time-bound (SMART) objective E.g. - Secure three specific lighthouse accounts as reference customers before the end of the year. - Land a man on the Moon and returning him safely to Earth before the decade is out. (NASA's annual budget in the 60s was about $50bn in today's money. They could take on a big mission). ### Vision - your aspiration for the future and the role you look to play in it To continue the example from above: - A decade from now, our rocket technology will have enabled humankind to become a multi-planetary species. - A decade from now, our software has enabled automotive manufacturers to make all cars using 100% recycled material. ## The power of a strong Identity Taken together, these three building blocks form the Identity of your company. - They drive your positioning statement. - They determine your Ideal Customer Profile. - They set the direction for your company and product roadmaps. And crafted well, they will help you inspire talent, investors and customers to join your rebellion. In turn, this means that you can serve your purpose and execute your mission. All so you can deliver on your vision for a brighter future. --- # Now that the venture boom is over, is it too late to get returns in Venture Capital? **Source:** https://www.superseed.com/madsjensen-xyz/now-that-the-venture-boom-is-over-its-too-late-to-get-returns-in-venture-capital/ **Published:** 2023-01-17 **Author:** Mads Jensen *This is not investment advice. Past performance is not a guarantee of future returns. You should always do your own research and speak with your IFA before investing.* Now that the venture boom is over, it's too late to get returns in Venture Capital. Or is it? 2022 was a difficult year for tech investors. EMCLOUD (an index of 75 listed cloud companies) is down 59% from the peak in November 2021. It's not because the companies are struggling as such. They are growing 29 %/year on average. But valuation multiples are down, and everything is cheaper. But this collapse is only the flipside of what came before. From 2018 to 2021, EMCLOUD rose more than 200%. A very healthy return for those who bought in on launch, and sold at the top. EMCLOUD is interesting because it serves as a proxy for private SaaS companies. As public valuations went up, so did private company valuations. And with it, so did the paper returns of venture capital investors and their limited partners. ## The attractiveness of venture capital Measured over a prolonged period of time, venture capital can be an attractive investment. According to Pitchbook, the average IRR for fund vintages from 2007 to 2021 was +20%. And for top quartile funds, 33%. Don't like averages? The median values were 18.3% and 29.3%, respectively. Now that the air is out of the balloon, does that mean that the good times are over? Are all the good returns gone? If history is any guide, the three years right after a correction are better than the three years leading up to a crash. For instance, compare Pitchbook's venture returns data for the periods 2007-2009 and 2010-2012. The median venture fund delivered a 19% higher return in the latter period. This also holds true if you compare 2006-2008 vs 2009-2011. For top quartile funds, the outperformance is even higher after the cash. Want to go further back? The median fund return was 22.6% higher for vintages 2001-2003 than for 1998-2000. ## The takeaway This might seem completely intuitive on paper. Yet human nature is such that many flock to assets that are in a bubble (with high valuations). And shy away after prices have corrected. But in reality, investors are likely better off keeping their heads cool when the bubble fever rages. And to buy in when prices have moderated. So next time someone is telling you that they are looking to "sit this one out", show them this chart. And remind them that - historically - the vintages right after a crash have been very good for venture capital investing. --- # Why should first-time B2B SaaS founders who just raised a seed round make a product roadmap? **Source:** https://www.superseed.com/madsjensen-xyz/why-should-first-time-b2b-saas-founders-who-just-raised-a-seed-round-make-a-product-roadmap/ **Published:** 2023-01-16 **Author:** Mads Jensen So - you are a technical founder building a SaaS company. And now you've raised your first seven-figure round from professional investors. Congratulations! The objective for the next phase is to nail your Ideal Customer Profile (ICP), win (more) clients and prove product/market fit. Doing this will take you to your Series A valuation milestone. Now your investors ask for a product roadmap. But you already know how to build great software. So surely making one is just a waste of time? ## Why do we need roadmaps? Your roadmap is likely going to iterate a lot. Just like your sales playbook. Is it even worth making one? A written roadmap helps you align your priorities with your team and your board. And writing one can even help crystallise your own thinking. It's a good tool. Smart people are often good at solving problems. And for smart founders with VC funding, it is tempting to try to solve too many of the problems they come across. But fundamentally, the purpose of strategy is to concentrate your resources at the point of the biggest impact. A clear ICP and value proposition will help direct everything you do. A well-aligned roadmap helps ensure you can deliver on your customer promise - before you run out of money. Sadly, more than 2/3 of the startups that raise a seed round never get to Series A. Even for those that get to Series A, more than half fail to return their invested capital. And, cash is now scarce. We can no longer burn $ like it is 2021. For a software company, your tech roadmap is your future. So your investors will want to sense-check that your roadmap is aligned with the ICP and the value proposition. And help you refocus if they think you are veering off course. That is an important role of your board. ## How does it relate to sales? Now, sales traction is key. That is the visible demonstration of product/market-fit. But it's not enough. Many companies brute-force their way to initial revenue traction, only for sales growth to stall. And there is a big difference between landing a few clients and then getting to actual p/m-fit. So your sales playbook helps clarify the direction your are taking. And your product roadmap explains how you get to the right destination, using the best route. You can create one in a tool. Or you can make a simple one in google slides. But make one. Because this is the best way to get clarity around your company's strategy and priorities. And in a fast-moving software company, that clarity is the key to your company's future. ## What are some objections? ### It's too hard / not worth the effort Making a good roadmap can be tricky. And like a sales-playbook, it will take lots of iterations. But the roadmap sets the courses and prioritises your experiments and iterations. And that is exactly what they should be doing. ### It is too difficult to estimate the effort required to deliver roadmap elements Some developers are reluctant to make roadmaps as they are unsure how long things will take. Those concerns are perfectly valid. It can be super hard to predict how long something that's never been done will take. So provide estimates as a starting point. And use those estimates to figure out whether you are on track. --- # How can B2B SaaS founders plan for cuts? **Source:** https://www.superseed.com/madsjensen-xyz/how-can-b2b-saas-founders-plan-for-cuts/ **Published:** 2023-01-15 **Author:** Mads Jensen **How can B2B SaaS founders plan for cuts?** *(possibly applies to others as well)* 2023 is going to be a tricky year in startup land. Cash is tight, and recession is looming. And most startups need to tighten their belts. When times are good, and the sky is the limit, we can all get exuberant. Then the laws of physics are reintroduced, and gravity reasserts itself. Maybe you've overhired. Maybe your customers are pausing their spending for a bit. And suddenly, burn races far ahead, and the runway shortens dramatically. ## A useful framework Many founders took action and trimmed in 2022. But some still have it all to do. So here are some suggestions on how to work with your board and investors to get it done: 1. Start with why: - Yes - you may need to cut to survive. But you need a wider strategic lens than making it to another day. So start by realigning you and your board on your strategic intent. Remind yourself and others what you are in this world to do. Then zero in on the immediate mission and objectives? Maybe it is about landing specific lighthouse accounts? Or proving the expand part of your upsell motion? Make sure to put some realistic growth assumptions in your plan. If you don't agree on the objectives up front, nothing else in the discussion will make sense. - Be clear on whether the plan is to make it to break even or to make it to a funding milestone. How much runway do you need to get to this milestone? 2. Then what: - What are your options (typically variants of "sell more" and "spend less")? - What are the implications and trade-offs (analyse each option with pros and cons. Make sure to cover trade-offs)? - What is your recommendation (have a proposed plan. But don't be too wedded, or you risk looking intransigent)? - What are the most difficult aspects of implementing your recommendation (that's the risk piece)? - What are the expected monthly revenue, cash burn and cash-out date of your new plan? 3. Finally how: - How do you propose to implement your plan? What are the headcount and salary spend per department before and after the cuts? How will you communicate the plan to those who sadly have to leave? How will you energize those you want and need to keep? - How do you address the major risk aspects (team, customers, product/tech)? ## The importance of getting it done Building a company is an incredible journey, full of ups and downs. Restructuring and cuts is one of entrepreneurship's hardest and most unpleasant aspects. But sometimes, it has to be done. And as founder/CEO, it is your job to do it. It takes a lot of different skills and tools to build a successful SaaS company. Knowing when and how to trim the sails is an important one for your kit bag. In startup land, time is always of the essence. And the sooner you get it done, the more runway you buy yourself and your team to achieve your objectives. And as long as you keep your mission aspirational and objectives clear, your team and your board will be motivated to follow you. Because that is what you want and need. A motivated team, the backing of your board/investors and the necessary runway to meet your objectives and the mission for your company. --- # The Best Product Always Wins **Source:** https://www.superseed.com/madsjensen-xyz/the-best-product-always-wins/ **Published:** 2023-01-12 **Author:** Mads Jensen The best product always wins. Or does it? Product-led growth is all the rage: 1. Develop beautiful software, 2. make it easy to adopt, and 3. empower users to become champions that drive expansion. It's a beautiful vision. And if you sell to small businesses, it is likely your only option. But should SaaS companies that target the enterprise be product or sales-led? ## Meeting Slack I remember the first time I saw Slack. What a beauty. It was back when I was building Sefaira (B2B SaaS company selling to architects and engineers). We'd been struggling with internal instant messaging for a while. Various teams had created their own chat channels in Skype. It was not a great collaboration experience. We needed something new. One day our developers came across this new instant messaging platform that epitomized the beauty of SaaS. With Slack, there is nothing to install. Setup is a breeze. Organic deployment. Viral adoption. Only pay for what you use. In some ways, perhaps one of the first true fulfillments of Salesforce founder Marc Benioffs vision for SaaS. Just so easy and beautiful. We adopted it overnight and never looked back. ## Microsoft Arrives A few years later (2016), Microsoft decided to join the fray with MS Teams. And bearing in mind the clunkiness of many MS products, it was hard to see how they could beat Slack. In fact, Legendary Slack Founder/CEO Stewart Butterfield went "all-out" hubris mode. He took out a full-page ad in the New York Times to welcome Microsoft to the era of modern collaboration software. The letter included lots of "friendly advice" for Microsoft. But the subtext was not so subtle. The dinosaurs of Seattle and their archaic software would be no match for the pioneers of product-led growth. Except they would. Because it turns out that it's great to have a superb product. But in enterprise, it is more important to have relationships with the CIOs of Fortune 500 companies. And Microsoft had that in spades. Within six years, Microsoft Teams had six times more users than Slack. ## The Aftermath Today, Slack is still the better product. Founders and early investors did well. But Slack is no longer an independent company. Salesforce obliged with an exit and a new home for the business. And Slack never became the overpowering force many of us thought it could have become. Because much as we all love beautiful products. In the end, superior distribution often wins. That brings me back to the opening question. Should SaaS companies be product-led or sales-led? In today's world, the answer is both. It's a pincer movement. Great products are table stakes (not that this makes them easy to build). But it takes great distribution to reach big customers. So get working on your sales playbook and call some prospects. And when you are in front of them, wow them with how quickly your beautiful product delivers ROI. That's the way to win big in today's market. --- # Three Lessons from 2022 **Source:** https://www.superseed.com/madsjensen-xyz/three-lessons-from-2022/ **Published:** 2023-01-11 **Author:** Mads Jensen 2023 is well underway. But there were some great lessons (or reminders!) from 2022. Let's dive in. ### Lex Twitter *AKA demonstrating that: yes - you can run tech companies with very little staff. And software does really scale incredibly well. * Elon Musk took over Twitter and laid off more than half of the company. And proved that it could still run seemingly just fine. Agreed - the story isn't over yet. But - like him or loathe him - this was a seismic event in the tech sector. ### Lex Gorillas *AKA What happens when you invest in companies with upside-down unit economics*. Delivery group Gorillas was valued at as much as $3bn in 2021. In 2022 it was sold to Getir for $1.2bn. That was less than the $1.3bn the company had raised. And - here is the clincher: Only $40m of the purchase price was in cash. So Gorillas investors swapped their 100% ownership of one delivery company for a small stake (12%) in another delivery company. Plus $40m spare change. Probably not the outcome they were looking for. ### Lex OpenAI *AKA what happens when tech's most unassailable moat suddenly looks vulnerable*. For years the world looked enviously at Google's profit margins. Google search was ubiquitous, and Google's ad revenue looked impervious to assaults. And suddenly, the partnership between Microsoft and OpenAI throws all that up in the air. Note: this movie is going to play out in 2023. And Google still looks formidable. But ChatGPT was enough of a scare for Google's CEO Sundar Pichai for him to declare a "code red". Lesson: even the seemingly most invincible organisations can be disrupted. In summary: 1. software is the ultimate scaling asset 2. unit-economics matter more than ever 3. even Goliath isn't invincible --- # How do seed stage B2B SaaS founders make a good product roadmap? **Source:** https://www.superseed.com/startupid/how-do-seed-stage-b2b-saas-founders-make-a-good-product-roadmap/ **Published:** 2023-01-10 **Author:** Mads Jensen I have seen a lot of overcomplicated roadmaps. And often, they are symptoms of unclear strategies. But a product roadmap shouldn't be overly complicated. At its heart, it is a strategy document that: - sets out the near and medium-term objectives for your product (/customer experience), - defines milestones that show that you are on track to meet your objective(s), - provides estimates for the effort required to meet the milestones, and - prioritises workstreams that get you to your milestones (and objectives). That's your roadmap. ## 9 Steps to a Solid Roadmap So how do you make one? Open a Notion or Google doc and answer the following questions: 1. Who do we serve? (Ideal Customer Profile (ICP) and Ideal User Profile) 2. What problems are we trying to solve for customers? 3. What user experience do we need to provide, to solve those problems better than alternatives? 4. How do we measure customer value to ensure we have delivered customer success? 5. What does our product need to do (features) to deliver that customer success/value? 6. Of all the features we can build, which next 3-5 features will deliver the most value for my ICP? 7. Roughly how much effort do I expect it will take to deliver those 3-5 features? 8. What is the rank of the features based on value/effort? 9. What do we need to learn to validate our hypotheses? The answer to step 8 is your roadmap. Put it as bullets on a page or as a colourful Gantt chart. And make sure to include it in your company and board updates. As your product matures, more questions and complexity will arise. But it is always helpful to keep things as simple as you can. Direction and priority - that's what you are looking for. What are the steps? ### Steps 1 and 2 Your company's identity is at the heart of everything you do. Your mission, vision and purpose. Who you serve and why you exist. There is a process to figure this out, which I cover elsewhere. Assuming you have mapped this out, the next step is to craft your sales playbook and your product roadmap. These exist in sweet harmony. And in fact, the two first questions are the foundation for both your sales playbook and your roadmap. If you already have the playbook, grab them from there. If not, get writing. ### Steps 3-6 At this point, you have hopefully already done lots of customer discovery. If not, you need to talk to customers. They will tell you what you need to know. Synthesize their insights to find answers to steps 3-6. ### Steps 7 Estimating how long it will take to build features can be tricky. And it is impossible to get 100% accurate. But a ballpark estimate beats not having any idea. So make the best estimate you can. And Let everyone know that they are subject to change. Besides, timeboxing is important. You could arguably spend either three months or nine months on a feature. But if you are at the seed stage, you probably want to think "MVP, MVP, MVP"). ### Step 8 Now you rank value vs effort. This effectively prioritises your development. That's it. v0 of your roadmap is ready. ### Step 9 As you go along, write a list of open questions and hypotheses still to be tested (that's step 9). And make sure to share these with your sales and CS teams. They are your eyes and ears on the ground (at least until you have a full-time product function). As you start growing a scaling, you'll hire a head of Product and scale out the product organisation. But for now, you have an important tool to help you prioritise and communicate. --- # US Venture capital activity continued to decline in Q4 of 2022 **Source:** https://www.superseed.com/madsjensen-xyz/us-venture-capital-activity-continued-to-decline-in-q4-of-2022/ **Published:** 2023-01-09 **Author:** Mads Jensen Pitchbook's preliminary Q4 numbers for US Venture Capital activity are now out. They show last quarter's investment activity (measured in number of deals) down by 25% from Q1. But they also show activity higher than any other quarter in the past ten years. All Q4 investment rounds have not yet been announced. So the preliminary figures adjust for this by estimating completed but as-of-yet unannounced deals. A few observations: - The analysis shows that numbers have declined. But anecdotally, most people I speak with feel that the decline has been much more severe than 25%. - This is possibly because Pitchbook overestimates Q4 activity. - More likely, it is because most of the new rounds are "internal" - i.e. companies raising from existing investors. - If most folks are raising internally, the market will feel much quieter. Why does this market feel difficult for founders (and some investors?) Venture Capital is the definitive "growth" industry. It's wired to grow all the time. Even a static market will feel tough, and a declining one doubly so. --- # Welcoming 2023 **Source:** https://www.superseed.com/madsjensen-xyz/welcoming-2023/ **Published:** 2023-01-08 **Author:** Mads Jensen Dear SuperSeed LPs, And then it was 2023. Last year was a crazy, busy, hectic, turbulent year. It was exciting and - for many - quite frightening. The world emerged from the Pandemic to look into the face of inflation, a stock market crash and war in Ukraine. And in the tech world, everything got turned upside-down. It's almost as if we'd been on autopilot for two years. Not a tranquil tour on the scenic route. But a journey on an ever-accelerating bullet-train, with rapid-fire investment cycles and capital galore. Then suddenly, it all just stopped. The world stepped off the QE-crazy of the pandemic era. Less money and oxygen were given to faster grocery delivery with questionable unit economics. And we could all get back to the real work: building proper companies. ## Major events of 2022 2022 saw some massive tech milestones emerge. It also saw some spectacular failures. Some of the highlights: - Tech stocks were down down down. Tech companies in S&P500 were down about 40%. SaaS companies in the EMCLOUD index down 51.5%. - Crypto collapsed. FTX spectacularly so. - Startups without proper unit economics were found out. - The crazy war for talent in the tech sector came to an end. All major tech companies have now announced freezes or significant layoffs. We shouldn't downplay the trauma caused by these events. A lot of jobs have been lost. A lot of wealth has gone up in smoke. There are real-world repercussions, and for many, these are challenging. But in many ways, the cleanout was overdue. Too much capital and too much human talent had been allocated to unproductive endeavours. And as we have been reminded (yet again), this ultimately destroys value. So we needed to clear the decks. The process will continue in 2023, but we are well underway. And in the meantime, there is so much exciting business to be getting on with. In 2022 we saw: - Quantum leaps in AI with the public release of ChatGPT. - Amazing next-generation (Web 3.0) companies come through. - A refocus from "growth at all costs" to smart growth with proper unit economics. ## What's in store for 2023 So what will happen in 2023? - A lot of startups deferred their raises in 2022 and will run out of money in 2023. - Startups that need to raise will have a tough time. It's just harder, even for good companies. And for the "not quite there (yet)" companies, it's impossible. - This means that the cleanup will continue. We will see more startup failures as overfunded companies with poor business models run out of cash. They will be unable to raise the next round, and will merge or fold. - Interest rate tightening will level off. But not as quickly as some people hope. Jerome Powell has been clear. "Whatever it takes" to combat inflation. - Valuations will continue to be moderate. This is an investors' market. Great returns will come out of this vintage. On the tech side, this will be the year of AI. And when it comes to AI, 2023 will make 2022 look like a sleepy year. OpenAI made huge waves when they released ChatGPT in November last year. ChatGPT is a chatbot that sits on top of the GPT-3 language model. ChatGPT made GPT-3 universally accessible. And it shows us all the power of Large Language Models. Well, GPT-4 is expected to arrive in early 2023. It has taken years to develop and has 100x more parameters than GPT-3. In simplified terms, parameters are the basic building blocks of an AI algorithm. So if GPT-3 is powerful, imagine what something 100x more powerful will be able to do. ## Our plans for '23 We continue to lean into AI as the main tech driver for change. This sits alongside two other major trends: 1. The reconfiguration of globalisation - primarily driven by US / China rivalry. 2. The reconfiguration of energy. 40% of the UK's electricity supply in 2022 was from renewable sources. The US was behind at about 22%. But the trend is incontrovertible. 2023 will see nations continue to change their energy supply. And companies redesign their supply chains. And AI will be there every step of the way, helping us all be more productive. This will profoundly change both geopolitics and economics. And provide ample opportunities for tech investors. Even if the outlook for inflation and interest rates looks higher than in the past few years. ## SuperSeed II In 2022 we started investing out of our new B2B SaaS fund - SuperSeed II. We hit the ground running, and have already had the opportunity to partner with eight great startup teams in the new fund. Our portfolio companies had a strong 2022. We were generally super proud of how the founders adjusted to the new economic reality, all while they kept growing their toplines. More detailed performance information will follow once accounts and audits are done. But we can already now say that 2022 was a solid start for the new fund. In 2023 we look forward to continuing our work to back Europe's smartest technical founders in B2B SaaS. And to help them build great companies that transform how business is done. Thanks for all your help and support in 2022. We are excited to work with you to deliver on this incredible opportunity in 2023 and beyond. Dan, Mads & Team SuperSeed --- # Simple steps to make a SaaS Sales Playbook **Source:** https://www.superseed.com/startupid/simple-steps-to-make-a-saas-sales-playbook/ **Published:** 2023-01-07 **Author:** Mads Jensen A sales playbook is a powerful tool for B2B SaaS founders. Done right, it will help you: 1. Define and target the right customers 2. Help the customer understand what you do 3. Evaluate whether the solution will drive critical business outcome for customer 4. Establish a compelling reason to act 5. Gain consensus across the people involved in the buying decision 6. Agree and formalise a relationship 7. Create blueprint for ensuring that the customer is successful But how do you make one? It's a lot easier to get started than you'd think. Open a Notion or google doc and answer the following four questions. 1. Who do we serve? (Ideal Customer Profile) 2. How do we create value for customers? 3. What factors have helped us win deals in the past? 4. How do we reach prospects in the ICP group? All the stuff you don't know goes under a 5th headline: 1. What do we need to learn? To unpack how you create value for customers, answer these four questions: 1. What problems are we trying to solve for customers? 2. What tangible ROI do we deliver to customers($)? 3. What are the 2-3 reasons why prospects should want to do this right now (not in 6 months)? 4. How do we measure customer value to ensure customer success? The founders make v0. Once you have some numbers on the board, hire a sales lead to refine the playbook. Keep iterating. And keep selling. Because in startup land, sales solve (nearly) all problems. --- # Well-run SaaS startups use four simple tools to succeed **Source:** https://www.superseed.com/startupid/well-run-saas-startups-use-four-simple-tools-to-succeed/ **Published:** 2023-01-06 **Author:** Mads Jensen The objective for a B2B SaaS company at the Seed stage is to develop and prove product/market fit. To do this, the founders must: - hire the right team, - build the right product, and - win the right customers. Customers, product & team. That's it. But it's not a linear process. Not six months of hiring followed by six months of building followed by six months of selling. If only startup life were that simple. Rather it's an iterative process. You sell something transformative. You build something amazing. You hire someone great. All in one, messy, iterative flow. And yes, it is messy. And unpredictable. But it doesn't have to be complete chaos. And in fact, the most successful founders have a clear plan. Even if they end up iterating a lot. ## The building blocks What are the foundational building blocks? How do you know you are on track? And how do you make sure you have enough runway to get there? At the early stages, you need four simple tools. You need: 1. a sales playbook, 2. a roadmap, 3. an org chart, and 4. a budget that ties hiring, sales and R&D spend together. Make sure you have all of those. Map them out. Track them. Update them. And use them to force you to think through difficult decisions and tradeoffs. Aren't plans obsolete the moment you make them? Yes and no. Your plans will change. For sure. But thinking through them helps you make the tough and the right decisions. Because plan beats no plan. Every single time. --- # Public SaaS companies are still valued completely differently from other companies. **Source:** https://www.superseed.com/madsjensen-xyz/public-saas-companies-are-still-valued-completely-differently-from-other-companies/ **Published:** 2023-01-05 **Author:** Mads Jensen Public SaaS companies are still valued completely differently from other companies. Or are they? In yesterday's post, I looked at how markets value public #SaaS companies as we enter 2023. The conclusion is that they are valued on (capital efficient) growth. But how does this compare to how #markets value other businesses? At face value, it looks like they are valued completely differently. The valuation multiples of S&P500 companies mainly correlate to profit. The three best correlations are: - EBIT Margin (0.42), - Free Cash Flow Margin (0.37), and - Gross Profit Margin (0.34). On the other hand, the value of SaaS companies correlates to revenue growth (0.49). The two approaches appear in stark contrast. But that is only until we take a step back and consider the overlaps. SaaS companies may not deliver profit today. But they are still expected to deliver plenty of Free Cash Flow in the future. And it's a combination of high growth and high gross margins that will get them there. So when you think about it, the difference in valuation is mainly a timing thing. Jam today vs jam tomorrow (or five years from now). And in an otherwise low-growth world, investors are still willing to pay well for the promise of future profits. --- # Growth for SaaS companies is back in vogue! **Source:** https://www.superseed.com/madsjensen-xyz/growth-for-saas-companies-is-back-in-vogue/ **Published:** 2023-01-04 **Author:** Mads Jensen Growth for SaaS companies is back in vogue! Wait, what? As we know, 2022 was the year when an all-consuming focus on growth was replaced by sensible business. Positive unit economics. Free Cash Flows. Those sorts of grown-up things. I'll come back to that in a minute. First, let's look at the macro picture around the valuation of tech stocks. 2022 was an abysmal year for most investors. The S&P500 closed down ~20%, with all almost all sectors battered (energy being the only exception). But why are stocks down even though company performance was respectable? It's true; Q4 earnings haven't been released yet. But the expectation is that 2022 revenue and profit are both up for SP500 as a whole. So although both profits and revenues were up, each profit $ is now valued less by investors. And valuation multiples (price-to-revenue and price-to-earnings) reflect this. As you can see, the multiple decline has touched all sectors. Even energy. But energy stocks are up because increases in energy sector profits have more than outweighed multiple compression. ## What's happening with SaaS companies? From 2018, the overriding driver of SaaS company valuation was revenue growth. Higher growth rates led to higher valuation multiples. Investors paid little attention to unit economics and profitability. This changed dramatically in 2022. Growth went from explaining 50-60% of a company's valuation to less than 30%. Meanwhile, the Efficiency metric increased in importance. Efficiency is a measure that combines Revenue Growth with Free Cash Flow. It tells us how efficient growth companies are at turning cash into growth. And by March 2022, Efficiency had become the most important metric to value companies. ### How does the scorecard look at the beginning of 2023? For this analysis, I use the Bessemer EMCLOUD index as a proxy for SaaS companies. It consists of 75 well know, public cloud companies. Category software leaders like Salesforce, Adobe, Atlassian and Datadog. But even these industry-leading software companies are, on average, still losing money. Of the 75 firms in the index, only 13 have reported positive net income over the past 12 months. So why are they valuable? For one, they are still growing rapidly. And once they achieve scale, they can become exceptionally profitable. Take Adobe. The design software behemoth has delivered $7bn of Free Cash Flow on $17.6bn of revenue over the past year. A Free Cash Flow margin in excess of 40% is what most companies can only dream of. And because of these economics, fast-growing software companies can be highly valuable. Witness Adobe's acquisition of Figma for $20bn in September. The deal was done at 50x annual recurring revenue. There is still a lot of money in growth. ## SaaS vs. S&P500 Growth Back to EMCLOUD: The median company grew 33.2% over the past year. And is forecasted to grow 25.6% over the next 12 months. This compares to the median S&P500 company that is projected to grow 6.4% in the next 12 months. In 2022, the median EMCLOUD company revenue multiple declined from 17x to 6.6x. But it is still 2.5x higher than the median S&P500 company at 2.6x. There is indeed value in growth. ## S&P500 valuations as we enter 2023 So what is the simplest way to accurately determine the value of a public software company as we enter 2023? Let's start by looking at the large-caps from S&P500. For those, profitability is the main driver. EBIT Margin explains more than 40% of a valuation multiple. This is followed by Free Cash Flow Margin and Gross Profit Margin. Net Income on its own is a relatively poor predictor. And importantly, it is all about the forecasted profitability. Future expectations are much better at predicting the share price than past performance. ## Public SaaS valuations at the start of 2023 How well does with work for SaaS companies? As mentioned, most of the EMCLOUD companies have negative net income. That would lead to negative valuation multiples - not useful. As a result, profitability is a poor predictor of SaaS company value. But Efficiency (growth + Free Cash Flow) is a great one. And forecasted Efficiency for the next year alone explains 52% of a company's revenue multiple. If you are looking for a simple revenue multiple, the best predictor is next year's forecasted Efficiency. This is closely followed by forecasted revenue growth. Note that there is almost no overlap between how the market values S&P500 and SaaS companies. Growth by itself was in the dog house last year. It is now again at a point where it explains nearly half of a SaaS company's valuation. That's a meaningful change. Note: in my view, this doesn't mean that we are back to growth at all costs. 2023 will be a year of continued "fiscal discipline" in startup land. But growth continues to be extremely important for value creation. ## What does this mean for private companies? Strong early-stage SaaS companies grow much faster than large, listed companies. So if growth is valuable, they should be even more valuable. But there are detractors to that valuation premium. 1. Yes, early-stage companies are full of potential. They are also full of risk. Market risk. Execution risk. Founder risk. All things that can railroad them well before they hit $100m in ARR. So what matters is the long-term growth rate as they grow into their Total Addressable Market. 2. Illiquidity premium. In the past few years, it was fashionable to see liquidity as old-fashioned. Who cares about liquidity when there is plenty of Quantitive Easing cash? But now interest rates are higher, and volatility is up. And it's useful to be able to sell shares if you need liquidity. Private companies don't offer that option - at least not easily. Therefore they should be priced at a discount to public companies, all else being equal. Valuing private companies is still as much art as it is science. But it is helpful to pay attention to how public companies value SaaS businesses. And interesting to see how this changes over time. --- # Someone Still has to Ask the Questions… **Source:** https://www.superseed.com/madsjensen-xyz/someone-still-has-to-ask-the-questions/ **Published:** 2022-12-21 **Author:** Mads Jensen # Someone still has to ask the questions… 2022 has been an incredible year. From "the bads" (war and stock market crash) to "the goods" (fusion energy and AI advances). It is worth considering how these will impact us in 2023 and beyond. Of all the above, my bet is that AI will have the biggest impact. AI algorithms (like the type we see in ChatGPT) will transform how we work and live. Not without challenges. But most of it for good. A criticism levelled at current AIs is that they are not "real intelligences". They make things up. They make mistakes. (Some of this admittedly sounds quite human). But the main limitation of AI algorithms is that they have no agency. They can't think for themselves. And as we know, all good thinking starts with good questions. ## The importance of questions IBM's founder Thomas J. Watson, Sr. once said: "Man has made some machines that can answer questions provided the facts are profusely stored in them, but we will never be able to make a machine that will ask questions. The ability to ask the right question is more than half the battle of finding the answer." Tech has come a long way in the past 100 years. And future AIs may be able to ask questions as well. But for now, computers are not sentient. So figuring out which questions to ask is still squarely the purview of humans. ## **The power of humanity** With computers and AI, we have more powerful tools than ever. Far from making humans redundant, this amplifies the value of what is uniquely human. Asking the right questions is no longer just "half the battle". When answers are easy, asking the right questions is increasingly the only thing that matters. The winner of tomorrow is the one who is most curious. Most exploratory. Most determined to ask the right questions. And to look for the most impactful answers. So as we sit around a marvel/worry about what AI is and can do, it's worth remembering one thing: *AI algorithms are great at providing answers. But someone still has to ask the questions. And right now, that someone is still very much us.* Happy holidays! --- # Why are early-stage valuations going up? **Source:** https://www.superseed.com/madsjensen-xyz/why-are-early-stage-valuations-going-up/ **Published:** 2022-12-19 **Author:** Mads Jensen Why are early-stage valuations going up? Growth stage valuations have collapsed this year. Like public market tech investors, private growth-stage investors have taken a bath. Earlier this year, Klarna raised money at an 85% lower price than its 2021 valuation. Ouch. But what's going on at the early stages? On Friday, Pitchbook released the [2023 US Venture Capital Outlook](https://pitchbook.com/news/reports/q4-2022-pitchbook-analyst-note-2023-us-venture-capital-outlook). Far from decreasing, it shows US Seed valuations going **up** in 2022. ![](https://www.superseed.com/wp-content/uploads/2022/12/image-1-1024x398.png)Pitchbook analysis - US Seed Valuations going up in 2022 Given the macroeconomic backdrop, this seems counterintuitive. But there is a simple explanation. The main driver has been a flight to quality: - the bar has gone up. Fewer deals are getting done, and only the best companies are getting funded. - the best companies, on average, raise more money than the median performer. - as only the best companies are raising, the median US valuation and round size is going up. - this is further compounded by the bigger firms doing more seed deals. Accel, Andressen, Lightspeed, Sequoia etc have been more active in US Seed in 2022. And they tend to write bigger cheques. So fewer and bigger firms writing fewer and bigger cheques. That's the story of US venture capital so far in 2022. ## How are things looking in Europe? According to [Dealroom](https://dealroom.co/) data, European Seed and Series A round sizes were stable between Q4 '21 and Q3 '22. ~$2.2m for the typical Seed and ~$7.9m for the typical Series A. But volumes are off by 20%, down from 1,150 investments in Q4 of last year to 934 in Q3 of this year. The expectation is that the number will decline further in Q4. Even adjusting for the usual lag in the data, the data matches what we see on the ground. Fewer deals are getting done. ![](https://www.superseed.com/wp-content/uploads/2022/12/image-2.png) Let's assume a similar pattern to the US - i.e. that only the best investments get made. This means that the average investment in Q3 of this year was into a higher-quality opportunity (traction, risk, team etc). And if the average investment is of higher quality, and the average price is the same - well - that's a relative price cut. So although it looks like valuations have continued to increase at the earliest stages, the reality on the ground is more mixed. This is a tough time for founders. Most have had to adjust their plans in light of the funding market. But ultimately, we see much better businesses being built. More judicious spending and better unit economics. And I expect that the near-term pain felt by entrepreneurs will translate into better long-term outcomes for all. Founders as well as investors. --- # Here is why I am bullish, despite short-term market volatility. **Source:** https://www.superseed.com/madsjensen-xyz/here-is-why-i-am-bullish-despite-short-term-market-volatility/ **Published:** 2022-12-16 **Author:** Mads Jensen A few days ago, I conducted a thought experiment. I explored whether SaaS valuation multiples could go as low as 3x ARR. In February 2016, SaaS multiples were at 3.3x forward revenue. At the current 26% median annual growth rate (EMCLOUD) that corresponds to 4.2x ARR. Not 3.3x. But not pleasant either. Not surprisingly, thoughts of valuations as low as 3-4x revenue were not popular amongst the SaaSserati. And, as I wrote, I don't find that to be the most likely scenario from here. Why do I still see volatility (and potential downside) in the stock market short term? This weeks' US inflation print was surprisingly benign, coming in at 7.1% vs market expectations of 7.3%. Markets responded favourably on Tuesday, with the S&P500 initially up 2.6% before falling back. Powell, much less so. He has been clear that he intends to continue to increase rates to stamp out any last vestige of inflation. So there. After raising rates by 0.5% on Wednesday (to 4.25% - 4.5%), he promised further increases in 2023. The Feds median estimate for the end of 2023 is now 5.1%. This is higher than most investors forecast. It is also likely to put a damper on economic activity in the new year. If we take Powell at his word, investors will inevitably be disappointed. So 2023 is likely to be wobbly. But that doesn't detract from the medium-term opportunity. And that is the exciting bit. ## Advances in 2022 2022 has been an odd year. Inflation has been raging, and Putin has been warmongering. And most people's pensions have taken a hit as stock prices have deflated. BUT 2022 has also been a year of reaching incredible technological milestones. - AlphaFold AI predicted all known protein structures. Scientists can now predict how more than 200,000,000 proteins fold. That's up 1,000x from what we could do before AlphaFold. This is transformative in drug research. - The James Webb telescope became operational. This helps us better understand the origin of our universe. It is also the culmination of two decades of scientific work. - Scientists at the Lawrence Livermore National Laboratory succeeded in creating Nuclear fusion 'Ignition'. It is a major milestone towards limitless, clean energy. And it is something scientists have been chasing for decades. - OpenAI released ChatGPT - a chatbot that passes a Turing Test. This milestone is something computer scientists have been pursuing for 70 years. These breakthroughs won't change the fabric of society overnight. But they unlock so much potential for the decade ahead. In energy, in medicine, in manufacturing and in transport/logistics. And AI/software will supercharge advances across all these domains. Markets will likely be wobbly in the near term as we unwind inflation. But the medium-to-long-term prospects are incredible. --- # B2B SaaS valuation multiples at 3x? **Source:** https://www.superseed.com/madsjensen-xyz/b2b-saas-valuation-multiples-at-3x/ **Published:** 2022-12-14 **Author:** Mads Jensen *This is not investment advice. Always do your own research and speak with your financial adviser before you invest.* In the world of tech investing, FOMO has given way to fear. Private company valuations have followed public companies. What used to be 20-30x revenue is now 5-10x. And there is even speculation that 2-3x could be on the menu in 2023. But is 3x ARR even possible? [EMCLOUD](https://cloudindex.bvp.com/) is Bessemer's Emerging Cloud Index. A collection of 75 public SaaS/Cloud companies like Salesforce, Hubspot and Snowflake. Industry leaders. Amazing businesses. As late as September 2021, the median ARR multiple was 18.4x. It's now hovering at 5.4x. That's a 71% drop. A proper shellacking. Feels like tech investors have had enough pain. ## Light ahead But there is hope! On Monday, Thoma Bravo (a Private Equity firm) announced [an $8bn acquisition of Coupa](https://www.thomabravo.com/press-releases/coupa-software-enters-into-definitive-agreement-to-be-acquired-by-thoma-bravo-for-8-billion). The eminent Tom Tunguz had [a great analysis on his blog the same day](https://tomtunguz.com/coupa-thoma/). As Tunguz points out, this is the most substantive acquisition since Adobe acquired Figma. The M&A market may be improving. That's good news. And importantly, the deal shows that there may be an upside in tech valuations. Forward MultipleGrowth RateEfficiencyCoupla7.8x17.0%39.6%Median SaaS (EMCLOUD)4.6x26.4%32.2% Thoma Bravo paid a 31% premium to the public price. They also paid a 7.8x multiple on forward revenue. That is higher than the industry median of 4.6x. The average public cloud company grows 56% faster than Coupla. Would investors be willing to pay 56% above Coupla's valuation multiple of 7.9x? I.e. 12.3x forward revenue? That would be a nice upside to where we are now! I am a technologist. So - by definition - I am an incurable optimist. However, there is a strong argument that we could decline further before markets rebound. Here is another interpretation of the price Thoma Bravo paid. A year ago, tech companies were mainly valued on growth. Faster growth = higher valuation multiple. But as of 2022, Efficiency has replaced growth. And what is Efficiency? It's revenue growth % + Free Cash Flow %. Revenue growth of 30% and a Free Cash Flow % of 10% give an Efficiency score of 40%. And it turns out that Coupla's efficiency was 23% *higher* than the median SaaS company. Coupla grew more slowly than the peer group. But their free cash flows were better. And Private Equity firms like free cash flows. By this logic, the implied valuation of the deal isn't 12.3x for the median SaaS company. It's 6.4x. Slightly higher than where we are today. But not terribly exciting. ## So is it up or down from here? The S&P500 is at roughly 4,000 at the time of writing. There are different ways to analyse the possible path from here. Historically, the S&P500 has been closely tied to liquidity and quantitative easing. As central banks expanded their balance sheets, the S&P500 went up, up, up. All that has come to an end now. ![](https://www.superseed.com/wp-content/uploads/2022/12/image.png)Correlation between SP&500 and QE. Source: Yardeni Research Based on an analysis of the likely Quantitative Tightening in 2023, Steno Research is [calling the floor at 3,500 next year](https://twitter.com/andreassteno/status/1602240289514684416). ![Image](https://pbs.twimg.com/media/FjxMmbuXEAQFopc?format=png&name=900x900) Alfonso Peccatiello [sees the floor at 3,250 based on recession and declining company earnings](https://twitter.com/macroalf/status/1602063929164763136). ![Image](https://pbs.twimg.com/media/FjuekojWQAEOKK_?format=jpg&name=medium) That's a further decline of 12.5% - 18.75%. The EMCLOUD Index is a great way to track public cloud/SaaS companies. It has a Beta of 1.16 to S&P500. This could mean that SaaS companies have a further 15-20% to go. And 20% off the current median 5.4x ARR multiple is 4.3x. Not quite 3x. But not pleasant reading either. Do I think we can see those valuations in 2023? It's not unthinkable. I also don't think it is the most likely outcome. But, whatever your expectations, it seems clear to me that we aren't out of the woods quite yet. Better get ready for another few rounds on the roller-coaster. *(Note - I am using EMCLOUD data which is slightly different than Tunguz'. Directionally, the analysis is the same).* --- # Venture-backed tech companies don't need board governance. Or do they? **Source:** https://www.superseed.com/madsjensen-xyz/venture-backed-tech-companies-dont-need-board-governance/ **Published:** 2022-12-12 **Author:** Mads Jensen Venture-backed tech companies don't need board governance ..seems to have been frequent wisdom over the past few years. Rather, the idea has often been to give founders maximum latitude to experiment and build. And why not? After all, building a successful tech company is unreasonably hard. So hard, in fact, that it seems only superhuman entrepreneurs can do this. In other words: the last thing founders need is a bunch of biscuit-eating suits holding them back. Or boards asking for a bajillion ESG reports on employee water consumption. And wasn't Steve Jobs, the grand supremo of entrepreneurs, fired by his own board? Only to be replaced by John Sculley. Someone with a deep understanding of how to.. eeek.. market soft drinks? Jeez. Besides, we have had a decade with low interest rates and "almost free" capital. If things didn't work out, you could always raise another round. So there. "No board governance for me, please", seems to have been the logic. ## When it goes wrong But it's not always gone well. There are plenty of examples where boards either were non-existent (FTX). Or were misled (Theranos). So fraud is an issue. But it's not just fraud that's causing problems. I am talking about something else here. Namely when the boards are there, but not quite doing their job. Turns out a decade+ of "free money" has made our ecosystem slack. - Too many founders preferred not to be held accountable. - Too many investors were comfortable cheerleading from the sidelines. - Or didn't know how to add value, even if they wanted to. It's easy to blame the founders. But I think investors bear the brunt of the blame. Writing cheques with no strings attached serves no one. Least of all the founders we are trying to support. Most of us have been guilty of sloppy board work. What are some of the symptoms? Unclear objectives. Wolly strategy. Lack of transparent reporting. ## How to get it right So how do boards provide proper governance? By ensuring clarity on the following - objectives - challenges - strategic principles - coherent plan of action - KPIs to track Company management (led by the founders) is responsible for putting the plans together. But for first-time founders, it's not always clear how. They deserve good boards to help them figure this out. And to ask the hard questions if the plan isn't clear. It's time for all of us to roll up our sleeves and get stuck into the board work. Because when done right, it can add a lot of value. Also for tech startups. --- # Tranched investment is a terrible idea. Maybe. **Source:** https://www.superseed.com/madsjensen-xyz/tranched-investment-is-a-terrible-idea-maybe/ **Published:** 2022-12-09 **Author:** Mads Jensen Tranched startup investment is a terrible idea. Or is it? First, some definitions. Venture capital investments are, by definition, "tranched". The tranches are called: Pre-Seed, Seed, Series A, Series B and so forth. Startups are risky little things. Most of them perish before they have a chance to flourish. But there is meaning to the madness. As companies hit milestones (team, product, customers, scale, profit), they become less risky. And as they become less risky, valuations go up. It might take $100m+ of investment for a company to go from inception to IPO. But writing a $100m cheque to a pre-seed stage startup with a likely failure rate of +90% seems cavalier. And then there is the question of dilution. So instead of investing $100m up front, investors start with a smaller cheque of - say, $500k. And each major milestone then unlocks a higher valuation and the next funding tranche. In other words: tranched investment. But VC investors often talk about something slightly different when they talk about tranched investment. Rather than tranching investments to major milestones, the idea is to use smaller waypoints. Same idea. But smaller tranches. Now the Seed or the Series A round is no longer a "big cheque" to hit the next major goal. It's a smaller cheque to hit one or more smaller goals on the way. At face value, this might seems like a pragmatic approach. Venture capital orthodoxy is that this is a bad idea. And generally, I agree. Startups are hard. It often takes superhuman effort and focus to make it to the next major milestone. They are also unpredictable. And founders have lots of pitfalls they need to navigate on the way to that milestone. As a result, the last thing founders need is to constantly worry about whether they will run out of cash in 2 weeks. It's not good for the founders. And it's not good for the business. BUT What should founders do if there is a mismatch between valuation and desired capital? If they need $4m to hit the next major milestone, but markets currently value their business at $4m pre? Of course, you could give up half of the company. But there might be times when it's better to do half and half. E.g. half at $4m and the remainder at a higher valuation once a few smaller milestones have been hit. Most investors would agree that a "fully funded plan" is ideal. Enough capital to get to the next major milestone. With a bit of buffer. That's the orthodoxy. But sometimes "best" isn't available. And in startup-land, orthodox ideals sometimes need to make way for pragmatic alternatives. In those situations, tranched investment is worthy of consideration. --- # ChatGPT (and AI) - a toy, a threat or a terror? **Source:** https://www.superseed.com/madsjensen-xyz/chatgpt-and-ai-a-toy-a-threat-or-a-terror/ **Published:** 2022-12-07 **Author:** Mads Jensen Last week, [OpenAI](https://openai.com/) released [ChatGPT](https://chat.openai.com/auth/login) to the world. It was generally [met with awe in tech and venture circles](https://www.superseed.com/journal/the-future-has-arrived/). But not everyone is excited. The criticisms are in one of three camps: 1. it's just a toy with limited practical application (and implication) 2. it's a threat to workers and to the democratic discourse. Thus, it is a threat to the foundations of civilisation 3. it's a terrifying technology that harbingers dire implications for humanity. Some of these criticisms are directed at ChatGPT. And others at AI in general. Let's unpack these - from benign to cataclysmic. ## It is a toy ### It has shortcomings that render it useless or harmful: ChatGPT uses probability to create content and responses. Sometimes it's "right". Sometimes it just sounds right, but it's factually wrong. But even when it's wrong, it is built to sound as if it is right. That makes it unpredictable. At best, that makes it useless. At worst, this makes it dangerous. ## It is a threat to workers and to democracy ### The potential to reduce human interaction If AI algorithms are compelling and stimulating, we will likely spend more time interacting with them. And as we spend more time interacting with algorithms, we spend less time interacting with each other. Humans are social animals, and human interaction is essential for our well-being. Given that, algorithms that reduce human interaction are bad for individuals. And as a second-order consequence, also bad for society. ### The potential for loss of jobs AI has the potential to do the work that is done by humans today. Consequently, it displaces workers, puts them out of a job and threatens their livelihoods. Having a job or access to work enables people to earn a living. It can also help to give people a purpose. In that light, it is harmful if AI algorithms like GPT permanently eliminate people's jobs. This is bad for both the individual and for society. ### The potential for bias AI systems are only as good as the data used to train them. If that data is biased, then the AI system may also be biased. This could lead to unfair and unequal treatment of individuals based on their race, gender, age, or other factors. ## It's one step from the Terminator ### The risk that a powerful AI will be set on destroying humanity ChatGPT is one further step towards [Skynet]. The premise of the films in the Terminator franchise is that a powerful AI develops sentience and decides to take over the world. Cue death and destruction. ### The risk that governments use AI algorithms to harm humans It is unlikely that GPT (or a similar algorithm) will develop sentience and set itself on world domination. A more likely scenario is that nefarious governments use AI algorithms to harm others. AI is a powerful tool. It has the potential to be used for good and for bad. And when powerful tools are used for "bad", very bad things can happen. ## All the "bads" In summary, here are some arguments for why AI is useless or even dangerous: 1. ChatGPT is prone to make up answers that sound plausible but could be wrong. That makes it a toy or a danger. 2. AI Algorithms reduce human interaction, making us lonelier. 3. Algorithms can displace workers and lead to permanent job losses. 4. Algorithms are biased, which can lead to unfair treatment. 5. AI algorithms can be used to harm humans. Or - worst case - do so on its own accord. These are all reasonable concerns. AI is a powerful technology. We should consider the implications carefully. ## How do we ensure AI is useful and safe? AI is one of the biggest technological advances in our lifetime. Such advances raise questions and challenges. It is beyond this blog to comprehensively address all AI-related questions. But here are three principles that might guide our thinking. ### Develop transparency to overcome inaccuracies and biases Algorithms like ChatGPT are probabilistic. This means they are prone to making up answers that sound right but are actually wrong. Sounds familiar? In many ways, this is something humans do all the time. Another thing we humans do all the time is to be swayed by our biases. And the concern is that AI algorithms will encode and amplify existing biases. One of the benefits of algorithms is that we can look inside them and audit them. We can get them to reveal the basis of their conclusions. And to grade the confidence of their answers. In many ways, we can make algorithms much more transparent than humans. But only if we make this a priority. It strikes me that open and transparent algorithms must be one of the top priorities in AI research. ### Invest in education to enable mobility We displace human workers when we automate work using AI Algorithms. Some argue that it will put everyone out of a job and destroy our economy. Of course, people have been arguing against automation for centuries. Witness how the Luddites smashed the automated weaving looms in 19th-century England. But human ingenuity has kept finding new things to do. And few would argue that we would be better off with society stuck in the 19th century than we are today. Yes, AI automation brings progress and a better world. But it also brings challenges. We must put in place programmes that enable life-long education and retraining. And we must start early. We need an education system that prepares us for a shifting world. So the second big priority is comprehensive investment in education. And speaking of education, consider some of the benefits of AI. Imagine a world where we have a personal tutor for every child. It's hard to imagine anything more transformative than that. ### Make sure that government policy embraces AI This leads me to the third criticism of AI. Powerful tools are dangerous when used for destructive purposes. AI is extremely powerful. It can be very dangerous. I think this critique is spot on. So does this mean that we should stop AI research? That doesn't seem like a smart move. At best, we'd love access to the powerful benefits AI can bring. And at worst, competing nations will continue their research and leapfrog our capabilities. Then we would lose out on the benefits while putting ourselves at risk. So as a third principle, we must ensure comprehensive mastery of AI. This should be through government policy that provides: 1. comprehensive public R&D investment, 2. a regulatory framework that encourages the development and use of AI, 3. close partnership with our allies, 4. support for deploying AI in education, business and healthcare, and government. ## AI - so much potential if we can get it right AI (and ChatGPT) wasn't built in a day. But it is getting better every day. Every advance brings new potential. And potentially new challenges. Like all powerful tools, we have to "handle with care". The three principles I propose to help us do that are as follows: 1. Make the development of open and transparent algorithms the top AI research priority 2. Invest in life-long education to enable personal mobility 3. Develop comprehensive government policy to embrace AI It is on us to act with purpose and compassion. And if we do that, AI has the potential to be the most transformative technology of our lifetime. --- # The Future has Arrived **Source:** https://www.superseed.com/madsjensen-xyz/the-future-has-arrived/ **Published:** 2022-12-04 **Author:** Mads Jensen This week, the world changed forever. There are small things that shift all the time. But once in a while, there is a distinct fork in the road. A shift that - once made - charts a new course for the world. I am, of course, talking about the release of ChatGPT - the unbelievably smart AI-powered chatbot unveiled by OpenAI on November 30th I'll get back to ChatGPT shortly. Firstly let me put it in the context of how we use AI today in the world. ## Everyday use of Machine Learning For years we have been talking about how AI will change everything. In many big and small ways, this has already been happening. Machine Learning enables computers to infer answers and insights from vast amounts of data. Machine learning algorithms are already at work figuring out which netflick to recommend to you. Which tweet to put in your timeline. Or which Instagram post or Youtube/TikTok video you need to see to keep you scrolling and scrolling and scrolling. Machine learning also helps Alexa understand what we are saying - even though we speak with all sorts of funny accents. And it helps Google Maps figure out how to get us most efficiently from point a to point b. Machine learning has made its mark in lots of subtle ways. Perhaps the most common way of interacting with machine learning is through google. That uncanny feeling when it just knows what you are looking for, so much so that it finishes the sentence for you. And the elegance with which it helps point us to that paragraph on that page that just has the answer to the question that's on your mind. But this pales in comparison to the power of ChatGPT. ## A Copywriter at Your Fingertips Imagine an AI algorithm that will write you a poem. Or a song. Or create a recipe. Or a block of computer code. Or a newspaper article. Or a movie script. Or a custom-built answer to pretty much any single question you can fathom. It's like custom-written Wikipedia entries for every question in your mind - except you don't have to scroll through the whole entry to find the answer to the specific question at hand. The algorithm isn't simply pointing you to a page on the internet it thinks best answers your question. It is creating genuinely new content to address the specific question you raise. You can [play with it here](https://chat.openai.com/auth/login). ## So How Does it Work? The purpose of GPT-3.5 (the algorithm that underpins ChatGPT, the GPT standing for Generative Pretrained Transformer) is to learn to write text that looks like it could have been written by a human. To do that, it has been trained on (i.e. "read") texts with billions of words. This is basically the equivalent of gobbling up library after library with tens of thousands of books. A voracious reader indeed. And the output is basically two things: 1. it has become so good at it that its writing is more or less indistinguishable from that of a human. 2. it has more "knowledge" readily available in its databank than any human ever could have I use quotation marks around knowledge deliberately. The GPT-3.5 algorithm doesn't have a sense of self or understanding the way humans do. It's just an incredibly competent writer with an unfathomable amount of information at its fingertips. ## What's the Real Innovation Here? Look, basically, ChatGPT is uber mega cool. Even really smart people are telling me that they can't stop playing with it. Your very own, all-knowing bot, always at your fingertips (or at least never further away than your smartphone). These types of models have been available for some time, but this is the first time such a powerful bot has been freely available. The future arrived overnight. Boom! But secondly, ChatGPT shows us the power of a natural language interface. You don't need to know the arcane tricks of programming to make it do what you want. Or learn a complicated set of UI tricks to unlock its power. Just write what you want, and you will get it. While Google (+ StackOverflow) will be able to show you an article on how to solve a specific coding problem, ChatGPT will **write the code for you**, just prompted by a normal English language query. Double boom! ## The World Will Never be the Same I started out by saying that we woke up to a new world last week. Today, it's hard to imagine a world without google and the instant access to information it gives us. ChatGPT feels like a similar step change, but it's one that landed overnight (yes, clever researchers have been working on Transformers like GPT for years, but the powers were put into the hands of the public on the 30th of November). ### Let's start with education. ChatGPT fundamentally changes the way education works. Are you looking for an essay on Mao's reforms - the Great Leap Forward and the Cultural Revolution? You got it. 1,200-word essay. Here you go. Not hard to see how this changes the nature of homework. But how about the other side of the equation? Could ChatGPT outline a relevant set of questions for students? And a grading rubric? And an example answer? And grading for an essay? And suggestions as to what could have been better? Yes - all of the above. Education will never be the same. ### So what about business? The advances shown with ChatGPT open up whole new ways to do business. AI-crafted, tailor-made letters for sales prospects? Automated replies to enquiries that actually have meaning? Transformation of customer service, corporate knowledge management, software development, accounting. The list is endless. Now that OpenAi has shown the world the power of a language-driven interface, there is no going back. As Sam Altman (OpenAI CEO) wrote: "[...]you will be able to have helpful assistants that talk to you, answer questions, and give advice." But this is just the beginning "Later, you can have something that goes off and does tasks for you. Eventually, you can have something that goes off and discovers new knowledge for you." It completely changes the way we do business. ChatGPT still has limitations. It's deliberately not "connected to the internet". The fine folks at OpenAI are at pains to stress that it is still just a "research release". But they are not fooling us. This is a watershed moment in technology and in the advance of AI. What an exciting time to be in technology. What an exciting time to be alive! --- # The roaring 20s: finally within sight? **Source:** https://www.superseed.com/journal/the-roaring-20s-finally-within-sight/ **Published:** 2022-11-30 **Author:** Dan Bowyer As 2022 draws to a close, I'd like to reflect on the year we've had, the global economy, and what is ahead for 2023. The Pandemic and the War in Ukraine have disrupted the global economy and bookended an era, but also laid the ground for growth opportunities in the decade ahead. I'll get to the growth opportunities in a minute. But first, let's take a look at how we got here. Let's think back to the start of the year. War in Europe wasn't on most people's bingo cards. And it's easy to think of the Russian invasion of Ukraine as the key driver of this year's misery for investors. The breakout of war was a shock to the system. But the seeds had already been sown. Globalisation has been driving the global economy for the last four decades. The seventies had been marred by stagflation. Following the oil crisis of the early seventies, spiking energy prices and inflexible labour markets led to a wage-price spiral. The result: moribund economies and high unemployment. Let's review what has happened since then. ## **Four Decades of cheap labour, cheap energy and cheap money**. Globalisation provided a steady stream of cheap labour. It kept the cost of imported manufactured goods down and kept western labour costs in check. The result: low inflation. ![](https://www.superseed.com/wp-content/uploads/2022/11/Screenshot-2022-11-30-at-15.20.47-1024x479.png)US inflation - a reasonable proxy for OECD in the same period As inflation stayed low, central banks could keep interest rates low. Cheap credit fuelled rising asset prices – especially for housing. But also for company stocks – both public and private. ![](https://www.superseed.com/wp-content/uploads/2022/11/Screenshot-2022-11-30-at-15.23.48-1024x563.png) ![](https://www.superseed.com/wp-content/uploads/2022/11/Screenshot-2022-11-30-at-15.24.04-1024x667.png) Globalisation created a global economic boom. It lifted hundreds of millions out of poverty in the East and helped the richest in the West amass vast wealth. ![](https://www.superseed.com/wp-content/uploads/2022/11/Screenshot-2022-11-30-at-15.25.31-1024x595.png) This wealth was not evenly distributed. Many in the Western middle class were left behind, leading to protests at the ballot box. This disrupted the post cold war political stability and led to the rise of autocrats from North to South. The end of history and the triumph of liberalism were cancelled (or at least temporarily put on hold), and we still feel the aftermath of these shocks to the political system. ![](https://www.superseed.com/wp-content/uploads/2022/11/Screenshot-2022-11-30-at-15.28.46.png) At the same time, vast global economic growth was sustained by abundant cheap energy. We became so efficient at extracting energy that oil and gas prices stayed low in the 80s and 90s, [despite rampant consumption](https://www.researchgate.net/figure/Production-vs-Oil-Consumption-in-the-world-1965-2020-Source-Made-by-the-author_fig1_357405912). ![](https://www.superseed.com/wp-content/uploads/2022/11/Screenshot-2022-11-30-at-15.35.33-1024x742.png) By the noughties, oil prices started spiking, leading to moderate inflation in the US. However, this was soon subdued by the Great Financial Crisis and [more or less kept in check](https://www.macrotrends.net/1369/crude-oil-price-history-chart) until Russia invaded Ukraine at the start of 2022. ![](https://www.superseed.com/wp-content/uploads/2022/11/Screenshot-2022-11-30-at-15.32.48-1024x653.png) We [can see](https://www.iea.org/data-and-statistics/charts/co2-emissions-intensity-of-gdp-1990-2021) more efficient use of oil and better use of renewables when we measure the CO2 emissions per $ of GDP. ![](https://www.superseed.com/wp-content/uploads/2022/11/Screenshot-2022-11-30-at-15.33.04-1024x548.png) However, because global economic activity has increased so much, [emissions have kept going up](https://www.statista.com/statistics/276629/global-co2-emissions), up and up. ![](https://www.superseed.com/wp-content/uploads/2022/11/Screenshot-2022-11-30-at-15.33.13-1024x601.png) So when viewed from the West, these were the defining three features of the past four decades: 1. Cheap manufacturing abroad and depressed wages at home 2. Cheap energy and increasing emissions 3. Cheap credit and inflating asset prices These trends have now come to an end. This will lead to the most significant shift in our global operating model in our lifetime. # **Globalisation - the next chapter** We see three tectonic shifts in the global economy. ### 1. Changing the way we **make and move** things Two factors are upending the way we manufacture goods, with knockon effects for supply chains and logistics:  1. Traditional offshoring to China is coming to an end; and 2. The Pandemic and war are exposing the brittleness of supply chains. Let's unpack these further... #### **The end of traditional offshoring** The era of offshoring and simple labour arbitrage is over.   Offshoring and labour cost arbitrage were key drivers of "operating efficiency" for decades. But China hit the "[Lewis turning point](https://www.ft.com/content/767495a0-e99b-11e4-b863-00144feab7de)" during the last decade, and the country now faces decades of a declining working-age population. This means that a near-unlimited supply of workers has come to an end. The cost of labour has already been increasing over the past two decades. This is set to accelerate further. #### **A bifurcation of the global economy** We are starting to see a decoupling between China and the OECD. During the nineties and the noughties, tighter economic integration between East and West was a key part of the Western playbook. As developing countries became more prosperous and more integrated into the global economy, they would naturally transition towards western liberal democracies. That was the way things were "supposed to work". But emerging economies refused to follow the script. Emerging BRIC economies weren't interested in simply aligning with the priorities of the West. Many policymakers in both Europe and the US were quite late to decipher China's geopolitical ambition. But when Russia invaded Ukraine at the start of 2022, everyone finally understood the need for a new playbook.  As Chinese labour costs have risen and political winds have shifted, manufacturers are now looking at rerouting manufacturing to "friendlier" nations. Or perhaps bringing it home entirely. For concrete examples, see [the US CHIPS Act](https://www.mckinsey.com/industries/public-and-social-sector/our-insights/the-chips-and-science-act-heres-whats-in-it) that aims to cut China off from advanced, Western semiconductor technology. Companies will keep looking for ways to lower costs to maintain and expand profit margins. And with cheap Chinese labour giving way to more expensive alternatives, those alternatives must be augmented by automation and technology. This is the first major driver of B2B technology investment over the coming decade. ### 2. **Transition to the low carbon economy** We need a new playbook to deliver "net zero".  The growth in carbon emissions has been rampant over the past four decades. We have a target to bring emissions to net zero by 2050 - just 27 years away. Barring a global calamity that eclipses the Pandemic, it is a safe bet that this won't happen through the curtailment of human economic activity. It is in our human nature to make, to travel and to *do*. So we must find ways to keep increasing economic activity while eliminating our oil addiction.  To deliver on the promise of net zero, we need to radically retool the way we make and the way we move: new ways to make things and new ways to generate, store, and transport energy, people, and goods. These are enabled by massive investments in technology and infrastructure. The transition to net zero will drive business investment in two ways:  1. Firstly, because business will be critical in developing, manufacturing, installing and operating the decarbonised economy. There is a huge economic opportunity in this.  2. Secondly, because the cheapest megawatt is the one we never need to generate. And there are still massive efficiency opportunities in manufacturing and transportation through investment in better technology. With the intelligent use of tech, every company can make more with less. I predict a wave of investment into improving both the supply and demand sides of the global energy marketplace. And, of course, much of this will continue to be through traditional interventions (leaky houses and inefficient means of transportation still waste a lot of energy). Still, there are nearly limitless ways to make factories and offices more efficient, enabling us to make and do more while consuming reduced resources.  The transition to the low carbon economy is the second major driver of investment in the coming decade. ### 3. **The end of "dumb money"** For forty years, investors have had a relatively easy ride. And especially the decade and a bit since the Great Financial Crisis have been straightforward. With low interest rates and quantitative easing, all assets have inflated. Stocks and bonds. Of course. But also property. And art. And wine. And cars, watches and pretty much everything else you could invest in. Over the last decade, stock market investors have reaped 11-12% a year after inflation. A few technology companies with incredible business models drove a big part of the increase. However, with both bellwethers Alphabet and Apple reporting declining EPS over the past year, investors are out of safe havens. So as investments go, it is time for all of us to be smarter. Of course, the S&P500 will likely still be a good investment over time. But it is unlikely that passive capital allocation will perform as well over the next decade as it did during the era of QE. Instead, savvy investors will look for opportunities that create tangible returns for customers. If the "greater fool" theory of investment no longer works, investing becomes about identifying and picking investments that create tangible ROI for business customers – either as growing top line or efficiency gains. ## A new paradigm for the global economy In summary, we see the global business paradigm migrate from: 1. dogmatic offshoring and low-wage arbitrage to a much more sophisticated supply chain strategy (this necessitates more investment in automation and other enabling technologies); 2. a high carbon to a low carbon economy (this will again necessitate investment in automation and other efficiency-enhancing technologies); and from 3. asset inflation and "the tide lifting all boats" to a climate that requires more thought to pick the best investments.  > **All of this speaks to more investment in technology that makes business smarter and more efficient. And at the heart of this sits AI-powered software.** ## Investment opportunities in Make, Move and Manage We see great opportunities emerge in the three verticals of Make, Move and Manage: 1. Make – Next-generation industrial automation (Industry 4.0) combining IoT, AI and edge computing to make our manufacturing processes much more efficient; 2. Move – next-generation logistics and supply chain management that drives better forecasting and demand planning and a more flexible and resilient supply chain; and 3. Manage – next-generation enterprise software that empowers businesses with the latest advances in AI to optimise effectiveness and employee productivity.  The coming 6-18 months will likely be choppy "in the real economy", which will impact both consumption and investment. But I don't think it will take too long to get inflation and interest rates under control. And this will unlock the next wave of investment in technology that will lay the foundation for a prosperous decade ahead. As humanity, we have significant challenges ahead of us. But this is precisely the type of environment that makes human ingenuity thrive. The easy options are gone. Now we have to do the hard work. And by pursuing that road, we will unlock a decade of creativity and growth ahead. Bring on the roaring 20s of the new century! --- # Why we invested in... Techsembly **Source:** https://www.superseed.com/journal/news/why-we-invested-in-techsembly/ **Published:** 2022-11-25 **Author:** Dan Bowyer Towards the end of 2019, we met an extremely bright and experienced 2nd-time founder, building a new kind of e-commerce platform. "We don't *do* e-commerce", I rudely spurted at our investment meeting. "Yeah, but this is different", Mads stated, "take a look, have a chat with Amy and see what you think". When Mads says that, I pay attention. As investors, it's too easy to become jaded by the many pitch statements, the hackneyed phrases, so it's super important to constantly keep an open mind. See everything that comes across the desk with fresh eyes. Mads was right, this was different, it was thoughtful, smart, untapped, with capacity to expand across multiple sectors and territories. They solve one particular challenge really well; If you're an international retail brand, with multiple e-com store-fronts in multiple languages, with multiple sets of inventory, rules, regulations etc. - it's super hard to manage and keep track of your whole organisation - the stores in each country, their back office, the dashboards, all at a group level. Many haven't even attempted to face the problem and consequently, their client's client experience is poor. You'd think offerings such as Shopify would cater, but like others, they can't due to how their infrastructure is set up. When we first met the team, they were approaching any multi-tenanted retailer, but it became obvious fairly quickly that the first persona they should reach, with real pain points, were large premium hospitality groups such as the Peninsula, one of their first clients. If you've ever stayed at a large hotel group like the Hilton, you've probably experienced how frustrating it is, post booking, to buy or manage hotel products and services - vouchers, massages, upgrades, F&B, or other. You've more than likely ended up picking up the phone (what's that?) and dialling 0. Nowadays, we expect it all on our phones, 3 clicks away - boom, at the door. ![](https://www.superseed.com/wp-content/uploads/2022/11/Screengrab-TS-261x300.jpg) That's what [Techsembly](https://techsembly.com/) does. It enables premium hospitality brands to offer a premium shopping experience to their guests. And extremely elegantly too. Increasing conversions by up to 70% and massively boosting revenue (keeping in mind this is for products and services they already offer. There's more to come). We've now supported Amy and the team with their last 2 funding rounds to drive global expansion. They are already working with many of the leading hospitality brands, with more major players to be announced in Jan. The company is on track to raise series A next year. As with all of our [portfolio companies](https://www.superseed.com/portfolio/), next time you're out and about, have a think about what's powering those clicks behind the scenes, making things easier as we make, move and manage our lives! --- # Why we get out of bed **Source:** https://www.superseed.com/journal/who-we-are-and-who-we-serve/ **Published:** 2022-11-09 **Author:** Dan Bowyer Video may well have killed the radio star, but it sure does bring a story to life! We recently made the video below to give a flavour of what we do, who we are and most importantly, who we serve. As a VC firm, our primary purpose is to make money. To state the obvious - If we don't, we don't exist! But to have real impact, Venture Capital has to be more than just the C. We get to start revolutions, change lives, and specifically for us here at SuperSeed - transform how the world works. > Startups are pure innovation. They are also net creators of jobs and opportunities. It's a privilege to be able to work with [the smartest European founders](https://www.superseed.com/portfolio/) and help them bring their visions to life. **That's why we get out of bed.** --- # What *is* impact? **Source:** https://www.superseed.com/danbowyer-me/what-is-impact/ **Published:** 2022-10-31 **Author:** Dan Bowyer **Can you make money *and* do good?** It’s obvious why the economy is front and centre of any political debate (apart from being Rishi’s raison d'être). Not only is money on everyone’s mind, especially right now, but every government department is being challenged on funding and spending. Even in Bull times, it always comes down to the economy, and therefore, business. Without tax receipts coming into HMRC, which are currently around £370bn pa in the UK, we can do very little. Business is the only band at the bar mitzvah, which leaves us in a moral quandary. > As a business, are you doing bad, doing no harm or doing good? Where is your impact along that thread? This topic comes up a lot when meeting startups in SuperSeed Towers. We all need money to survive at home or in business. As VCs, it’s even more raw: it’s how we place bets on the future, trying to outwit power law and probabilistic thinking. Making winning bets gives us the opportunity to survive and recycle, as we fuel the creation of products and services for the next generation. I’d be so bold to state that without venture capital, there is no innovation (Corporates are crap at it, and it gets worse - they’re also net destroyers of jobs and opportunities). Just look around you right now and ponder what was venture backed? Starting with the super-computer on your desk, or in your pocket, plus the software they both rely on. **Now that’s impact.** But is it the right impact, for right now? *Because the game has changed.* Most accept that there are too many of us on this rock, doing too much too quickly, powered by too much fossil fuel. Consequently, our survival as a species is under threat. Yet business must go on - it too being the very root of our survival. (No Elon, moving to Mars isn’t any part of the answer.) I was speaking to a fresh-faced German VC last week who raised his first €50m fund in 7 months from first ask to close. This is enviously unnatural for most of us in the emerging manager community. How did he manage it as a solo first-time manager? ***It’s a climate fund.*** Chris Sacca recently closed $800m for his LowerCarbon Capital fund designed to “unf**k the planet” stating “the commitments came exceedingly fast — in just a few days.” As a theme, climate looms large over the economy. No air to breathe and, well… oh. However, I’m going to be obtuse now: apart from carbon capture, heat pumps or solar panels, what really *is* climate tech? And is an impact fund the only way to create ‘impact’? Which is where it gets interesting, and a big part of why I get out of bed in the morning. There are many ways to slice and dice positive impact with two types of investment activity that drive the triple bottom-line - those being people, planet and profit. The explicit Sacca style ‘save the planet by funding impact startups’, and the implicit - making business smarter via efficiencies that boost productivity. If I was being mean and self serving I’d suggest that the former are great at ‘impact’ headline grabbing, while firms like ours are busy making big business smarter from behind the scenes - transforming how the world works. I passionately believe we need both strategies, although there are two more important caveats to note: Impact in private markets *must* align with making profit or it simply won't work, and secondly, we must accept that politicians will not make too many bold moves as their only real mandate is re-election (smarter regulation would be nice). **So business must fill the gap. ** What we *don't* need are more zombie unicorns, faster e-scooters or grocery deliveries. So thank goodness those days are over, and we, as a community, can get back to investing in real business models solving real problems, which I believe is happening. As you may have read in a previous note, we recently invested in [a demand sensing / supply chain management platform called Garvis](https://www.garvis.ai/). They have a unique offering that saves up to 10% off the bottom line for large CPG, manufacturing or FMCG companies. If you’re a multinational with revenues in the billions, like many of their clients, that efficiency gain is not insignificant. Or, look at [ThingTrax](https://www.thingtrax.com/) who make legacy factory machinery smart with tiny IoT devices to feed actionable performance data to management teams. Or [Ai Build](https://ai-build.com/) who are literally creating the production processes of tomorrow via additive manufacturing. > There is a real and sizeable opportunity to create positive societal impact by transforming the everyday mundane services that power our lives.  Ultimately, I believe the smartest money is investing in enabling 'more, for less'. Come bull or bear. But of course I would say this and my wife laughs at me when we discuss what I do and how B2B SaaS makes me fizz - yes I *am* that popular at dinner parties.  What I also love about what we do is that it’s not about making money doing questionable things, to then give back at some later guilt driven date. Personally I believe it’s important to do good *and* have a positive impact along the way - which is why we invest in startups that create real transformative change for the largest organisations on the planet.  To put all of this into a UK context, we’re around 20th on the list (26th on some) of most productive countries in the world per capita, with nearly all of northern Europe beating us.  **We can and must do better.** As we realign supply chains, labour markets, and onshore production to mitigate the risks from shifting-sands monetary policy, China, Russia/Ukraine and weakened economies, I believe it’s paramount to counter these offensives by investing in Europe’s smartest founders, who are building the smartest tech, to create smarter business.  As a small aside - I do take issue with some of the nonsense headlines about Ai. It will not replace us humans - we will only see robots in sunglasses with pew pew laser guns roaming Camden in James Cameron flicks. In the real world, Ai and other smart technologies will give us superpowers, making *us* part of the machine to radically improve efficiency and therefore productivity.  > Productivity is for robots. So yes - I believe there *is* real and positive impact out there being created in the strangest of places, in different shapes and sizes, from the unlikeliest of sources. You *can* (read must) do good as you make money. Just don’t get caught up in the greenwash.  Unless green as in greenback, and making profit.  Nothing wrong with that. *... Dan* --- # October Market Commentary: Poking the Bear **Source:** https://www.superseed.com/journal/october-market-commentary-poking-the-bear/ **Published:** 2022-10-07 **Author:** Dan Bowyer *The below should be considered as general market commentary and does not constitute investment advice. You should always seek advice from your independent financial adviser before making investments.* So, now we are in Q4. Smart investors took the bearish sentiment at the end of September to sell out and book losses, ensuring that S&P500 ended Q3 on a 2022 low of 3585 - more than 25% down since the start of the year. Yep - we are officially in a bear market. [We’ve had 15 bear markets since 1942 (including this one)](https://www.ftportfolios.com/COMMON/CONTENTFILELOADER.ASPX?CONTENTGUID=4ECFA978-D0BB-4924-92C8-628FF9BFE12D). This means that the bear has come out once every 5.5 years. The last bear market in 2020 was barely a one-month blip (albeit with steep losses as the covid panic engulfed markets). However, on average, bear markets between 1942 and 2022 had a duration of 11.3 months and led to losses of 31.7%. ![](https://www.superseed.com/wp-content/uploads/2022/10/Screenshot-2022-10-06-at-18.39.10-1024x345.png)Chart courtesy of First Trust So far, the 2022 bear market has lasted a little over nine months and measured a 25% drop from peak to trough. So not far off the place where bear markets have historically bottomed out. And some commentators have suggested that it might soon be time to reenter markets. And indeed, following September's clearing-out, we started October and Q4 with a bang. Once investors had sold shares and booked losses at the end of September, many investors felt it was time to pile back in. As a result, the start of the new quarter has been buoyant, with the bellwether S&P500 index up 5% in the first few days of trading. However, the index gave up most of those gains by the end of the week, and it looks like we may not have touched the bottom quite yet. Indeed, several factors suggest that this bear market might still have some life in it. ## Demand crunch On the demand side, there is a looming interest rate crunch coming to mortgage owners everywhere - especially if you are on a variable interest rate. US mortgage rates have now [jumped to a 16-year high](https://www.bloomberg.com/news/articles/2022-10-05/us-mortgage-rates-rise-for-seventh-week-to-highest-in-16-years), putting pressure on new home construction and leading to lower construction sector activity. Furthermore, [about 11% of US mortgages are variable, representing about 19% of the total value](https://www.cnbc.com/2022/05/11/adjustable-rate-mortgage-demand-surges-to-14-year-high-as-homebuyers-try-to-afford-this-pricey-spring-market.html). Interest hikes flow through to disposable household income, meaning less demand in the quarters ahead. On the UK side of the pond, we are in for another shellacking. Here, about 20% of mortgage borrowers are on variable rates. Moreover, 40% of fixed-rate mortgages expire at the end of 2022. This means that by Q1 2023, [60% of the UK’s homeowners will be at the mercy of the massive recent spike in interest rates](https://www.ft.com/content/31ea5115-c253-42b4-b734-a92191c257ae). So homeowners need to spend much more on servicing their mortgages. In addition come price hikes on energy, groceries and other goods. This means that homeowners and consumers, in general, are facing a challenging macroeconomic climate. ## Supply-side crunch It's not just consumers that are facing challenges. In the US, the [Labour's share of GDP is at a historic low](https://ercouncil.org/2019/chart-of-the-week-week-7-2019/). On the other side, corporates' share is at a historic high. But this level is unlikely to persist in light of the following trends: 1. Geopolitical risks mean that corporates are reversing offshoring. [World trade as a % of GDP has already dropped from nearly 60% in 2011 to 56% in 2019](https://tradingeconomics.com/world/trade-percent-of-gdp-wb-data.html), with expectations of further declines as more corporates are reshoring manufacturing. This adds resilience but also increases costs. 2. At the same time, the [proportion of the population that is of a working age is shrinking](https://www.insurancejournal.com/news/national/2021/07/07/621605.htm). This tips power back towards employees - something we have started seeing materialise in the form of strikes (that invariably will lead to higher labour costs). So at the same time as demand is likely to contract, the corporate cost base is being put under pressure. All else equal, we expect this to put earnings under further pressure. ## Unwinding QE As of the start of 2022, central banks across the world had amassed [$31trn of assets as part of the various quantitative easing programmes](https://www.yardeni.com/pub/peacockfedecbassets.pdf). It is a staggering amount. The QE programmes are now being reversed, and the expectation is that this reversal will continue. In many ways, quantitative easing was instrumental in inflating asset prices over the past decade+. The expectation is that QE reversal will put asset prices (including equities) under pressure). ![](https://www.superseed.com/wp-content/uploads/2022/10/image-1.png) ## Geopolitical risks Finally, there are still real tail risks from e.g. the war in Ukraine - particularly Putin’s threat of using tactical nuclear weapons. While the expected probability of a strike still is relatively low, the implications for investments and equities would be significant. So overall, it looks all but certain that we’ll see recession rear its head in 2023 and that equities will be put under continued pressure. Given all of this, it seems premature to rush back into the markets at this point. The question then becomes, how far are we from the bottom? Some commentators have suggested that the summer of 2023 is when markets will have bottomed out, and it’s time to get back on board the equities train. It’s worth bearing in mind that stock markets usually are ahead of the recessionary curve. Historically, the S&P has hit its high [seven months before the start of a recession](https://www.forbes.com/sites/sergeiklebnikov/2022/06/02/heres-how-the-stock-market-performs-during-economic-recessions/?sh=763e97696852). Conversely, it typically bottoms out four months before the end of a recession. In other words, if you think the recession is over by next summer, Q1 might be the right time to start moving back into equities. ## **How to think about venture capital during a bear market?** The pullback in public markets has made many investors reevaluate their venture capital allocations. If listed equities are going through a tough time, will small tech companies be facing the same (or worse)? However, it turns out that small tech startups play by entirely different rules to listed behemoths. There are many reasons for this, some of them being: 1. B2B tech companies typically help their customers save money. And that’s something everybody is looking to do in a tough economy; 2. Tech startups often compete in small and less congested market segments, meaning that their performance is tied more closely to individual deals and much less to the macroeconomy. Small teams that execute well are great at finding ways to retain and grow revenue, even if macro-trends are contractionary; and 3. Startups often pursue opportunities tied to new technologies, regulation changes or other factors giving tailwinds. This means that while the macro-factors can be contractionary, startups can still find ample boost through micro-factors. Growth-stage unicorns have been the exception during the last few years. These companies have stayed private even after their valuation has hit multiple $bn. In many ways, the performance of unicorns has been closely tied to public market equities, as their size and shape are much closer to that of a listed company (with a higher cost base and lower growth) than an agile, early-stage startup. A few years ago (before “New Venture” and the unicorn economy), Invesco published [this](https://apinstitutional.invesco.com/dam/jcr:1f35880c-bdf9-42ea-8afe-ab69b85bc7a4/The%20Case%20for%20Venture%20Capital.pdf) whitepaper showing no [correlation between venture capital and large-cap equities](http://apinstitutional.invesco.com/dam/jcr:1f35880c-bdf9-42ea-8afe-ab69b85bc7a4/The Case for Venture Capital.pdf): ![](https://lh6.googleusercontent.com/us5eqQt7v1X7YFnMOFdikXJLbQDpmRAP7ND7U5Q15_EvlpVVPT7w_HVjK8VDYzzZgVQf7KxJ3e_F2kzrlUqo12fs_1OO4pgITDnImftAwzx76lFMiiG1aXAkqGffdcG4PfyaOMwkQZ4Jv6vm7qfkYT1NXzflKjXvYYKQLgCkCoTnfZPO_KvO8I0ZYA) ***So*** rather than seeing the current market as a reason to pull back from venture capital investing, venture capital should be seen as a powerful way to add uncorrelated diversification to investors’ portfolios. Because, as we know, [Diversification is the only free lunch in finance](https://www.ft.com/content/31991efb-3a88-413c-83dd-fea5c7aded58)! --- # SuperSeed leads Finteum’s Seed Round **Source:** https://www.superseed.com/journal/news/superseed-leads-finteums-seed-round/ **Published:** 2022-09-30 **Author:** Mads Jensen We are super excited to announce SuperSeed’s investment in [Finteum](https://finteum.com/) – a London-based software company that helps tier1 banks better manage their liquidity. Distributed Ledger Technology (or DLT, of which blockchain is one variant) has had massive focus over the past few years – especially due to the excitement, hype (and controversy) surrounding cryptocurrencies like Bitcoin. But Distributed Ledgers have uses far beyond cryptocurrencies, and we love the practical ways in which it is transforming how business is done. Banks are facing ever-increasing regulation, and this is driving them to hold a lot of liquid assets on their books. Holding too few liquid assets gets you in trouble with regulators – not where any bank wants to be. And holding too many liquid assets can be costly – even more so now that interest rates are increasing. Since 2018, Finteum founders [Brian Nolan](https://www.linkedin.com/in/nolanbrian/) and [Zbi Czapran](https://www.linkedin.com/in/zczapran/) have worked closely with several leading European banks to develop a better way to manage their short-term liquidity needs. They found that when it comes to intra-day FX and repo swaps, there is no better technology for managing and clearing trades than distributed ledgers. And with Finteum, Brian and Zbi have built a beautifully elegant DLT-based solution to do just that. ![](https://www.superseed.com/wp-content/uploads/2022/09/finteum-overview-1024x422.jpeg) Finteum already has several major European banks signed up to the platform that will go live in 2023, and many more are lining up to join the system. When we met Brian and Zbi, we were struck by their deep knowledge of the intricacies of how banks manage their treasury and their clear vision of how to transform the space, coupled with technical prowess in how to realise their vision using distributed ledgers. Having recently been joined by [Martti Palosuo](https://www.linkedin.com/in/marttipalosuo/), we know that the Finteum triumvirate will take the company to great heights. Congratulations to team Finteum on closing your round. And here at SuperSeed, we are super proud to partner with you for the next phase of the journey! --- # Why did we invest in Garvis? **Source:** https://www.superseed.com/journal/news/why-did-we-invest-in-garvis/ **Published:** 2022-09-29 **Author:** Dan Bowyer We’re pretty bad at sharing why we do things. That changes now. And starting *right* now I’m going to start a series of ‘Why we invested’ posts. Obviously we want to showcase our founders and their startups but what's also important, I think, is that we share what a great match looks like to us, so other founders can start to pattern match and find the investors they want to work with. We may be a great fit for you, we may not be.  I hope this series helps in your investigation. Let me know what’s missing. **Back to [Garvis](https://www.garvis.ai/).** Garvis, is a bionic demand forecasting business designed to help organisations better manage their production and distribution. Imagine you’re Unilever or Coca Cola - how much product should you make and ship, to where and when? Too much and you waste cash and resource, too little and you’re leaving money on the table. In August we led the £3m seed round to help Garvis further develop their bionic Ai platform and radically transform enterprise supply chain management. I met Piet the CEO through a deal scout back in April ’22. It was immediate intrigue as we love supply chain startups, especially in the light of Russia-Ukraine flip-flopping global supply chains. Our thesis has always been focused on this space but right now it’s just super important. So we dug into what the startup really does, who they are, who they serve and how they're different compared to some serious incumbents such as SAP. When looking at new startups you need a thesis with yardstick. A 'what you do', with a 'how you know' list. Yes we invest in B2B but there are layers deeper than that, and everyone here has their own particular lens or viewpoint.  **Here is my own personal startup ICP. My cheat sheet:** - *Founders* - Tech and domain smart who’ve lived with the problem. - *Opportunity* - It’s potentially a global offering, with potential local clients who really care. - *Transformative* - It leans into a trend and truly transforms how we make, move, or manage business. - *Territory* - It’s a UK business, or from a smaller market & want to use the UK as a global springboard. - *Working Product *- It works and has some form of real world client validation. (Usually some revenue) - *Trojan* - What they sell today is not how they’ll make money in 5 years' time. - *It’s different* - There’s magic, and well funded competitors are not dominating this wave. - *Relationship* - It will be a healthy marriage and we can add value to the mix. ## How did Garvis measure up? It was an initial hit across all the lines at first glance. The only question I had was around Trojan'ing - should that be important in this context. I’ll come back to that.  - *Founders - *The team, driven by Piet are in the top tier of experts who are brilliantly suited to solve this problem. They collected organically via various projects over several years, invested their own cash, built product 1 and took it to market. So deep domain experience is baked in, with genuine buy-in.  - *Opportunity* - It’s obvious that supply chain challenges are a global issue but is this product the solution and can this team drive global growth? Yes is the short answer. There was nothing restrictive, they’re a distributed global team across London, Pakistan, the US and Belgium - and they already had clients in 6 countries. Tick. - *Transformative* - will this change the world. Absolutely. In spades. Their key differentiators being two fold: 1 - The solution is open or white-box - Competitors are closed, i.e. the software is not available for clients to interrogate. The Garvis solution is the opposite where clients can see how decisions are being made in real time. 2 - Time to value is measured in hours not months. Where competitors are closed books they also take months to install and configure. The sales process for Garvis is simply 'try it and see'. The proof is in the data so they compare with their existing solution. Analysing historic data, clients can quickly see that Garvis, out of the box, is more accurate, faster and totally transparent. Regularly hitting 5% improvements with a target of 10%. In real money this means 2-5% added to the bottom line. Which is truly transformative for any organisation that makes large numbers of ‘things’ and ships. - *Territory* - Starting as a Belgian startup we flipped the top-co to UK to take advantage of available capital, brand connections, legal strictures and other leverage that the UK has to offer. Next stop USA as part of global domination. - *Working Product* - With 30 happy clients the proof was pudding shaped. Doesn’t matter what we think - only them. We spoke to them, plus a selection of potential customers to test the draw. The open-box decision making was a true differentiator in clients’ eyes. The ability to give operational teams real-time open insights was obviously a game changer. - *Trojan* - This was an unknown and in context, irrelevant. Normally I would look for a play where what you’re doing today truly unlocks tomorrow, from a product level. Garvis were already in this camp with differentiation already so strong that this could be parked. One for a future board meeting should the need arise.  - *Differentiation* - See above. They have the jump. Clients are jumping. Sales are already rocketing. - *Relationship* - Piet could have worked with many other investors with multiple term sheets on the table. He chose us, Bosch the German engineering and tech group, and Scalebridge. All for different and important reasons - Jamie from Scalebridge a seasoned fund raising maven and trustee, us as kindred founders supporting the GTM, and Bosch for network, credibility and access. As an investor group we agreed how we should best work together which took numerous meetings to define clear lines of responsibility, value-adds and access. There was a healthy marriage to be had.  To date Garvis is working with 50 multinational organisations, the team is rapidly expanding, and they’re closing 5 new deals per months.  We’re super proud to be on the Garvis rocket ship. *Dan* --- # The 2022 valuation deflation **Source:** https://www.superseed.com/journal/the-2022-valuation-deflation/ **Published:** 2022-07-31 **Author:** Mads Jensen We follow both public and private tech markets closely, and 2022 has been an interesting year for both companies and valuations. As of the end of July, the Nasdaq 100 (the tech-heavy index of large US companies) is down 22% YTD. Meanwhile, the broader S&P 500 is down 14%. But the cloud-focused BVP Emerging Cloud Index (EMCLOUD) is down a whopping 42% YTD (EMCLOUD contains a range of great B2B SaaS companies like Adobe, Salesforce and Shopify). However, from March 2020 to the peak in November 2021, EMCLOUD was up a whopping 200%. That’s 200% in about 18 months for an index – an almost unbelievable appreciation in such a short amount of time. And as we know in hindsight, this bubble was driven by the unique combination of Covid-induced lockdowns and massive quantitative easing. However, someone investing in the EMCLOUD Index at the start of 2020 (i.e. before the pandemic) would still be up 15% today, and if you’d invested in 2018, you’d be up a respectable 50%. Not an astronomic return over four years, but also not a complete disaster. In parallel, venture capital investment continues at a healthy clip. In fact, according to Pitchbook, there was $60bn invested in venture capital in the US in Q2 of 2022, more than in any quarter before 2021 (perhaps bar the dot-com bubble at the end of the 1990s). So, the venture capital market remains healthy. In effect, what has happened is that the bubble we saw in particular in late-stage valuations (growth / pre-IPO stocks) over the past two years has “reverted to mean”, and we are now back on a healthier trend. We think this is overall a great thing for the venture ecosystem, as it means less capital going to unproductive companies (and sucking up talent, customer attention etc.) ### What does this mean specifically for SuperSeed? As we didn’t participate in the “pay above the odds” bubble in later-stage companies, our portfolio companies are not suffering the backlash from large down rounds. And as we continue to invest in healthy software companies that deliver tangible ROI to their customers and drive solid, recurring revenue, our forward-looking view remains extremely positive. In fact, knowing that many of the best companies come out of crises (the Great Financial Crisis led to companies like Airbnb, Slack and Stripe), we see now as a great time to invest. So while we continue to stay disciplined and look to only invest in companies with great founding teams and solid technologies, we see the current climate not as a time to pull back but as a time to invest and back the amazing founders that are going to build the tech giants of the coming decade. --- # SuperSeed - ESG Policies and Investment Strategy **Source:** https://www.superseed.com/journal/superseed-esg-policies-and-investment-strategy/ **Published:** 2022-06-25 **Author:** Mads Jensen Business is a powerful force. It can harness human ingenuity to solve problems, create value and drive change. And as all powerful things, it can lead to both positive and negative consequences. With power comes responsibility. Applying ESG (Environmental, Social and Governance) thinking can help businesses deliver positive outcomes beyond value to customers and return to investors. ESG factors are sometimes bucketed into one category, but they are perhaps better thought of as three distinct lenses through which to view investment policy and strategy. ## **Environmental** At the onset, most B2B software startups have an exceedingly small direct impact on our planet’s natural ecosystem. Initially, they are tiny operations with no or limited customer reach and negligible resource use. However, in time, their impact can be vast. SuperSeed’s investment strategy is all about helping automate and optimise the way business is done. And in turn, automating and optimising business is all about driving resource efficiency. An inefficient business process is a wasteful business process. If a process requires more of anything than it needs to (be it equipment, material, energy or labour), by definition, it is using precious resources that could be better used elsewhere. SuperSeed invests in companies that automate and transform business processes to make them more efficient, reducing the waste of resources in developing and supplying goods and services. With this lens, almost every SuperSeed investment has the potential to make business more resource efficient and, therefore, most sustainable. Seen through this lens, helping businesses become more sustainable is an integral part of what we do. ### Some specific examples Many manufacturing processes are much less resource-efficient than they could be. An example of this is subtractive manufacturing (CNC Milling). With CNC milling, cutaway manufacturing materials often can’t be reused directly. And even where they can, substantial energy is used to reprocess the material so that it can be used again. Contrast this with additive manufacturing (3D printing), where there is almost no material waste. SuperSeed is invested in **[Ai Build](https://ai-build.com/),** a leader in software for large-scale additive manufacturing. Ai Build is currently working with a range of automotive and aerospace manufacturers to transform how large-scale manufacturing is done. Another area is extrusion or injection moulding as used in plastics manufacturing. Notwithstanding the drawbacks of plastics, they remain a vital part of the global supply chain, used for everything from food and drinks containers to automotive interiors. And as long as we use plastics, it is essential to ensure that they are produced efficiently. SuperSeed’s portfolio company [**ThingTrax**](https://www.thingtrax.com/) enables manufacturers to make things using fewer resources. In fact, one of their customers recently told us that ThingTrax enables them to produce in 5 days what was previously done in 7 (and using only the same equipment), with much less energy consumption as a result. SuperSeed’s investment strategy is to invest in companies that make business better and more efficient, resulting in lower prices for customers, better economics for suppliers and a reduced burden on the environment. ## **Social** Economic access is one of the important byproducts of business efficiency. When businesses become more profitable and resource efficient, prices can be reduced, and more consumers can access innovation. As a society, automation enables us to produce much more with fewer resources. This ultimately makes it possible for more people to enjoy greater wealth. Automation is the only reason we can share the benefits of our economic and technological progress with as many people as is the case. In 1943, Thomas Watson (then president of IBM) predicted that there would be a world market for “maybe five computers”. At the time, computers were exceedingly big and exceptionally expensive. When the Apollo 11 mission landed on the moon 26 years later, they were supported by several computers from IBM, including 5 IBM/360 Model 75s, the prototypical original mainframe. These systems cost $3.5m a piece in 1965 when they were launched, which equates to about $29m today. And according to Gene Kranz, who was NASA’s flight director for the Apollo missions: “Without IBM and the systems they provided, we would not have landed on the Moon”[[1]](#_ftn1). However, today, we can all purchase a smartphone which is a vastly more powerful supercomputer than the IBM S/360 mainframe NASA used to land on the moon. And because these have become so inexpensive, more than 3.5bn people in the world have a smartphone[2]. Interestingly, this is more people than the entire global population of 3.3bn in 1965[3]. Only automation in materials extraction, design, manufacturing and logistics has enabled us to deliver such phenomenal technology access to such a broad group of humanity. ## **Governance** We see corporate governance as an essential aspect of creating both shareholder and stakeholder value. This is supported by academic research, which has found that good governance significantly increases the return of public companies[[4]](#_ftn4). Good governance starts early, but it should be applied intelligently. It is especially important for seed stage companies to look for governance measures that are commensurate with the limited resources they have available. In particular, we look to appoint independent directors to boards (in addition to founder and investor directors). We also arrange frequent board meetings (between 6-10/year) to ensure that founders are both supported and held accountable. As startups grow, we typically work with them to implement additional governance structures, such as audit committees, remuneration committees etc. All the while bearing in mind that any governance structure has to be appropriate for and proportional to the size and type of business. [[1]](#_ftnref1) [https://www.ibm.com/thought-leadership/space/](https://www.ibm.com/thought-leadership/space/) [[2]](#_ftnref2) [https://www.oberlo.co.uk/statistics/how-many-people-have-smartphones](https://www.oberlo.co.uk/statistics/how-many-people-have-smartphones) [[3]](#_ftnref3) [https://www.worldometers.info/world-population/world-population-by-year/](https://www.worldometers.info/world-population/world-population-by-year/) [[4]](#_ftnref4) [https://www.jstor.org/stable/25053900?seq=1](https://www.jstor.org/stable/25053900?seq=1) --- # Tips for hiring in early stage StartUps **Source:** https://www.superseed.com/playbook/tips-for-hiring-in-early-stage-startups/ **Published:** 2022-04-22 **Author:** Dan Bowyer > Tips on hiring the right people in early stage startups Having got this hideously wrong a number of times I’m now trying to steer the founders I work with away from making the same mistakes. Hiring into early stage startups is hard. Expectations are high when you make your first hires and getting the right people on the rocket ship is fraught with oh f**ks. A few things that worked for me: * Hire though your network if you can. Obvs. Ask everyone, with a memorable brief in hand that’s culture and commercial specific. Be referable. * Find and cherish the right recruiters. There are some gems out there so exhaust personal channels quickly and then don’t be afraid to pony up. Your time is a premium. * With founding teams / early staff - there is often a strong argument to hire great generalists with a bent towards the functional role you really need, i.e. a great operator that leans into sales. Or a great lead developer who isn’t *just* comfy behind a keyboard. Generalists generally are your early stage Generals! * Time-box early working projects with newbies and enable them to sink or swim according to their own metrics. Let them set their mission and goal. It’s a 2-way test keep in mind. This is chemistry and commerciality stuff. * Let people roll through, don't try and keep anyone, but make it patently clear that while we're all working together, we make magic. We are a unit, we are family, [we are one](https://www.youtube.com/watch?v=TGtWWb9emYI&ab_channel=PitbullVEVO) (they tend not to leave with that much explicit freedom anyway.) Hire slowly fire fast. No prisoners. Rocketeers only. 🚀🚀🚀 *...Dan* Hiring in early stage start-ups isn't the only important consideration. For more advice about building your early-stage startup, [head over to SuperSeed's Playbook](http://www.superseed.com/playbook). We have tips on everything from board pack composition to Ideal Customer Profiling and business strategy. --- # Why entrepreneurship is tough (Really) **Source:** https://www.superseed.com/playbook/why-entrepreneurship-is-tough-really/ **Published:** 2022-04-18 **Author:** Dan Bowyer ## A little reflection on why entrepreneurship is tough. It breaks down to 3 core reasons IMO... I started my first business 25 years ago, it took me 3 years to start, I got everything wrong and it took me 3 years to ‘get it’ ("beautiful lessons", as my coach used to tell me). A core early learning was that it was going to be a never ending conundrum wrapped in an enigma and that's ok. Entrepreneurship *is* tough. **BUT... ** ...in that 4th year things started to slot together. I was over the oh f**k hump. Now I invest in people who are at the stage I was at 25 years ago, but they're smarter, more energetic and mostly come with less baggage. Brilliant - because they need to be smart enough to ‘get it’, and dumb enough to try. Looking at these wonderful individuals now, and looking back at how I was then, so many aspects mirror. Nothing has really changed in how startups start up, even though the whole industry, how it’s viewed, and the tech has all moved on significantly. However, all of the important crunchy aspects are timeless and universal. It is still very much the case that entrepreneurship is tough, but here are the three core reasons I think front and centre: * **Firstly**, just because you know about selling houses, that doesn’t mean you know about building a business that sells houses. They are completely different skillsets and the latter is even more of a beast to pin down. There are around 20 roles in any business anywhere in the world, and as a founder you have to have a solid grip on most of them to survive. Painful. You’ll be sh*t at some, ok at others, while the rest you’ll shine at. Keep learning. * **Secondly**, every important lesson you really need to get will feel like a punch in the face because you flat out will not get it until it takes your eye out. Learning smart new stuff is hard and it only really comes in time-sucking jaggedy punchy packages. You can’t learn from the good stuff. It doesn’t create meaningful change. And often you have to f*ck up to truly get it. My goodness mine make me wince. I once took down multiple email servers for days with a simple marketing click of a button. * **Finally**, it’s lonely. As a founder you’re top of the tree with more clients and hands out in your direction than when you worked in corporate. You think you’re the boss but you have more bosses now than you’ll ever have. And worst to boot, you’ll never feel like you got there. One hump just leads to the next and the next - and none are the fun type. [As Prince once said, “I’ve been to the top of the mountain and there’s nothing there”.](https://www.lovecomedown.co.uk/musicblog/2016/12/16/princes-closest-friends-share-their-best-prince-stories) All I can say is that it’s a fascinating personal and work experience. Drop any expectations and go for a ride. It's once-in-a-lifetime stuff where you will experience many things most will not. Enjoy every day like it’s a new adventure, turn everything into a learning project, do good, share honestly and openly while sucking up every moment with relish. There is no good or bad. There is no right or wrong. It’s all a trip! *... Dan* If you're finding entrepreneurship *especially* tough, have a read through [SuperSeed's Playbook](http://www.superseed.com/playbook). We've compiled some really useful lessons and tips for startup founders. --- # Staying focused after a raise **Source:** https://www.superseed.com/danbowyer-me/staying-focused-after-a-raise/ **Published:** 2022-04-14 **Author:** Dan Bowyer I’d like to share an observation with B2B founders, especially those who have just raised their first sizeable round. tl;dr focus is key. > Staying focused now is probably more important than at any other time.  **The below** are some examples of mistakes I made at this pivotal moment. Perhaps some will resonate with you. 1. **I stopped selling personally and handed that function over to an amazing salesperson with a stellar CV.** I hadn’t yet bottled the founder sales. This needs to be nailed before you delegate. **2. I started to sell anything to anyone because I could.** My ideal customer profile (ICP) wandered and wavered. I should have stayed with ICP1 for a while longer. Learned from something manageable, really nailed it, before moving on from there. 3. **I went on a hiring spree to GET STUFF DONE!** Assumed that hiring is the way to get stuff done. More people more power right? The process of hiring was lengthy, and took a lot of energy. The noise and politics around hiring became a distraction. I should have tranched it against milestones. 4. **I hired from corporate.** The CVs looked stunning, but that’s no guarantee that they’ll be right for a role in startup. Startups require a very specific type of person, and mostly corporate types aren’t a great fit. Key lesson learned. 5. **I hired specialists**. Yes, you need the right skills for the role. But the ‘job’, when you’re still very early, is a lot wider than it seems. Even when you need a specialist, they still have to be able to play the generalist role when their specific house isn’t on fire. The earlier the stage, the wider the competencies have to be. No Ninjas. 6. **I didn’t listen.** All that cash made me arrogant. “I’ve made it, right!?” Wrong. Doh. This is simply the beginning of a new and different part of the journey. If in doubt get back to first principles and back to your identity: who do you serve, and why do you serve them? For a more detailed look at business strategy ideas for B2B founders, why not have a read of our article: [Business Strategy For Startups](https://www.superseed.com/playbook/business-strategy-for-startups/)? *Written by Dan* --- # What I learned as a B2B founder turned VC **Source:** https://www.superseed.com/danbowyer-me/lessons-from-a-founder-turned-vc/ **Published:** 2022-04-12 **Author:** Dan Bowyer As a founder turned VC, here’s what I’ve learned so far from the other side of the table. For context, here at [SuperSeed](https://www.superseed.com/about/) we invest in what I call deeper tech business automation. i.e. anything that transforms how we work, make or move things. Early stage, which to us means anything before Series A. We do a deal about once every six weeks and our average ticket size is ~£1m. Below are a few key lessons I've taken from the transition from B2B founder to VC investor. **Volume down, value up** Early-stage deal volume is way down, but the deal sizes across all stages are up. This makes the space look more voluminous than it really is for the new kids on the block. This will shift as markets shift, which will probably happen later this year. That said, there is still a lot of money available for new startups. **It takes time** We’re 4 years in and started investing 3 years ago. It seems to always takes 3 years to 'get it'. I’m old. I’d like to think I’m smart. But yet again, after all these years and businesses, the rule still stands. It is the magic number. It’s taken 3 years to build the fund to a state that feels like we’ve camelled the hump. The model works. We’re off to the races. It will take you time too, and that's ok. **VC is probabilistic not deterministic** VC is as much gambling and luck as it is systems and hard work. We live by the fact that most deals will fail. We only need 1 in 10 to work. In startup it’s the opposite paradigm. **We have investors too** Just like B2B Founders, VCs have investors, and they expect outcomes and returns. We're here to make money. It's not charity, as ugly as that sounds. The more we make, the more we can recycle. The more founders we back, the richer the ecosystem, and the network recycles. **VC is not sexy** It looked it from the outside. It’s just not. Don’t believe the hype. But likewise don’t be *too* anti. I don’t really care to be a VC or for some of the ego mania (though some of it is fun, I will concede). I simply love **startup** and **working with founders** who are **changing the world**. That’s magic. VC is my vehicle of choice to get a job done. ** VCs don’t always get the mechanics of early stage startup** Or we might have a very specific, myopic view on certain sectors, spaces, plays. Don’t lean on us for knowledge we may not have. Check us out first. In our case in particular, there is lots of information available about who we are, how we can help, and who we can help. **Venture will become more democratised** Crowdfunding kicked it all off and there’s more coming. We have an IPO. There are new VC models coming. More niches are being served. More people are understanding VC and its attractions. Try to read and learn this side of the desk. We're an open book, and would welcome any questions you might have. **It's really all about opportunity creation** Early stage businesses are net creators of jobs. Big business is the net destroyer. We need this ecosystem more than people appreciate. B2B Founders, you are the lifeblood of this world, and without you, we have nothing. **Venture is the fuel for the engine of innovation ** A pithy phrase, but I know you’ll get the meaning. Love or hate us, we’re a really important cog in the machine. I didn’t quite realise to quite what extent until getting into the weeds. **It’s an adventure. Adventure Capital.** Good name for a fund... *... Dan* --- # SuperSeed Fund II - Using a Sales Lens to Unleash European B2B Startups **Source:** https://www.superseed.com/journal/news/superseed-fund-ii-using-a-sales-lens-to-unleash-european-b2b-startups/ **Published:** 2022-02-09 **Author:** Mads Jensen In 2018 Dan Bowyer and I launched SuperSeed with a mission to build a true founder-led venture fund for European B2B startups. Since then, we’ve partnered with 15 amazing startups and more than 50 fund investors who have backed our strategy to fund and support great founders commercialising B2B technology.  ## Technology, Human Progress and Sales As passionate technologists, we believe that technological innovation unlocks all human progress. But while “the Tech” is the foundation of progress, no real progress happens until the tech is in the hands of actual users. And to get it there, we need to sell. In other words - nothing happens until someone sells something. Europe has approximately 50% more researchers and software developers than the US, yet  - the US has created more than twice as many unicorns as Europe, and  - only 1 European tech company is in the global top 25 when measured on market cap, and not a single European tech company is in the top 25 when measured on revenue. One could ask if the domicile of companies matter in a global economy. In 2021, US GDP per capita was $68k, with the EU and the UK both hovering around $47k (IMF figures). And even with globalisation, companies continue to create more / better jobs and to place the lion’s share of their R&D investment (which boosts future growth) in their home geographies. So if we want Europe to benefit from the outstanding R&D done in our part of the world, we need to create more global champions. And this starts with our approach to how we build startups.  Most of today’s big tech companies (such as Apple, Amazon and Google) were once venture-backed startups. And while great founders have built amazing unicorns in Europe over the past decade, almost all of the biggest tech companies are still located in the US.  ## Accelerate Commercialisation to Create Impact Because while Europe has excellent technical talent, we Europeans often fall into the trap of pursuing technology perfection while US competitors focus on commercialisation. And so, before we get started selling, a US competitor has delivered real customer traction and used this traction to raise more capital and attain a leading market position.  In other words, too often, great European technology doesn’t create a global impact because it isn’t successfully commercialised.  For Europe to remain relevant, we have to be the best not just in R&D but also in commercialising R&D.  Startups are the engines of R&D commercialisation. And startup founders and their teams are truly the heroes that move the needle of human progress. But it takes a lot of energy to build a startup, and often venture capital is the fuel that accelerates the startup engine and helps founding teams achieve launch velocity to build a viable business.  ## How Operator VCs Can Help So our startup engines do need capital, but often this isn’t enough. They also need expertise and best practice. And this is where European venture capital still has room to grow. There are many pitfalls when building a startup. Many first-time founders with a technical background are looking for true partnership from their investors. Ideally, they are looking to partner with investors who have themselves built tech companies and who know from first-hand experience how to avoid the worst bear-traps. However, by some measure, less than 10% of venture capital investors in the UK have even worked in a startup. There is nothing wrong with financial or strategy skills – especially when companies reach the scale-up stage. However, getting a company successfully from Seed to Series A requires a lot of practical skills that are hard to embed in spreadsheets. This is why Europe needs more venture capital investors with operator backgrounds, and indeed why Dan and I decided to launch SuperSeed.  As a VC firm, we work to deliver the best practices on how to build successful B2B startups. We work with technical founders and help them build their teams and craft their commercial processes. This way, we help smart technical founders lay the foundation for successful companies and to get faster from Seed to Series A This is not about “taking over” from the founders or “steering the car from the back seat”. The founders are absolutely in charge. It is their companies, after all, and we back them to build great businesses. And then we provide capital and expertise on how to get it right so the founders can truly deliver on their vision. We look to be investors and board members who have been in the trenches, know where the pitfalls are, and intimately understand what levers can help accelerate B2B start-ups to become commercially viable businesses.  We are proud to partner with a group of amazing fund investors who are backing us for our second fund. And we are thrilled about the work we are doing in partnership with current and future portfolio companies and founding teams.  Small teams with a clear mission can conquer the world. It’s our job to make sure they have the right capital and right support to succeed.  Here is to the amazing things startups all over the world will build and achieve in 2022 and beyond! Mads Jensen Managing Partner, SuperSeed --- # SuperSeed launches £50m fund to back early-stage technical founders who are creating the future of business **Source:** https://www.superseed.com/journal/news/superseed-launches-50m-fund-to-back-early-stage-technical-founders-who-are-creating-the-future-of-business/ **Published:** 2022-02-04 **Author:** Mads Jensen - SuperSeed is a London based seed-stage venture capital firm investing in startups focusing on business and industrial automation. The firm has now completed the first closing at £31m of its Fund II, which will target a final close of £50m. - The firm was started in 2018 by Partners Dan Bowyer and Mads Jensen, both ex-founders with multiple exits, who passionately believe that venture capital investors need to better support early-stage entrepreneurs. - The partners launched their first fund in 2019 and have since invested in[15 B2B startups](https://www.superseed.com/portfolio/). - The SuperSeed model is designed to support technical founders in commercialising B2B offerings to help them develop better sales playbooks. A startup's ability to sell is existential. - SuperSeed invests in startups that transform how business works. [Growth in business automation](https://www.mckinsey.com/business-functions/operations/our-insights/the-imperatives-for-automation-success) is enormous, recently accelerated by Covid. Next-generation technologies such as 5G, quantum computing, AI and blockchain accelerate this. At the same time, they help businesses secure vital supply chains and find new ways to manage, manufacture and sell. - SuperSeed will invest in 30-35 B2B software companies over the next 3-4 years, helping them kick-start sales and building the foundation for global market reach. - SuperSeed Fund II is an Enterprise Capital Fund with a cornerstone investment from the British Business Bank’s Enterprise Capital Fund programme. **LONDON – February 4th 2022** SuperSeed, a venture capital firm investing in the future of business automation, today announces it has completed the first closing of its new fund at £31m, targeting a final close of £50m. The fund will invest between £500k and £1.5m in 30-35 startups and focus on companies based in the UK or looking to use the UK as a springboard for global expansion. Europe continues to develop world-class technology, and the European startup ecosystem has exploded over the last decade. However, far too often, European startups are outfought by their American counterparts, and the venture ecosystem needs to do more to support them. This was why SuperSeed was created. The founders believe that startups are the primary engine of positive change. With venture capital supplying the fuel, European venture capital firms can and should play a much more supportive role at the delicate early stages. The opportunity for smart business transformation is bigger than ever. Recent research from [McKinsey](https://www.mckinsey.com/business-functions/operations/our-insights/the-imperatives-for-automation-success) shows that 66% of large companies are piloting new automation processes, and [WEF](https://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf) found that 80% of business leaders are considering ‘speeding up’ their automation processes following the pandemic. **Mads Jensen, Founder and Managing Partner at SuperSeed, says** “Technological innovation is the foundation to unlocking human progress. In simple terms - tech will save us! But tangible benefits can only be achieved when the technology is commercially viable and successfully deployed in the hands of real users. Unfortunately, when it comes to marketing and sales, the Americans have historically had an edge on us here in Europe, and we have to do better if we want to compete on the world stage.  “At SuperSeed, we play a highly active role in helping early-stage founders create powerful go-to-market strategies. We provide best practice on how to build a successful B2B startup and work with founders to build out their team, craft processes, and truly accelerate the journey from Seed to Series A.” **Dan Bowyer, Founder and Partner at SuperSeed says,** “I have quite a lofty take on what automation really is. To me, it’s the technological bedrock that will enable us to keep the promises we’ve made to the climate, our planet and children. On the surface, it helps businesses lower costs and create efficiencies; great, that’s a massive tick. But underneath, it’s a lot more than that. “So our thinking when we started the fund was - how do we enable as many smart startups as possible to make it AND have a meaningful impact? “It’s well known that most startups fail, which is mostly due to a lack of product-market fit. In other words, to succeed, it’s imperative to find and focus on the right customers while filtering out faster horses. All the while avoiding the natural tendency to get sucked into polishing the product, thinking if it’s better, they will come. As entrepreneurs, we’ve experienced the allure! “How can you fastest get to product-market fit? Our experience as founders is that the process of selling is crucial to finding and expediting this journey. This is what Mads and I learned the hard way and want to pay forwards. In simple terms: “sales solve all problems”.” SuperSeed’s Partners share the view that there is a multi-decade long opportunity to help companies automate business and manufacturing processes. **Mads Jensen says,** “In business and especially manufacturing, there's so much that automation can improve. This is why we expect it to be one of the biggest long term economic trends, even without the natural accelerant of a pandemic. Software lies at the heart of this opportunity. Except for the most sophisticated manufacturers like Boeing and Rolls Royce, around 80 percent of manufacturers still use equipment that isn’t ‘smart’, or connected to the internet.” This is a great opportunity to invest in automation.  In this area also lies one of the biggest opportunities to address  climate change: “It’s easy to miss how crucial business automation is to solving some of the biggest challenges humanity faces. We are not going to stop consuming but must be much more mindful about how we make the things we consume. Automation opens up huge sustainability opportunities: from the application of smarter materials to the way we reuse and recycle post-consumption. From picking and packing to putting on the table. From hiring to managing to paying, or finding to buying to delivering. I could go on and on, but the fundamental challenge is how we bring global communities up to the Western standard of living without destroying the planet. We must therefore be much more efficient in the way we make, distribute and recycle things - automation is the only answer.” **Ken Cooper, Managing Director, Venture Solutions, British Business Bank, says**, “The British Business Bank’s Enterprise Capital Funds programme is key in helping to develop and maintain effective venture capital provision in the UK, lowering the barriers to entry for emerging fund managers and for those targeting under-served areas of the market. Our cornerstone commitment to SuperSeed’s fund will help them to provide successful finance and support to early-stage, high-potential entrepreneurs and businesses.” **Andrew Thomas, Founder and CEO of SuperSeed portfolio company**[** ****Kleene**](https://kleene.ai/)**, says**, “SuperSeed stood out to us for their drive and passion for our space. We've found their support to be invaluable, providing a robust and intelligent input to strategy based on their deep experience of how to build SaaS companies.” **Daghan Cam, Founder and CEO of SuperSeed portfolio company**[** ****Ai Build**](https://ai-build.com/)** says**: “Having SuperSeed as our lead investor was a game-changer for Ai Build. Their team truly understands the challenges faced by first-time technical founders, and they are always there to help us overcome setbacks and figure out how to progress faster. We consider them an invaluable part of our team.” **Amy Read, CEO and Co-Founder at SuperSeed portfolio company**[** ****Techsembly**](https://www.techsembly.com/)**, says: **“From the first call I had with SuperSeed, I knew they were the VC I wanted to work with. Straightforward, down to earth honest advice and support. With SuperSeed, there is no gender bias; they value you for who you are and what they believe you can deliver. I know they're always at the end of the phone if there's anything I need, and most importantly, that they have my back.” Since its first fund launched in 2019, Superseed has invested in 15 companies, including AI Build, Integrated Finance, Kleene and Duel. **- ENDS -** **Contact** Ben Goldsmith ben@goldsmithcomms.com +44(0)7788295321 **About Superseed** Dan Bowyer and Mads Jensen are both life-long entrepreneurs and operators. In 2018 they founded SuperSeed to help the next generation of technical founders build great companies. --- # Looking Backwards to look Forwards **Source:** https://www.superseed.com/journal/looking-backwards-to-look-forwards-2/ **Published:** 2021-12-08 **Author:** Mads Jensen It’s always so simple when you look back while looking to the future can create more anxiety than is useful, and (cheese alert) a wise person once told me that today is called the present because it’s a gift.  So in this letter, I’d like to quickly look back on the easy stuff, whilst committing the cardinal sin of predicting the future, all with a healthy dose of being very grateful and privileged to work with partners like you, the founders we meet, and the team we’ve built. What a gift.  # One and done for ‘21 As another annus horribilis comes to a close for many, it’s worth reflecting on what could also be described as an annus mirabilis. The first Covid vaccine was granted regulatory approval in the UK on the 2nd of December 2020, and the [first jab administered on the 8th](https://www.bbc.co.uk/news/uk-55227325). While it is frustrating that Covid has become a permanent houseguest, it is perhaps worthy to consider how far we’ve come in just 12 months, and how we’ve pulled together as a nation and global community. As of today, [55% of the world population has received at least one shot](https://ourworldindata.org/covid-vaccinations). Yes, there have been many mistakes along the way, and politics has often been a single point of failure. But still - it’s a marvel how we’ve applied science, technology and human ingenuity to deliver solutions.  *When was the last time we managed to get an innovation to more than half of the planet within 12 months? * If nothing else, it shows that we can collectively create change, and maybe this is a forerunner to solving climate change, as well as a starter-template for other challenges? As Omicron is currently stealing headlines and rattling markets, I remain fundamentally optimistic for the year ahead. It might be bumpy but I expect that we will continue to find solutions to the global challenges that arise. And here at the local SuperSeed level, we’re extremely proud to invest in founders who are building the solutions businesses need to solve real problems. In 2021 we invested in 5 incredible startups – [Seyo](https://www.seyo.io/), [Kleene](https://kleene.ai/), [Techsembly](https://techsembly.com/), [Duel](https://www.duel.tech/) and [Integrated Finance](https://integrated.finance/). Across Fund I we still have had no casualties, revenues are up nearly 5x over the last 18 months regardless of Covid and fund investors have enjoyed an IRR of 25% (net). In the last few months, we slowed our investment activity as we focused on closing our Fund II, refining our processes, and building the pipeline for 2022. As we are closing the new fund, we thank the many investors who are partnering with us for the next phase of the journey. If you’d like an update on the fund, companies listed or co-investment opportunities do let me know. # What’s new for ‘22? I see four major challenges ahead for 2022:  1. Covid. There is a continued threat that future mutations will continue to impact the economy 2. Climate. The rapidly accelerating threat that climate change could lead to significant economic disruption 3. Geopolitical tension. Driven currently by two key flashpoints - China/Taiwan and Russia/Ukraine. Under pressure there may be more. 4. Inflation & Interest rates. Partially as a result of supply chain bottlenecks, partially due to overheating in certain economic sectors.  These factors are currently causing jitters, and there are many views on what this could mean for both private and public markets. There is no doubt that these four factors could increase market volatility and shake valuations. However, as venture capital investors we take a longer-term view and are somewhat sheltered by investing in business and industrial automation, which we expect to do very well in the long term, regardless of the noise above. # Where are the Opportunities? Supply chain disruptions are creating pressure to change how we make things. Following thirty years of off-shoring and labour arbitrage, the question is now no longer: where in the world can we find the lowest-paid labour. It’s rather: how can we make things in a way that is cost-effective, sustainable and resilient.  Add to that the (long overdue) [ESG](https://www.investopedia.com/terms/e/environmental-social-and-governance-esg-criteria.asp) focus from investors and it’s obvious that we are heading out of one paradigm (labour arbitrage) and to another (business and industrial automation). Sensors, AI, and advances in additive manufacturing now make it cost-effective to create and recycle things closer to where they are used. In this new world, low hourly labour rates are no longer the primary source of competitive advantage. This enables a retooling of the manufacturing base from the lowest hourly wage to one that is more about skill and expertise. This investment opportunity is right here and now as we enter 2022.  The prospect of higher inflation will further catalyse business automation. Because while inflation puts pressure on labour costs, it will at the same time improve the business case for investment in automation to balance the books.  Even though it may feel like we’re at the pinnacle of all things technical (every generation must feel the same), there is still so much to do from a technology perspective. We have the basic components, we have the R&D, we have the teams - all we now need to do is to develop the applications that will truly take business and manufacturing to the next level. It’s an exciting time to get involved. An opportunity we’ve seen for some time and will continue to lean into. # The 2022 Investment Strategy Rolling this all into our 2022 pipeline the broad strategy remains unchanged: to partner with the smartest technical teams solving difficult problems, and transforming the way business & manufacturing is done.  With the above in mind, we’ve continued to build the pipeline and have a dozen promising startups lined up for investment. Looking at companies using software to: 1. Transform how enterprise automates back-office processes 2. Improve how consumer companies deliver customer happiness 3. Reduces time and cost required to create large-scale infrastructure projects  4. Replace Excel in financial services with next gen modelling tools 5. Design sustainable cities and urban environments # Until Next Year… Recently it’s been great to start face to face meetings again, go to events, and remind ourselves that Zoom isn’t always the answer.  We’d all love to see more of you in 2022. Until then, best holiday season greetings from Dan, Mads and the SuperSeed team *This article is published by SuperSeed Ventures LLP, authorised and regulated by the Financial Conduct Authority. The article does not constitute substantive research or analysis and should not be construed as an investment recommendation.* --- # Dark October Clouds? **Source:** https://www.superseed.com/journal/dark-october-clouds/ **Published:** 2021-11-14 **Author:** Dan Bowyer To some investors, October represents a tumultuous and historically challenging month. This largely psychological reaction called the "[October Effect](https://www.investopedia.com/terms/o/octobereffect.asp)" has its origins in the many dramatic financial crashes that have happened in October. The Bank Panic of 1907, the 1929 stock market crash and Black Monday 19th October 1987. And, oh - October 2008 was pretty grim too, with the S&P 500 delivering a negative 16.79% hit to investors. However, when you look over the[last 50 years](http://www.moneychimp.com/features/monthly_returns.htm), October has, on average, been a positive month. And since 2000 the tally has been 14 up years (including the October just closed) vs only 8 down years. It turned out that this year’s “dark month” delivered a smooth and steady [5.8% monthly climb](https://www.cnbc.com/2021/10/31/stock-market-futures-open-to-close-news.html) for the S&P 500 from 4357 to 4605. Halloween also came and went, but it seems we avoided the worst of the October ghosts this year. ## **What’s new in Venture Capital**? At SuperSeed we invest in business automation which includes AI and industrial tech. Both of these areas are growing rapidly, and often-times in an interlocking way, as advances in AI are paving the way for progress on the industrial automation front. And much as it can feel like automation “has been done”, it is surprising how many factories still operate with machines that don’t deliver continuous sensor data, and therefore can’t be optimised using machine learning. Until now, that is. Exciting startups in our portfolio, like [ThingTrax](http://www.thingtrax.com), are working to address this, and they are not alone. A significant number of startup founders (and venture capital investors) are looking to make our manufacturing base more efficient including resource efficiency which is key from a sustainability perspective. Let’s look at some of the latest numbers. State of AI & Industry 4.0 tech Funding for European AI startups hit $4.6bn in the first 9 months of 2021, already outpacing the FY 2020 number by 35% (CB Insights). But it is not just investments that are taking off. European exits are surging again with 72 AI exits recorded in 2021 Q1 - Q3 with the majority being M&A activity (CB Insights). On the industrial side, we are seeing a rapid increase in investment in industrial automation (Industry 4.0) startups, with investments in the first nine months of the year nearly 2x of 2019 which was the previous [record year](http://Dealroom.co). And while the pandemic slowed industry 4.0 investments in 2020, it’s likely to accelerate it going forward as the pandemic has given manufacturers a big incentive to accelerate the deployment of digital transformation projects. We have a strong pipeline of AI and industry 4.0 companies and are planning several investments in these areas in the coming months. ## Portfolio News Q3 portfolio revenue numbers for SuperSeed Fund I are now in, and topline numbers were up almost 35% quarter-on-quarter (+10%/month). After a very quiet August that generally saw B2B buyers down tools and take overdue holidays, sales engines roared back to life in September to deliver another great quarter for sales and revenue. Portfolio company revenue is now at 2.85x year-over-year on a like-for-like basis. We had several up-rounds in Fund I and IRR across the portfolio is now 25% net. We are aiming for this to increase over the coming 6 months as more companies graduate to Series A. Fund I investors can find the updated Q3 valuations from this week via the [fund portal](https://superseed.mainspringfs.com/Login) The team at [Kleene](https://kleene.ai) have had a phenomenal year and have just closed their [$14m Series A](https://kleene.ai/blog/series-a-announcement/)() led by Octopus Ventures. We met Andrew Thomas and Matt Sawyer more than a year ago and have been working closely with them since then. The round is a great milestone, but only the beginning for an incredible team that’s making magic in a super hot space. Following hot on the $18m Series A by [Dopay](https://www.dopay.com) last month, we are now starting to see the first batch of companies hit the next valuation milestone as they graduate to Series A. [ThingTrax](https://thingtrax.com) had a great quarter, winning several major contacts and growing recurring revenue by 73% quarter-on-quarter. [Kluster](https://www.kluster.com), [Duel](https://www.duel.tech/), [Kamma](https://www.kammadata.com/), [Techsembly](https://www.techsembly.com/) and [Integrated Finance](https://integrated.finance) have all continued to win solid contracts and have posted robust growth in recurring revenue. Based on their current trajectories, these companies are all on track for Series A in 2022. Overall it has been a very strong Q3 and the current outlook is for an equally strong Q4. ## Events We forwent our usual monthly pitch event to host a special Investor only event on 17th November to discuss our success to date and also our future plans. The strategy remains to back Europe's best entrepreneurs in the business automation space but please get in touch if you would like to find out how we plan to do that going forward (only open to qualifying investors). ## Oh, that’s interesting. - **Amazon keeps delivering** - Amazon’s dominance in almost every area of business it touches continues as they [surpass FedEx](https://www.chargedretail.co.uk/2021/09/17/amazon-now-delivers-more-parcels-than-fedex-as-it-takes-aim-at-3rd-party-fulfilment) in number of delivered packages. - **Buffets Brazilian Bank** - NuBank, who received a [$500m investment](https://www.ft.com/content/3f92fb0b-9c57-4d1a-9682-7910b970e408) from the Oracle (Buffet) in June, is set its sights on going public at a reported $50bn valuation. - **Flipping Business Shut** - Zillow’s ill-fated house [flipping business](https://www.foxbusiness.com/real-estate/zillow-sells-homes-shutting-down-house-flipping-business) has been shut, with a recent deal to sell 2000 of the nearly 18k homes it has. This move is set to cost the company $500m and lay off ¼ of its staff. - **Sequoia gently changes the VC landscape** - Their [new fund model](https://techcrunch.com/2021/10/26/sequoia-dramatically-revamps-its-fund-structure-as-it-looks-to-rethink-venture-capital-model/) removes some of the structural pressures that have historically led to needing to liquidate positions based on an arbitrary 10yr timeline rather than what's the best investment decision. Their new model allows for truly patient capital plus liquidity. - **The artist formerly known as . . .Facebook** - After who knows how much spent on advertising and branding consultants, Facebook has changed its name to [Meta](https://about.fb.com/news/2021/10/facebook-company-is-now-meta/). Wow, I know you are as moved as we are. Who knows if it will be enough to distract from the [disturbing revelations](https://www.ft.com/content/dcc9c9bf-2abe-4167-aaac-efc067d5a359) of Frances Haugen. - **And finally, what makes a good seed investor?** Napala Pratini recently shared her experiences from [100 investor meetings](https://sifted.eu/articles/good-bad-seed-investors/). Not surprisingly, she found that working with investors who themselves have startup backgrounds makes a positive difference. Best November greetings from Dan, Mads and the SuperSeed team *This article is published by SuperSeed Ventures LLP, authorised and regulated by the Financial Conduct Authority. The article does not constitute substantive research or analysis and should not be construed as an investment recommendation.* --- # Are markets cooling down? **Source:** https://www.superseed.com/journal/are-markets-cooling-down/ **Published:** 2021-10-08 **Author:** Mads Jensen September has, for years, had a reputation (deservedly) as a month best avoided in stock markets (or perhaps one to lean into, if you look to “buy the dip”). In fact, since 1982,[September has been the **only** month to deliver negative S&P 500 returns on average](http://www.moneychimp.com/features/monthly_returns.htm). September 2021 didn’t disappoint. The S&P 500 index was down 5% in what has been the first real blip since May. While September was disappointing for public stock markets, Q3 was an excellent quarter for venture capital. Unfortunately, we don’t have real-time reporting in quite the same way as for public markets. Still, even based on the investments that have already been reported, Q3 has been another record quarter for venture capital, with more than $150bn invested on a global basis (Dealroom.co). ![](https://lh6.googleusercontent.com/vjNYp5tl0OUapaSO92_KkbaW2T3cEtZuGU7ZR8_I6SfMjknfSrUElvQ2ulL1OE_5qMEuf0UCtO1r1CeKOX786_9TB9AS47sNSsd7VwKv-EXHvfRbFCQmXSXh_ZIrx8oSORfcqTop=s0) Once again, this expansion was mainly driven by growth in later stage rounds. While it isn’t surprising that most of the capital is deployed in larger rounds, it is interesting that pre-seed and seed rounds now account for only about half of all funding rounds, down from 80% just a few years ago (Dealroom.co). However, the shift from smaller to larger rounds has been significant since 2016 so we have enough data to see that the trend is real. ![](https://lh5.googleusercontent.com/sYF6XZSRh-kaUaMjCGUeR2XLIjzEiYN5gv68wTSukQQ1zMI9VZm13aFu4UieaPSbpdEXmAWBJhNYKEFPiyN5QF6vH3nTRYd_u3u-NP8sOIWHVIHVdWKJiPHBEeptw-nrfq5Qohrh=s0) In other words, we continue to see many investors shift their focus to later stages where they can deploy more capital and companies can be more easily diligenced based on financial KPIs. However, as we know, these rounds are getting very competitive, and valuations have increased rapidly.  From our side, we remain entirely focused on the seed-stage where valuations and risk-adjusted returns remain attractive, and there is a rich opportunity to partner with great teams before the rounds get too competitive. ## **What is the future of work? ** Over the past 18 months, we have all been part of a fascinating experiment on the impact of remote working. While there are undoubtedly benefits, we are still not fully aware of the long-term implications. A[recent piece of research by Longqi Yang](https://www.nature.com/articles/s41562-021-01196-4)et al. took the pandemic as an opportunity to analyse the effects of remote working in more detail. Looking at those already working remotely alongside those forced to. This background enabled the researchers to separate the effects of firm-wide remote work from other pandemic-related confounding factors. They used rich data from emails, calendars, instant messages, video/audio calls and workweek hours of 60k Microsoft employees over the first six months of 2020 to understand the effects of firm-wide remote work on collaboration and communication. > Their results showed *that firm-wide remote work caused the collaboration network of workers to become more static and siloed, with fewer bridges between disparate parts. Furthermore, there was a decrease in synchronous communication and an increase in asynchronous communication. Together, these effects may make it harder for employees to acquire and share new information across the organisation*. This certainly won't be the last word on remote working and the future of work. ## Portfolio News Following more than a year of break-neck growth, most of our portfolio reported a quiet August as customers and partners took the month off. However, we have now closed September and Q3, with preliminary reporting showing that customers returned from holidays with plenty of buying appetite. From what we know so far, Q3 was overall a great quarter fuelled by a strong September – more next month once all the numbers are in. We had several up-rounds in Fund I and returns across the portfolio are good. - [Dopay announced an $18m Series A](https://dopaynews.pr.co/202558-fintech-dopay-raises-us-18-million-series-a-round-to-grow-its-next-generation-virtual-banking-platform) and now have plenty of capital in the tank to expand following the regulatory approval of their new platform back in June. - We also have three additional portfolio companies soon to close their Series A rounds. - [Integrated Finance](https://integrated.finance/) are looking to hire a couple of superstar Java developers if you know anyone in your network? - [Kluster](https://www.kluster.com/) are flying with growth driven by both strong upsells and returning customers. 2 new feature releases and 4 new team members. - [Duel](https://www.duel.tech/) are also keen to hire hungry [junior salespeople and a head of customer success](https://careers.duel.tech/). They’ve just signed Mint Velvet, Beauty Pie, Charlotte Tilbury and Spectrum as new customers in Q3. - And last but not least [Ai Build](https://ai-build.com/) having just closed their £820k round are also proud to share that they just signed their 5th subscription client with several large automotive OEMs almost ready to sign. ## Events On October 27th we have our next startup pitch event, showcasing several companies in our pipeline plus a fireside chat with Henry Whorwood from [Beauhurst](https://www.beauhurst.com/) talking about data! Discussing crazy valuations, who is moving the needle, how attractive is the UK really, the angel deal landscape and so much more. Put October 27th, 430-530pm in your diaries and please [REGISTER HERE](https://www.eventbrite.com/e/superseed-startup-pitch-event-oct-tickets-183337325877). (We are also planning a live event towards the end of November - more news on that to follow.) ## SuperSeed Fund II In September, we had pre-closing on the new fund and will be making the first investments out of the warehousing facility in October (these investments will be rolled into Fund II). The strategy remains to back Europe's best entrepreneurs in the business automation space.  We are targeting the first formal closing on Fund II at the end of November. Please get in touch if you would like to learn more about the fund and SuperSeed's investment strategy (only open to qualifying investors). ## Oh, that’s interesting - **China Bans Crypto** - In a very bold move, China’s central bank declared [ALL crypto transactions to be illegal](https://www.bbc.co.uk/news/technology-58678907). China is one of the world’s largest crypto markets. PBOC stated cryptos "seriously endangers the safety of people's assets."  - **Chase’n Retail Banks** - JP Morgan Chase debuted their [first overseas retail bank](https://www.pymnts.com/news/digital-banking/2021/jpmorgan-chase-debut-omniservice-united-kingdom-digital-bank/) in over 200 years in the UK but don't expect to see a branch near you anytime soon, it is digital only. We shall now see what deep tech pockets can do to the challenger bank landscape. ​​ - **Down, Down, Down** - With Facebook, WhatsApp and Instagram all having global outages, many millions turned to Twitter for answers. Did you know that [over 55% of Twitter users](https://www.pewresearch.org/journalism/2021/09/20/news-consumption-across-social-media-in-2021/)regularly get their news from the site?  - **When the boat comes in **- Ports are [bursting at the seams](https://qz.com/2065671/cargo-ships-are-so-full-that-ports-are-struggling-to-unload-them/), with some ports showing container loads up to 70% more than pre-pandemic rates. Shortages and delays are becoming commonplace as ports struggle to adapt to demand. - **Ever (not so) Grande** - Evergrande, the world’s most indebted property developer, is missing bond payments, trading on its [shares has been suspended](https://www.thetimes.co.uk/article/teetering-property-developer-evergrande-suspends-trading-for-transaction-update-2p02qjzmh) and its fire sales are not generating the liquidity needed. This could become something to watch. Best October greetings from Dan, Mads and the SuperSeed team *This article is published by SuperSeed Ventures LLP,* authorised and regulated by the Financial Conduct Authority. *The article does not constitute substantive research or analysis and should not be construed as an investment recommendation. Please note, investments in unlisted companies are illiquid and expose investors to a significant risk of losing invested capital. Please always seek independent financial advice before making investment decisions.* --- # Cool Summers and Hot Markets **Source:** https://www.superseed.com/journal/cool-summers-and-hot-markets-2/ **Published:** 2021-09-06 **Author:** Mads Jensen Although it feels like summer never quite arrived in the UK (or is it making a late comeback?), we are now in September, and autumn lies ahead. However, in contrast to our somewhat tepid British summer, financial markets have continued to be on fire, with the SP500 setting new records and being up roughly 22% this year (roughly 4,500 as of September 5th). Let’s unpack that and also put it in a context for what's happening in the startup market. ### What’s ahead for financial markets? The crystal ball So far this year, stock markets have grown on the back of continued positive earnings and a benign interest-rate climate. Of course, nobody knows precisely what the stock markets will do in the future but Goldman Sachs' recently released a comprehensive analysis of what the S&P500 might do over the coming 18 months (“Sharpen your pencils” Goldman Sachs Global Investment Research August 2021). Their report analyses the historical drivers of stock market returns. They also look at what will drive returns in the near future. The headline takeaway is that GS expects the S&P500 to continue growing, with estimates of 4,700 by the end of 2021 and climbing to 4,900 by the end of 2022. ![](http://dev.superseed/wp-content/uploads/2021/08/SP500-forecast.png) GS sees the main drivers of stock market movements as being low corporate tax and interest rates. The expectation is there will be an adjustment to corporate tax rates and that treasury yields will climb modestly.   ![](http://dev.superseed/wp-content/uploads/2021/08/SP500-scenarios.png) ### Interest rates and inflation Interest rates continue to be one of the key drivers of valuation. And as we have discussed in recent posts, inflation is the key driver of interest rates ([Inflation and Venture Capital Returns](http://dev.superseed/journal/inflation-and-venture-capital-returns/), [the End of Inflation?](http://dev.superseed/journal/the-end-of-inflation/)) that in turn drive valuations: 1. Interest rates are used when discounting the expected future cash flows of public companies. 2. Low interest rates mean that more business projects can deliver positive expected net discounted cash flows. This leads to more projects being financed, fueling future growth and corporate earnings. 3. Low interest rates have, over the last decade, been accompanied by quantitative easing. This has provided liquidity to markets, pushing valuations up further. So like many others, we look to inflation for signs of where the macroeconomy is headed. And if we further break down the sources of inflation, the main driver has been raw material prices. In fact, [raw materials have risen almost universally throughout 2021](https://www.bloomberg.com/news/articles/2021-05-01/the-price-of-the-stuff-that-makes-everything-is-surging). ![](http://dev.superseed/wp-content/uploads/2021/08/sources-of-inflation-2.png) Is this inflationary spike likely to be permanent or transitory? Economists at both GS and the Federal Reserve view the spike in raw materials inflation as temporary. They point to disruption to global supply chains caused by the pandemic. And both sets of economists see inflation as pulling back to more manageable over the next 18 months (see chart). ![](http://dev.superseed/wp-content/uploads/2021/08/inflation-forecast.png) ### How is this likely to affect the startup and venture landscape? Different sectors are affected in different ways. However, based on the scenario above, we see the B2B and specifically the business automation segments as heading further towards a Goldilocks zone. It looks like there will be a modest increase in inflation which will likely lead to upward wage pressure. This strengthens the business case for automation and investment in technology (Martin Sandbu [on Wages and productivity](https://www.ft.com/content/c980423c-ec39-4f29-9c4b-cac802ffe1e5) - FT paywall). At the same time, if inflation remains at a modest 2-2.5% (rather than anything more dramatic), this means that major interest rate increases are unlikely. This in turn means that capital will continue to be available to support private and public market companies. There are still many global issues we need to resolve – not least climate change (and we see intelligent business automation as a key part of addressing this). But when it comes to venture capital for B2B companies, it looks like we are entering ideal conditions for continued growth. ## Portfolio News We mentioned last month that several of our portfolio companies were moving towards their Series A. Around half of the current cohort of 15 are currently jockeying to reach the milestone, with Kluster, Kleene, SeeQuester, Thingtrax, Vaticle and Ai Build all racing ahead. The rest are making solid progress to catch up by focusing on product-market fit. - [Kamma](https://www.kammadata.com/) (the property licensing startup) is hiring 2 new mid-level sales resources. Referrals welcome. The company is also seeing good traction for their partner agency product which helps unlock further revenue streams [as explained here by one of their clients](https://www.linkedin.com/posts/kammadata_kamma-is-a-fantastic-revenue-stream-we-activity-6830519367910916096-JlGZ). - [Scribeless](https://www.scribeless.co/) is hiring a senior sales leader to amplify the company's recent 61% MoM growth. - [Integrated Finance](https://integrated.finance/) has just signed their 6th customer and are continuing their solid growth. They've just hired a new account manager and are looking for a solid CSM, product designer and to extend the dev team. - [Techsembly](https://www.techsembly.com/) is soon to release a multi-store e-commerce solution for the Chinese markets, which was extremely difficult to execute but has huge revenue potential. If you'd like to know more or can help the founders in any way please let us know ## Events We are excited to be trying our first fireside chat hosted by the inimitable Dan Bowyer. He will be joined by a special guest to talk through their experiences in industry, business and investing. The event is at 16:30 on the 14th of September 2021. Registration is free for investors: [HERE](https://www.eventbrite.com/e/superseed-startup-pitch-event-tickets-163486300927) ## SuperSeed Fund II Subscription forms are now ready, and we have started taking commitments for Fund II. The strategy remains to back Europe's best entrepreneurs in the business automation space. Please get in touch if you would like to learn more about the fund and SuperSeed's investment strategy (only open to qualifying investors). ## Oh, that’s interesting - Mads Jensen recently wrote in Maddyness on how [**the UK can lead the next wave of economic growth by embracing automation**](https://www.maddyness.com/uk/2021/08/11/the-uk-can-lead-by-embracing-automation/). - **VC has transformed the US economy** - In the last 50 years, venture capital has transformed the US economy, and [the Venture Capital industry is responsible for the rise of one fifth of the current largest 300 US public companies](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2681841), according to a recently updated study by Will Gornall and Ilya Strebulaev - **NFT Axie sales exceed $90m in 30 days - **NFT and CryptoCurrency game, Axie, is exploding, but how does a combination of Pokemon and CryptoKitties go from $670k revenue in April to[over $90m in July](https://www.notboring.co/p/infinity-revenue-infinity-possibilities). - **270x more likely to summit Mt Everest than click on Banner Ads - **Banner ads are simply not being clicked, but does that mean they are useless? [Possibly not.](https://www.businessinsider.com/its-more-likely-you-will-survive-a-plane-crash-or-win-the-lottery-than-click-a-banner-ad-2011-6?op=1&r=US&IR=T) - **Robot Gets Fired - **Pepper, SoftBank’s [robot](https://www.wsj.com/articles/humanoid-robot-softbank-jobs-pepper-olympics-11626187461), malfunctioned during scripture readings, taking breaks during an exercise class and couldn’t recognise the faces of family members. - **Dogs = Buns - **It may have seemed trivial, but now food behemoth [Heinz](https://www.cnet.com/news/heinz-starts-petition-to-make-hot-dogs-and-buns-come-in-equal-packs) is getting behind a petition to sell hot dogs and buns in equal numbered packs to reduce food waste. With best wishes, Dan, Mads and the SuperSeed team *This article is published by SuperSeed Ventures LLP,* authorised and regulated by the Financial Conduct Authority. *The article does not constitute substantive research or analysis and should not be construed as an investment recommendation. Please note, investments in unlisted companies are illiquid and expose investors to a significant risk of losing invested capital. Please always seek independent financial advice before making investment decisions.* --- # Inflation and Venture Capital Returns **Source:** https://www.superseed.com/journal/inflation-and-venture-capital-returns/ **Published:** 2021-08-08 **Author:** Mads Jensen We are now well into the summer period, and this month we look at liquidity in venture capital and the threat of a return of inflation. #### **Liquidity in venture capital** Last month we examined how H1 of 2021 has been a record year for the amount invested in venture capital. A significant portion of this investment was in mega growth rounds (+$250m) that have set records. From our perspective, it is positive to see so much capital allocated to grow tech companies at the later stages. One of the critical drivers for this growth is the success of the IPO market. Although there have been initial disappointments like the Deliveroo IPO in April, that stock has since recovered (up over 17% since April 1st). And many others (like Darktrace) have done phenomenally well post IPO. Half a decade ago, there was a dearth of IPOs, which meant that it took longer for VC investors to turn profits into cash. This situation has now been turned entirely on its head. In the first half of 2021, [the amount of dollars raised through IPOs in the US was more than triple all of 2020](https://superseed.mxfig.com/5a739915c16886760f9a374a/l/hK2pHknLzFOenvY2n?messageId=!!!MESSAGE_ID!!!&rn=!!!RECIPIENT_NAME_ENC!!!&re=!!!EMAIL_ADDR_ENC!!!&sc=!!!IS_SENDER_COPY!!!). Some of this is driven by SPACs, and there are reasonable criticisms of the recent SPAC surge. However, a large part of the IPO proceeds is ultimately finding its way back into the venture eco-system - first as distributions to investors and then as investments into the next wave of early startups. In 2015, [VC backed companies accounted for 44% of the R&D](https://superseed.mxfig.com/5a739915c16886760f9a374a/l/9H262cpSpxK2Khboy?messageId=!!!MESSAGE_ID!!!&rn=!!!RECIPIENT_NAME_ENC!!!&re=!!!EMAIL_ADDR_ENC!!!&sc=!!!IS_SENDER_COPY!!!) spend of all US public companies - a number that’s likely only gone up since then. Therefore, we see the IPO liquidity flowing through the startup ecosystem as positive - both for investors and the wider economy.  ![](https://lh3.googleusercontent.com/CFh7iHl0tXbH4mOYXdpjBExErJfFqDd3cXossQ-EG2-LN_IWgqn7r-quYaafN_otZMjQJWvRwCznszXsbngHSfiZ23FFXhT9QV-qxP47oP49Bh3HiouQMUmbjgkNalfv2oQ7ytGi)Courtesy, Tom Tunguz #### **Is inflation coming back? ** For the past 30 years, we have lived in a blissful world of declining inflation, with OECD averages down [from ~10% p.a. in the 80s to ~2% in the noughties](https://superseed.mxfig.com/5a739915c16886760f9a374a/l/sDmoi7xrcFldKhpMr?messageId=!!!MESSAGE_ID!!!&rn=!!!RECIPIENT_NAME_ENC!!!&re=!!!EMAIL_ADDR_ENC!!!&sc=!!!IS_SENDER_COPY!!!) (FT Paywall) However, this year, the spectre of inflation has been rearing its head again, with the [US Core Consumer Price Index (CPI) up 4.5% year-over-year in June](https://superseed.mxfig.com/5a739915c16886760f9a374a/l/IuaiQJFfqra8Ya6h7?messageId=!!!MESSAGE_ID!!!&rn=!!!RECIPIENT_NAME_ENC!!!&re=!!!EMAIL_ADDR_ENC!!!&sc=!!!IS_SENDER_COPY!!!). This is the largest increase in the CPI since November 1991, and it has rightly gotten many observers concerned that inflation is on the march.  ##### Why should we care?  Besides the obvious (rising prices and erosion of purchasing power), increasing inflation might force central bankers to raise interest rates. This could have a double whammy effect. It could:  1. put a damper on economic activity, which would slow earnings growth, and 2. mean that a higher rate of interest should be used when discounting expected future cash flows (still the primary way to value public companies). In other words - not a promising scenario.  *But is there a likelihood that the inflation fears are overblown? * The Nobel Prize-winning economist Paul Krugman recently analysed this issue, and he suggested two major reasons for [why inflation might not be as threatening as some think](https://superseed.mxfig.com/5a739915c16886760f9a374a/l/jeiV52SamBwQhk4QI?messageId=!!!MESSAGE_ID!!!&rn=!!!RECIPIENT_NAME_ENC!!!&re=!!!EMAIL_ADDR_ENC!!!&sc=!!!IS_SENDER_COPY!!!): 1. A large part of the increase in CPI has been driven by the increasing price of cars, which, in turn, was caused by shortages in the supply chain - primarily from semiconductors (yes - cars are really driving computers nowadays - [Marc Andressen was not wrong](https://superseed.mxfig.com/5a739915c16886760f9a374a/l/LzO1dx4vlc7Exmf1E?messageId=!!!MESSAGE_ID!!!&rn=!!!RECIPIENT_NAME_ENC!!!&re=!!!EMAIL_ADDR_ENC!!!&sc=!!!IS_SENDER_COPY!!!)). TSMC, which is one of the largest suppliers of chips to the automotive industry, said in mid-July that [they would ramp production by 60% this year](https://superseed.mxfig.com/5a739915c16886760f9a374a/l/RXPT4zRdyCEAA1PNy?messageId=!!!MESSAGE_ID!!!&rn=!!!RECIPIENT_NAME_ENC!!!&re=!!!EMAIL_ADDR_ENC!!!&sc=!!!IS_SENDER_COPY!!!). As these types of supply chain bottlenecks get cleared up, that will help take the pressure off inflation. 2. Although consumption was down in 2020 (and with it - GDP), most of the slump was in perishable goods and services (vacations, restaurant visits, haircuts). In contrast, investment in durable goods like TV’s and refrigerators held up just fine. So although savings rates were up and there theoretically is a lot of money ready to be spent (which could cause inflation), it is unlikely that we will all suddenly go have a year’s worth of haircuts or restaurant meals all at once. There is just a limit to how many of those perishable goods we can consume - even if we have been deprived for too long.  So what does this mean for UK interest rates? According to the Bank of England, [it doesn’t look like rate increases are imminent](https://superseed.mxfig.com/5a739915c16886760f9a374a/l/JLVNMC7QcGTw63Z8I?messageId=!!!MESSAGE_ID!!!&rn=!!!RECIPIENT_NAME_ENC!!!&re=!!!EMAIL_ADDR_ENC!!!&sc=!!!IS_SENDER_COPY!!!) (FT: paywall). But it’s certainly worth continuing to keep an eye on inflation as we enter the next phase of the post-covid recovery.  --- # Using the Ideal Customer Profile (ICP) to Accelerate B2B SaaS sales **Source:** https://www.superseed.com/playbook/ideal-customer-profile/ **Published:** 2021-07-31 **Author:** Dan Bowyer “Sell me this pen!” How would you answer Jordan Belfort, or the countless others before and after him, if they asked you this question? The truth is, you can’t answer this question until you know who you’re selling the pen to. Different audiences have different wants and needs, and understanding these are the first step to building a successful Go-to-Market Strategy. ICP stands for *Ideal Customer Profile*. In other words, this is the core market you’re selling to. ## Why Does This Matter? In many ways, you can think of the ICP as the tip of the arrowhead. The thing that directs all the activities of your company. Once you have a clear understanding of *precisely *who you are selling to, that helps inform who you are reaching out to through marketing messages and sales campaigns. And this in turn determines who you will end up talking to and therefore what market feedback you will get. And since market feedback drives your product roadmap, your ICP ends up determining the direction of your product.  The more specific and better targeted your ICP, the easier it is to align all activities of your company and the faster you will be able to accelerate towards and beyond product/market fit.  Some other benefits of a clear ICP: - It makes it easier to prospect - We’ll sell to customers who are a good fit for our solution, meaning that they will use the product, get value, renew and expand - Our sales organisation can learn to sell to that type of customer, shortening sales cycles Ultimately, early-stage startups are aiming to prove they have product/market fit, which is about proving that they have a fit with one particular ICP. If you have that, then you can raise your series-A, scale the company, build towards a great exit and those fabulous yachts in the Maldives. Conversely, if you are not clear about who you are selling to, you may end up talking to too many different segments that in turn give you too many conflicting priorities. It then becomes much harder to manage your product roadmap and get to product-market fit. ## How Do We Define an ICP? In B2B sales, an ICP is first broken down into Company and Person/Role, these are the steps we need to follow: 1. Define your unique and defensible product differentiators. 2. Identify pains that your differentiators solve. 3. Map pain points to specific use-cases. 4. Quantify the business benefits of eac use-case. 5. Identify the negative consequences of not solving the pain points. 6. Identify companies/industries which have those specific pain points/use-cases 7. Define the job roles/titles that own those pain points/use-cases 8. Prioritise companies and/or roles based on value achieved by solving these pain points. Ultimately we want to define a “beachhead” use-case for us to target, as trying to sell multiple use-cases to multiple markets too early will end up spreading you too thin and dilute your messaging. ## Unique, Defensible Product Differentiators In a head-to-head with your competitors, what capabilities do you have that are unique, difficult for you competitors to build AND that create meaningful value for customers? ## Quantifiable Business Value Put simply: businesses only want to buy new software if it either makes them more money or saves them money, so how can you do one or both of those? This can either be hard numbers: increased revenue or decreased costs; or soft numbers: increased productivity or decreased risk. The first group is easier to quantify, the second more difficult but you still need to. E.g. does an increase in productivity mean you can do the same amount of work with 50% of the workforce or does a decrease in regulatory failures save X in fines per year? ## Company Profile This is typically broken down by industry, geography, size (revenue, headcount, etc.), plus other defining factors such as existing technology used, size of certain teams, etc. By defining an Ideal Company Profile we can start to figure out both the size of the market we have to fish in and where to start fishing first. ## Role Profile Role profiles are broken down into 2 or 3 groups: - Buyers - these are the main decision-makers, who ultimately we’re trying to get to sign on the dotted line (with the pen we’ve sold them!) - Users - the people using the product, they may not make the final decision on whether to buy, but if they hate using the product there’s little chance the DM will buy - Influencers - not the Instagrammer/Tik Tok kind; think Finance, IT, HR - not the intended users/managers but ancillary teams who may need to buy-in to the purchase  Each role we want to define by: - Role title or keywords to look out for in title, e.g. Sales Director, ...Additive Manufacturing... - Seniority, e.g. CXO, Managerial, etc. There are other defining factors we can use when creating a role ICP.. For example, previous role/company history may help infer whether they are stuck in a rigid corporate mindset or open to new cutting edge ideas. Identifying whether a prospect is based in the global HQ can point to how much decision-making authority they have, both at a local and global level. ## Example ICP **Company**: - Location: UK, Ire, Benelux, Nordics - Sectors: Plastics, building materials, windows & doors, flooring, food & beverage (other discrete manufacturers) - Sites: 3-15 - Machines: 50-300 - Employees: 200-1,000 - Annual Revenue: £25M-£125M **Economic Buyer:** - Title (Primary): CEO, GM, MD, Chief Manufacturing Executive, Operations Director, Production Manager - Title (Secondary): Manufacturing Engineer, Process Engineer, Manufacturing Manager, Operations Manager - Seniority: VP, C-level, Founder, Owner, Partner, Head, Director, Manager - Based in head office or regional HQ ## Be Like Goldilocks Two important things to consider are *don't think too big *and *don’t think too small.* This may sound contradictory but both pitfalls can happen when defining an ICP. It’s great to have an aim of conquering the world market for what you’re building, but when you’re starting up or scaling you need to anchor yourself by what is the *best *customer for you *now. *You may plan to sell to CEO’s of Fortune 500’s one day, but probably not in your first year. Equally, don’t be too limiting: a too narrow-minded approach may lead to you missing out on opportunities that are clearly in reach. Defining a range of companies and/or roles that you want to target allows you to test what works and what doesn’t. But remember the aim of defining ICPs is to narrow down who you are selling to so don’t go overboard and create too many, otherwise the messaging will either become too generic or just a plain mess. ## In Closing (remember, Always Be Closing…) As with everything, don’t forget that ICPs can become out of date. As you grow and both your product and the market matures your ICP may change with it, so it’s super important to review and refresh. How else are you going to take over the world? --- # How to make good board meetings for seed-stage B2B startups **Source:** https://www.superseed.com/playbook/how-to-make-good-board-meetings-for-seed-stage-b2b-startups/ **Published:** 2021-05-31 **Author:** Mads Jensen > *Note: this article does not contain legal advice. If you are unsure of your legal/fiduciary responsibilities as a company director, please seek advice from your legal counsel.* # Why boards have a role I have worked on dozens of boards. Some for companies that rapidly gained traction and success. Others for companies that fought their way through several years of challenges, and then eventually took off. And then some that never quite got there. It would be disingenuous to suggest that the boards of those companies were the only, or even the main, reason for each success or failure. But it would also be defeatist to suggest that there is nothing a board can do to improve the trajectory of a business. And I would go as far as to suggest that – especially for early-stage companies, there is quite a lot a board and its directors can do to help a company towards success. # Why not avoid boards altogether? I know of both founders and investors who shun company boards. Many of them have had bad experiences with boards and board meetings. Examples of things that don’t work at all: - Meetings where founders are expected to go through lengthy slide packs on a slide-by-slide basis, spending most of the meeting educating non-execs and investor directors about the business. - Lengthy operational reviews where the CEO is being quizzed (or second-guessed) by directors who don’t quite know how to contribute constructively – perhaps because they don’t understand the business or the market well enough to bring new things to the table. For this reason, some founders stop bringing the most important issues to the board, or perhaps give up on the board altogether. And some (otherwise skilled) investors actively promote this by shunning director roles, instead advocating more active involvement outside the board. We have all sat through unproductive board meetings, and I have every sympathy with those who feel they’ve had enough. That said, I think it’s a shame to throw the baby out with the bathwater. In my view, the best answer to bad board meetings is not to give up on the board, but to make the meetings good! Because when a board works, it can be such a powerful catalyst for propelling a company forward through greater strategic clarity and marshalling of the wider resources that can be activated through the board, including non-executive and investor directors. # How can boards help? To figure out what makes them good, first, let’s look at the purpose of boards and company directors. Let’s leave aside for a minute the (actually quite sensible) prescriptions from the Companies Act ([I outline them here](http://dev.superseed/playbook/the-7-general-duties-of-directors/), for those who are keen) and focus more strategically on why the directors are there. A simple way to think about this is that the board is there to help the business succeed. Assuming the business has a legal (and hopefully also positive) purpose and mission, the directors should then do everything they can (again, within the law) to help the business prosper. Start from that perspective, and it becomes surprisingly easy to think of sensible ways for a board of directors to do what it is there to do. At its simplest this is about helping - founders formulate and decide on the best strategies to overcome the obstacles that stand in the way of business success - to make sure founders and management are held constructively accountable To the last point, in my experience, most founders (at least those we work with) are incredibly motivated. If something isn’t quite working for them, it’s not for lack of motivation on their part. So in my experience, constructive accountability is more about having a structured framework for measuring and reporting progress. And by the way – good directors can help with best practice for those frameworks. There is a lot of "best practice" to learn from. Here are some more things good directors can do: - Ask questions to help stimulate debate and uncover opportunities and pitfalls the founders may not have considered. - Help founders evaluate strategic options, by bringing an external view to a discussion. It can sometimes be hard to see the forest for the trees, and founders are often deep in the jungle! - Help think about the priorities and timing of initiatives. Giving founders the “air cover” to execute in a focused way (rather than having them feel they need to try to do everything at once). - Help founders make good hiring decisions. If you are world-class technical founders but have never managed, let alone hired a sales leader, it’s not always obvious what “good looks like”. Good directors can help you here. # So how do you make board meetings good? ## 1. Make sure you have the right directors on your board The first rule is to make sure you have the right people in the room. This includes aspects of chemistry (we’ve all met toxic businesspeople). Still, assuming that the people you have around you generally are collaborative and trying to do the right thing, it’s then more a question of getting the right skills on board. So what should boards know (collectively)? - How to build companies in your industry - How to sell to your customers - How to hire well and build your team for success - How to raise money for startups and keep them funded - When to lean in and when to get out of the way to let the founders get on with execution This is not to say that all directors should be experts at everything. But collectively, you should be able to cover all of those bases. If your board doesn’t cover all of the above yet, it may be time to upgrade your board. This is also not to say that your non-executive directors will be doing all the heavy lifting. The hard work (and the glory!) belongs to the founders. But there are just so many pitfalls when building a company, and it’s incredibly helpful to be able to discuss those pitfalls with folks who have real experience on how to navigate them. Finally, no two businesses are alike, and one should always be careful to assume that you can just copy/paste. Still, there is a long list of things that probably won’t work, and it’s helpful to be able to stay on the shoulders of those who’ve tried those out themselves so you don’t have to explore every blind alley on the way (as you may not have the runway to do that!). So do you have the right directors on your board? Good investors should help you with board composition. If they don’t, it may be time to refresh your cap table. ## 2. Prep well The founders (management team) should make a good pack and circulate it a few days in advance. And the directors should make sure they’ve read it before the meeting. And how do you craft a good pack? I [discuss that in more detail here](http://dev.superseed/playbook/board-packs-for-seed-stage-b2b-saas-startups/), but as a rule of thumb, begin the deck with a couple of slides on the biggest issues facing the company. If you start there, you are more likely to have the bulk of the meeting focused on how to solve those issues. And what can be more valuable than spending 1-2 hours a month discussing with some smart people who understand your sector how to solve the biggest challenges facing your business? ## 3. Remember what a board meeting is and also what it isn’t. A board meeting is an opportunity for founders and directors to come together and discuss how to solve the biggest challenges facing the business. Yes – there are also some practical considerations (e.g. stock option sign-off), but if the principles of your option plan has been agreed and the pack has been circulated in advance, the actual signoff of this and other minor matters can be done in 15 minutes towards the end of the meeting. What is a board meeting not? It’s not a weekly ops meeting. As I mentioned above, some directors aren’t quite sure how to be strategic, and so they default to do what they might have done in their prior lives as operators – they review. A common pitfall is for boards to spend 75% of the meeting reviewing each deal in the sales pipeline. Yes, sales is incredibly important, but it is also highly tactical and operational. And a good pipeline is far too rapid moving to wait 4-6 weeks between boards for a good review. The founders should be reviewing their pipeline weekly, and – if they don’t yet have the expertise on how to do this, their directors should help them with sales management coaching or with the hiring of a good sales leader who can put in place weekly pipeline reviews. I promise you, spending 45 minutes at a board meeting discussing how to achieve this will be far more effective in the long run than spending 45 minutes on the pipeline itself. The board is there to help the company evolve and grow through strategic decisions, not to act as an operational steering committee. # In summary At the heart of all successful business is a solid strategy (sitting alongside hard work and just enough luck when it matters). Not strategy as some far-flung philosophical concept, but in terms of getting focus on what the real problems and priorities are, and what things to do (and not to do) to solve them. It’s not endless lists of activities, but a few well-chosen decisions that can mean the difference between success and failure in the high stakes world of building startups. Done well, good boards can mean the difference between getting faster to the right answer or spending 6 months walking down the wrong path. Given the limited runway of startups, you can’t afford too many of those 6-month blind alleys. So, my advice is to bring together the best board you can find, prep them (and yourself) well and then go have some great board meetings to help your business get to the right answers (and results!) faster. Good luck! --- # Board Packs for Seed Stage B2B SaaS Startups **Source:** https://www.superseed.com/playbook/board-packs-for-seed-stage-b2b-saas-startups/ **Published:** 2021-05-31 **Author:** Mads Jensen ### Why do startups need board packs? So, you’ve raised capital from seed VC’s, and you have fresh money in the bank. Congratulations! You also have new investors on your cap table and a board with new directors. Depending on your investors, this can either be a blessing or a curse. But assuming you chose well, you could have yourself a real asset on your hands. The question is now: how do you make the most of your new directors? If you have value-add investors, chances are that they will be helpful to you in between board meetings. But even if you speak with them on a weekly or bi-weekly basis, the board meeting can provide a helpful platform for getting your investor-directors to add value to what you do. The question then becomes, how do you prepare for a good board meeting for a Seed stage B2B SaaS company? A key tool in your kit bag is the board pack. ### What is the purpose of the board meeting? The main purpose of the board is to help you and your business succeed. The board also has a governance role (which is a fancy way to say “checks-and-balances”). But if your company doesn’t succeed, there won’t be anything to govern. So really, it’s all about making sure that you and the company succeed. What might be a helpful way to use board meetings at this stage? Some people feel that board meetings are a distraction and that founders should just be left “to get on with things”. In some circumstances, that may be valid. Especially if the founders have a lot more experience in building tech companies than their investors and board members. However, that begs the question: if your board can’t add any value, did you find the right investors and get the right board directors for your company? Assuming that you have a board with people and knowledge who are there to help you win, what’s the best way to prepare for the meeting? There are different views on what is helpful, but in my experience, the best board meetings are spent discussing the big challenges facing the company. Those where getting the decision right can really move the needle and help accelerate the evolution of your business. And the recipe for getting board meetings to be just that is surprisingly easy. Select good, value-add investors and non-execs / advisers to your board. And then prepare your board pack so that the “big issues” are front and centre. I discuss the role of the board and directors in more detail here. ### The structure of the pack One way to start the pack is simply with a page as follows: - What are the biggest issues you are facing as a business right now, and - What are some options to address this (feel free to indicate the “preferred” or “suggested” way)? Discussing the “big issues” is really the key topic for any board meeting. Rather than getting stuck in the weeds, better to focus your board’s energy on strategic issues. Some founders (encouraged by some investors-directors) feel they have to “report” to the board in a similar way to what they might do with a corporate boss. “Look — here is a long list of all the activities we are undertaking”. Listing activities might be helpful for context, but you can put that in the back of the deck. The board's purpose is not to second guess every marketing campaign you are running, but to provide mainly strategic coaching and support.¹ So feel free to use a simple board template with big questions up front and then the remainder of the deck being a series of appendices with raw data. So back to “the big issue(s)”. What are those things? How do you determine what “the biggest issue” is? Strategic analysis can help you here: 1. What is your current mission? 2. What is the next milestone? 3. What are the challenges standing in the way of reaching the milestone? And once you have mapped out the challenges, you can also list the options you have available to overcome the challenges. The important part of this pack is to outline the challenges. Spend some time really thinking about what they are. If you get this right, you help yourself, your leadership team, and the board focus all their energy on how best to solve what’s most important. And solving the most important issues affect your business outcome disproportionately relative to everything else you do. Here is a useful framework on how to develop a strategy for a Seed Stage B2B Startup if you want to dive deeper: [Business Strategy for Startups](http://dev.superseed/playbook/business-strategy-for-startups/) ### Deep Dives In addition to the key strategic issue, it can be helpful to have a rotating set of “deep dives” where you look more deeply at a certain part of the business. This can help uncover “problem areas” or even opportunities you might not have been thinking of. For instance, you can rotate between: - product roadmap - team / hiring - GTM playbook and strategy ### The rest of your pack The rest of your pack can be a series of appendices #### Operations 1. A time series of your MRR and CMRR data. [More on CMRR here](http://dev.superseed/playbook/using-cmrr-to-track-the-progress-of-your-saas-company/). 2. A time series of your North Star Metric. Don’t have one yet? Work with your team and your board to develop one. #### Sales Sales are vital to business success. The most successful businesses have both great products and great sales operations, but it’s possible to succeed with less than stellar products if you have a good sales approach. And sadly for many great product companies, it is also possible to lose to an inferior product, if your sales and marketing aren’t quite right. On the positive side, initial sales traction can be the thing that allows you to raise more cash to improve your product. Make sales excellence a core competence in your B2B SaaS organisation, and you’ll have taken an important step on the way to succees. 1. List of wins and losses since the last meeting. Ideally, you’ll know why deals were lost. If so, include a small description. 2. Overview of sales funnel. If you sell to enterprise and you have a limited number of opportunities, include a view of your opportunity Kanban with opportunities and deal values listed in the relevant sales stages. 3. ARR Bookings, including expansion and churn data (David Skok has a great chart that [you can see here](https://www.forentrepreneurs.com/wp-content/uploads/2013/01/ARR-bookings.png)). 4. Meetings booked and pipeline added. 5. Sales forecast (which should be split into commit and best case). Forecast quarterly bookings at least once a month. By the last month of the quarter, start forecasting the next quarter (so for June, do the first forecast for Q3 alongside the final Q2 forecast). You should do the sales forecast as a time series so you can see how it changes month-on-month. This will help your organisation get much better at forecasting sales - a critical ability for fast-growing SaaS companies. Accurate forecasting may be tricky at first, but it will get much easier as soon as you hire your first head of sales. S/he can also report on opportunity conversion ratio, which you can measure on a cohort basis, by month. #### Cash & finances When companies run out of cash, they typically fail. Therefore, the first commandment of startup building is “thou shall never run out of cash”. Put the following in your pack: 1. A forecast with an expected cash-out date 2. Monthly burn-rate — (ideally as a time-series) 3. Monthly management accounts and a balance sheet #### Team Include your team slide and show us where you are planning to hire. It doesn’t need to be fancy — but salaries are likely to be your biggest expense, and a team slide helps align everyone on where the money is really going. #### Product Include a one-slide product roadmap (just include what you use internally). To make it extra useful, tie it back to your “biggest issues” from the strategic session. It will help your board understand your priorities and help your internal team better link their priorities to the company strategy. ### Typical cadence Cadence is the rhythm or “beat” that makes a crew of rowers all pull as one team. That can also be a helpful analogy when you think about the “heartbeat” of your company. As your team grows, you’ll start doing regular reviews with your senior leadership team (head of sales, head of marketing, head of product etc.). A helpful cadence can be to do monthly reviews with your whole team, where you have each team-lead prepare a section for you to review as a group. You can then reuse those same slides with the board (in the appendix) for a meeting the week after. This cuts down on board prep time and means that everyone from your reports to the board works off the same data. One cadence that is popular is as follows: - Week 1 — close monthly books. Develop product KPIs. Senior Leadership Team meeting (founders and other key company leaders) - Week 2 — board meeting. Review progress. Discuss obstacles and any tweaks needed to strategy - Weeks 3–4 — iterate and execute There is no one-size-fits-all, but it’s helpful to have a regular rhythm to the way your company works, to help everyone move rapidly forward. ### Time Commitment It takes a bit of time to produce a good board pack, especially in the beginning, where you are working through the best way to capture the information that’s critical to your company. Your board can help, so don’t hesitate to get their input on the format. Ideally, your pack contains mainly content that you already use to run your company, and a lot of this will be produced by your functional leaders as per above. This has the double benefit of saving time and improving transparency. ### Do’s and don’t Some people prefer to put lots of data up front in the pack. Personally, I don’t think that’s necessary. You should never feel like you have to walk the board through a lot of slides to bring them “up to speed”. To make the most of the meeting, circulate the deck at least 2 days in advance and assume that everyone has read the deck. We see founders that make incredible board packs, almost like investor (or customer!) pitches. While we appreciate all the hard work that goes into these, my advice is clear: don’t do it! You shouldn’t be marketing to your board. Every hour you spend making beautiful decks for us is an hour not focused on improving your product, building your team and winning customers. ### In Summary Board reporting is a key topic as start-ups think about how best to engage their investors and other stakeholders. With a bit of investment, good board packs can form valuable tools both for internal audiences and also for your board and investors. It’s easy to go overboard and spend too much time on reporting. Still, in general, a group of start-up founders should probably spend at least a few hours every month tracking their KPIs and analysing these in a deck — both for themselves and also for their key stakeholders. ¹ Note: the board coaching could be operational, but if you spend a big chunk of your board meetings dealing with operational things that perhaps should be dealt with internally, that suggests that you may be lacking key skills in your team. So rather than spending time with your directors trying to resolve an operational issue, try to get them to help you figure out a) if you do need additional skills in the team, and b) actually finding someone to fill the role. --- # Using CMRR to track the progress of your SaaS company **Source:** https://www.superseed.com/playbook/using-cmrr-to-track-the-progress-of-your-saas-company/ **Published:** 2021-04-14 **Author:** Mads Jensen B2B SaaS companies live and die by their ability to grow Monthly Recurring Revenue (MRR). From a valuation standpoint, highly profitable recurring revenue is the thing that sets them apart from so many other business models. High margin revenue that keeps rolling in every month, what’s not to like? But it turns out that MRR isn’t always the best metric for keeping track of what’s happening with your business. Imagine that one of your customers is telling you that they are not going to renew their contract. Now imagine that the last day of the contract is Dec 31st, and that the customer is worth 20% of the company’s MRR. When the company is reporting MRR for December, everything looks great. The recognised revenue doesn’t decline until January and won’t show up in reporting until a month later. But in fact, the company lost 20% of the MRR base in Q4 (assuming no new customers). That might have been helpful information to report. Now consider the opposite example. Consider the same business signed new business in December that’s expanding MRR by 40%. But the new contracts aren’t starting until February 1st the following year. If we only look at reported MRR, December looks fine (flat month), January looks like a disaster (20% contraction), and February looks amazing — a 50% increase on January! Numerical example below : `Previous run-rate: $100k MRR Churn: $20k MRR (20% contraction) Growth: $40k MRR (40% growth) Jan MRR: $80k (20% down from Dec) Feb MRR: $120k (50% up from Jan)` In this case, we’d report amazing progress in our February numbers. However, these aren’t reported until March, a full 3 months after the deals were done in December. Not a useful way to manage the business. ### What’s the solution? Enter Committed MRR (CMRR) Whereas MRR is an accounting term that reflects the revenue we recognise from delivering our contracts, CMRR is a business operations metric that shows the total pool of committed revenue we have attained. As an internal and board KPI, it is often a much better metric than MRR. This doesn’t mean that we shouldn’t track MRR (we have to — otherwise, we can’t do our accounts), but it’s good practice to report both. ### CMRR in practice So how do we calculate CMRR? It’s not a formal accounting term, and there is more than one way to calculate the figure. One way is as follows: - Start with your MRR - Add upsells and new business that has been signed by your customer, but which doesn’t yet figure in your MRR. Example: Sign contract in December for a start on Feb 1st. The December MRR does not include these numbers, but the December 31st CMRR does include them. - Subtract downgrades and churn that have been communicated by the customer, but which hasn’t yet been included in your MRR. Example: Contract expires in December. The customer didn’t sign by December 31st. When you do your reporting in the first week of Jan, you know that the contract churned, so you subtract that MRR from the December CMRR number. ![](http://dev.superseed/wp-content/uploads/2021/05/MRR-CMRR-1024x577.png)MRR and CMRR Chart This type of graph gives you an up to date view of what’s happening with your sales and revenue retention + expansion efforts, so it is super helpful to include in your internal + board reporting. You can find a google sheet with a worked example here: [**CMRR Example**](https://docs.google.com/spreadsheets/d/1jvsU-h7UUzBvdR_j0puwHpMjPJ25o_jZskGhc-dMlMc/edit#gid=1607296309) ### FAQ > What do I do with [implementation revenue, one-off fees etc]? Report them separately. In a SaaS company, it is mainly the MRR that makes your business more valuable. Collect the other revenue. Book it. Use it to cover burn. It’s valuable. Just don’t mix it into your MRR. --- # Why we invested in Ai Build **Source:** https://www.superseed.com/journal/news/why-we-invested-in-ai-build/ **Published:** 2021-02-10 **Author:** Mads Jensen ## Making things by printing them Manufacturing is an enormous part of the global economy. By some measures, the [sector contributes more than $12trn to global output](https://amfg.ai/2019/02/27/additive-manufacturing-industry-landscape-2019/). There are many ways to manufacture things. Three of the main ones are formative (injection moulding), subtractive (CNC milling), and additive (3D printing). **Injection moulding** if the process of injecting a material (e.g. plastic) into a mould to produce many copies. This is useful if you need to make, e.g. a million Lego bricks. The drawback is that it is costly and timely to make the first mould. So, if we just need a few copies of something, injection moulding is not the answer. **CNC milling** is the process of cutting pieces away from a block of material (e.g. metal). This is useful if you need to create a few hundred parts of something. The process is highly precise and scales to produce exact replicas. The downsides are that a) it is relatively time-consuming to make each part, b) it requires quite skilled operators and c) there is a lot of wasted material. **3D printing** is the process of depositing layers of material on top of other layers until you have a final product. This is extremely useful if you need a custom component at low volume – for example, a prototype of an item. It is also excellent at really complex geometries. However, the process works best at a small scale (e.g. a desktop item). For larger things like machine, aerospace or automotive parts, the process has been too inaccurate and too error-prone. It has also been too time-consuming, with competent operators having to re-run print-job after print-job (with job failure rates for large jobs typically cited at 65%). Every time this happens, the print operator must adjust the job parameters and then rerun the process. It can take weeks. That is – until now. ![](http://dev.superseed/wp-content/uploads/2020/10/image-1.png)A large-scale, 3D printer ## Next-generation 3d printing Enter [Ai Build](https://ai-build.com/index.html) – founded in 2017 by Daghan Cam and Michail Desyllas. Over the past three years, the founders have put leading-edge research within generative algorithms, robotics and computer vision to create the Ai Build platform. Their solution automates and accelerates large parts of the 3D printing process. It does this by pre-optimising 3D print jobs and then – once the print is taking place - auto-correcting the many small issues that can happen during a print-job. This way 3D print operators can create much bigger geometries, much faster, and with higher quality. ![](http://dev.superseed/wp-content/uploads/2020/10/image-1024x572.png)AiSync - Ai Build's platform for next generation large-scale additive manufacturing The technology reduces the human effort required to produce a large 3D printed object by up to 86%. It also opens up 3D printing to much larger components than have been the case in the past. Finally, it reduces material wastage, as it is now possible to create very large objects by using just the necessary amount of material. When starting out, the Ai Build team found that commercially available 3D printers were too limited in capability to support their ambition for high-quality, large-scale 3D prints. Therefore, they initially made their own hardware, so that they had printers that were sufficiently sophisticated to meet their exacting standards. As Ai Build is a software company at heart, they have now partnered with equipment manufacturing specialists [Weber](https://www.hansweber.de/en/) and [Kuka](https://www.kuka.com/en-gb). This means that Ai Build can now focus on perfecting the software platform. Ai Build is already supporting several large industrial companies with their rapid prototyping needs. We see Ai Build’s technology as having wide application across aerospace, automotive, construction and other manufacturing areas. Team SuperSeed is excited to support Daghan and Michail as they continue on their journey to build a great company in the additive manufacturing space. [Learn more about investing with SuperSeed](http://dev.superseed/investors/) *This article is published by SuperSeed Ventures LLP which is authorised and regulated by the Financial Conduct Authority. The article does not constitute substantive research or analysis, and should not be construed as an investment recommendation in relation to any publicly traded company. Please note, investments in unlisted early stage companies are illiquid and expose investors to a significant risk of losing all money invested. Please always seek independent financial advice before making investment decisions.* --- # Business Strategy for Startups **Source:** https://www.superseed.com/playbook/business-strategy-for-startups/ **Published:** 2021-01-05 **Author:** Mads Jensen How do you develop a meaningful strategy for your startup? What should founders be putting on the agendas of board meetings? Let's explore how to craft a good strategy for your startup. ### Strategy in Startup Land We are lucky to work with a bunch of super talented founders tackling big problems. When you are trying to change the world, the to-do list is never-ending. So we try to help our founders get their strategy right, as this helps prioritise their activities and make sure that all the hard work translates into tangible results. #### What’s the Problem? Recently I was working to solve a thorny business problem, and I reached out to a good friend (and highly accomplished entrepreneur) for advice. I talked her through the business’s current status, some relevant aspects of what we were working on, and then asked her for her thoughts. What would you suggest, I said? Well, she replied, that depends on what problem you are trying to solve. Because being clear about the problem we are trying to solve is essential to coming up with the right solution. It is also, often, much more challenging than one would think. More than half a century ago, IBM’s founder Thomas Watson Sr. said: “The ability to ask the right question is more than half the battle of finding the answer.” This might seem obvious. But the essential insight is still helpful. Namely that we should often spend a little more time thinking about what problem we are trying to solve before we charge headlong into looking for the “answer”. In 2011, Professor Richard Rumelt codified this insight in his book Good Strategy/Bad Strategy. Here, he presents a simple four-step framework for strategic analysis. I will discuss below how we use this with startups. Getting back to the conversation with my friend, I could see how I had fallen into the trap of looking for suggested solutions without being clear about what problem I was trying to solve. #### Strategy and Startups There is probably no environment as chaotic as early-stage startups. Founders are bombarded with an endless series of existential questions, they often find themselves burning their candles in both ends in an attempt to simultaneously boil the ocean and create an overnight success. Building a successful startup is super hard. It isn’t surprising that founders are sometimes struggling to see the wood for the trees. Because of this, they often bring this endless set of activities into the board room. The implication is that directors spend board meetings talking about a long list of operational matters. These are seemingly important, but they can take focus away from the big things that truly make a difference: namely pivotal decisions that will likely determine the future success or failure of the startups themselves. To help our founders, we often work with them to go through Richard Rumelt’s strategic framework. It is deceptively simple, but surprisingly powerful if you make an effort to apply it to your business problems. Because it turns out that the hardest thing often isn’t so much identifying and prioritising a set of activities. Rather, it is to acknowledge what challenges you face as a business, and prioritise which problems are the biggest obstacles to achieving success. #### The Historical Context To put the value of strategy in perspective, it helps to consider the origin of the word strategy. Derived from ancient Greek, the word Strategos means “general” or (literally “army leader”). Today, the word strategy is used in a plethora of contexts. But to the ancient generals, the idea of strategy was clear: namely to develop a plan for overcoming obstacles (e.g. having a small army, suffering from a lack of supplies, only possessing inferior weapons) to achieve an objective (defeat the enemy, or — perhaps — simply to survive). Today’s embryonic field marshals (startup founders) can similarly think about strategy as the means through which fledgeling startups overcome incumbent behemoths by making good decisions about how to deploy the limited resources they have. ### A Framework for Strategy Formulation So — how does one do this in practice? Rumelt’s framework consists of 4 steps: 1. Define your proximate objective 2. Identify the challenges to be overcome to achieve the objective 3. Formulate a set of policies on how to overcome the challenges 4. Create a coherent plan of action for implementing the policies. Let’s step through an example. #### 1. Define your Proximate Objective This step in itself has multiple layers - starting with defining your long term purpose as a business, and then identifying a shorter-term milestone (also known as a "proximate objective"). ##### Business Purpose At its essence, this step is about formulating your long-term business objective (also known as business “purpose”). One of the beautiful things about startups is that founders have such wide latitude in defining their companies’ long-term visions and purposes. Unencumbered by the legacy of an existing organisation or customer set, you can treat this exercise as a blank sheet of paper and truly dream big. Some well-known examples of this are Elon Musk’s objective at SpaceX (making humanity a multi-planetary species) or Larry Page and Sergey Brin’s objective at Google, which was to catalogue all the world’s information. Defining the right purpose (or mission) is one of the most powerful levers founders have in setting the businesses on a trajectory for success. But having a compelling long-term vision and purpose for your business is not enough. ##### Proximate Objective You also need a proximate objective, by which we mean something close enough at hand to be feasible. To continue the SpaceX example from above: it’s impossible to go from being a fledgeling startup to making humankind a multi-planetary species in one step. First, you need to prove that you can even get a rocket into orbit. So the key here is to define a purpose for your business that is big enough to be meaningful and compelling for you and your team. And then define a proximate objective that you can feasibly achieve within a defined amount of time. A well-defined proximate objective helps prioritise and coordinate all the activities of your business. Choose it well. Defining your proximate objective is such a big topic that I will cover this in a separate article. But once you have determined the business’s proximate objective, the strategic analysis becomes a relatively straightforward (if emotionally challenging) exercise for founders. #### 2. Identify the Challenges to be Overcome to Achieve the Objective Correctly identifying the major challenges is often the hardest exercise for founders. To be a successful founder, you have to live in a little bit of a dream world. Who in their right mind would attempt to launch with a team of 2–3 people and no financial or branding resources to speak of, in an attempt to take on the corporate behemoths of this world? It’s a little bit like trying to defeat the Roman Empire with a ragtag band of adventurers. Noble, but perhaps a bit foolhardy? I say this with all love and respect, having myself been one of those adventurers on a foolhardy quest! But to succeed in the overthrow, you need to have slightly reckless courage to even make the attempt. This is the superpower and Achilles’ heel of entrepreneurs. On the one hand, you need to be courageous (/reckless) to keep up the conviction required to push through all the adversity you will encounter. On the other hand, you need to have enough self-reflection to acknowledge the very real challenges that stand in the way of getting to your objective. And **this** is the hard part. Because how can you keep up hope that you can defeat Goliath against all odds, yet still be ruthlessly honest with yourself about all the things that don’t work? When we work with our founders, this is often one of the more challenging exercises we go through. There is inevitably a temptation to stay at the superficial level (“our only real challenge is that we don’t have enough cash”). But if you don’t drill down one layer, you won’t get to the insights that genuinely help you identify the problems you need to solve. So how do you get there? The important thing is to be brutally honest. Maybe you don’t have a working product? Perhaps you don’t even know what your customers want? Write those things down. And get help from trusted outside mentors and advisers to go through this, if need be. An honest inventory of the things you need to address is your best chance at formulating a successful strategy. A typical list for an early-stage startup might have some of the things below: 1. We don’t really know what our customer prospects most important business problems are 2. Our product doesn’t yet solve our customer’s business problem 3. Or we have a working product, but we don’t yet have any reference installations from customers 4. We only have six months cash in the bank (and the company we think we are competing with head-on have just raised $30m) Develop a good and detailed list of 2–8 things. If you have more, it is possibly because you are including problems that are more long-term in nature (and less critical to the immediate attainment of your next milestone). Focus is key. Once you have the list, prioritise your top 3–5 challenges. These should be the challenges you absolutely must solve to reach your proximate objective. #### 3. Formulate a set of Policies on how to Overcome the Challenges Once you have a prioritised set of challenges, you’ve likely accomplished at least half of the challenge in coming up with a good strategy. Now the fun begins. Step 3 is all about making real decisions about **how** you will overcome the obstacles that stand in your way. What could these decisions look like? If the challenge is: we don’t know what our customers want, the policy could be so say that we will: - define a hypothesis of our ideal customer profile, and then interview 30 prospective customers with that definition, - hire someone with a background in this industry, or - choose another target segment we know better (which then creates a new challenge that needs to be overcome). Be super clear about which problems you are addressing and how you plan to tackle them. All policies are not equally good, and there are lots of patterns and best practice on how to solve common startup problems. But a good board meeting (or conversation with your startup mentors) can be about going through a list of challenges and proposed policies for tackling them. You hopefully have good advisers and mentors. If you do, they will have a lot of relevant experience of how other companies in your situation have tackled and overcome similar challenges. These examples might not apply to you, but that’s fine. Don’t take what comes from your advisers and investors as gospel. Instead, treat the conversations as a source of inspiration for how to solve the challenges you face. This will likely lead to a much more fruitful discussion than if you present a long list of operational activities. "Here are all the marketing activities we plan to do next quarter” might stimulate debate. But my bet is that it won't be the most productive use of your time with the board. #### 4. Create a Coherent plan of action for how to Implement the Policies Step four is the operational foundation of running and building your business. Did we decide on a policy of working with new channel partners rather than hiring more inside sales reps? Does this mean that we need to hire dedicated channel managers? Well, what is the detailed plan for doing that? Do we advertise on LinkedIn or use a recruiter? How do we interview and onboard the new channel expert? In startup land, no plan survives contact with the enemy. But plan definitely beats no plan. And the good news is that — if you are super clear about 1. **why **you are looking to do something, 2. **what **problem you are trying to solve, and 3. **how **you are going to address the issue at a strategic level (your policy), then 4. the detailed** who, how and what **of the action plan are much easier to get right. Your detailed action plan often lends itself to iteration, in case you don’t get it quite right on the first go (we didn’t manage to find the right resource? Let’s tweak the plan and try again). This is not to say that execution isn’t essential, but just an honest reflection of how unpredictable and uncertain things can be in the startup world. As a result, we have to adjust the detailed execution along the way. ### Summing up President Eisenhower (someone who knew the value of both military and civilian strategy) said that “[Plans are worthless, but planning is everything.](https://quoteinvestigator.com/tag/dwight-d-eisenhower/)” And as we discuss above, the plans you make for your startup are unlikely to survive contact with the enemy (or the reality of rapidly evolving business conditions). But even so, it is essential to identify and prioritise your strategic obstacles, and then decide how you will address them. Otherwise, you leave your business's success to chance, with you as founder hoping for success. And — as the saying goes — hope is not a strategy. --- # Welcome to SuperSeed **Source:** https://www.superseed.com/playbook/welcome-to-superseed/ **Published:** 2020-10-31 **Author:** Mads Jensen Dear Founder(s), If you are reading this, it is probably because you and we have decided to become partners in building the amazing startup you are working on. Congratulations for taking the step to becoming a founder, and thank you for choosing to work with SuperSeed for the next crucial phase of your company’s journey. This resource is an overview of some of the support we offer our founders. Here is an overview ## Table of contents - [The next phase.. ](#h-the-next-phase-nbsp) - [How we help - SuperSeed Workshops](#h-how-we-help-superseed-workshops)[Board meetings](#h-board-meetings) - [What you can expect from us and what we expect from you](#h-what-you-can-expect-from-us-and-what-we-expect-from-you) [Sales and marketing](#h-sales-and-marketing) - [New Website](#h-new-website) - [Brand](#h-brand) [Team & Hiring](#h-team-amp-hiring) - [Sales](#h-sales) [Finance](#h-finance) - [Financial strategy and planning ](#h-financial-strategy-and-planning-nbsp) - [Bookkeeping](#h-bookkeeping) [Technology](#h-technology)[Other Resources](#h-other-resources) - [Founder coaching](#h-founder-coaching) - [Peer Networking](#h-peer-networking) - [Free Cloud Credits - AWS Activate and Microsoft Azure](#h-free-cloud-credits-aws-activate-and-microsoft-azure) - [Recommended Reading List](#h-recommended-reading-list)[Books](#h-books) - [Blogs](#h-blogs) ## The next phase..  We back smart and ambitious founders who are looking to change how business is done. The journey to building a successful and enduring company is one of the most meaningful and rewarding paths one can take in a lifetime. It is also one of the toughest, with countless pitfalls along the way. As founders, we have been through the startup journey from start to exit. And we aim to use our experience to be as helpful as possible for you and your company in your own journey. You now have some fresh fuel in the tank (in the form of cash from the most recent funding round), and most founders at this stage have a longer to-do list than they have hours in the day. We have tried to compile a list of things that we hope will be helpful over the coming weeks and months. There are many ways to think about the journey from Seed to Series A. One way is to think about it in terms of these 3 phases: **Problem / Solution fit**. What is the problem we are trying to solve? Is it relevant to the audience we are targeting, and can we propose a solution that can credibly solve this problem in principle?Phase 1 is often the thing we are dealing with at the pre-seed stage. This is the stage when we are still discovering user requirements and building our launch product. However, some companies launch a product and start generating revenue without fully solving step 1.**Product / Market fit.** Can we deliver a user experience that solves our customer’s problem so that they become repeat users and reference customers?Phase 2 is about building the right product for the problem and user-base we care about, and prove that product/user (and product/market) fit through strong user engagement and repeat sales. This is the mainstay of our work at the Seed stage.**Go-to-market fit**. Can we build a system that enables us to distribute our solution in a repeatable, profitable, and scalable way?Phase 3 is about developing a commercial model that allows us to distribute our software in a commercially viable (and attractive) way. This is really the focus of late Seed and early Series A. Companies often iterate around these three phases, jumping back and forth as founding teams learn and discover the real requirements. However, firms that start phase 3 prematurely often find that they are investing sales and marketing $ inefficiently. SaaS products without strong user engagement might attract initial customers and generate sales, but the whole SaaS model is predicated on delighting customers and renewing accounts. If customers churn after 12 months, the firm will never develop a sustainable business. Then it is back to phase 2 (or even 1). ## How we help - SuperSeed Workshops One way we try to help our startups is in terms of crystallising their strategic thinking. This often starts with some foundational definitions around who they are and what they are trying to build. We have a set of structured workshops that explore the following:   1. Identity & positioning. Who we are, what we are trying to do and for whom, and what are we looking to become in the future: Purpose, Mission, Vision and value proposition. 2. Strategy and Milestones: objectives, challenges and strategic principles to overcome them. Read more on [our proposed framework for startup strategy](https://www.superseed.com/playbook/business-strategy-for-startups/). 3. Sales playbook: Ideal customer profile and GTM funnel In our upcoming sessions, we’ll talk about the specific challenges you face and explore how we can best help. ### Board meetings We typically have a minimum of 10 annual board meetings, with a duration typically of 90-120 minutes. Initially, we suggest starting with 90 minutes, and then expanding if we consistently need more time. Board meetings done right can be an incredible resource for you as a founder. It is your chance to pick the most important topic to you right now and then pick the brains of some (hopefully) smart and experienced people to help you solve the issue. This is not to say that your investors and your board have all the answers. But if they are good, they can sometimes help you ask questions and consider options you may not have thought about in your team. That’s powerful. Use it wisely. The best board meetings take a bit of prep. The board pack should be circulated 2-3 days before the meeting (so everyone can read it in advance). It should contain monthly management accounts, minutes from the last meeting, the KPIs you use to run the business (sales, pipeline, org chart etc.) and a summary of progress against the plan/target milestones since the last meeting. Most importantly, it should contain a synopsis of the biggest challenge you face in getting to your next milestone and some questions for discussion with the board. That’s it. Please don’t prepare the deck as a presentation where you plan to step through each slide. The data in the pack should be for reference. Most of the meeting should be spent discussing and exploring options for solving the most significant challenges you and the company face. - More on how to have [good board meetings](http://dev.superseed/playbook/how-to-make-good-board-meetings-for-seed-stage-b2b-startups/), and - how to make [good board packs](http://dev.superseed/playbook/board-packs-for-seed-stage-b2b-saas-startups/) here. ### What you can expect from us and what we expect from you We are here to help you succeed. Your success is our success. We aim to do whatever we can to help you overcome your challenges and shine. We don’t always get it right, but we do try hard, and we know you do the same.  In the first few months after taking seed financing, there are hundreds of things to get right. The business develops very quickly. It is a time of significant change and high velocity. Some people have likened it to building the airplane whilst flying it. That certainly is what it feels like sometimes. Because so much is happening, we usually try to stay close to our founders during the phase right after closing the round. This is typically in the form of a weekly call between the founder/CEO and someone from the SuperSeed team. This call allows us to talk about all the things that need consideration.  During this phase, you can expect us to generally try to help you with the most pressing problems (what’s keeping you up at night?) but also to gently suggest things we’ve seen work well for other companies at your stage. All businesses are different, but there is a well-rehearsed playbook on how to get from Seed to Series A. We want to make sure we make you aware of best practice so you can maximise your velocity and impact.  As we move beyond the three months, we often switch to bi-weekly calls and then fall back to a combination of board meetings and ad-hoc calls. But our focus remains the same - how can we help you and the business become as successful as possible.  What do we expect from you? Please bring us your biggest problems. We can sometimes guess, but it’s much easier if you just tell us what’s keeping you up at night. The more transparency we have in the relationship, the better we can help. All the glory belongs to you, so whatever we propose is just our attempt to try to help you get there faster. We try to be collaborative, open-minded and transparent, and we expect you to be the same.  ## Sales and marketing ### New Website For B2B companies that sell to small businesses, the website is likely one of the most, if not the most important, sales channel. If you don’t have a sales force, the website is the one place where all your prospective customers get to interact with you. You almost can’t invest enough in making that website user experience slick and powerful. But even if you sell to mid-sized and larger companies, your website is one of the only direct touchpoints prospective customers may ever have with you. Sure – you’ll talk directly to your customer champion. But you may never manage to get in front of many of the other stakeholders who need to sign off on the purchase – the CMO, the FD, the head of sales.. the CEO (or whoever the other stakeholders are). But if they are considering your solution, they’ll likely visit your website. It may be your only chance to wow them. Make it count. We find that Seed-stage startups often underinvest in their website or don't manage to clearly articulate their positioning on their site. This is a important place to focus early on. ### Brand This is difficult but important. There is a penalty for trying to build brands with names that are turn-offs or tricky to say. Hard to get right, but not rocket science.  If you are not sure about your name, you are better off changing it early. And don’t worry about “lost brand equity” at the seed stage. This stuff only becomes important once you have invested many $m in brand building. By focusing on your customer, building your brand reputation can increase trust in the market and position your business ahead of competitors. Getting your brand right will set you apart, with customers showing a willingness to buy. ## Team & Hiring ### Sales Unless your team already has a natural head of sales (perhaps one of the founders), it will likely be helpful to bring in someone who can be the commercial focal point for the next part of the journey. Yes – the founders (and in particular the CEO) are always salespeople #1, #2 and #3. BUT – if you are a small founding team, you will have a million things to do, and the way to start scaling revenue is to start building a sales team. The first hire is important. We have hired scores of salespeople and sales leaders over the years, and we can often help in the interview process to give you an “outside perspective”. And if you are looking for help in finding talent, we can help introduce you to high-quality recruiters and search firms who specialise in commercial roles for SaaS companies. There are firms that charge everything from £5k for strong sales reps to £40-£50k for a top-flight Chief Commercial Officer who has experience in taking you from $0 - $10m in revenue (and beyond). And remember, search takes time, so start building the pipeline early.   ## Finance ### Financial strategy and planning  Congratulations. You are now a venture-backed founder. Building a business is a great privilege. It is also hard, and there is an endless string of things you need to do. Above all, you must remember the 11th commandment. Thou shalt not run out of cash. As founder/CEO, you are now above all “fundraiser in chief”. You are building a team to build a company. You have to make sure that there is always enough fuel in the tank to pay your team so you can keep building and iterating until you become cash-flow positive (which could be several years down the road, if things go well and you grow fast!) On the journey from Seed to Series A, most startups find that they need to upgrade their level of financial management and forecasting. Series A investors usually expect more rigour than the founder-made Excel models companies use when raising their seed rounds. Not just because VC’s love spreadsheets, but because a good model is a vital tool in helping to plan the build-out of the business and forecast cash needs (and cash out dates!). At the seed stage, businesses often don’t need a full-time FD. We work with a number of great part-time CFOs / FD’s who can help upgrade financial models to get the business ready for the next round of financing. ### Bookkeeping As you now have professional investors, you are expected to deliver monthly management accounts. These must be done timely and accurately – otherwise, they are not helpful to you or the investors. Good bookkeepers are not expensive, and they’ll quickly pay for themselves in terms of time saved for the founders. We know some good bookkeepers we can introduce you to if you need one. ## Technology If we have invested, it is most likely because you have built a genuinely differentiated technology and which we think has massive commercial potential. That’s our starting assessment of all the companies we invest in. Even so, we find that many of our great founders haven’t built widely scaling enterprise applications before. As you start thinking about scaling, there is a ton to consider: how to get the architecture right, what testing regime to adopt and how to make sure that the platform is secure. We work with a number of highly experienced CTOs who have ‘been there’ and built SaaS platforms that scaled to a global reach and millions of users (or even hundreds of millions!) They’ve likely made all the mistakes and learnt the lesson and will be happy to share. Let us know if you’d like an intro. ## Other Resources ### Founder coaching Few things can be as lonely as being a startup founder and, in particular, a founder/CEO. We know how hard it can be, as we’ve been there (stressful to always be three weeks from running out of money!). As investors, we try to be supportive, but some founders feel that it can be helpful to talk to someone who understands the pressures of startups who is not also their investor and on their board. We work with some great founder coaches and can introduce you to one if you like. As founders, it is all about making the most of your precious assets, and you have no asset more valuable than your own time. Sometimes minor tweaks can come out of a coaching relationship that can help unlock massive productivity gains. It is well worth it – we highly recommend it! ### Peer Networking Sometimes, you just want to chat with another tech founder facing (or has faced) problems similar to yours. A great network to meet other venture-backed tech founders is [ICE](https://theicelist.com/). The network features both annual social trips (both skiing and sun) and monthly founder roundtables where founders meet and discuss what’s most challenging in full confidence. It’s “invite-only”, but let us know if you are interested, and we can refer you for an invitation.  ### Free Cloud Credits - AWS Activate and Microsoft Azure SuperSeed has a partnership with AWS, Microsoft Azure and Google Cloud Platform, providing you with free credits for all three platforms. Please get in touch if you want intros to the cloud teams!  ### Recommended Reading List There are endless amounts of content on how to build startups. We don’t want to add to the excess, so this is just a short list of some of the gems.  #### Books The hardest thing in startups is to focus on one problem long enough to win. Nothing is more important than focus, and strategy is the discipline that helps you find it.  If you read just one business book, make it the delightful [Good Strategy/Bad Strategy](https://www.amazon.co.uk/Good-Strategy-Bad-Difference-Matters/dp/1846684811/) by Richard Rumelt.  #### Blogs There is a delightful wealth of good startup and venture blogs today. Some of the best ones include:  - [https://bothsidesofthetable.com/](https://bothsidesofthetable.com/) by two-time entrepreneur turned VC Mark Suster - [https://www.forentrepreneurs.com/](https://www.forentrepreneurs.com/) by David Skok. It’s perhaps slightly wonkish, but there are some great models and insights for how to analyse your company - [https://tomtunguz.com/](https://tomtunguz.com/) - Tom Tunguz analysis is razor-sharp and always delightful to read. --- # The end of inflation? **Source:** https://www.superseed.com/journal/the-end-of-inflation/ **Published:** 2020-09-28 **Author:** Mads Jensen *Asset prices have sky-rocketed over the past decade, but retail price inflation is muted. Meanwhile, the monetary environment is as loose as it has ever been. Is this truly the end of inflation? We look the economics and put startup investing in the context of the global macro economy.* ## The longest bullmarket On the 2nd of September, the S&P 500 peaked at 3,580. By then it had staged a remarkable rally since the [CARES Act was enacted into US law on the 27th of March earlier this year](https://www.jdsupra.com/legalnews/congress-passes-largest-ever-economic-55140/). The CARES Act followed a round of [quantitative easing announced by the the Federal Reserve on March 15](https://www.federalreserve.gov/newsevents/pressreleases/monetary20200315a.htm). This included purchases of $500 billion in U.S. treasuries and $200bn in mortgage backed securities This year’s rally is itself an extension of a much longer bull market which started in 2009. Following the financial crisis, the American Recovery and Reinvestment (ARRA) Act was passed to breathe some fiscal stimulus into the economy. This fiscal stimulus also came on the back of the first quantitative easing (QE) programme, [announced by the fed in November 2008](https://www.federalreserve.gov/newsevents/pressreleases/monetary20081125b.htm). The combination of QE and the 2009 ARRA stimulus kicked off [the longest bull market in history](https://www.investopedia.com/market-milestones-as-the-bull-market-turns-10-4588903). Since March 9th, 2009 and September 2nd 2020, the SP500 index grew from 676.53 to 3,580. This means a 5-fold in a little over a decade. Quite extraordinary. So it seems that twice in the same economic cycle, growth in asset prices have been kick-started by significant fiscal programmes backed by quantitative easing. And due to QE, it has been easy for governments to fund the fiscal stimulus. Essentially, the fed and other central banks have purchased whatever debts governments wanted to issue, keeping interest rates ultra-low. Although asset prices (like the SP500) have skyrocketed, Governments have been able to run fiscal deficits without causing any meaningful retail inflation. Indeed, Fed Chair Jerome Powell specifically said that he was not concerned about the increase to the Fed's balance sheet [because inflation isn’t currently and issue]((https://www.thebalance.com/what-is-quantitative-easing-definition-and-explanation-3305881). But doesn’t this seem to run counter to traditional fiscal patterns? Let’s take a brief trip back in history. ## The “good old days” The asset price inflation of the 1920s gave way to the great depression and economic collapse. The extent of the depression was perhaps made more so by US President Herbert Hoover’s policies. His Treasury Secretary [Andrew Mellon sought to use the crisis as an opportunity to “Liquidate labor, liquidate stocks, liquidate farmers, liquidate real estate” in order to clear up the financial system](https://en.wikipedia.org/wiki/Andrew_Mellon). As a response to the general economic malaise of the 30s, John Maynard Keynes wrote his hallmark book [‘The General Theory of Employment, Interest and Money’ in 1936](http://www.bbc.co.uk/history/historic_figures/keynes_john_maynard.shtml). He argued that rather than using crisis as an opportunity to liquidate everything, the government should use crises as an opportunity invest aggressively, so as to create demand when markets slump. Keynes' policies forged the basis of the post-war consensus, more or less, until the oil shock of the 1970’s. At that point, it seemed that fiscal expansion started to [simply lead to higher inflation and stubbornly high unemployment (stagflation)](https://en.wikipedia.org/wiki/Keynesian_economics). Enter Milton Friedman and the monetarist economists who had a [strong focus on managing inflation by managing money supply in the economy](https://en.wikipedia.org/wiki/Monetarism). Managing inflation became the new economic mantra up through the 80s, and reserve banks across the developed world were very successful at bringing down the rate of inflation. [And as inflation came down, interests came down, and asset prices started to grow](https://fred.stlouisfed.org/series/FPCPITOTLZGUSA). Then things started to get really interesting. ## Back to “the present” In the past 12 years, we have twice seen crises followed by big economic stimuli. Both times, these have happened without either interest rates or inflation taking off. Government have been able to run large deficits without increase in interest rates due to QE. As governments have needed to borrow more, central banks have just expanded their balance sheets to buy the new government debt. Government have been funded and interest rates stayed low. Presto! And inflation? Where did that go? Didn’t it use to be that running large government deficits would push demand up in the economy. And that this would lead to both retail price and wage inflation? Enter another actor - globalisation. ## Globalisation - an “infinite” labour pool Government fiscal stimulus used to put pressure on local supply chains (especially labour) and this could simultaneously push down unemployment and push up inflation. However, the globalisation we’ve seen since the 1980s have meant that the wider impact on demand have happened not just in the national economies, but globally. A few years ago, Branko Milanovic and Christoph Lakner (formerly of the World Bank) showed how [most of the world had seen significant income growth from globalisation, except for the lowest earners in countries such as the US and the UK](https://www.bbc.co.uk/news/business-37542494). While these groups might historically have benefited from economic stimulus (due to more local jobs in e.g. manufacturing), many of these jobs have now gone overseas. This boosted the incomes of workers in e.g. China, but leaving workers in developed economies short-changed - sometimes even with negative income growth. There are several implications of this. Some of them are political, and we have seen how trade and immigration have become more dominant in the political debates. But there is also an economical implication, which is that we have more elasticity in the labour market. This in turn means that it has been possible for governments to run big deficits for a sustained period of time without worrying about inflation (as per Jerome Powell’s analysis). The effect of globalisation is not likely to disappear immediately, and we are likely to see a continuation of low interest rates and a relaxed monetary environment for a period of time to come. The question is, what follows that? ## The age of disorder In a recent analysis, Deutsche Bank talked about the coming decade as the [Age of Disorder](https://www.db.com/newsroom_news/2020/the-age-of-disorder-the-new-era-for-economics-politics-and-our-way-of-life-en-11670.htm). The DB analyst team highlights brewing US / China tensions are likely to lead to some reversal of globalisation. This - in turn - could lead to higher overall import prices that will have both first and second order impact on inflation: 1. Prices of imported goods are higher (--> inflation), and 2. Local production will become more cost competitive, which could push up local labour demand, wages and therefore costs (--> inflation). So in many ways, the big economic experiments begun in the late 70s and early 80s (globalisation + inflation management and low interest rates) enabled the type of quantitative easing we have seen from 2008. And all of this has now led us to quite a unique position. If a reversal of globalisation leads to more inflation, this could make QE and fiscal deficits more difficult to manage in the future. This could in turn suggests that we might be in for some asset price turbulence in the medium term, as both national economies and the global trading system looks to reshape itself to the changing political pressures ## What are the implications for start-up investing? At SuperSeed we invest in startups that make technology for business automation. That is essentially a way for companies to use software and associated technologies (such as Internet of Things) to produce their goods and services more cheaply. While it is likely that there will be pressure on many industries and perhaps even overall asset prices in the years ahead, we continue to see a strong opportunity in startups that help businesses be more efficient. Especially in an environment where easy gains from labour arbitrage (“off-shoring”) start to wane, the focus only likely to intensify on technologies that can help companies manage costs in an uncertain economic environment. The start-ups we investment in develop exactly such technologies, which is why we see such a good opportunity in the decade ahead. [Click to learn more about investing with SuperSeed](http://dev.superseed/investors/) *This article is published by SuperSeed Ventures LLP which is authorised and regulated by the Financial Conduct Authority. The article does not constitute substantive research or analysis, and should not be construed as an investment recommendation in relation to any publicly traded company. Please note, investments in unlisted early stage companies are illiquid and expose investors to a significant risk of losing all money invested. Please always seek independent financial advice before making investment decisions.* --- # Not quite a quiet summer **Source:** https://www.superseed.com/journal/not-quite-a-quiet-summer/ **Published:** 2020-08-14 **Author:** Mads Jensen Covid has left the global economy reeling. Despite the global turmoil, we've had an action-packed July with lots happening in the world of tech and venture. First, let’s round up some of the macro trends. ### US Overall, the economic numbers for the past few months have been grim. [US Q2 GDP was down 9.5% year-on-year – the largest drop on record.](https://www.forbes.com/sites/chuckjones/2020/07/31/ignore-gdp-plunging-33-pay-attention-to-the-95-decline) ![](http://dev.superseed/wp-content/uploads/2020/08/960x0.jpg)U.S. GDP growth, year over year U.S BUREAU OF ECONOMIC ANALYSIS. FEDERAL RESERVE BANK OF ST. LOUIS This took $2.1trn out of the economy, effectively erasing all growth over the past 5 years (the US economy was smaller in Q2 2020 than in Q1 2015). ![](http://dev.superseed/wp-content/uploads/2020/08/960x0-1.jpg)U.S. economy. Billion of chained 2012 dollars U.S. BUREAU OF ECONOMIC ANALYSIS, FEDERAL RESERVE BANK OF ST. LOUIS We just haven't seen anything like this in recent memory. ### UK UK GDP numbers are – if anything – looking worse. [In Q2, GDP declined a whopping 21% from Q1, and the UK is in the unenviable position of having the worst performing economy in the G7](https://www.theguardian.com/business/2020/aug/09/uk-to-fall-into-deepest-slump-on-record-with-worst-fall-in-gdp-among-g7). ## The big picture The US continues to be the economic locomotive of the global economy, and it’s clearly been hit badly by a combination of Covid and [what looks like a lacking policy response from Washington.](https://www.theguardian.com/us-news/2020/mar/28/trump-coronavirus-politics-us-health-disaster) ![](http://dev.superseed/wp-content/uploads/2020/08/Picture-1-1.png)US Job Market - Source: the New York Times & the Bueau of Labor Statistics Although the US labour market has started recovering over the summer, [jobs remain far below pre-pandemic levels.](https://www.nytimes.com/live/2020/08/07/business/stock-market-today-coronavirus) But even so, it looks like the economy has started to heal; with that comes hopes that the worst is behind us, and that we can focus on rebuilding businesses and the wider economy. But big questions remain as to how quickly we can get through this. Although governments have tried to cushion the impact to livelihoods and the economy by enacting substantial fiscal stimulus, [this has come with a hit to the public finances not seen since the 2nd WW](https://www.bloomberg.com/news/articles/2020-07-21/u-k-budget-deficit-swells-to-record-in-june-on-virus-stimulus). In terms of getting the economy back on track, there are multiple forces fighting each other: 1. **Positive underlying trends in the economy. **Green shoots of the economy, such as we have seen from many companies (including our portfolio) reporting that business is picking up. 2. **Drag from unwinding stimulus and possible tax rises. **Government stimulus starts to unwind as furlough payments are reduced and other stimulus measures pulled back. The [Chancellor, Rishi Sunak is trying to soften the blow with his most recent stimulus package](https://news.bloombergtax.com/daily-tax-report/sunak-announces-cut-to-stamp-duty-in-bid-to-revive-u-k-economy), and even the relatively upbeat [Bank of England recently projected that unemployment will increase by 1 million to 2.5m by Christmas](https://www.theguardian.com/business/live/2020/aug/06/bank-of-england-unemployment-rise-downturn-covid-19-us-jobless-business-live). And then of course there is a big bill which needs to be settled. [Some analysts believe tax rises are likely, if not inevitable](https://www.ft.com/content/8175c594-c809-11ea-9d81-eb7f2a294e50), and depending on how they are implemented, this could lead to a drag on the economy. 3. **The drag of business failures that are still to materialise. **Perhaps surprisingly, [the number of insolvencies in the UK **fell **in Q2](https://www.gov.uk/government/collections/company-insolvency-statistics-releases) – just as we were at the peak of the pandemic. This was driven mainly by government measures which included extended credit from HMRC and the bounceback loans that were underwritten by the government, enabling otherwise struggling business to carry on trading. However, insolvencies are likely to snap back once the government support tapers away, making a ripple effect through the economy as one businesses failure means the loss of customers for other businesses, who in turn will struggle. Whichever way you look at it, it seems like the economy will be quite bumpy in the next 6-12 months. Proceed with caution. ## So, what does this mean for investing? Investors who kept invested broadly in the stock-market throughout the crisis (e.g. through index trackers) have done well, [with the SP500 now back at its all-time-high.](https://theconversation.com/the-sandp-500-nears-its-all-time-high-heres-why-stock-markets-are-defying-economic-reality-142707) So is this a sign that things are better than they look, or just an indication that the stock market has formally decoupled from the economy? Here is one possible interpretation: 1. The long term prospects for the best run companies are still positive. If anything, Covid has improved the prospects for companies like Amazon, Microsoft and Google. 2. Continued quantitative easing means that liquidity keeps flowing into the market. 3. With interest rates at rock bottom, investors have fewer places to turn. This doesn’t rule out the possibility that markets could get rocky again over the coming months - especially if the pandemic starts to spin out of control. But it does go a long way towards explaining their current heights. From a startup investing perspective, our thesis is unchanged. The companies that are doing the best are those who are the most sophisticated with their technologies and business models. Tech continues to drive business transformation, and startups continue to contribute a lot of the underlying innovation – both in terms of tech but also in terms of business models. So if you have a long investment horizon, we continue to see what we believe are very good opportunities in early stage tech investing, also in the current market. ## In other news ### Intel loses the crown? [Intel recently announced a delay to their 7-nanometer transistor technology](https://www.cnbc.com/2020/07/27/tsmc-shares-jump-as-intel-faces-next-generation-chip-delays.html), meaning that they for the first time in decades don't have the most sophisticated microprocessor manufacturing capability. This puts them behind Taiwanese TSMC, whose shares jumped $34bn on the day of Intel's announcement. Intel had already had a knock, with Samsung overtaking the company as the largest semiconductor manufacturer in 2017. Although Intel regained the top spot last year, it looks like they have now fallen behind TSMC in terms of technological sophistication. As Apple is also looking to migrate away from Intel chips, it will be interesting to see what moves Intel will make to try to fight their way back to the top of the semiconductor industry. ### Apple has a win in court [Apple had its €13bn Irish tax bill overturned.](https://www.bbc.co.uk/news/business-53416206)The European Commission had initially argued that Apple owed the additional tax on competition grounds. However, the EU’s general court overturned the ruling, saying that there was insufficient evidence that the Irish tax breaks were anti-competitive. The ruling is likely to be appealed to the European Court of Justice. ### GPT-3 - major news in AI And in exciting AI news, the [GPT-3 is an exciting new algorithm from OpenAI that shows the promise of what AI can achieve.](https://www.bbc.co.uk/news/technology-53530454) The algorithm has the ability to construct not just sentences, but write whole documents, poetry, computer code or answering medical questions. Although the results are impressive, there are still quite a few issues with the algorithm, and [it does’t look like it is about to put us all out of a job just yet.](https://www.theverge.com/21346343/gpt-3-explainer-openai-examples-errors-agi-potential) That's it for this month. Not quite a quiet summer, but quite possibly a prelude of what's to come in the months ahead. *This article is published by SuperSeed Ventures LLP which is authorised and regulated by the Financial Conduct Authority. The article does not constitute substantive research or analysis, and should not be construed as an investment recommendation in relation to any publicly traded company. Please note, investments in unlisted early stage companies are illiquid and expose investors to a significant risk of losing all money invested. Please always seek independent financial advice before making investment decisions.* --- # Tesla, overvalued or undervalued? **Source:** https://www.superseed.com/journal/tesla-overvalued-or-undervalued/ **Published:** 2020-07-09 **Author:** Mads Jensen On June 10th, [Tesla’s market cap reached ~$190bn](https://www.telegraph.co.uk/technology/2020/06/10/tesla-shares-surpass-1000-putting-valuation-188bn/), making it [the most highly valued automotive company in the world, ahead of Toyota](https://www.visualcapitalist.com/tesla-is-now-the-worlds-most-valuable-automaker/). At that point, [Tesla also become worth more than Ford, GM and Fiat Chrysler put together.](https://www.forbes.com/sites/jamesmorris/2020/06/14/how-did-tesla-become-the-most-valuable-car-company-in-the-world/#2c4c2f77f473) This is eye catching, given that [Tesla only sold 367,500 cars in 2019](https://www.theverge.com/2020/1/3/21047233/tesla-2019-deliveries-q4-record-model-3-sales), while [Toyota sold more than 10m](https://www.statista.com/statistics/267274/worldwide-vehicle-sales-of-toyota-since-2007/). And the valuations of the companies speak their own language. While [Toyota was trading at 8.4x last 12 months earnings back in January, Tesla was already at that time trading at 50.2x earnings](https://www.visualcapitalist.com/teslas-valuation-surpasses-ford-and-gm-combined/), suggesting an extremely high confidence in Elon Musk’s ability to grow the company (and its earnings) in the years ahead. This earnings multiple has only gone up with the most recent increase in the share price. ### What's going on (and what is the bull case)? Why are people so excited? And how does this relate to venture capital? From a venture perspective, it is very interesting because Tesla works across multiple areas of tech innovation, such as AI (autonomous driving) and energy efficiency, including energy storage (battery technology). If we think about a future of ubiquitous autonomous vehicles, and a much bigger reliance on renewable energy, both of these technology areas will be important. So, while there are many reasons why people are excited about Tesla, there seems to be two key ones: 1. **Prospect of market dominance**: Tesla currently produces electric cars with [the longest range](https://www.carmagazine.co.uk/electric/longest-range-electric-cars-ev/), and in the views of many, quite possibly also the best. If you can keep making the best cars, this could translate into a dominant market position as the world transitions from petrol to electric over the next decade ([15 years from now it will be illegal to sell new petrol, diesel or hybrid cars in the UK](https://www.bbc.co.uk/news/science-environment-51366123), so the days of the internal combustion engine as the dominant engine for mainstream transport are literally numbered). So, Tesla might then become the dominant car company – if not by volume then by profit? That would certainly justify a high valuation. 2. **Proprietary Technology**: Tesla is delivering lots of technology innovation to create competitive advantages, and this could translate into long-term advantages in battery and autonomous vehicle technology. Both of these areas could give Tesla large sources of revenue and profit outside their own car production. So, there is an argument that betting on Tesla is a bet on more than just their ability to make good electrical vehicles. It all sounds plausible, but does it stack up? ### The bear case Are there arguments why Tesla might still – ultimately – be priced too highly? Tesla CEO Elon Musk certainly seems to think so. On May 1st [Elon tweeted that he thought Tesla was overvalued](https://www.nytimes.com/2020/05/01/business/tesla-elon-musk-tweet.html). At the time, the valuation was around $130bn – much lower than today. So, what’s going on? Tesla still loses money on every single car they make. Yes – [they did manage to eke out a $16m profit for the first quarter of 2020](https://www.caranddriver.com/news/a32324475/tesla-posts-profit-q1-2020/), but of this, [a whopping $354m came from regulatory credits](https://www.caranddriver.com/news/a32346670/other-automakers-paid-tesla-record-354-million). So, what’s that about? Regulatory credits are basically money paid directly or indirectly from other automakers, who still record too high emissions on the vehicles they produce. Without these credits, Tesla would have been $338m in the red. If you compare that to [the 103,000 cars produced in Q1](https://ir.tesla.com/news-releases/news-release-details/tesla-q1-2020-vehicle-production-deliveries), that’s a loss of almost $3,300 per car! One could argue that this is the cost of developing a market leading position, but the question is whether Tesla can maintain that lead as other automakers start ramping up electric vehicle production. In years gone by, mainstream automakers haven’t gone all in on electric cars (and if you know that Tesla sells every car for less than it costs to make, you can more or less see why), but the major automakers are making a big push now, with [Volkswagen targeting one million EVs produced by 2023](https://www.volkswagen-newsroom.com/en/press-releases/volkswagen-significantly-raises-electric-car-production-forecast-for-2025-5696). If anything, there is a risk that this is going to push the retail price of EVs down and reduce the amount of regulatory credits Tesla can get from other automakers, making it even harder for them to reach sustainable profitability. But what about the technology? Leadership in battery technology and manufacturing capacity, and also in autonomous driving? Not all industry observers are certain that Tesla has a sustainable technological lead. On June 22nd, Berstein tech analyst Toni Sacconaghi highlighted in a report that although Tesla currently has an advantage, it is uncertain that they can maintain this gap to competitors, as most of their current lead in vehicle distances comes not from proprietary technology, but from [really good EV engineering](https://www.barrons.com/articles/tesla-battery-technology-electric-vehicles-engineering-51592842119). It is a safe bet that companies like VW and Toyota will be able to copy a lot of Tesla’s engineering innovation, and likely introduce a few new things of their own. So, what about leadership in autonomous driving? Tesla certainly get a lot of data from their current fleet of cars. But [in Waymo (re: Google) they have a formidable competitor](https://www.theverge.com/transportation/2018/4/19/17204044/tesla-waymo-self-driving-car-data-simulation) , and as other automakers start fitting their cars with sensors to capture data, Tesla’s lead in data access could also be eroded. And finally, Elon Musk is starting to see some real headwind over [his reasonably generous pay deal](https://www.theguardian.com/business/2020/jun/30/tesla-shareholders-urged-to-oust-elon-musk-over-55bn-pay-deal). Whether one agrees with it or not, you can't help but worry that these types of things distract from the still significnant work Tesla has to do to make good on all the expectations baked into the share price. As we can see, there are weighty arguments on both sides as to whether Tesla is currently overpriced or underpriced. So, should you buy or sell Tesla stock? Whatever your view, remember John Maynard Keynes’ important insight around contrarian investing: “The market can stay irrational longer than you can stay solvent". On the other hand, if you are looking for long-term investments as an antidote to the daily volatility of the public markets, there is always investment in early stage, capital light tech companies tackling sizable global markets. That's what we specialise in at SuperSeed. SuperSeed investors can see further updates on the [SuperSeed Investor Portal](http://dev.superseed/investorupdates/). (Login required) *This article is published by SuperSeed Ventures LLP which is authorised and regulated by the Financial Conduct Authority. The article does not constitute substantive research or analysis, and should not be construed as an investment recommendation in relation to Tesla Inc. or any other publicly traded company. Please note, investments in unlisted early stage companies are illiquid and expose investors to a significant risk of losing all money invested.* *Please always seek independent financial advice before making investment decisions.* --- # Why We Invested in ThingTrax **Source:** https://www.superseed.com/journal/news/why-we-invested-in-thingtrax/ **Published:** 2020-06-25 **Author:** Mads Jensen ## **Why SuperSeed Invested in ThingTrax** Artificial Intelligence (AI) enabled by machine learning is rapidly changing the world. McKinsey estimates that [AI has the potential to replace as much as 800m jobs before the end of the decade](https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages). Manufacturing is an area that is likely to see significant disruption. Already, robotics is having a major impact, with [the number of industrial robots shipped per year growing from ~300,000 in 2016 to 550,000 forecasted for 2020](https://www.sam-solutions.com/blog/five-reasons-to-implement-robotics-in-manufacturing/). AI promises to usher in the next phase of productivity gains in the manufacturing sector. However, as with all machine learning, the algorithms we deploy are only as effective as the data they are fed. And today, [as much as 90% of manufacturing equipment remains unconnected](https://www.reuters.com/brandfeatures/intel/businessintelligence/adlink/smart-factory-4). Unconnected machines = no data, and no data = no way to use machine learning to improve manufacturing performance. Manufacturers could upgrade all of their legacy manufacturing equipment to new, connected models, however, with the cost of a machine measured in millions, the up-front capital costs can be prohibitive. And even when machines are upgraded, manufacturers often buy equipment from different vendors, meaning that the data supplied can be heterogeneous and not easily analysed cross platform. And finally, even when we have a steady flow of data from the machines, the plants are still only as efficient as the employees working in them. And in many plants, employees are still critical to the overall success of the production process. This is where ThingTrax comes in. ### **ThingTrax – the Chromecast of Manufacturing** ThingTrax provides a solution that transforms legacy manufacturing plants into modern, fully connected manufacturing facilities with capability for automation and intelligent planning. Co-Founded by the exceptional IoT and software engineers Aman Gupta and Imran Shafqat, ThingTrax has developed a solution with three components: 1. Small IoT devices that monitor individual manufacturing equipment 2. Video cameras that look at how workers are working in the plant 3. A cloud platform that takes data from both sensors and cameras, and produces actionable insights for plant managers. This helps managers: 1. Optimise plant throughput by uncovering production process inefficiencies 2. Facilitate production scheduling (i.e. optimising throughput, scheduling staff etc). 3. Improve health and safety (making sure workers are moving in the right way, not too close to dangerous equipment etc). So the solution adds lots of value to manufacturers, and often has ROI of < 6 months. The hardware is inexpensive, and the bulk of the solution is subscription based. It’s a good combination. ### **But wait, there is more!** As we were finalising our investment in ThingTrax, the Covid epidemic broke out. And the ThingTrax team responded quickly by enabling a series of new features on their platform, specifically to help manufacturers deal with the epidemic. Specifically, they have rolled out features that: 1. provide thermal monitoring to see if any employees have fever, 2. social distancing analysis to see if employees are working too closely, 3. Personal Protective Equipment analysis to see if employees have the right PPE kit, and 4. contract tracing to figure out who employees have been close to, if they should later show symptoms of Covid infection. ![Thing Trax Heatmap v2 page 001 1](http://dev.superseed/wp-content/uploads/2020/07/ThingTrax-Heatmap-v2-page-001-1-1024x737.jpg) This innovation was all undertaken in response from existing customers, and we see this kind of rapid response to customer needs as a great testament to the product ability and engineering proves of the ThingTrax team. We are excited about what Aman, Imran and their crew can build over the coming years - both in terms of the solutions they make for their customers and also in terms of the company they are creating. --- # The Greatest Crisis since WW2? - SuperSeed June 2020 **Source:** https://www.superseed.com/journal/2020-june-update/ **Published:** 2020-06-07 **Author:** Mads Jensen On June 8th, the S&P500 (an index of 500 large publicly listed US companies) reached 3,232, very close to the previous all time high of 3,386 from February 19th. In the days since then we have seen market gyrations, but these notwithstanding, the rebound since March 23rd when the market plunged to 2,237 is on the back of US unemployment hitting 40m. With the number of unemployed in the US now at the highest rate since the Great Depression and markets close to an all time high, what's going on? At the face of it, it seems like there is a disconnect between the stock market and the "real economy". And if you have a business or work in the travel or leisure sector, you probably don't feel the economy is even remotely close to being back to where it was. So why are stock markets doing so well? 1. **Company valuations are tied to future expected cash flows. **The value of a business is much less about what happens today, and much more about what might happen in the future. As long as investors expect companies to do well in the next 3 years+, there is a view that some short-term choppiness in earnings is manageable. 2. **Asymmetric recovery**. When looking at S&P500 - almost 20% of the value is determined by just 5 companies - Microsoft, Apple, Google, Facebook and Amazon. Their market cap is so large, and their performance has been so strong during the crisis, that they just in themselves have made up the bulk of the gains from the index (see chart below. 3. **Government intervention**. Governments have acted much more swiftly this time round than was the case previously. During the Financial Crisis of 2007-2009, it took more than a year until the $~800m American Reinvestment and Recovery Act (ARRA) was passed on February 17th, 2009, more than 15 months after the crisis had started. Conversely, this time round, US congress passed a $2.2trn stimulus bill on March 25th, just a few months after the crisis began. This is a massive difference in speed which has had an immediate impact on stock markets. ![](http://dev.superseed/wp-content/uploads/2020/06/image-1024x613.png)Source: finviz.com So what are the takeaways? For now, governments are propping up the economy, and markets believe that this is a credible strategy - at least for the largest and most successful tech companies. However, when it comes to the real economy, the picture is much more nuanced. It's obvious that for millions of people, we are nowhere near "back to normal", and the repercussions could take a long time to percolate through the economy. ### What does this mean for venture investing? As seed investors, we are always trying to work out what might be the successful technologies and companies of tomorrow, and so we have a built-in bias towards the long-term rather than the here and now (see point 1 above). This means that our macro-strategy remains unchanged - we are still looking for companies that have the potential to disrupt industries in the future, and judging from the many highly innovative businesses we see, the future has lots of positive technology disruption in store. On a tactical level, we continue to work with our portfolio companies to help them remain well capitalised and run in a capital efficient way, as customer sales cycles can be impacted and new fundraising rounds can take longer. And for new investments, we work with founders to help them build extra runway into their raises, so they have extra reserves to get to the next milestones - even if the next 1-2 years will be more challenging from a business climate perspective. And finally, we keep investing in innovation that provides real ROI for customers. Whether good times or bad times, businesses are always looking for technology that can deliver real return on investment. And that is the type of technology we invest in at SuperSeed. We'll close this update with a few parting words from General Stanley McChrystal (now retired) who has led forces in both Iraq and Afghanistan. He is very used to dealing with crises, and was recently interviewed by Reid Hoffman (co-Founder of LinkedIn) on the Masters of Scale podcast. It is well worth a listen. > "as soon as it [the economy] starts to sort itself out, certain organisations are going to sprint ahead because they have been figuring it out and they've been going to school on this" Find it [here](https://mastersofscale.com/rapidresponse/) or on your favourite podcast app. All the best, Dan, Mads and the SuperSeed team. --- # Adapting to a new reality - SuperSeed May 2020 **Source:** https://www.superseed.com/journal/2020-may-update/ **Published:** 2020-05-08 **Author:** Mads Jensen We are now well into what's starting to feel like a "new normal" in the "lockdown economy". Other than the challenges of juggling home-schooling with investment work, our team has not been significantly impacted as everyone has quickly adjusted to remote working. For the wider economy as well as for many individuals the Covid pandemic has been devastating. However, from a seed investment perspective we have seen a 2-3x increase in dealflow at more attractive valuations, so in some respects there is a silver lining to the situation. ### What does this mean for venture investing? Many of us have had a feeling that we were drowning in analyses discussing "what this means", and it does feel like many observers are trying to read the tea leaves and come up with insights that are more or less well baked. The truth is - we may not know yet. In the recent words of legendary investor Howard Marks: "it's my view that if you're experiencing something that has never been seen before, you simply can't say you know how it'll turn out". At least in the longer run. In the short run, we still have relatively limited data, but anecdotally the trend seems to be that we are shifting to more of an investors market in early stage venture investing. Here are a few recent analysis that we think merit reading: - [McKinsey on the "next normal"](https://www.mckinsey.com/featured-insights/leadership/the-future-is-not-what-it-used-to-be-thoughts-on-the-shape-of-the-next-normal) - [Analysis on the implications for Private Equity in general](https://www-altassets-net.cdn.ampproject.org/c/s/www.altassets.net/knowledge-bank/by-pe-focus/large-buyouts/private-equity-gearing-up-for-once-in-a-generation-buying-opportunity-as-coronavirus-hammers-markets.html/amp) (more buy-out focused, but with some parallels to venture) - Analysis from Tom Tunguz showing a [sharp contraction in round sizes](https://tomtunguz.com/fundraising-market-q1-2020/) - particularly at Seed stage. This will have implications for valuations. - And finally, [Howard Marks' most recent investment memo](javascript:openPDF('Knowledge of the Future','/docs/default-source/memos/knowledge-of-the-future.pdf')). From a SuperSeed perspective, we keep pushing forward as we are seeing exciting companies raising at reasonable valuations. If you'd like to discuss opportunities or the markets in general, please don't hesitate to reach out. All the best, Dan, Mads and the SuperSeed team. --- # SuperSeed invests in $1m Kluster round **Source:** https://www.superseed.com/journal/news/superseed-invests-in-kluster/ **Published:** 2020-05-03 **Author:** Dan Bowyer We’re excited to announce that SuperSeed has closed an investment into [Kluster](https://kluster.com/), a start-up whose SalesTech platform uses machine learning to accurately predict revenue forecasts and manage pipelines for high-growth businesses. Kluster is solving the problem that businesses aren’t leveraging their data effectively when it comes to pipeline management and reporting. Existing CRM systems generate vague reports that often are based more on hope than on facts. This leads to targets being missed, long sales cycles, and disparity between executive level strategy and function level execution. Revenue uncertainty is causing many businesses to miss targets by as much as 50% and which are often set top down. Pipeline meetings create hours worth of admin for the sales team and management, and often the ultimate result of manual forecasting is vague and incorrect data. The C-suite then relies on poor data to set and execute strategy, and the vicious cycle continues.  ![](http://dev.superseed/wp-content/uploads/2020/05/kluster_image-1024x520.jpg) The market for sales and CRM analytics is already worth over $8 billion and is growing at a 12.8% CAGR through 2025. There is a real demand to support revenue growth, and optimise sales processes and reporting through technology and cloud based services as these businesses scale - and Kluster are well positioned to capitalise on this opportunity with their AI enabled, intelligent reporting and analytics. Kluster’s solution has demonstrated considerable improvements for customers who are using the platform, and have been able to achieve a 24% increase in win rate, 16% reduction in sales-cycle, and an 8% increase in AOV. The team have secured a number of high-growth customers including Cognism, Go-Cardless, Cision, and Saba Software and with the new funding are preparing to scale into more clients and capitalise on the market demand. Kluster has consistently outperformed the major incumbents in revenue management space for a number of reasons; the level of flexibility and customisation combined with AI driven forecasting, and taking the human out of the loop has greatly increased Kluster’s ability to provide more accurate reports, in real-time - something no one else is currently able to do. ![](http://dev.superseed/wp-content/uploads/2020/05/kluster_rory_dan-1024x684.jpg) We have been wholly impressed by co-founders Dan Thompson and Rory Brown who have a real understanding of the problem with Rory’s background in corporate sales, and how to apply technology to uncover otherwise hidden value with Dan’s experience in building statistical models and pricing software for the insurance market. It is a rare combination when you have two founders who are clearly very successful in their respective fields, carrying a high level of confidence in their abilities - but at the same time are also entirely open to learning and applying new ideas in the business. Both Dan and Rory are supremely focused on delivering their vision to scale Kluster into the leading sales analytics and business intelligence company globally and are continuing to grow a very strong team which is another point which really stood out to us. In a start-up, the team is crucial, and as a founder you need to be able to lead and grow an all-star team and this is what we’ve seen Dan and Rory do, in a very short period of time. We’re delighted to add Kluster to the SuperSeed portfolio and look forward to working with the Kluster team on accelerating their revenue growth and go-to-market in the UK and overseas. We also believe that they are well positioned to help businesses in the new ‘wartime’ we find ourselves in. There is an increased need for businesses to optimise their sales activities and Kluster have developed a solution which will serve these forward thinking organisations and further strengthen their commercial functions beyond the pandemic. Like many investors we’re very much open for business - but certainly not ‘as usual’. We clearly owe a great level of care to our LPs for the investments we make in these uncertain times and we’re confident that Kluster have the ingredients that give them a shot at being one of the winners who will emerge post Covid-19 stronger than before it - and with an even larger customer base! --- # The 7 general duties of directors **Source:** https://www.superseed.com/playbook/the-7-general-duties-of-directors/ **Published:** 2020-03-31 **Author:** Mads Jensen *Note: this article does not contain legal advice. If you are unsure of your legal/fiduciary responsibilities as a company director, please seek advice from your legal counsel.* UK Companies Act 2006 prescribes 7 general duties all directors must observe. These are specific to UK company law, but they are quite sensible, and it might be sensible to follow them in spirit wherever you might be doing business (while of course observing local laws and regulation). 1. Act within powers – meaning that directors should only do the things their company constitution (articles, shareholders’ agreement) allows them to do, and only for the purposes they were intended. 2. Promote the success of the company. This objective is very instructive. Directors are not there to look out for their own interests, but to promote the success of the business itself. That is the job at hand! 3. Exercise independent judgement. The purpose is for directors to act independently (to promote the success of the company) without being swayed by other interests – either those of the founders/management or other stakeholders. 4. Exercise Reasonable care, skill and diligence. Being a director is a proper responsibility that should be taken seriously. 5. Avoid conflicts of interest. Meaning: don’t do things that conflict you, and if you think there might be a conflict, discuss it openly with the other directors. 6. Not to accept benefits from third parties. Do not get into a situation that could get you accused of being bribed and not having the company’s best interest at heart. 7. Declare interest in a proposed transaction or arrangement. If there is a potential conflict of interest in relation to a specific transaction, you must declare this to the other directors. Interestingly, in defining the role of a director, UK company law doesn’t distinguish between executive directors (“management”) and non-executive directors (investors, NEDs), despite their actual roles being vastly different. Given some of the corporate governance scandals seen in places like Carillion I am not sure that’s helpful, but that’s a discussion for another day. The key point is that – when it comes to the UK Companies Act, the role of the director applies equally to management and outsiders, and it is helpful to remember that it is not for one group to come to meetings feeling that their job is purely to educate another group, but for both groups (execs and non-execs) to come together as one to discuss how best to solve the most pressing problems of the business. ---