Tag: Startups

  • The Tyranny of Revenue

    The Tyranny of Revenue

    Private markets have inherited the curse of myopia

    It’s a long held belief that public markets may be a poor environment for innovation because of the short-term focus driven by meeting quarterly targets.

    However, there’s an indication that this has reversed.

    Research from the 1990s and early 2000s did indicate that public companies invested less, and were less responsive to investment opportunities than matched private peers, as a reflection of limited long-term thinking in their strategy.

    “Listed firms invest less and are less responsive to changes in investment opportunities compared to observably similar, matched private firms, especially in industries in which stock prices are particularly sensitive to current earnings.”

    Comparing the Investment Behavior of Public and Private Firms, by John Asker, Joan Farre-Mensa and Alexander Ljungqvist

    However, this does not appear to be the case in more recent analysis. In a relative sense, comparing public companies to matched private companies, public companies now appear to be more long-term oriented than their private peers.

    “IPO firms respond more to investment opportunities and have higher productivity in their early public years. Our results on public firms’ sensitivity to growth opportunities hold under several robustness tests, including when we consider firms’ total growth including acquisitions.”

    Do IPO Firms Become Myopic?, by Vojislav Maksimovic, Gordon Phillips and Liu Yang

    What has changed over the course of the last 25 years which could explain this? Primarily, the volume of capital in private markets, and the scale of exits.

    Venture capital has evolved from the business of funding cheap / high ROI experiments to the business of fuelling market expansion. As a result, it has inherited what was once a public market problem; the need to regularly demonstrate growth in order to boost share price.

    Today, revenue targets for the various staged funding rounds are surging, as expectations climb higher and creep earlier.

    In the late 2010s it was assumed that a company could comfortably hit a Series A with around $1M in revenue. That rose to $2-3M by 2024, and with AI the bar has been pushed even further in the years since.

    Consider data from Wing. Only 32% of Series A companies had any revenue at all in 2010, growing to 92% by 2023.

    More than ever, venture capitalists want to see revenue. And once you have revenue, it will never be enough.

    The resulting myopia problem is a real problem for innovation, and there’s both a robust empirical case and a important anecdotal case to be made.

    The Academic Case

    First up, the data. There’s a strong case made across a number of papers about the dangers of startups scaling too soon, and too quickly.

    “We find that startups that begin scaling within the first 12 months of their founding are 20–40% more likely to fail… [We also find] no evidence of a countervailing benefit in terms of successful exit.”

    When Do Startups Scale? Large-scale Evidence from Job Postings, by Saerom (Ronnie) Lee and J. Daniel Kim

    “For the last 6 months the Startup Genome Project has been researching what makes high growth technology startups successful and has gathered data on more than 3200 startups. In our research one reason for failure has shown up again and again: premature scaling.”

    Startup Genome Report Extra on Premature Scaling, Max Marmer, Bjoern Lasse Herrmann, Ertan Dogrultan and Ron Berman

    To provide a summary of the obvious, it is crucial that startups scale properly across all dimensions as they develop. They must be testing and validating assumptions, adding the right skills at the right time, and building the right core of knowledge.

    If you want long-term compounding growth from a genuinely important company, it probably doesn’t start with a mad dash to build a flimsy book of contracted ARR.

    When startups are pushed — by the incentives presented by investors — to pursue “growth at all costs”, this obviously produces much more brittle businesses. As a result, they fail more often, innovate less, have higher rates of fraud, and produce worse exits.

    The Anecdotal Case

    It’s an interesting thought-experiment to apply this question of short-termism and long-termism to the world of AI.

    On one hand, there is Google, a public company. Fundamentally, today’s AI industry would not exist without Google, DeepMind, and their pioneering work on the transformer architecture.

    While they’ve been slower to commercialise the technology than some of their private peers, they have caught up quickly and regularly trade the top-spot on benchmarks with the other labs.

    Perhaps, more importantly, is the calm demeanour and steady leadership reflected by Demis Hassabis. He talks about science, curing disease, and other productive applications of the technology with giddy optimism.

    There’s a particularly powerful scene in the Thinking Game documentary where Demis comes to the conclusion that they should predict every protein sequence in existence and release the data for free to spur further discoveries.

    On the other hand, there is the temperamental nature of leadership at the private labs. Altman and Amodei have spent years making reckless claims about AGI, the end of employment and parallels to weapons of mass destruction.1

    This is not intended to downplay the impact of these models. There are real concerns about changes to the job market, and to cybersecurity. But for a technology is so consequential, who would you prefer as a steward? Sam “Code Red” Altman, Dario “Oppenheimer” Amodei, or Demis Hassabis?

    The truth is that Altman and Amodei are bound by the need to play the private market game. If either one of them falls significantly behind on revenue growth, investors will herd toward their competitor. So, they are stuck in an incremental battle for market share and short-term narrative victories. They are slaves to the needs of their investors, inflating the impact of their work beyond all reason.

    Meanwhile, Hassabis is quietly building. He does not need to go on speaking tours, drum-up investor interest or pump the market with irrational sentiment. He’s responsible (along with the other great researchers across Google) for far more important innovation than the other private labs combined.

    In this example, it is clearly the public market tortoise that is beating the private market hare in the long-run.

    Private Market Myopia

    The tragedy of a more myopic private market is that even as it grows in scale and scope, it is not able to fund the innovations which have been crying out for more patient capital.

    Instead of driving capital into long-horizon infrastructure and industrial capacity, swollen private markets pursue endless incrementalism — an acusation they have commonly levelled at the public markets.

    Worse still, is this myopia feeds investment in short-term opportunities that cannibalise long-term prosperity. Predatory lending companies, “pay-to-earn” scams, expense gambling apps and crypto casinos. These well-funded corners of the market are not only an opportunity cost for real innovation, but are actively cannibalising future potential.

    So, yes. The world will need more great public companies to help dig us out of this casino economy.

    1. Occasionally with times and locations that coincided with major private market fundraising activity. [↩]
  • Designers of Problems Worth Solving

    Designers of Problems Worth Solving

    Why the most investible founders might have the least fundable ideas

    “What do you look for? Grit — sure… everyone likes to say that… intelligence… obviously — the right balance of taking feedback & independence — sure. But you have to go deeper — have a deeper framework on really what makes for great founders who are going to stick it through and be winners AND what is the shorthand you can share with your partners / each other on WHY you as a lead investor actually believe in this person / the bet you are making.”

    Sam Lessin, GP at Slow (Draft letter to LPs)

    Four basic premises for early-stage investing:

    Accepting all of these means acknowledging that first-check VCs should generally source their own deals without relying heavily on signals like traction, market activity, or credentials.

    This is the torturous reality of being the first money in: reaching conviction on little more than a story.

    And you must be able to do that in a thoughtful manner that can be explained to others, and learned from in future.

    A Complicated Package

    The “jockey’s versus horses” debate goes back to the very beginnings of venture capital. It has also become painfully reductive: given a choice between markets, technologies or people, being explicitly pro-founder has the best optics.

    Of course, it’s never that simple. All information is useful in the decision-making process, and each aspect tells you something valuable about the others.

    • The approach described by Peter Thiel is to consider the whole pitch as “some kind of complicated package deal,” combining people, ideas and technology.
    • Or, as Nabeel Hyatt puts it, the way to “separate hucksters who do a good pitch from people who are real executors is to look at the thing that comes out of their hands”.
    • Indeed, Sam Altman described the Y Combinator screening process as looking closely at the “non-obvious brilliance of the idea” being presented.

    Indeed, the problem a person chooses to solve, and how they approach solving it, can tell you a lot about the person..

    Another way to frame this is outlined by Mattia Bianchi and Roberto Verganti in “Entrepreneurs as Designers of Problems Worth Solving“:

    “Finding meaningful problems to address is a critical driver of innovation and entrepreneurship in today’s turbulent environment, possibly even more so than problem solving.”

    “Designing a problem is mainly based on inventively reframing a situation through its expansion, elevation, and prospective sensemaking. The outcome is a more meaningful problem when it aligns the entrepreneur’s purpose with how society, technology, and culture are changing.”

    Entrepreneurs as designers of problems worth solving

    Bianchi and Verganti’s suggestion is that the act of identifying important problems is itself indicative of potential, and a precursor to “designing problems”: the addition of novel aspects which weren’t part of the original problem scope.

    Problem expansion, elevation, and speculative leaps for designing novel
    problems

    This process demonstrates that a founding team has a deep understanding of a particular problem (and the universe around it) and a novel and thoughtful solution.

    Finding Outliers and Avoiding Hucksters

    Understanding and evaluating qualitative traits is obviously very difficult, especially when the playbook of what VCs say they look for is now wide-open for study.

    However, while there’s a strong canon of common wisdom about the attributes of “good founders”, very few VCs have build a framework for recognising, documenting and reflecting on them.

    An example framework that overlaps with the “problem design” approach is the list of attributes used by 1517 Fund, as described in Michael Gibson’s book, ‘Paper Belt on Fire‘:

    • Edge control
    • Crawl-walk-run
    • Hyperfluency
    • Emotional depth & resilience
    • Sustaining motivation
    • The alpha-gamma tensive brilliance
    • Egoless ambition
    • Friday-night-Dyson-sphere
    • Wily & wondrous
    • Alchemy

    (Descriptions and examples for the first 8 are included in the book. The final two may be included in a forthcoming update.)

    If you study these attributes, and the story behind them, you will understand an important qualification: they reflect markers of outlier behavior. The intent is to seek out extraordinary people, rather than create template for ordinary brilliance.

    Indeed, the industry’s more common reliance on gut-feel and credentials is likely to exclude outliers, and limits the potential for constructive reflection on (and true accountability for) investment decisions:

    “For example, VCs often discuss the ‘chemistry’ between themselves and the entrepreneur. The deal often falls through if the chemistry is not right. Such intuitive, or “gut feel”, decision making is difficult to quantify or objectively analyze.

    The added complexity from subjective information further clouds the decision making process and invites the decision maker toward more biases. Due to the complexity of the decision and the VCs’ intuitive approach, VCs may have a difficult time introspecting about their decision process. In other words, VCs do not have a comprehensive understanding of how they make the decision.”

    A lack of insight: do venture capitalists really understand their own decision process?

    There are two typical outcomes of this laziness:

    Instead, the best way to understand something like determination or clarity might be to look for founders with a well-designed problem. For example, making humanity multi-planetary, making energy too cheap to meter, tapping atmospheric rivers, or building the telescope for drug discovery.

    • If a founder has designed a problem which seems high on importance and low on popularity with investors, they’re likely in it for the right reasons.
    • If they can articulate the problem in a novel and coherent manner then there’s a good chance they’ll be able to find a solution where others will fail.
    • If the story of that “problem design” process feels deeply personal (it keeps them awake at night, and busy on weekends) there’s a good chance they will persevere.
    • If their approach to the problem shows a willingness to go back to basics and do the work to learn whatever is required, they likely have the agency required for entrepreneurship.

    It doesn’t matter if the founder ends up pivoting. That may even be desirable. It doesn’t alter the value of what you can learn from their approach to designing and solving problems.

    Thus, some of the least obviously “fundable” ideas may be the most deserving of investment, if you use the right lens. Investors should seek founders that have picked good quests over those that choose lower friction.

    Conspicuous Consumption

    At the opposite end of this spectrum are rage bait startups and the blight of limbic capitalism.

    It is no coincidence that these companies are always at the dead-centre of consensus. The playbook is simple:

    1. Build in a competitive category
    2. Pursue viral stunts that make you unignorable
    3. Raise a huge round to fund more virality / growth

    This exploit a basic premise of scaled venture capital: the largest firms cannot afford to miss potential winners.

    A startup can look like a potential winner by having a track record of success, a density of technical talent, or by commanding enough attention to become an object of desire; the ‘conspicuous consumption‘ of venture activity.

    As long as they pass one of these legibility tests, they get onto the treadmill of “tier-1 VC” convention bidding where success is hitting milestones to earn staying on the treadmill. Capital feeds growth on hype-adjusted multiples, producing fuzzy venture math which is capable of any conclusion you desire.

    The only problem these companies truly solve is scaled capital allocation and systematic AUM growth for venture capital firms. The founder becomes a minor player in a largely commensal relationship designed to support the VC’s ambitions.

    “The hyper-normative world of consensus-seeking is dripping with incentives that would make you their slave. And if you don’t have anything, in particular, that you believe, then they will likely succeed.”

    Build What’s Fundable

    Fund Good Quests

    Without revisiting the topic of bifurcation yet again, it is clear that one of the models described above works for traditional VCs, while the other is geared toward the scaled allocators.

    So, if you are an early stage investor, one of the best things you can ask yourself in the early stages of evaluating a company is whether the founders are designers of a problem worth solving.

    It may not lead you to companies that grow rapidly, which may make raising your next fund more of a challenge. But, like proper risk management, it’s the right approach if you really care about maximising returns and building a durable legacy as a venture investor.

    (top image: “Astronomer Copernicus“/ “Conversations with God”, by Jan Matejko)

  • Coordinating Capital

    Coordinating Capital

    The role of startup valuation in staged capital deployment

    In his article VCs should play bridge, Alex Danco described the Capital Coordination problem in venture capital, created by the staged nature of investments.

    I recommend reading the whole piece, but the summary is that investments are de-risked by staging capital over future milestones (with the implied valuation step-ups) rather than investing everything up-front and hoping for the best.

    The heart of this strategy is the signalling game where investors offer affirmation of the investment for their capital partners.

    VCs should play bridge

    “This gives the next investor cover to say, ok, I’ll do the same thing. I’ll invest at $20M, sending a signal: I believe that this price reflects a discount to the next round. This 20 million price, by current convention, means we believe this hand will play out as another X million in GMV run rate”, or whatever it is your signalling for this new round of bidding.”

    Danco’s assessment speaks to the oddly myopic perspective of “venture math“: investors are primarily focused on understanding incremental progress (measured with ARR) rather than the ultimate outcome. This has come at a cost to sectors that are slower to start generating revenue but solve much more important problems.

    He’s right to frame venture capital as collaborative (Bridge) rather than strictly adversarial (Poker), this also highlights issues like enmeshment and collusion which emerge when so much stress is put on relationship-driven outcomes over objective measurement.

    The approach that Danco describes as “convention bidding” is framed as an alternative to the zero-sum attitude of formal valuation — where investors are primarily concerned with securing their own returns rather than participating in affirmative semaphore.

    Indeed, this reflects a revealed preference for market signals over conviction which should trouble anyone who still believes venture capital is responsible for risk capital formation.

    Discounting the Future

    Further to the above, Danco’s article is a good reflection of venture capital’s poor grasp of valuation, characterised by three common misconceptions:

    1. Valuation is transient, and at each stage a new valuation would need to be calculated — creating uncertainty that threatens signal-driven coordination.
    2. Valuation is the same as price, and entry points are more a function of market norms and comps than they are of fundamental value creation.
    3. The practice of valuation is built on too much uncertainty to be practical for early stage companies.

    While the first is inaccurate, the second is actually dangerous. The general practice of market-driven pricing has created huge structural fragility in venture capital, exacerbating the already painfully boom-and-bust nature.

    The third, however, is just silly. All investments are predicated on valuation, at least in theory (the alternative being mindless momentum investing). The only difference is whether it’s written out with explicit assumptions or a mental calculation with implicit assumptions.

    Indeed, valuation is essentially the story of a startup (“What happens if things go right?”) translated into numbers, with some discounting for the cost of capital and risk of failure. You can think of it as the financial source code of a pitch.

    Usefully, by calculating a terminal value based on that story and discounting it using the expected rate of return in venture capital, you can also ascertain a healthy entry point for future rounds — assuming the startup remains broadly on track.

    Consider the below: the valuation of a Seed round, calculated on Equidam at just over $20M. As a part of this calculation, and the future it’s predicated on, you can also see the valuation trajectory for the forecasted period. If the company raised a Series A at ~30 months, the valuation would be around $85M.

    Expected Valuation Growth – Equidam

    Of course, all of the usual caveats: the future is uncertain, startups pivot, markets shift. However, it’s fairly easy to built those adjustments into a model and see how they change both today’s valuation and the future trajectory.

    Assessing valuation with a higher resolution view of performance enables better judgements: Perhaps top-line revenue is growing on track, but costs are surging. Maybe revenue is lagging, but margins are way better than expected.

    The hope is not that forecasts play out precisely, but that they give you a useful relative perspective on performance over time.

    If venture capitalists wanted a way to coordinate staged capital in the future, to finance a company efficiently (with minimal time spent agonising over terms) while ensuring a good risk-adjusted return for all investors, this is the logical approach.

    Valuation is essentially the process of aligning expectations across market participants by transparently exploring data and assumptions. Typically this occurs between buyer and seller (VC and founder) in a specific transaction, but there’s no reason why downstream capital providers couldn’t benefit from that work.

    It also offers the vital benefits of helping investors better understand the value of novel innovation, and limiting exposure to systematic risk of market-based pricing — getting caught in the trap of “money chasing deals“.

    (top image: “The Bulls and Bears in the Market” by William Holbrook Beard)

  • What is Valuation

    What is Valuation

    Whether you’re investing in mature companies in the public market, or fast-growing startups in the private market, one question separates good and bad investors:

    Do you understand valuation?

    Valuation is the rationale by which you determine which opportunities to pursue. To develop your understanding of valuation is to develop your ability to recognise potential.

    Despite the central role in investment decisions, valuation is often misconstrued as financial engineering or market-driven pricing exercises.

    Valuation is an opinion

    Here’s three things valuation is not:

    • Based on verifiable inputs
    • Provably accurate in output
    • A mirror of market sentiment

    Instead, valuation is always an opinion based on a set of assumptions about an unknowable future.

    “People act like it’s an award for past behavior. It’s not. It’s a hurdle for future behavior.”

    Bill Gurley, VC at Benchmark

    Whether that valuation is based on a detailed DCF model or napkin-math, it’s an opinion. And it’s no more or less of an opinion as your process gets more or less sophisticated; the only difference is how clearly you outline the assumptions.

    When one investor states that a company is overpriced, and another that it is undervalued, neither is right or wrong in the moment — they just have differing opinions.

    Valuation can be a simple, implicit part of the process, or it can be an explicit exercise used to better understand an opportunity and check assumptions.

    Valuation is a story about the future

    In order to form an opinion about a particular future, you must first listen to its story.

    “The value of a stock is what people believe it is and could be. A stock is a story.”

    Lulu Cheng Meservey, Founder of ROSTRA

    All investment decisions are bets on today’s fiction.

    • Elon Musk is telling you that we must get to Mars
    • Brian Armstrong is explaining the importance of Bitcoin
    • Parmita Mishra is describing the future of medicine
    • Aaron Slodov is laying out a reindustrialisation roadmap
    • Augustus Doricko is talking about rivers in the sky

    Do you believe them, when they have nothing but a story?

    Does that story involve unleashing a huge amount of economic energy? How much capital does it require?

    Based on the credibility of that story, and the potential scale of that economic energy, is it worth paying-up today?

    “The scarcest resource isn’t capital or talent. It’s the ability to make people believe in your specific tomorrow strongly enough to fund it today.”

    Howard Yu, LEGO® Professor of Management and Innovation

    Valuation is the art of using stories to develop opinions about the future

    Valuation can be broken down into a few pieces:

    • How do you judge the credibility of a story?
    • How do you estimate the economic potential of a story?
    • How do you estimate the risk associated with a story?

    You can think about this via two extremes:

    1. You’re looking at a company you’re familiar with. You’ve got a good mental model of the industry, the technology, the market forces, trajectory, and risks. It’s relatively simple for you to make a rough judgement on value in your head. This reflects Kahneman’s “System 1” thinking.
    2. You’re looking at a company you have no familiarity with. The technology is novel, the market is emerging, and there’s no real precedent. In order to make a good judgement, you have to submerge yourself in details and scenarios. This reflects Kahneman’s “System 2” thinking.

    In the latter case, a more sophisticated valuation process can help you understand the credibility and economic potential of a story. It provides a framework for ingesting information that can control biases, while allowing you to recognise and scrutinise the main drivers of value.

    Valuation is essentially the act of running simulations of the future described in a story, focused on the numbers rather than the narrative, to explore the potential.

    Pricing is not valuation / Trading is not investing / The present is not the future

    Venture capitalists often choose to focus on relative pricing, instead of valuation, as an attempt to proxy experience through crowdsourced activity — allowing for speedy “System 1” style investment decisions.

    This means analysing industry activity, which biases investment towards categories with low information friction like B2B SaaS, at a cost to sectors with more idiosyncracy, like deeptech.

    Unfortunately that focus on market data also means these investors are not developing their ability to make judgements about the future, only to pattern match today. This compounds into a ‘knowledge worker atrophy‘ problem, weakening venture capital’s institutional competence at funding creative endeavors and novel solutions.

    That would appear to be a critically weak link in the value proposition of venture capital, and the premise that it funds important solutions to humanity’s problems.

  • -1 to 0: Notes on Origination

    -1 to 0: Notes on Origination

    “Small hinges swing wide doors”

    Eric Bahn, Co-founder and GP at Hustle Fund

    Consider this simple truth: All value created from Series A to exit is downstream of the first check. Without initial belief, the foundation of VC collapses.

    This relatively thin slice of venture (by capital) is therefore disproportionately important. All future returns are directly attributable to the industry’s ability to surface new opportunities.

    Despite this fundamental importance, venture capital is structurally rigged against origination:

    • Origination is inherently a small check pursuit, as these ideas need minimal capital to validate propositions.
    • Origination is primarily a small partnership or solo-GP pursuit, versus the consensus-building nature of large firms.
    • LPs are biased against highly diversified VC strategies, particularly with the growth of Fund of Funds, which makes scaling an origination strategy more challenging.

    Thus, origination is the domain of smaller firms, with smaller funds. This runs contrary to the compensation incentives in venture capital, which push successful investors to raise larger funds and invest at later stages.

    “In exploring its sources, we document several additional facts: successful outcomes stem in large part from investing in the right places at the right times; VC firms do not persist in their ability to choose the right places and times to invest; but early success does lead to investing in later rounds and in larger syndicates.”

    The Persistent Effect of Initial Success: Evidence from Venture Capital (2020)

    So, in addition to the existing problem of VC’s lack of institutional knowledge and high churn, good investors are often squeezed out of this origination focus if they wish to achieve greater scale.

    Origination is the foundation on which all future VC returns are built, yet it is systematically underresourced and underallocated

    You should be dubious of any venture capitalist that claims “there’s too much capital, and not enough good opportunities”.

    You will never this claim from VCs focused on origination, who have to spend a distracting amount of time on their own fundraising — even when their track record is strong. Again, their size prohibits them from more easily raising from larger institutions.

    This chronic misalignment sets the ceiling on what venture capital can achieve in returns and accelerated innovation

    Multiple studies of the innovation ecosystem in Europe, and how funding is distributed by bodies like the EIF, have come to the same conclusion: Capital allocated to later stages does not solve the problem of underpeformance and slow progress.

    Instead, shifting allocation earlier, to improve origination, without any increase in capital, has an effect equivalent to doubling the total capital pool.

    Industrial Policy via Venture Capital (2025)

    It’s bad for innovation

    Research indicates that origination struggles directly influence which startups are created: Founders who have a hard time finding VCs willing to write their first check end up pivoting to more generic ideas with lower information friction.

    It’s bad for returns

    If all venture returns are downstream of origination, you would expect that a specific focus on origination would lead to outperformance amongst VCs. You would be right:

    “The consensus in recent literature is clear: proactive deal origination is pivotal for achieving superior outcomes in venture capital investments.”

    Where Are the Deals? Private Equity and Venture Capital Funds’ Best Practices in Sourcing New Investments (2010)

    You’re not mad enough about VC incentives

    Fundamentally, this issue will not be resolved as long as the incentives in VC mean the primary opportunity is to maximise fee income by optimising for proxy metrics.

    There is very little intrinsic motivation for VCs to take excessive career risk on highly idiosyncratic ideas, which generate slower markups than consensus startups, in the hope that they may generate some carry a decade down the line.

    In summary: Venture capital is structurally set up to pile capital into categories with low information friction, which is diametrically opposed to innovation.

    You’d have to be brave, crazy or stupid to do anything else.

    The brilliant exceptions

    Fortunately, for everyone involved, there are a few mission-driven firms that have made it their business to focus on origination.

    In today’s market, you’re either a lifecycle capital partner to founders, or you’re a niche operator originating unique opportunities for those who can be.

    To originate deal flow, one needs some combo of:

    – unique networks;
    – top tier reputation in a niche; or
    – uncommon levels of investment rigor for company stage

    All of these help drive signal for the company, which is incredibly valuable in a noisy market.

    Mike Annunziata, Founder and GP at Also Capital

    They have each developed specific proactive strategies to find high potential opportunities, first — sometimes before there’s even a company to invest in.

    • Firms that focus on early conviction and speed, like 1517 Fund and Hustle Fund — who write tiny inception checks to help prospective founders take the first step.
    • Firms that focus on research and expertise, like Compound and Not Boring Capital — who find contrarian needles in consensus haystacks by processing vast quantities of information.
    • Firms that focus on process and scale, like BoxGroup, Boost VC and First Round — who manage the greater idiosyncratic risk of origination with discipline and portfolio strategy.
    • Firms that focus on community and talent density, like South Park Commons, and Entrepreneur First — who allow opportunity to manifest from pools of brilliance.

    (They all share the key elements: community, research, process and speed.)

    It’s hard to state just how much value can be attributed to these firms, and a few others like them. Relative to their size, they are responsible for a disproportionate amount of founder opportunity and downstream potential.

    To understand the potential for these models, Y Combinator and Founders Fund are a good illustration of the same principles at scale. For a range of reasons, these two firms have broken out of the orbit of VC’s structural limitations.

    To conclude, a quote from Danielle Strachman on 1517 Fund’s approach to origination, and some links to great conversations with the other firms above:

    “We call it turning over rocks. It’s like, oh, I went to this campus and I found an interesting young person; we’re turning over rocks. You go to a hackathon and my favorite thing is to look for the young person who is staring off into space. They’re by themselves because they built their own thing. You go up and you talk to them and you’re like, whoa, okay, you are really super nerded-out on security and you’re talking to me like I should know what you’re talking about, and I have no idea. But we’re gonna talk for a while and we’re gonna figure this out.”

    Danielle Strachman, Co-founder and GP at 1517 Fund

    Interviews with the other firms mentioned:

    (top image: “Supersonic”, by Roy Nockolds)

  • The Venture Capitalist’s Paradox

    The Venture Capitalist’s Paradox

    Relying on market efficiency while investing in idiosyncrasy

    Venture capital is the hunt for outliers; ideas that are not well understood by the wider market. This strategy is rooted in the need for risk capital to finance frontier businesses.

    You can see how this played out in previous generations of tech: Amazon, Airbnb, Canva, Coinbase, Dropbox, Google, Shopify, Slack, Uber… All of these companies faced an uphill battle with investor interest, and went on to produce incredible exits for the early believers. Similarly, there are many examples where the founder’s own capital paved the way: SpaceX, Tesla, Palantir, Anduril…

    On the other hand, it seems relatively more difficult to find stories of competitive early rounds leading to great outcomes. Stripe, perhaps?

    This understanding of venture capital is reinforced by the data:

    We find that consensus entrants are less viable, while non-consensus entrants are more likely to prosper. Non-consensus entrepreneurs who buck the trends are most likely to stay in the market, receive funding, and ultimately go public.

    The Non-consensus Entrepreneur: Organizational Responses to Vital Events

    Despite this, today’s venture capitalists have an absurd reliance on the market as their lens to understand value. Herd behavior drives investor attention, and (what passes for) valuation is primarily derived from relative measures like ARR multiples.

    Investors set out to generate alpha with their unique ability to recognise novel opportunities, but rely on broad market sentiment as a lens to understand what is worth pursuing.

    This paradox has stumped observers.

    This heavy reliance on comparable companies in the VC valuation process is perhaps unsurprising, given that relative pricing methods are not uncommon in M&A markets overall. What is less clear, however, is the exact driver behind this method, and the understanding of why a relative pricing methodology will impact startups’ valuations.

    What Drives Startup Valuations?

    Whether it’s marketplaces, crypto or today’s AI boom, capital herds into the category and drives up prices, and those prices then become “comps” for others.

    Ultimately, the surge in deals creates a greater volume of market data for that category, reducing information friction for investment, allowing for more deals to be made more quickly. This compounding influence quickly overheats activity.

    In addition to competition driving up prices and compressing due diligence timelines, there’s a further pernicious consequence: while information friction is reduced around the consensus, it is increased elsewhere.

    Founders building truly innovative products find themselves facing a wall of blank-faced investors programmed with category multiples. The difficulty of raising capital becomes so great that many simply reorient their ambition to lower quality projects closer to the experience of venture capitalists. This is clearly a fundamental failure:

    Information frictions in valuation can lead startups to select projects that align with the expertise of potential venture capital (VC) investors, a strategy I refer to as catering […] where a startup trades off project quality with the informational benefits of catering.

    Startup Catering to Venture Capitalists

    Fundamental Value

    If all of this was to hold true, we should see an increase in performance by the few VCs that maintain a lens on fundamental (rather than relative) value. In theory, these investors would be better able to recognise outlier value and unique opportunities.

    Indeed, that does appear to be the case. Multiple studies on venture capital investment performance and decision making processes have reached the conclusion that VCs would benefit from a better understanding of fundamental value:

    Venture capital funds who base their investment strategy on fundamental values and a long-term view seem to have a measurable advantage over those who engage in subjective short-term trading strategies.”

    How Fundamental are Fundamental Values?

    Ideally, a startup would combine a full fledged fundamental cash flow-based approach with a thorough comparable companies analysis to cover both its intrinsic value and a market-based measure.

    What Drives Startup Valuations?

    So, the paradox is laid bare and the remaining question is why this is the case. There are two potential explanations:

    1. VCs do not understand valuation. A decade of ZIRP-fuelled spreadsheet investing has destroyed the institutional understanding of risk and the purpose of venture capital.
    2. There are other incentives at work which explain this phenomenon; reasons why VCs would be reluctant to closely analyse fundamental value.

    Explanation 1: Hanlon’s Razor

    Unsurprisingly, there’s plenty of evidence to make the case that this is unintentional; a result of simple incompetence and herd behavior. Indeed, herding is a well-studied phenomenon in professional investment, particularly when insecurity is high:

    Under certain circumstances, managers simply mimic the investment decisions of other managers, ignoring substantive private information. Although this behavior is inefficient from a social standpoint, it can be rational from the perspective of managers who are concerned about their reputations in the labor market.

    Herd Behavior and Investment

    Chronic groupthink, and the atrophy of independent reasoning, further explains why VCs are also often unable to clearly articulate the process that goes into their investment decisions:

    The findings suggest that VCs are not good at introspecting about their own decision process. […] This lack of systematic understanding impedes learning. VCs cannot make accurate adjustments to their evaluation process if they do not truly understand it. Therefore, VCs may suffer from a systematic bias that impedes the performance of their investment portfolio.

    A lack of insight: do venture capitalists really understand their own decision process?

    Many VCs have simply emulated the practices of their peers without fully understanding why. Indeed, those peers probably couldn’t provide a rationale either. It’s an industry of investors copying each other’s homework while pretending to be original thinkers.

    Action without understanding purpose naturally erodes standards. Not only does this make VCs bad fiduciaries, it also precludes any learning and institutional development.

    Almost half of the VCs, particularly the early-stage, IT, and smaller VCs, admit to often making gut investment decisions. We also asked respondents whether they quantitatively analyze their past investment decisions and performance. This is very uncommon, with only one out of ten VCs doing so.

    How Do Venture Capitalists Make Decisions?

    Explanation 2: cui bono?

    The alternate theory is that there may be some genuine motive for VCs to avoid looking too carefully at fundamental value. That somehow they are able to profit from a reality in which it is not an important driver of activity.

    Perhaps it is simply because today’s VCs behave more like traders, rather than investors. That the easier opportunity is to glom onto hot categories and ride the exuberance to an overpriced exit somewhere down the line, rather than trying to find good investments.

    This is also not an original accusation:

    For those who are holding on to the belief that venture capitalists are the last bastion of smart money, it is time to let go. While there are a few exceptions, venture capitalists for the most part are traders on steroids, riding the momentum train, and being ridden over by it, when it turns.

    Putting the (Insta)cart before the Grocery (horse): A COVID Favorite’s Reality Check!

    Venture capital, as a discipline, runs an existential risk of invalidating itself by becoming institutionally what crypto is colloquially. If venture capital is just about “[creating] the impression [of] recoupment”, then its no better than the pump and dumps of the crypto bros.

    Institutionalized Belief In The Greater Fool

    Yet again, there’s evidence that this is the case.

    VCs are heavily influenced by market conditions, optimism and FOMO. None of these are original accusations, although it might be interesting to learn this has been demonstrated in research:

    The optimistic market sentiments and fears of missing out in hot markets can significantly shift VCs’ attention towards cheap talk, such as promises of high growth. Such conditions may even prompt VCs to neglect costly signals such as the profitability of new ventures.

    Venture Capitalists’ Decision Making in Hot and Cold Markets: The Effect of Signals and Cheap Talk

    This is confirmed by research looking at this question from the other side, where it is demonstrated that VCs are commensurately less likely to herd in periods with greater uncertainty and less optimistic momentum to channel capital:

    The study finds a significant negative relationship between economic policy uncertainty (EPU) and herding behavior, indicating that venture capitalists are more likely to make independent judgments when EPU rises.

    Economic policy uncertainty and herding behavior in venture capital market: Evidence from China

    Even when a VC talks about wanting to find category winners, they are also implicitly talking about riding a category. Were they hunting for genuine outliers, the category wouldn’t matter. There is a reason, for example, why some VCs widely publicise the categories they invest in as great opportunities, and others choose to keep any alpha for themselves.

    The final reason is simple: subjective, market-based pricing is opaque and open to manipulation. VCs can collectively produce and support higher marks regardless of underlying value. They can also choose to be “opportunistically optimistic” about a portfolio company:

    During fundraising periods the valuations tend to be inflated compared to other periods in the life of the fund. This has large effects on reported interim performance measures that appear in fundraising documents. We find a distinctive pattern of abnormal valuations which matches quite closely the period up to the first close of the follow on fund. It is hard to rationalize the pattern we observe except as a positive bias in valuation during fundraising.

    How Fair are the Valuations of Private Equity Funds?

    Indeed, much of the fallout post-2022 involved LPs feeling fairly miffed that managers weren’t being honest about underlying portfolio company value. As long as VCs broadly maintained marks, there was still hope to raise another fund on those mostly meaningless proxy metrics of performance.

    In Conclusion,

    • Many VCs choose to play a short-term trading game
      By focusing on market momentum, rather than companies, through relative valuation methods, VCs operate more like traders.
    • Most VCs take the path of least resistance when adopting practices
      While analysing fundamental value offers outperformance, relative value is easier to understand and offers strategic advantages.

    Most VCs identify as investors, and some of them still are. Mostly the smaller, boutique firms who were quick to realise that they could not compete in consensus categories against the multi-stage platforms.

    There is value in being a trader if you have multi-billion dollar funds and can both manifest and then coast on the beta of technology markets. They behave like market makers on the way up, extracting opportunistic liquidity, and can aim to concentrate resources into the handfull of winners before the market turns.

    This strategy is toxic to smaller investors, many of whom are simply washed out in the boom-and-bust cycles that this amplifies. Instead, these investors, focused on identifying true outliers, need to consider polishing their lens on fundamental value.

    (top image: Ascending and Descending by M.C. Escher)

  • Explorers and Industrialists

    Explorers and Industrialists

    The two faces of what we sometimes call venture capital, and the cognitive dissonance they create.

    This blog started out as a place to write about the intersection of science fiction and technology. The first article about venture capital, and the opium of consensus, emerged from watching VCs herd into thinly-veiled crypto scams in the name of “web3“.

    The influence of consensus remains poorly understood, and the subject of distracting counterfactuals, despite being widely discussed for as long as “investor” has been a profession.

    Words must have meaning

    Venture capital is the practice of identifying nascent business opportunities with radical potential; the application of process to extract outsized value from extreme idiosyncratic risk.

    In this context, venture capital is clearly not a consensus-oriented discipline. Both theoretical and quantitative perspectives support this reality; the intention, goals, behavior and performance.

    We find that consensus entrants are less viable, while non-consensus entrants are more likely to prosper. Non-consensus entrepreneurs who buck the trends are most likely to stay in the market, receive funding, and ultimately go public.

    The Non-consensus Entrepreneur: Organizational Responses to Vital Events

    Simply look back at the early-days of venture capital’s largest outcomes, where the majority did not have investors competing for access, driving up the price. Fundraising was a struggle for those founders, and in that struggle was the opportunity for those on the other side of the table.

    It’s very hard to make money on successful and consensus. Because if something is already consensus then money will have already flooded in and the profit opportunity is gone. And so by definition in venture capital, if you are doing it right, you are continuously investing in things that are non-consensus at the time of investment.

    Marc Andreessen, a16z

    Indeed, if consensus were to play a meaningful role in venture capital, it would fail on the only two things it sets out to achieve:

    • Maximise ROI for LPs through idiosyncratic risk
    • Finance the development of novel innovations

    In addition, there is no such thing as a downstream supply of “non-consensus capital”. If you fund non-consensus companies then you have to be able to support them to the point where their potential is obvious and larger/later generalists can be convinced. It’s paradoxical to believe that other investors will share your non-consensus viewpoint.

    Cognitive dissonance

    The root of the confusion about consensus (and entry price, portfolio construction, attention, relationships…) is the mixing of two different private market strategies. One is venture capital, and the other is something else that just falls into the venture capital allocation bucket.

    This other strategy emerged in the period from 2011 to 2021, enabled by low interest-rates, as described by Everett Randle in “Playing different games“:

    Tiger identified several rules / norms / commonly held ideas in venture/growth that are stale & outdated and built a strategy to exploit the contemporary realities around those ideas at scale.

    Everett Randle, Kleiner Perkins

    The capital mechanics of this strategy were explained further by Kyle Harrison, in “The Unholy Trininty of Venture Capital“:

    So you have massive capital allocators looking for places they can farm yield and they don’t want to be slinging hundreds of $30M checks. Capital agglomerators represent the perfect solution. Hit your 7-8% yield target, be able to park $250M a pop without being the majority of the fund, and sit back and relax.

    Kyle Harrison, Contrary

    Versions of “bigger/faster/cheaper capital” have since been employed by other multi-stage platform firms, and the consquences are dramatic:

    If success in venture capital is driven by non-consensus investments, in less competitive ecosystems with more rational pricing, why would an investor pursue a strategy directly to the contrary?

    The answer is indicated in Martin Casado’s episode with Harry Stebbings, in which he described missing the category winner as the greatest sin of investing.

    This hunger to capture the most extreme “power law” category leaders is a requirement of the exit math involved in multi-billion dollar funds. Consequently, these platforms also raise substantial early-stage funds to index any emerging theme, putting an option on the future of those companies.

    A mega-VC with $5-10B annual funds is really searching for only one thing: a company they can pile over $1b into with a potential for 5-10X on the $1b. With this, seed fund is inconsequential money used to increase the odds of main objective.

    Bill Gurley, Benchmark

    The startup industrial complex that has emerged is not optimised for non-consensus investing, and nor does it need to be. Investors at these platforms are hired to compete and win in known areas — and have attitudes to match:

    Successful startups fit into a mold that investors understand, and that more often than not those startups attracted meaningful competition among top investors.

    Martin Casado, a16z

    If a company has tons of hype and seems overvalued, don’t run away. Run towards it. Hype is good. Means they’ll raise, exit at higher valuation. And the price likely won’t feel overpriced after the startup exits.

    Andrew Chen, a16z

    The output is a rough index of high-growth private technology companies. The platforms are harvesting the beta from innovation, rather than finding the alpha in outliers. Even the giant outcomes become so well/over-priced that their returns risk converging with benchmarks.

    Categorically, this is not venture capital in any meaningful sense.

    The only reason we call this venture is because LPs need to call it venture so CIOs can hit annual allocation targets they promised boards. This is venture allocation, not venture returns.

    Will Quist, Slow

    Venture Alpha and Venture Beta

    To repeat a point already well-hammered, the platform strategy is entirely different, with a different LP base, a different return profile, and different rules.

    The difference between the Andreessen quote above, and the more recent quotes from Chen and Casado, is simply that in 2014 Andreessen Horowitz was a venture capital firm. Today it is an asset manager, an RIA with a multi-stage platform strategy, perhaps best described as a venture bank.

    Many VCs have sought to assimilate “tier 1” multi-stage behavior, acting out what they believe LPs and peers expect to see despite the fundamentally incompatible models. This herding around identity and behavior reflects the extreme level of insecurity in venture capital, a product of the long feedback cycles and futility of trying to reproduce success in a world of exceptions.

    Venture Banks

    There is no harmony with venture capital, and the behaviors of these platform investors should not be emulated by venture capitalists — whether or not they also call themselves venture capitalists.

    To put a point on this: The platforms explicitly benefit from consensus and price inflation. Both are toxic to venture capital.

    As long as both strategies are discussed under the banner of venture capital, we’ll keep wasting time on pointless debates about markets and incentives, and investors will keep making dumb, confused decisions.

    (top image: A Steam Hammer at Work, by James Nasmyth)

  • The IPO Decision

    The IPO Decision

    The influence of productivity shocks, peer effects and cost of capital on AI IPO ambitions, and what happens next.

    While leaders at xAI, Anthropic and Mistral have been silent on their plans to go public, OpenAI is starting to open up.

    Back in May it was reported that negotiations with Microsoft included provisions that allowed OpenAI to file for an IPO. The transition to a Public Benefit Corporation (PBC) the following month made that technically possible. Both Altman (CEO) and Friar (CFO) have made statements alluding to the process since. Indeed, simply the fact that Friar is being put in front of the media more often as a leadership figure is a significant signal.

    OpenAI’s most recent release may underline this direction. By prioritising model economics (focusing on the “router” capability) rather than model performance, the reception to GPT-5 was poor. In reality, this may reflect a shift in posture toward public market metrics, and their willingness to take the PR hit.

    The prompt router also lays the groundwork for OpenAI to provide selective access to more expensive models. The next generation of LLMs will be powered by NVIDIA’s Blackwell chips, offering ~30x faster real-time inference. The chips are rolling out at the moment, with an impact on model releases expected next year.

    Assuming these models will be a major step-up in competence, this could be the tipping point for a wave of AI IPOs in 2026.

    Factor 1: Productivity Shocks

    In our model, two firms, with differing productivity levels, compete in an industry with a significant probability of a positive productivity shock. Going public, though costly, not only allows a firm to raise external capital cheaply, but also enables it to grab market share from its private competitors.

    IPO Waves, Product Market Competition, And the Going Public Decision: Theory and Evidence

    When an industry experiences (or anticipates) a significant positive productivity shock (an inflection point in their ability to generate value), this may trigger an “IPO wave”.

    Essentially, if there’s a significant step-up across the industry then there’s a real incentive to be the first (or at least be early) to tap public markets for capital to drive market-share expansion.

    LLMs have continued to improve over the last few years, with a number of hyped releases and growing experimentation amongst enterprise users, but there has yet to be a truly significant “productivity shock” moment.

    Factor 2: Peer Effects

    We find that observing a peer go public within the previous 12 months raises the propensity to undertake an IPO from a baseline rate of 0.31 percent per quarter to 0.44 percent per quarter, amounting to a 40 percent increase in IPO propensity. This result is robust to accounting for hot market effects and other common shocks that may affect competing firms’ IPO decisions.

    IPO Peer Effects

    The first to market has an advantage in that they may capture the demand for that industry amongst public market investors. They also bear all of the cost and the risk of blazing that trail, primarily that they might have greatly overestimated demand.

    There is some benefit to being a “fast follower” in these circumstances, which is often what triggers the “IPO wave” dynamic seen in the culmination of tech cycles. However, the later you are in that wave, the less of the benefit you capture.

    These periods, often characterised as “IPO windows” have been referred to in literature as “windows of misopportunity” for investors due to the increased failure rate. However, it’s also true that the few survivors tend to appear more innovative (in patent quality and quantity) than IPOs issued in other periods.

    Overall, this chapter tend to conclude that “windows of opportunity” provides real opportunity to the most inventive private firms and allow them to raise public capitals to further their innovations.

    Essays On Ipo Cycles And Windows Of Opportunity

    Factor 3: Cost of Capital

    We find that less profitable companies with higher investment needs are more likely to IPO. After going public, these firms increase their investments in both tangible and intangible assets relative to comparable firms that remain private. Importantly, they finance this increased investment not just through equity but also by raising more debt capital and expanding the number of banks they borrow from, suggesting the IPO facilitates their overall ability to raise funds.

    Access to Capital and the IPO Decision: An Analysis of US Private Firms

    There’s a common perception that going public is for mature companies who are past the period of aggressive growth, looking for more stable access to capital. This does not appear to be true.

    In fact, companies that IPO are often doing so in order to increase their investment in growth, including intangible assets including R&D spend. This is particularly true in heavily competitive markets and capital-intensive businesses.

    This has obvious relevance to LLM providers, who check a lot of these boxes. Certainly, in the phase of investing in infrastructure to support scale, lowering the cost of capital is a major priority.

    Factor 4: Beyond Hedging

    Similar to going public, hedging mitigates the effect of risk on a firm’s product market strategy, and, thus, results in greater product market aggressiveness. Therefore, in the presence of product market competition, hedging has a strategic benefit similar to that of an IPO. Importantly, we show that the availability of hedging reduces, but does not eliminate, the incentives to go public.

    Strategic IPOs and Product Market Competition

    Not necessarily a reason why LMM providers may look to IPO, but rather why they haven’t until now: “Hedging” in this context effectively reflects the relationships that many model providers have with large public companies like Microsoft, Amazon or Apple.

    Rather than going public themselves, they can rely on these partnerships to fund investment and distribute risk, offering some of the benefits of going public without any of the costs.

    However, the example of OpenAI’s relationship with Microsoft illustrates that it’s possible to outgrow these arrangements.

    IPO Windows

    Generally speaking, “IPO windows” are a mirage chased by liquidity-starved venture capitalists. A truly great company, like Figma, can IPO more-or-less whenever it wants to.

    However, that dynamic changes when you have a group of peer-companies in a fiercely competitive (and capital intensive) industry. At that point, it is likely that there will be some strategic clustering of IPO ambitions.

    A true “IPO window”, 1999/2000 or 2021, involves ~1,000 companies listing in the space of about six months. Diligence collapses, the quality of companies goes in the toilet, and public markets are torched for years afterwards. Sarbanes-Oxley killed this behavior in 2002, and it didn’t appear again until the low-interest-rate pandemic briefly drove public markets insane in 2021.

    Why Wait

    Assume that OpenAI is likely to be the first out. As today’s AI leader, with the widest consumer adoption and biggest brand, it seems the best positioned.1

    What are they waiting for?

    Primarily, they’ll be waiting to see if Blackwell unlocks the kind of productivity shock they are looking for. To clear their recent $500B valuation they’ll need to go public with undeniable momentum and a great story to tell new investors about future potential.

    Secondly, going public is just a huge amount of work. Both in a technical sense, preparing the company’s books for intense scrutiny, and in a brand and PR sense. Prospective investors may need educating about the product, or the image of leadership may need some rehabilitation.2

    IPOs are remarkably intense, and represent the most thorough inspection that a company will endure in its lifetime. This is why companies and their board of directors agonize over whether or not they are “ready” to go public. Auditors, bankers, three different sets of lawyers, and let us not forget the S.E.C., spend months and months making sure that every single number is correct, important risks are identified, the accounting is all buttoned up, and the proper controls are in place.

    Investors Beware: Today’s $100M+ Late-stage Private Rounds Are Very Different from an IPO

    Where Bubbles Emerge

    There has been endless talk of a bubble of AI investment, and certainly there seems to be a disconnect between price and value.

    This is true in private markets, reflected in transaction data, and in public markets, reflected by the delta between the MAG7 (all somewhat AI-connected) and the other 497 companies in the S&P.

    However, the truth is that bubbles only really happen in public markets. They require liquidity, enabling the wild sentiment-driven swings in price that characterise a bubble.

    In illiquid markets, like venture capital, you have what William Janeway called “speculative episodes”, which may be derived from a bubble playing out in public markets (via comps) but do not behave in the manner of a bubble.

    It’s almost as if wherever there is a liquid trading secondary market in assets, there you will find a bubble.

    The Upside of Wasteful Speculative Bubbles and the Downside of Efficiency

    Indeed, the concept of bubbles has been used in VC to disguise what is better described as simple greed and myopia. Investors behaving like traders — “riding the momentum train, and being ridden over by it, when it turns” — to quote Damodaran.

    It’s illogical to describe what is happening today as a bubble if all of the current participants (including Altman himself) acknowledge that it looks like a bubble. A key feature of speculative bubbles is surely that the participants do not realise it’s a bubble? A more honest characterisation is simply that VCs are choosing to gamble on AI because their LPs believe they should.

    This all changes when AI companies hit public markets, and the pool of investors (and capital) grows dramatically.

    Consider the environment: post productivity shock, with an IPO wave led by the largest model providers but cascading into related industries and any company tha can crest the narrative.

    This is precisely, and clasically, when we’d see a bubble emerge; in the volatile public markets, rather than the sluggish and opaque private markets.3

    Until then, call it what it is: degenerate trading behavior.


    (top image: Allegory on Tulipmania by Jan Brueghel the Younger)

    1. Arguably, xAI is the second in-line. [↩]
    2. As a minor footnote here, I believe Altman’s recent comments about AI as a bubble, his uncertainty at leading a public company, and an AI CEO in 3 years, are all deliberate narrative prompts. [↩]
    3. This also fits neatly with Howard Mark’s comments about the markets feeling ‘expensive’, and the potential for a major correction in the not-so-distant future. [↩]
  • Startup Shock

    Startup Shock

    In 2014, Mark Schaefer coined the term “Content Shock“, to describe the point at which the amount of content being produced outgrew our available time to consume it. The implication being that a lot of content would be written that would be entirely overlooked.

    The upshot: increased spend on SEM and social media platforms, and content that was increasingly designed to grab attention.

    A similar phenomenon is happening with startups today, as highlighted by Sam Lessin, in his draft letter to LPs.

    A summary of the problem:

    Investors can only view so many pitches, so they rely on early signals of competence (pitch quality, MVP, early traction) to filter through opportunties efficiently. Not perfect, but it works.

    Today, with AI, it’s easier for anyone to develop a high quality pitch, a slick looking MVP, and even some initial revenue. Some of these are companies that would have emerged without AI, but many are just riding the wave and trying to get lucky.

    There’s a narrative violation happening right now that a lot of people aren’t talking about. The AI hype is certainly frothy and those frothy rounds are making the news. But the reality is that most AI companies just don’t get funding…

    Elizabeth Yin, Co-founder of Hustle Fund

    This happens in every cycle:

    • Some founders see genuine opportunity to do something important with the new technology.
    • Some see an opportunity to exploit investor enthusiasm to raise a load of money.

    With AI, the technology driving the enthusiasm also makes it easier to build a startup. Specifically, that it makes it easier to build a startup that passes a typical VC’s filters.

    The outcome is a huge amount of time spent chasing dead ends. Which may or may not involve capital deployed.

    Screening for Outliers

    This isn’t necessarily a problem at the first stage of screening inbound deals, where it’s relatively easy to select for a two basic truths:

    • Do we think the problem is important?
    • Do we think the founders are credible?

    Or, in a pragmatic sense, is the startup on a path that seems likely to unlock vast amounts of economic energy?

    For an example of how to scale this process, look no further than Y Combinator. For obvious reasons, that organisation has become very good at screening applications in large volume.

    A central element to this process is the single slide format which partners use to review applications. It contains much of the information needed to make the judgement mentioned above, but much more importantly it doesn’t include information that may introduce bias.1

    Y Combinator Application Review Slide, shared by Rachel Ten Brink

    This isn’t something you should farm out to associates or agents, and neither should you rely on warm intros for signal.

    You just have to do the work™.

    Recognising Talent

    The hard part begins when you turn your attention to a pool of viable opportunities and need to determine which have real potential.

    You can make a judgement on whether you think the problem is important, which reflects on the quality of the founders in the “package deal” nature of pitches.

    What you can no longer do as easily is test for founder credibility, given the glossy-finish provided by AI tools. This can be broken down in two main categories:

    • Do they understand the business model?
      Acquisition costs, unit economics, procurement timelines, cashflow, customer appetite, margins and moats?
    • Do they understand the implementation?
      The physics, transaction costs, regulation, R&D, infrastructure requirements and scalability?

    Previously, you could get a good understanding of this from a well-developed pitch deck and an MVP. Today, AI can fill in a lot of those answers for otherwise incompetent or poorly-motivated founders.

    One clue for how this problem may be addressed lies in Sam’s letter to LPs:

    A generation ago to make an investment VCs would say ‘where is your detailed business plan’. The business plan as an artifact served two purposes for investors; (1) it allowed you to ideally actually know / get up to speed on the business, (2) the form of the business plan and the thinking inside it told you something about how smart / talented / diligent the team was.  It was easy for investors to process relative volume of inbound because they could read a few pages of a bad business plan (just like a bad script) and say ‘pass’ vs. ‘oh this person is smart’.

    Sam Lessin, GP at Slow

    Sam suggests that business plans fell out of favour because (similar to today’s problem) it became easier to build a prototype and put together a nice deck. I’d take that a step further, with some observations learned via Equidam’s 14 years in the business of valuation and fundraising:

    The prolonged zero interest-rate period and the surge of SaaS solutions destroyed financial literacy in venture capital. It reduced much of the logic to a simple formula:

    CAC: $20
ARR/customer: $4
Net cash: -$16
Multiple: 20x
$1 Invested = $4 in (gross) NAV

    For over a decade, all many VCs cared about was how soon cash-incinerating SaaS growth engines could convert capital calls into NAV inflation and enable subsequent funds.

    The upshot was that investors, founders, advisors, and accelerators all forgot the basics of finance and economics. Cash flow doesn’t matter that much for SaaS… Projections are just extensions of the typical growth logic, and don’t serve a useful purpose… Theyr’e all just the same assumptions… The atrophy accelerated as capital flowed more freely, with fewer questions asked.

    Another (infuriating) consequence was that in a decade of unprecidented investment in venture capital, most of the money was shovelled into recursive NAV inflation, and very little progress was made on important problems in biotech, energy, infrastructure etc.

    By way of example, Equidam would have had a MUCH easier time over the last 15 years if it conjured up some bullshit private market multiples to value SaaS companies. Instead, with the mission to help get funding to the best opportunities, it has stuck by a principled and rigorous approach to valuation.

    Particularly, this includes using projections properly, and consequently most of what Equidam does is actually teaching founders how to do projections well, and teaching investors how to parse them.

    Fortunately, this too can be reduced down to a simple-ish formula:

    • Is the founder’s pitch coherent and credible?
    • If so, where do you expect the company to be, financially, in 3-5 years?
    • What is the implied value at that point?
    • Given typical success rates, anticipated dilution, and time to exit, what is the value today?

    Essentially, the difference is that this approach looks at the specific future of the company in order to understand value, rather than applying a generic multiple to past performance. This is a much more appropriate lens when you’re looking for outliers.

    The main criticism from ZIRPers is that the future is too uncertain, and projections are too unreliable. This earns them a blank stare and a strong cup of coffee.

    The entire purpose of venture capital is to make well informed, rational judgements about the future. If you aren’t comfortable investing based on assumptions, then one of two things is true:

    1. You should not be working in venture capital.
    2. You don’t understand the assumptions.

    (Though perhaps the second point also points to the first.)

    This leads to one of the most misunderstood points about valuation, which I’ll try and use to pivot this article towards a conclusion:

    Valuation is not intended to determine the right price.

    It is an exercise which, when done properly, helps both sides of a transaction understand the assumptions and align their expectations. From that scenario, you get an indication of value which can inform the price you agree on, but the value is in the process itself.

    Thus, the key here, and what has been missing from venture capital for more than a decade now, is a return to financial literacy, rather than financialisation.

    Sit down with founders. Talk through their strategy together, using both the deck and the financial model as a guide. Connect it with questions about the business and technical implementation. Their answers, guided by your questions, should slot together to create a coherent and credible picture of the future.

    Done well, this should give you a huge amount of insight into the founders, and their company.

    Indeed, it turns out that founders who go through this process actually have a significantly easier time raising capital. This is one of the many reasons I’m so irate when people tell founders to “let the market price the round”. The process of valuation is so valuable, so useful, and so important to innovation.

    So, you’re going to see a lot more startup pitches in your inbox over the next couple of years. It would be a grave mistake to ignore them, so you need to figure out how to embrace this process.

    Step 1: Learn from Y Combinator’s process for screening deals. Particularly, learn what not to look at in order to reduce the vectors for bias in your process. Limit as much as possible.2

    Step 2: Find a way to dig into this “complete package” of strategy, business model and technical implementation in a way that establishes founder credibility and can’t be faked with AI.

    It doesn’t have to be all at once. You can start by asking them to send you a video talking through their financial model and deck to explain the underlying strategy and how it connects to revenue/profit targets and milestones. Eventually, though, it should probably be in-person, where hopefully you can get a sense for their passion and tenacity.

    (header image: ‘Deluge’ by Ivan Aivazovsky)

    1. I can see why YC includes traction for their objectives, but I’d argue it’s perhaps a negative to include for a seed fund. i.e. it may bias your review towards companies with traction, rather than the best companies. [↩]
    2. To be honest, I don’t even like that the YC template includes the logo. It seems like unnecessary noise which may colour opinions early on. [↩]
  • Downstream of Seed

    Downstream of Seed

    One of the concepts we emphasize at Equidam is the inversion of qualitative and quantitative factors in startup valuation, as you go from Seed1 to pre-IPO funding.

    The archetypal Seed startup (perhaps just an idea) has nothing to measure. Investors must use their imagination, peer into the future, project a scenario. On the other hand, a startup raising the last round of private capital before an IPO will be weighed and measured almost entirely on financial metrics.

    Even as early as Series A you have access to some useful data. Can they actually build the thing? Do customers really want it? Does anyone want to work there? All sources of rich signal to help you make an objective decision about the company.

    Seed is different. Success comes down to the quality and consistency of your subjective, independent judgement. In many ways it is a unique discipline within the strategy of venture.

    What happens if you try to find a path in the data?

    You find…

    Essentially, it’s a hunt for outliers with no shortcuts and two specific qualifiers for any investment strategy:

    1. Any attempt to pattern-match to past success is going to dramatically limit your pool of opportunity, with no clear upside.
    2. Any constraints built into your investment strategy (sector, region, industry) are essentially a sacrifice of volatility (potential alpha).

    Thus, the ideal Seed investor is likely to be a generalist, with no preconceptions about what great founders look like, where they come from, or what they might be building.

    Rather than the hubris of a (supposed) rockstar stock-picker, Seed investors will find confidence through constructing solid processes, systematically rewarding good decisions and mitigating bad ones.

    They wont necessarily benefit from operational experience, but they will benefit from being able to recognise a good business. Indeed, some basic financial principles, often overlooked by today’s managers, can help uncover the potential in novel opportunities.

    Finally, and perhaps most importantly, they’ll have a firm grip on the biases which manifest in all forms of investing. Particularly the curse of overconfidence which erodes the positive influence of success.

    In summary, considering all of the above, we should expect Seed investors to present with an idiosyncratic worldview, some robust fundamental skills and an appetite for risk.

    Sadly, reality is the opposite: Seed investors are often risk-averse herd animals with little real competence. They have Rick Rubin-esque affectations, pontificating on ‘taste’ and ‘craft’, while copying each other’s homework and hiding deep insecurity.

    Why? Predictably, it’s the incentives.

    In the last 15 years we’ve seen the emergence of a Startup Industrial Complex, where a treadmill of capital, services and brand-strength was offered to participating firms and startups. If you wanted quick, reliable markups and easy downstream financing for your portfolio then you hopped on board.

    This movement destroyed the institutional contrarianism of Seed investing. Billions of dollars were piled into safe SaaS money-printers when capital was cheap. When the market for safe investments was saturated, investors responded by dumping huge sums of capital into silly ideas (remember NFTs?).

    That’s “risk”, right?

    This worked during ZIRP, because everything was going up and to the right. Public markets were so cracked-out on COVID and cheap capital that they grabbed anything at IPO. But it was never going to last.

    Seed VCs (and their LPs) need to recognise that role is, and always has been, to find breakout companies before they are obvious. Not to compete for deals. Not to seek validation from colleagues. To find those outliers. To be uncomfortably idiosyncratic. That’s it. That’s everything.

    Critically, while it may lag by a decade or so, everything else is downstream from Seed.

    The entire venture capital strategy depends on Seed investors doing their job properly. The entire premise of venture-backed innovation, and the promise of venture-scale returns, are entirely dependent on the health of Seed.

    (image source: “Venice; the Grand Canal from the Palazzo Foscari to the Carità”, by Canaletto)

    1. Pre-seed isn’t real [↩]
    2. There is a variety of perspectives you can use to confirm this. [↩]