• Limbic Capitalism

    Limbic Capitalism

    If the house always wins, build a casino

    “Should we be concerned about a ‘Venture Deep State’? A ‘FOMO Industrial Complex’? Limbic Capitalism?”

    Joe Milam, Founder of AngelSpan

    A previous article discussed why venture capital has such a myopic focus on backward-looking metrics connected to ARR.

    The summary is that people struggle with uncertainty, and there are two ways to cope: embrace it scientifically, or build a structure that lets people gamble on it.

    Enter “casino culture”.

    “Sports betting, shitcoins, meme stocks, vibe coding 100m in six hours, etc, are all expressions of the same deep cultural rot. If youth don’t believe there’s legitimate ways to get rich through work, all of culture will become a rotten sports book for the soul.”

    Will Manidis

    If you want to bet on startups, ARR provides a convenient metric to compress complexity and uncertainty into one dimension, reducing venture capital to a simple horse-race.

    Investors make bets on momentum categories, riding the wave of revenue driven by large injections of capital, and hope they can raise another fund or exit before the music stops.

    Combine that with the compounding influence of the “power law” meme, and the incentives to concentrate capital, you’ve got a real lottery on your hands.

    Like an actual lottery, the net cash return is negative.

    Net cash flows in venture capital head south

    “It’s very difficult to invest money well, and I think it’s all but impossible to do time after time after time in venture capital. Some of the deals get so hot, and you have to decide so quickly, that you’re all just sort of gambling.”

    Charlie Munger, Vice Chairman of Berkshire Hathaway

    The beneficiaries of this limbic shift are the agglomerators, who play the role of “the house”; appearing to participate in the risk while harvesting beta from the associated activity.

    For other investors, there are incentives to play:

    Primarily, it gives GPs a simpler story to sell to LPs, inspiring greater confidence. They can enthusiastically shill consensus ideas and relationship-based access — which is exactly what many LPs want to hear, despite the poor returns.

    Secondly, it diversifies away accountability. As more investors jump on-board the momentum train, individual career risk is exchanged for greater systematic fragility. When the market collapses, they can survive on relationships.

    By turning venture capital into a game of chance, abusing the principal-agent problem, their job is greatly simplified: It’s the blue pill of steak dinners and congeniality, not the red pill of grinding hackathons and building portfolios.

    Unfortunately, by abstracting the performance if investments from the underlying asset value with crude proxy metrics, the concept of “innovation” is mostly a mirage. It may be difficult to perceive at the time, when the traction feels real, but few truly important companies emerge from this environment.

    Value creation in the metaverse

    “Consistently surprised by general lack of world changing ambition with seed stage cos and “early stage” VCs. Era of incrementalism still prevalent.”

    Jimmy Yun, Investor at 8VC

    Limbic capitalism produces what Byrne Hobart and Tobias Huber have described as “virtual innovation” in Boom: Bubbles and the End of Stagnation.

    Essentially, a category emerges as the focal point for venture allocation, concentrating capital, and participants begin flipping coins under the long-standing premise that the upside of winners is always much greater than the downside of losses.

    How could they possibly lose?

    Image
    The Jackpot Age

    In the end, everyone eventually loses — except the house.

    It turns out that the very behavior they engage in is destroying returns, and the only winner is the person collecting fees to manage the bank.

    “Risk too much hunting jackpots and the volatility will turn positive expected value into a straight line to zero. In the world of compounded returns, the dose makes the poison.”

    The Jackpot Paradox

    (top image: “The Romans of Decadence , by Thomas Couture)

  • Venture Math

    Venture Math

    Creative ways to hide financial engineering

    A rough example of the logic driving investment decisions amongst the most degenerate venture investors:

    1. You’re evaluating a company with $7.5M ARR
    2. $5M is net new ARR, annual burn is $10M
    3. That’s a 2x burn multiple (BM)
    4. You invest $30M at a market-rate of 20x ARR
    5. Assuming 2x BM, $30M produces $15M in net new ARR
    6. At 20x ARR, it gets marked up from $150M to $450M

    On paper, that checks all the boxes: an investment that was subsequently marked up 3x using logic that could be decoded on an iPhone calculator.

    Indeed, the role of multiples is to produce calculations that are easier and faster; to get deals done and put capital to work.

    There are two major flaws with this approach:

    The first is that multiples are backwards-looking. By applying to current revenue or burn, they rely on past performance. All assumptions about the future are squashed down into the multiple itself. Venture capital relies on making good judgements about the future, not the past.

    The second is that multiples assume that all revenue is created equal. They ignore unit economics and capital expenditure, and nor do they fully appreciate churn. Finally, and particularly relevant today, they encourage founders to engage in creative accounting to boost ARR.

    “We’ve actually come back to saying there’s a real advantage to seeng the GAAP revenue accounting, to make sure all the money is showing up for real.

    There’s a lot of noise in that multiple, and when they were all SaaS recurring revenue businesses — all seat based, all 90% or 80% gross margin with no CapEx, all enterprise sales with low churn — it absolutely made sense. You could compare two companies. Thats’s why by 2019 or 2020 it almost felt like ‘fill in the form to give me the valuation’.

    None of those conditions are true now.”

    Rory O’Driscoll, Partner at Scale Venture Partners

    Consider, for example, the venture capitalist’s typical disdain for discounted cash flow (DCF) style thinking in valuation: There are too many assumptions, the future is too uncertain.

    So, instead, they price with ARR multiples, which include all of the same assumptions about the future, but obfuscates them into simple calculation. Out of sight, out of mind.

    “Some investors swear off the DCF model because of its myriad assumptions. Yet they readily embrace an approach that packs all of those same assumptions, without any transparency, into a single number: the multiple. Multiples are not valuation; they represent shorthand for the valuation process. Like most forms of shorthand, multiples come with blind spots and biases that few investors take the time and care to understand.”

    Michael Mauboussin, Head of Consilient Research at Morgan Stanley

    Indeed, multiples are a popular tool precisely because they hide all of that detail. Remember, venture capital is (unfortunately) a game of building confidence with simple stories, not demonstrating competence with complex truths.

    Financial engineering built on multiples is the bedrock of venture capital’s creeping financialisation.

    Imagine you invest $20 in a company that generates $4 in ARR from each customer, with a CAC of $20. It trades at a multiple of 20x ARR.

    • In practice, that company is losing $16 on every customer in the first year, with payback over 5 years assuming no churn.
    • On paper, every $1 into the company produces $4 in marked value for the investor. It’s a great looking investment.

    This is precisely the mechanic which incentivises insane capital consumption on negative unit economics. Investors trade financial health for faster markups, knowing that it’s a bust if the environment shifts.

    “Guess what happened once Founders realized that VCs were valuing startups using revenue multiples? They started playing a game of Hungy Hungry Hippo with the goal of accumulating as much revenue as they could!”

    Frank Rotman, Co-founder of QEDInvestors

    “When market turns, M&A mostly stops. Nobody will want to buy your cash-incinerating startup. There will be no Plan B. VAPORIZE.”

    Marc Andreessen, Co-founder of Andreessen Horowitz

    To quickly round-out a few points, lazy thinking with multiples…

    • …biases venture capital towards business that generate revenue quickly, which led to the neglect of deep tech.
    • …promotes pushing companies to scale as quickly as possible, which produces worse outcomes for everyone.
    • …contributes to VCs losing the ability to build independent conviction, encouraging herd-behavior.

    In conclusion, multiples are a tool for quick comparison across peers. Not for pricing, and not for understanding performance or potential of a startup. They include far too many important and unchecked assumptions, limiting an investor’s understanding of the specific future of a company.

    Proper valuation, with multiples used as a sanity-check afterwards, is the way.

    “We often pursue this kind of rationalization as a spot check, generally after going through the valuation process. When the multiple is implied, investors will then compare it to others seen in the public and private markets to get more comfortable. Think of it like a gut check, a way to determine if the valuation feels ‘reasonable’.”

    Alex Immerman, Partner at Andreessen Horowitz, and David George, GP at Andreessen Horowitz

    (top image: “The School of Athens“, by Raphael)

  • Systematic risk-management in VC

    Systematic risk-management in VC

    Why the industry loves confidence men

    “Venture capital has thrived in uncertainty: uncertain technologies, uncertain market trends and uncertain capital availability.”

    Michael Eisenberg, Founding Partner at Aleph

    Early-stage venture capital is characterised by uncertainty. Success lies in high-risk, non-consensus ideas that take many years to play out.

    There are two ways that LPs grapple with this uncertainty, as institutional allocators to VC:

    1. Seeking competence: managers that understand how to extract value from uncertainty.
    2. Seeking confidence: managers that inspire the most certainty about future success.

    For reasons best described by Daniel Kahneman, LPs are inclined towards the latter. This manifests as poorly managed risk; under-developed portfolio construction and overconcentration.

    This is despite a large body of research illustrating the following:

    1. Overconfidence is a prevalent bias in venture capital.
    2. Larger portfolios are likely to outperform smaller portfolios.
    3. Concentration creates more downside than upside.
    4. Diversification encourages VCs to take more idiosyncratic risk, which drives outperformance.

    Indeed, this is not without precedent. Some of the greatest early-stage VCs have unusually large portfolios: Boost VC, First Round, BoxGroup and Precursor are obvious examples.

    Despite the data, and the many anecdotal success stories, LPs are notoriously hesistant to back diversified strategies. By believing they can diversify at the LP level, across a portfolio of firms, they miss the systematic benefits of diversification and inhibit the compounding gains from process refinement.


    Portfolio Maths

    Earlier this year I posted a breakdown of expected venture capital returns based on simulating two seed portfolio models: one with 100 small checks, the other with 20 larger checks.

    Portfolio simulations

    The graphic was meant to illustrate a simple point: based on a typical range of VC outcomes, the more diversified portfolio was likely to outperform, delivering a narrower and more attractive band of potential return scenarios.

    “Typical” is the operative word there, as while the graphic implies the diversified portfolio wont exceed a 6x return, that’s only if you deliver average performance. It’s entirely possible for a GP to outperform in either strategy — although research indicates that the diversified portfolio is likely to have more upside.

    The statistical basis for this is simple: given venture capital’s reliance on outliers outcomes, the low probability and low predictability of those outcomes, it is more beneficial to make more investments (with lower ownership), than to have more ownership (with fewer investments).

    There are similar simulations and portfolio calculations from a number of other sources, which all point to the same conclusion: venture capital is systematically and unnecessarily overconcentrated.

    Picking winners chart
    Picking Winners is a Myth

    The coversation that followed (credit to the always-insightful Peter Walker for sharing it with his audience) highlighted a deeper problem with the industry’s understanding of portfolio strategy, risk management and cognitive biases.


    Overconfidence

    A good place to start is Daniel Kahneman’s work on cognitive biases in investing:

    “The confidence we experience as we make a judgment is not a reasoned evaluation of the probability that it is right. Confidence is a feeling, one determined mostly by the coherence of the story and by the ease with which it comes to mind, even when the evidence for the story is sparse and unreliable. The bias toward coherence favors overconfidence. An individual who expresses high confidence probably has a good story, which may or may not be true.”

    Daniel Kahneman, “Don’t Blink! Hazards of Confidence”

    As a crude summary, imagine an LP speaks to two VCs:

    • One says they’ll invest in 100 companies and expect that ~75 of them will be be writen-off. The fund returns will primarily hinge on ~1-5 investments.
    • The second explains that they have a powerful network advantage, and they’ll deliver huge returns from concentrated ownership in just 15 companies.

    The LP is likely to go with the second VC, who builds more confidence by telling a simpler and more coherent story; they simply have access to “better investments”. What’s not to love?

    And few investors demand diversified funds, so GPs don’t offer them. A slow and steady “venture is a numbers game” pitch is much less emotionally compelling than “I am a rock star who can consistently beat the odds.” And GPs need an emotionally appealing pitch to get funded.

    The Pervasive, Head-Scratching, Risk-Exploding Problem With Venture Capital

    LPs want to hear confidence, so that’s what VCs offer. Thus, “overconfidence” is by far the most prevalent and dangerous cognitive bias in VC behaviour.

    Overconfidence and disappointment in venture capital decision making: An empirical examination
    Overconfidence and disappointment in venture capital decision making: An empirical examination

    This mode of favouring confidence over competence leads to a number of dogmatic beliefs amongst the LP community:

    1. That relationships drive better investments in VC — which is only vaguely true in hot markets when it’s easy to collect markups from your friends.
    2. That venture capital funds themselves operate with power law outcomes — which is only true because of the unnecessary and toxic levels of concentration.
    3. That VCs are supposed to “pick” their way to success — rather than relying on well designed origination strategies and a systematic approach to capturing outliers.


    Overconcentration

    Overconfidence primarily manifests as overconcentration; portfolios with poorly managed risk. Too much capital, concentrated into too few investments, with high levels of uncertainty. This is typically measured with the Sharpe Ratio in grown-up investment strategies.

    The influence of concentration is obviously double-edged, as it compounds both good and bad investment decisions.

    However, research shows that this amplification is not evenly distributed: overconcentration hurts underperformers more than it helps overperformers.

    Fund Concentration: A Magnifier of Manager Skill
    Fund Concentration: A Magnifier of Manager Skill

    So, if venture capital were systamatically overconcentrated, you would expect to see a wider distribution of returns and a lower average return, relative to other strategies. As it happens, that’s an accurate description:

    Performance Dispersion in Alternative Asset Classes
    Performance Dispersion in Alternative Asset Classes


    Persistence

    There’s a good argument that the poor persistence of performance in venture capital can be attributed to shallow narratives around “picking” which undermine basic theory around portfolio construction and behavioral economics.

    Allocator Solutions: Evaluating Persistence in Fund Performance

    Not only does this damage persistence, it’s particularly toxic for new entrants who may attempt to piece together a strategy from the “common wisdom” available to them.

    Instead of being encouraged to adopt practices that are optimal for more consistent above-benchmark returns, emerging managers are pushed toward the “rockstar” narrative of outsized promises.

    Thus, they end up taking excessive risk and, more often than not, imploding; only 1/3 managers make it to fund 2, and only 1/10 make it to fund 4 .

    You might raise an eyebrow at this, if you are familiar with the history of venture capital incumbents strategically freezing out new entrants in order to maintain advantageous pricing power.


    Intitutional Insecurity

    “Perhaps the most powerful lesson from Marks is the idea of ‘uncomfortably idiosyncratic’ investing. Citing the late David Swensen of Yale, Marks emphasizes that successful investment management requires taking positions that feel uneasy because they go against the grain.”

    Howard Marks on ‘Behind the Memo’:

    If investment performance is driven by adopting “uncomfortably idiosyncratic” positions, it’s reasonable to assume this is especially true for early stage venture capital — where non-consensus investing drives outperformance.

    “You will know you are doing real venture capital when you aren’t competing with other investors to finance a deal but are instead offering to invest in people, industries and ideas that don’t yet have access to capital. That is where money can be most useful, and also where returns can be the highest.”

    Sam Lessin, GP at Slow

    VCs take a systematic approach to managing idiosyncratic risk through portfolio construction; optimising for larger outcomes and failure rates than other strategies. Indeed, VCs with larger portfolios appear comfortable accomodating more idiosyncratic risk, which ultimately contributes to stronger performance.

    In fact, you could probably summarise the VC strategy as the art and science of systematically extracting value from idiosyncracy.

    The lingering question, given all of this evidence, is why aren’t larger early-stage portfolios the default in venture capital?

    The answer is agglomerator-leak; the “loudest model” of the mega-funds, which ends up influencing practices and perceptions across the whole venture market.

    For example:

    When you are investing billions in each cycle, you must pry your way into hottest companies of each vintage. The future of your firm depends on those bloated private-market darlings, and there is significant career-risk associated with missing them.

    Thus, a number of ideological artefacts are spawned:

    • “You must be in the category winners, at any cost.”
    • “Only a handful of outcomes drive returns for each vintage.”
    • “Concentrated ownership drives outperformance.”

    These artefacts latch onto insecurity like Pinterest self-help quotes. GPs and LPs looking for confidence through coherence, biased toward simple ideas, lap up this accessible wisdom from venture’s most influential characters.

    Unfortunately, it means they apply the same thinking to early-stage investing, and much smaller funds operating a very different strategy, even when all evidence suggests that’s a very silly thing to do.


    Process Alpha

    From a well-diversified base of initial investments, it is possibe for a VC to double-down on winners over subsequent rounds. This is a process that Joe Milam, founder of AngelSpan, has dubbed “process alpha“.

    Staging your capital deployment properly over multiple rounds dramatically improves the IRR for investors, regardless of the MOIC/DPI. And given the natural failure rates of startups (usually within the first 2 or 3 yrs), optimizing on how much you invest in each round improves the risk-adjusted returns available.

    The Impact Of Proper Venture Portfolio Construction-Optimized DPI & IRR

    Essentially, this is how a VC can take the solid foundation of a well diversified initial portfolio and then build on that position with staged deployment into the best opportunities that emerge in subsequent rounds.


    Conclusion

    While it’s difficult to get into specific recommendations, it seems safe to make the case that early-stage venture capital firms should probably be significantly more diversified.

    More importantly, the LP:GP interface is clearly problematic, and LPs need to think carefully about whether they bias towards confidence over competence.

    In order to do so, they must grapple with the economic theory, and the reality of portfolio construction, to recognise why it’s important that there be a good level of diversification at the VC portfolio level.

    “You should not take assertive and confident people at their own evaluation unless you have independent reason to believe that they know what they are talking about. Unfortunately, this advice is difficult to follow: overconfident professionals sincerely believe they have expertise, act as experts and look like experts. You will have to struggle to remind yourself that they may be in the grip of an illusion.”

    Daniel Kahneman, “Don’t Blink! Hazards of Confidence”

    (top image: The Cardsharps, by Michelangelo Merisi da Caravaggio)

  • “VC”: What comes next?

    “VC”: What comes next?

    There is no shortage of think-pieces on the state of venture, bemoaning concentration, slipping returns, and the challenging environment for smaller managers.

    Few have tried to answer the underlying question:

    The perspective that VC consolidation is poisonous for both innovation and returns is slowly but surely becoming mainstream.

    Can’t wait for this to become a given so we can (finally!) move onto the much more interesting topic of “What comes next?”

    Geri Kirilova, September 2024

    First, the most concise formulation of the problem:

    LP allocation to “VC” grew so quickly that there was no opportunity for the strategy to adapt and properly allocate the additional capital. Instead, it was captured by opportunists.

    Thus, a majority of today’s “VC” activity is simple financialisation.

    • Venture capital involves making long-term investments in innovative, high-growth companies, with the goal of capturing outlier returns at exit.
    • “VC” is the process of using financial engineering to optimise fee income. Maximising proxy performance metrics by manifesting herd-like market momentum.

    “VC” isn’t intrinsically bad: the agglomerators are responsible for pulling billions of dollars into technology investment, by developing a product suitable for the largest and wealthiest LPs with the lowest expectations.

    However, there are three points the market needs to grapple with in order for venture capital to move forward:

    1. Agglomerators have no business investing prior to Series C

    The argument for their existence is that technological development often requires vast pools of growth capital, so why do they invest at seed?

    The truth is rooted in financialisation: it’s easier to produce a mirage of proxy metrics if you can pick startups that fit the momentum narrative, and founders that will go along with it.

    Another part of the reason is that these firms are doing very nicely from absorbing capital in the “VC” allocation bucket. Registered RIAs moving further away from early-stage investing might invite some unfavourable comparisons to their PE peers.

    Indeed, exactly whose allocation are they displacing? Is it venture capital? Or is it the PE firms they maybe more closely resemble? Or is it the public markets they have drained of new growth opportunities?

    We also know:

    • The hypercapitalising behavior of these large firms systematically breaks young startups.
    • They poorly built for early-stage investing, as large firm dynamics favour more obvious investments.
    • A decade of deal activity shows these firms are actively concentrating into consensus anyway.

    The early-stage activity of these firms achieves very little of any merit. They bend the market to their consensus narrative, suffocate genuine innovation, and turn startups into volatile commodities in their hunt for “market winners”.

    2. The agglomerator model of “VC” is an experiment

    For all that many of these firms are well-established names in the venture capital ecosystem, their strategy is not.

    In describing this bifurcation of the venture market, back in 2020, Nikhil Basu Trivedi stated “the next decade will be a referendum on agglomerators“. Essentially, until we see how these multi-billion dollar funds play out, it’s unclear what future the strategy has.

    Indeed, we can learn from history: after the dotcom bubble burst, the industry collectively swore off >$1B funds (for a while), with GPs reflecting on the challenges of scaling venture capital.

    There are similar lessons from the history of “mega-buyout funds”, as described by Meghan Reynolds of Altimeter:

    “Mega Fund dynamic in VC mirrors the meteoric rise of Mega Buyout funds in ’05-’08. Post GFC, Mega Buyout went out of favor w/ many LPs. What happened after was an emergence of strong, highly sought after <$2B “middle market” funds that were thought to have greater alpha.”

    Meghan Reynolds, October 2022


    3. Agglomerator practices have corrupted venture capital

    Perhaps the most painful and pernicious aspect of all of this, is that the conflation of these multi-stage agglomerators with venture capital has led to a corruption of standards and practices.

    Let’s be clear: venture capital has very little in the way of standards and practices. What there is, tends to be a mimmicry of “the loudest model“, which is the agglomerator playbook.

    So, when they say things like “entry price doesn’t matter”, or “non-consensus investing is dangerous”, or “the only thing that matters is getting into the best deals”, they are talking their book. It reflects a strategy that simply does not apply to venture capital, where entry price does matter, all alpha is non-consensus, and nobody can predict the best deals.

    The same is true for LPs, who derive much of their understanding of venture capital, and what “good” looks like, from these “loudest models”: When Marc Andreessen says “AI will save the world”, LPs demand that GPs have an AI thesis, even when a smart GP may be looking beyond that horizon, or at overlooked alpha elsewhere.

    So, what comes next?

    Hopefully, a number of things play out over the next five years:

    • We see the limitations of the agglomerator model, and venture capital can reclaim the early stage, yielding more effective and diverse origination with saner pricing and less volatile outcomes.
    • LPs slowly wise-up to this bifurcation of strategies, re-educate themselves about venture capital, make better allocation decisions and feel less inclined to impose upon GP strategy.
    • Agglomerators (re-classified as “venture growth”) become a defined sub-category of LPs private book, like venture capital. Publications like Pitchbook and CA start disaggregating performance into this new bracket.
    • Anyone who wants to play a fee-driven financialised game can go to venture growth, those who are driven by performance and impact can work in venture capital. Two different systems, each better understood and playing to their strengths.
    • Increasingly, venture capital will be able to tap into venture growth for liquidity as their portfolio matures, accelerating liquidity and feedback cycles to produce compounding performance gains.

    Slowly but surely, venture capital may return to being a better-performing and more positive-sum strategy, focused on finding outliers and the discipline of properly managed risk. Less herd behavior, and less cognitive dissonance.

    (top image: Meindert Hobbema’s The Avenue at Middelharnis)

  • 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 an opinion on a story

    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 developing better 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)

  • Venture Capital in 2035

    Venture Capital in 2035

    Risk capital in an age of AI and DeFi

    In the decade since 2025, algorithmic capital has swallowed much of the private equity opportunity.

    In response, “smart private equity” moved downstream to late-stage venture capital. Chasing the remaining alpha, VCs were compressed into the earliest stages.

    At the same time, venture’s LPs had long been hungry for a better product with stronger returns and greater transparency.

    2027 marked the end of “relationship capital” and sleight-of-hand with proxy metrics. Since the GFC, LPs had tolerated bloated funds, dangerous concentration and slipping performance. The need for a reset was clear.

    By 2030, LPs began the transition to pooling their capital in algorithmic treasury DAOs, offering:

    • A scalable, automated vehicle that could balance allocation across a range of strategies.
    • The transparency of operating on-chain, addressing many of the misaligned private market incentives.
    • A decentralised approach, allowing venture capital to reach into every segment of the economy.
    • A natively evergreen structure that is less negatively exposed to market cycles and platform shifts.
    • A competitive industry with vastly superior returns, where managerial skill earns allocation.

    The Role of the DAO

    A treasury DAO, in this context, is a pool of LP capital, such as endowments, pension funds, sovereign wealth, fund of funds, or high-net-worth individuals. An LP may choose to manage their own DAO, or join an existing DAO.

    Indeed, DAOs are fluid organisations that can merge or split as required. For example, a collective of high-net-worth individuals may form a temporary DAO around a particular opportunity. If a member leaves the DAO, or the DAO is dissolved, the division of outstanding investment economics is handled by the existing smart contracts.

    Larger DAOs benefit from access to strategies with more demanding allocations, and the ability to balance the pool of capital across a more diversified set of asset classes. A smaller DAO, on the other hand, may set a much higher hurdle for GP performance but offer above-market compensation contracts.

    The DAO automates all financial operations (treasury management, fund transfers, fee payments, report generation, and distributions) through smart contracts.

    The Role of Venture Capital

    As a result of the market compression, venture capital is now primarily focused on origination. Successful VCs recognise opportunities before they generate enough data to be legible to algorithmic capital. They explore qualitative signals, human psychology and future scenarios in a way that a machine still cannot.

    Access to the DAOs is as simple as submitting a ledger of private market transactions. If the ledger demonstrates a good risk-adjusted return in a particular strategy they may be awarded an allocation contract and the associated fee and carry contracts.

    Each DAO federates a broad base of independent VCs or small partnerships, effectively solving the top-of-funnel for investment opportunities and fundraising friction for founders, as in every industry, market or region there is likely to be someone on the lookout for opportunities.

    Indeed, if their performance is sufficient, it does not matter where a GP lives, works, or went to school. They can participate in the DAO full-time, or part-time. For example:

    • In some cases, specialists in fields like biotech or defense can supplement their income with a modest fee stream by surfacing the occasional interesting company.
    • Elsewhere, a successful angel investor might convert their track record into a large enough allocation to make investing a full-time career, without jumping through any hoops.

    Further downstream, specialist VCs (with deep industry, CFO or internationalisation experience) are still able to find alpha within the later-stage opportunities, shaping successful exits. The highly-automated nature of investment (with investments often made directly by the DAO) at these stages allows them to manage a large book, and focus on signals of overlooked value to pre-empt funding rounds(which, in-turn, the AI learns from).

    As each GP’s track record improves, they’re able to compete for incrementally larger allocations at more prestigious DAOs that offer more favourable compensation terms.

    Track record is understood through a blend of efficiency (realised IRR), risk (Sharpe ratio) and persistence. This allows a DAO to recognise potential in even relatively amateur investors, as well as downgrading or ejecting underperforming investors.

    Emerging Performance

    Structural secondaries are core to this model of venture capital, enabled by reduced information friction. The question of how long a startup chooses to remain private is redundant in a market with regular release-valves for liquidity.

    This reality aligns with LPs, who want to see faster cash returns. Similarly, VCs that can stay above benchmarks (typically 12-15%, realised IRR) are able to supplement their fee income with a regular drip-feed of carry from liquidity events.

    This generally means exiting a significant portion of each position in year 2-4. For example, if a GP sells half of their stake in a company in year 3, at a 3x valuation increase, that’s already a 14.5% realised IRR.

    Shortening the liquidity horizon to ~3 years from ~12 years makes understanding GP performance more practical, particularly with an evergreen fund strategy. It also shapes the incentives of downstream investors who are more likely to scrutinise fast-growing investment opportunities for the sustainability of that growth.

    Radical Transparency

    With all transactions signed with their on-chain identify, each investor (angel or GP) holds immutable and portable deal attribution which may be shared and compared. Thus, performance-based competition is real and important.

    “Top VC” lists are based on clear methodology and hard data, and each publisher competes to provide the gold standard of measuring performance across the metrics mentioned above.

    VCs that opt-in to sharing their ledger can use their ranking as a public recognition of their ability, feeding into other opportunities outside of access to investable capital.

    Venture Firms

    While there is no strictly necessary role for venture firms in a world where smart contracts handle a lot of the central functions such as financial operations, compliance and reporting, VCs may still choose to assemble where “the whole is greater than the sum of the part(ner)s”. Most of the time, this manifests as a brand-building exercise to develop inbound interest or a goal of providing more stable results with the aggregate firm performance.

    In these cases, informal partnerships may still manage attribution on an individual partner basis, or more formal partnerships may attribute to the firm, with an individual partner as the secondary signature. This way, the firms themselves may compete for ranking (and potentially provide more stable returns) without eliminating the portability and accountability of a partner’s attribution.

    Conclusion

    In this reimagined model, venture capital works because the structure is built around the desired outcome: managerial skill is focused where it makes the most difference, performance is rewarded with capital, transparency breeds healthy competition, and the broader distribution is able to surface more opportunities and potential outliers.

    This represents a new social contract for funding innovation, expanding who gets to invest, who gets funded, and where new category leaders may be found.

    Venture Capital 2035 Whitepaper

    (top image: “Webs of At-tent(s)ion”, by Tomás Saraceno)

  • 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)

  • It’s not a bubble until we don’t know it’s a bubble

    It’s not a bubble until we don’t know it’s a bubble

    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. [↩]