Tag: venture capital

  • 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. [↩]
  • 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 workℱ.

    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 CaritĂ ”, by Canaletto)

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

    Risk Capital

    The history of human progress is predicated on the history of efficient risk capital formation.

    WIll Manidis

    The story of venture capital (and its precursors) is a story of risk. You can take this back as far as you like, from ARDC to Christopher Columbus. From whaling expeditions to space exploration.

    Risk is the product.

    And, essentially, it boils down to this calculation:

    The merit of any investment depends on whether the probability of success multiplied by the forecasted return is greater than the cost.

    • Investments that are perceived to have a high probability of success attract a lot of competition.
    • Investments that are perceived to have a low probability of success attract very little competition.

    Venture capital is at the far end of this spectrum, where the ‘skill’ is in recognising when the market has mispriced risk because an idea is unconventional rather than bad.

    This brings us to the first category of risk in this conversation: idiosyncratic risk.

    Idiosyncratic Risk

    (the specific risk of an investment)

    Idiosyncratic risk reflects the specific potential of an investment: the probability of success, and the assumed return if it is succesful.

    Assuming you cannot change the probability of success or the assumed return, there are two ways to handle idiosyncratic risk:

    • Making low probability investments profitable by diversifying away total failure.1
    • Making low probability investments profitable by pricing the risk appropriately through valuation.

    These are the two main levers of venture capital, which is focused on what Howard Marks refers to as uncomfortably idiosyncratic investments:

    The question is, do you dare to be different? To diverge from the pack is required if you’re going to be a superior in anything. Number two, do you dare to be wrong? Number three, do you dare to look wrong? Because even things which are going to be right in the long run, maybe look wrong in the short run. So, you have to be willing to live with all those three things, different, wrong, and looking wrong, in order to be able to take the risk required and engage in the idiosyncratic behavior required for success.

    Highlights from a conversation with Howard Marks

    Idiosyncratic risk contrasts with the other main category of risk that investors must consider: systematic risk.

    Systematic Risk

    (broader market-related risk)

    If idiosyncratic risk is typified by venture capital, then systematic risk is typified by index funds. Consider the extent to which index fund performance is influenced by individual companies versus major political or economic events.

    Nevertheless, systematic risk is a consideration in venture capital, and there are two ways to handle it:

    • Avoid consensus, where competition drives up prices without increasing success rate or scale.
    • Avoid market-based pricing, where macro factors can drive up prices without increasing success rate or scale.

    Exposure to systematic risk essentially destroys an investor’s ability to properly manage (and extract value from) idiosyncratic risk.

    Alpha vs Beta

    If we consider idiosyncratic risk as the source of ‘alpha’ (ability to beat benchmarks) in venture capital, systematic risk reflects the ‘beta’ (convergence with benchmarks).

    A striking shift in venture capital over the last 30 years, particularly the last 15, is the extent to which the balance has shifted from idiosyncratic risk to systematic risk. This is a consequence of prolonged ‘hot market’ conditions, where consensus offers a mirage of success.

    Consider a typical VC in 2025. They’re likely to be focused on AI opportunities, guided by pattern-matching and market pricing (aka, “playing the game on the field”). Investing, in this scenario, is reduced to a relatively simple box-checking exercise.

    All of this implies significant systematic risk; the firm is riding beta more than they are producing alpha. This creates extreme fragility.

    Systematic risk has always been a concern, but it has been amplified in recent years by cheap capital and social media. The herd has grown larger and louder; more difficult for inexperienced or insecure investors to ignore:

    • Taking systematic risk means following the crowd. It’s an easier story to sell LPs, and there’s less career risk if it goes wrong as accountability is spread across the industry.
    • Taking idiosyncratic risk means wandering freely. It’s tough to spin into a coherent pitch, and there’s more obvious career risk associated with the judgement of those investments.

    Despite mountains of theory and evidence supporting idiosyncratic risk as the source of outperformance, it’s just not where the incentives lie for venture capital.

    The Jackpot Paradox

    There are fundamental consequences of the drift towards systematic risk in venture capital:

    • The muscles of portfolio construction and valuation atrophy, as consensus-driven ‘access’ dominates behavior and idiosyncratic risk falls out of favour.
    • The typical ‘power law’ distribution of outputs collapses as few genuine outliers can be realised from a concentrated pattern of investment.
    • As returns converge on a mediocre market-rate, investors manufacture risk by feeding power law back into the system as an input, trying to create outlier returns.
    • Success is further concentrated in a system that becomes increasingly negative sum overall.

    This broadly summarises where we’re at today. A disappointing scenario that represents failure to the actual bag-holders on the LP end, failure to founders, and failure to innovation.

    A lot of the blame falls in the lap of LPs. The low fidelity interface with GPs means that LPs have a general bias towards compelling stories which invite systematic risk.

    Thus, venture capital is reduced to a wealth-destroying competition for access to the hottest deals, fundamentally at odds with the concept of ‘uncomfortably idiosyncratic’ risk and generating alpha.

    Note 1: While idiosyncratic risk can be managed through diversification, diversification doesn’t necessarily produce greater systematic risk.

    Note 2: Another way to look at the ‘venture bank’ versus ‘venture capital’ paradigm is that venture banks are deliberately set up to embrace systematic risk.

    (image source: Rembrandt’s “Storm on the Sea of Galilee”, used on the cover of “Against the Gods: The Remarkable Story of Risk” by Peter L. Bernstein.)

    1. Though this is deliberately not a post about portfolio construction, which I have written about too much already. [↩]
  • Incentives and Outcomes

    Incentives and Outcomes

    Show me the incentive and I’ll show you the outcome

    Charlie Munger

    One of the most thought-provoking articles in venture last year was Jamin Ball’s “Misaligned Incentives“, in which he talked about the difference between 2% firms and 20% firms.

    The 2% firms are optimizing for deployment. The 20% are optimizing for large company outcomes. There’s one path where the incentives are aligned.

    Jamin Ball, Partner at Altimeter Capital

    The article was significantly because it was represented a large allocator acknowledging the issue with incentives in private markets. Not a novel take on the problem, but resounding confirmation.

    Ball stopped short of suggesting an alternative incentive structure, which was probably wise given visceral opposition to change. Many influential firms have grown fat and happy in the laissez-faire status quo of venture capital.

    Ball — like many people, myself included — framed carried interest as the ‘performance pay’ component of VC compensation. The problem is implicit: we have therefore accepted that fees are not connected to performance.

    For decades, we’ve accepted the wisdom that carry = performance, and fees = operational pay. Nobody thought to question that reality.

    Unfortuantely, for many firms (and certainly the majority of venture capital dollars under management), carry is a mirage. It exists so investors can pretend that performance is a meaningful component of their compensation while they continue optimising for scale.

    European Waterfall vs. American Waterfall

    European waterfall is a whole-fund approach to carry, whereby GPs don’t receive carried interest until LPs have had 1x of the fund (plus a hurdle) returned to them. American waterfall operates on a deal-by-deal approach, with a clawback provision if the fund isn’t returned (plus a hurdle).

    Paying for Performance in Private Equity: Evidence from VC Partnerships

    We know the american waterfall model (while imperfect) has historically outperformed, and yet the european waterfall has become standard. Venture capital has biased towards the ‘LP friendly’ approach to carried interest, even though it reduces their carry income, because it enables more easily scaling funds.

    We find strong evidence that GP-friendly contracts are associated with better performance on both a gross- and net-of-fee basis. The public market equivalent (PME) is around 0.82 for fund-as-a-whole (LP-friendly) contracts but is over 1.24 for deal-by-deal (GP-friendly) contracts.

    Paying for Performance in Private Equity: Evidence from VC Partnerships

    In summary, the problem is not that VCs have picked fees over carry as the more attractive incentive, it’s that carry has been used as a smokescreen for the exploitation of fees.

    Consider these few points, from the perspective of a seed GP:

    • If you charge a fee to manage the fund, you should not raise a successor fund without a serious step-down in those fees. Otherwise, what are you being paid for?
    • You should not charge management fees on investments you’re no longer truly managing. If you have no meaningful influence over a company in your portfolio, what are you being paid to do with it?
    • Indeed, if you’re no longer truly managing those investments, it’s incumbent on you to sell enough of your stake to lock-in a reasonable return when the opportunity is available.
    • If you raise a larger subsequent fund, you should be able to explain how that strategy allows you to extract a similar level of performance from a larger pool of capital. Otherwise, how can you rationally justify a larger total fee income?
    • Everybody knows that markups are bullshit. If you want to raise a second fund, get at least 2x back to your LPs through secondaries first. DPI is the only proof that there’s value in your investments.

    None of this should be surprising or even unintuitive, and yet…

    • Successor fund step-downs are remarkably uncommon.
    • Most US funds still do fees on total comitted capital, not even fees on invested capital, never mind fees on actively managed investments.
    • GPs are being paid to hold companies when they’d be better off securing an exit, or propping up zombie portfolio companies just to continue harvesting fees.
    • Few GPs have a sophisticated view on early returns, with most still focusing on MOIC rather than IRR and assuming late-stage price inflation will continue.
    • VCs expect founders to present a coherent pitch covering growth strategy and the implicit capital requirements. The LP-GP relationship is far cruder.
    • The whole venture ecosystem knows markups are barely worth the paper they are written on — and yet these incremental metrics continue to drive fundraising activity.

    Over the past 15 years, LPs have become so preoccupied with getting into the hottest name-brand funds that there has been little scrutiny given to the fundamental logic of terms.

    Today, with the market bifurcated into ‘venture banks’ and traditional venture capital, there is the opportunity to return to first principles.

    We can start by examining the extremes:

    Ending the charade; 100% fees

    In an entirely fee-based environment, without carry as a smokescreen for bad actors, fees would likely be more clearly connected to performance — addressing the concerns laid out above.

    This has the benefit of being a more predictable approach to compensation, likely attracting more responsible fiduciaries and level-headed investors. Less swinging for the fences, and more methodical investing and steady DPI.

    However, it would also mean losing an important minority of brilliant investors who are genuinely motivated by carry.1

    Ending the AUM game; 100% carry

    In a scenario where investors only ‘eat what they kill’, performance would matter so much — across so many dimensions — that VCs would have to very quickly develop better practices on portfolio management and liquidity.

    Of course, the downside is that compensation would be heavily backloaded, with no compensation for the early years of deploying capital and developing exits. A deeply unhealthy barrier to entry for emerging managers.

    What’s interesting about these two edge-cases, on opposite ends of the spectrum, is that both produce the same outcome: a greater level of professionalism, with a more sophisticated view on portfolio management and liquidity than we see today.

    Clearly, neither extreme is a good option and the ideal is somewhere in the middle — with both fees and carry in the mix. However, central to incentivising better outcomes is an end the fee exploitation game, with two key realisations for LPs:

    1. Fees must be connected to performance, in that a GP should not be able to raise another fund if they have not yet demonstrated concrete performance.
    2. The only meaningful demonstration of performance is DPI. Fortunately, as the market embraces secondaries, it’s possible to generate meaningful DPI much sooner.

    Venture capital needs to evolve alongside more distant exit horizons by making better use of secondary liquidity, more cleanly dividing the market into early and late stage strategies — which can each then better play to their strengths:

    Venture Trifurcation

    Indeed, some of the best early stage investors around today are already adapting to this reality:

    The times that it’s easiest to sell into these uprounds, are probably the times you should think seriously about it if you’re a seed fund.

    Mike Maples, Jr, Parter at Floodgate

    We were able to take a 1x or a 2x of the entire fund off [the table] and still be very long in that company. That locks in a legacy, locks in a return, and shortens the time to payback.

    Josh Kopelman, Co-Founder of First Round Capital

    For funds like [mine], selling stock of private startups to other investors will be “75% to 80% of the dollars that [limited partners] get back in the next five years.

    Charles Hudson, GP at Precursor

    You’re told ‘illiquidity is a feature, not a bug’ and ‘let your winners ride.’ But when the physics of the model shift, you often need to with it. 

    Hunter Walk, Partner at Homebrew

    I’ve been doing the ‘anti-VC’ strategy of selling my winners […] When a company gets overvalued, and I can no longer underwrite a 10x.

    Fabrice Grinda, Founding Partner at FJ Labs

    You sell at the B, and you actually — for us, with the way our math worked — could have a north of 3x fund. But I also wouldn’t want to give up the future upside. We actually ran that through the C and the D. The big ‘Aha’ for me was that selling at the Series B, a little bit, was actually very prudent for a couple of reasons.

    Charles Hudson, GP at Precursor Ventures

    With all of this in mind, it no longer unreasonable for LPs expect something like a 2x return on their capital by year 6, and for VCs to raise new funds based on hitting that 2x target. Ensuring a decent return (on an IRR basis) for their LPs while companies are still within their orbit of influence.2

    Unsurprisingly, proposals to fix fee income are unpopular, and not only with those who profit from the status quo. There is a lack of systems thinking which would allow participants to grasp the interconnected factors which shape outcomes, and see the opportunity for change.3

    Indeed, many of the objections are based on silly recursive logic, e.g.

    • secondaries aren’t a good market ➝ because they’re only used to sell poor quality assets ➝ so they’re not a good market
    • returns in venture come from a few giant outcomes ➝ so we hold to IPO ➝ so more value accrues to a few survivors ➝ so most of the returns come from a few giant outcomes
    • you can’t get liquidity on markups➝ because they’re optimised for fees not liquidity ➝ so markups aren’t liquid

    In essence, power law and illiquidity are both absolutely realities of the venture strategy, but both have also been used to excuse and entrench suboptimal practices.

    The Opportunity of Secondaries

    A common misconception: the value of investments increases consistently (even exponentially) over time, so GPs should always hold to maturity. This idea has played a significant part in slowing down the use of secondary transactions. It’s not really true.

    Investments often don’t increase in value. Quite often, they fail outright. Failure rate does reduce over time (39% at seed, 13% at series D), but it remains significant throughout.

    Analyst Note: VC Returns by Series: Part IV

    To quote the brilliant thread from Rob Go:

    Typically, you think of a series A startup as less risky than a seed startup, and a series D startup as less risky than a series A startup. This is often true, but because VC dollars both add and remove risk, the move down the risk curve is less linear.

    This is especially true for ‘the biggest winners’ who are often absorbing huge amounts of capital from the ‘venture banks’:

    But in recent years, this picture has been skewed even more, especially if the capital raised comes from a mega VC fund. At each funding round, there is a significant re-risking of the startup, to the point that you are not moving meaningfully down the risk curve for a long long time. And even at a late stage, a mega funding round can bring you right back up to the point of maximum risk.

    “Re-Risking” by Rob Go

    These rounds are also often highly dilutive; particularly with the proclivity of large firms to ignore pro-rata and cram-down early investors.

    So, in an absolute sense, there is a sustained risk of failure which slowly concentrates portfolio returns into fewer companies over time, which will decelerate TVPI growth (or even turn it negative).

    On top of that, there are often terms included in later rounds which mean that shares held by early investors become relatively overvalued. Particularly, IPO ratchet clauses and automatic conversion vetos. Thus, even if the theoretical TVPI of a seed fund remains flat, in reality it may be falling:

    “In November 2015, Square went public at $9 per share with a pre-IPO value of $2.66 billion, substantially less than its $6 billion post–money valuation in October 2014. The Series E preferred shareholders were given $93 million worth of extra shares because of their IPO ratchet clause. This reinforces the idea that these shares were much more valuable than common shares and that Square was highly overvalued.”

    Squaring Venture Capital Valuations With Reality

    Looking at AngelList data, the best time for a fund to sell (on an IRR basis, and ignoring the clauses above) would be year 8 — with value concentrating (but not really net expanding) in years 9 through 12.

    What to Know About TVPI

    That means the typical investment (assuming a 3 year deployment period) would be best positioned for a (partial) sale in years 5-7. Considering this, it’s difficult to make the case that GPs should be holding 100% for the ultimate outcome, every time. If they do, they are concentrating their risk without necessarily improving the portfolio outcome.

    To take this a step further, we could assume in a more rational market, less dominated by hype (more secondary activity driving more pricing tension, fewer bullshit markups), the illustrated TVPI would flatten out more gradually — so less of an obvious time to sell.

    In short, the story here is not about opportunistic secondaries to drive better IRR. The real case to be made is for a comprehensive secondaries strategy, and opportunistic holding. For too long, there has been ideological friction around secondaries which has held back venture performance and enabled some very bad habits. It’s time to change that.

    If there’s a chance to wipe the slate clean for venture capital, for LPs and GPs to return to first principles on compensation, incentives and ideal outcomes — to begin aligning venture capital with a high-performing meritocracy — it’s here, today.

    Ironically, innovations in venture capital haven’t kept pace with the companies we serve. Our industry is still beholden to a rigid 10-year fund cycle pioneered in the 1970s. As chips shrank and software flew to the cloud, venture capital kept operating on the business equivalent of floppy disks. Once upon a time the 10-year fund cycle made sense. But the assumptions it’s based on no longer hold true, curtailing meaningful relationships prematurely and misaligning companies and their investment partners.

    Roelof Botha, Managing Partner of Sequoia Capital

    [EDIT 24/06/2025: Added a quote from Charles Hudson]

    1. Listening to Jack Altman’s podcast with Josh Kopelman of First Round helped change my mind on this. [↩]
    2. I believe a 2x return is not an unreasonable target, but the market would adapt if it were. [↩]
    3. This observation is based on the feedback I had to an initial proposal. [↩]
  • Venture Capital’s ‘Knowledge Work’ Problem

    Venture Capital’s ‘Knowledge Work’ Problem

    While GenAI can improve worker efficiency, it can inhibit critical engagement with work and can potentially lead to long-term overreliance on the tool and diminished skill for independent problem-solving. Higher confidence in GenAI’s ability to perform a task is related to less critical thinking effort.

    source: The Impact of Generative AI on Critical Thinking

    This article is broken down into five segments:

    1. Priorities in venture capital
    2. The importance of cycles
    3. Forcing a reset
    4. The ‘knowledge work’ problem
    5. Operating from strength

    Priorities in Venture Capital

    In colder markets, founders just need capital on reasonable terms, and it doesn’t really matter where it comes from. Value-add propositions and brand strength are less important; access to hot companies doesn’t move the needle as much for LPs. Instead they care more about differentiation through strategy.

    In hotter markets, the opposite is true. Investors will be chasing the fastest growing companies in the most attractive categories, out on the thin ice of excess risk. LPs, sold the same dream, care only about how GPs can parlay their way into those deals. How you invest is irrelevant, what matters is your network and your brand.

    Strangely, at the peak things begin to come full circle. In 2021, when there the incredible amount of capital was spread across a record 1,594 firms, there was a horseshoe effect: with such abundant opportunity for investment, LPs and VCs once again saw the opportunity in strategy-driven alpha.

    The Future of Venture Capital Early signs of disruption suggest how the industry may be impacted

    In normal circumstances, the next stage of this cycle is the crash. The firms that leaned the hardest into chasing heat would be the most exposed, with portfolios that are the most obviously out of alignment with value. What’s left are the firms who chose to focus on solid strategy, who can begin harvesting deals in the down market.

    In 2022, this shift was derailed by the emergence of venture banks, designed to escape the typical cycles of venture. The largest firms raised the most capital in subsequent years. Access remained an important part of the story for LPs, especially with the convenient rise of AI.

    In fact, you could argue that this was the second time that cycle was disrupted, as many experienced investors called the top in 2016-2018 only to be thwarted by COVID. Two years of intense, irrational enthusiasm for digital only exacerbated the problem.

    The Importance of Cycles

    Consider how much of the natural world has evolved alongside fire. Wildfires serve an important purpose in preventing ecosystems from choking themselves to death on redundant biomass, and there are even species that have evolved to use fire as a mechanism to spread their seeds.

    Humanities view of fire as a threat, and the goal of suppressing it entirely in the natural environment, has had disastrous consequences. We have seen the emergence of ‘mega-fires‘, where biomass accumulates to the point where spread is fierce and inevitable.

    There are clear parallels here in venture. The extent to which the market is suffering today is proportional to the amount of time it took to hit a correction. What’s worse, for reasons described above we haven’t yet really allowed the full cycle to complete.

    Forcing a Reset

    While venture banks steam off into the distance, and venture capital tries to figure out how to navigate this environment, there are three signs of change.

    • Increasingly, there’s talk of smaller LPs like family offices looking to pursue direct investing strategies. In theory, this affords them a similar level of risk with better economics, but questions remain about their bandwidth to do this properly.
    • There’s been a surprising number of high profile GP departures, both launching their own funds and not. In many cases, this means partners are giving up wharever carry incentive they had. This suggests some discomfort in the status quo.
    • An increasing number of founders talking about bootstrapping or ‘seedstrapping’ (one and done fundrasing), or other strategies to avoid the problems assocaited with getting on the venture capital treadmill and the expectations involved.

    For the GPs that remain, it’s time to consider what the world would look like if the cycle had completed. How would they be forced to act in a truly ‘down market’ environment. Indeed, if you consider that many smaller firms have been priced out of AI, that may already feel like their reallity.

    It is clear that the bar for performance is significantly higher in a cash constrained environment with higher interest rates. While that may not change the reality for venture banks, it is existential for traditional venture capital.

    The ‘Knowledge Work’ Problem

    In hot markets, where investors take a prescriptive approach to investment, there is a huge problem with atrophy. Completely separate to the poor investments that come out of these periods, it’s also worth examining the practices they establish.

    Investors that spend all of their time chasing hot deals based on a number of set criteria have the same basic problem as knowledge workers that rely on Generative AI solutions: they are not using their critical thinking muscles. Executing orders, not problem solving.

    Consider how little actual thinking you have to do about an investment if your process is focused on second-order factors. Is it on an a16z market map, is it on YC’s Request for Startups, are other “tier 1” investors are in the round? Will downstream investors will give you the markups you need, and will LPs will be excited about it?

    This behavior, geared towards capital velocity, is focused on second order information and pattern matching. It is a prescriptive approach that informs what gets investment, displacing the first-order considerations about things like team, opportunity, valuation, market and strategy.

    This dissertation focuses exclusively on moral hazard, which refers to a venture capitalist’s propensity to exert less effort and shirk their fiduciary duties to the investors to maximize their self-interest; specifically, a VC’s propensity to choose subjective selection criteria over more cognitively taxing objective criteria when faced with multiple options and fewer resource restrictions.

    “Venture Capitalists’ Decision-Making Under Changing Resource Availability”, by Noah John Pettit

    While this approach might broadly work for venture banks, with an army of low-impact investors looking to index across new technology trends, it will not deliver the returns required by the traditional venture model.

    Operating from Strength

    The contrary to prescriptive investing is quite literally the hunt for outliers. Backing illegible companies. Being consensus averse. Resisting what have been the loudest models.

    This might sound like cowboy investing. A Rick Rubin-esque vibes based approach to venture capital. It certainly can be, and if you happen to be Rick Rubin it might just work — but why take the risk?

    The way for these investors to operate from a position of strength is to build process alpha. That is, do everything you can to prevent being wrong for predictable reasons (controlling for bias), and to manage the risk of being completely wrong a lot of the time (portfolio construction). Not to overintellectualise investment decisions, but to give yourself the strongest foundation to embrace the risk of uncertainty.

    To take the analogy a bit further, for all that Rick Rubin is a total eccentric, guided by his own taste without the need for external validation, he is not cavalier about it. He pays immense attention to environment and routine in order to help him get the best return on his time.

    There’s never a need for investors to stray from this disciplined mode of operation, it just so happens that most are prey to the cycles of venture capital and the temptation to inflate fees when opportunity arises.

    Discpline is easy when opportunity is limited.

  • Escaping the Cycle

    Escaping the Cycle

    I had a meeting once with Howard Marks, who I had wanted to meet for a long time. He’s a famous bond investor that does a lot of writing. For 15 minutes he asked me questions about the venture industry, a lot of structural questions, and I answered him as best I could. 

    He said, “That’s a really shitty industry”. I said, “Why do you say that, what do you mean?”, and he said “Cyclical collapse is built into the structure.” 

    Bill Gurley on his conversation with Howard Marks, All In Summit, May 2022
    “VC Downturns“, by Ali Afridi of Equal Ventures

    ‘2024’ could have been a short chapter in the history of venture capital. Another post-boom year of declining deal activity, recovering valuations, recaps and shutdowns. We left it with the public markets looking healthy and hope that IPOs might reappear on the horizon. All good reasons to flip the page to 2025. 

    Before you move on from 2024 entirely, consider the boiling frog. Venture capital has been quietly outgrowing traditional definitions over the last 15 years. Last year, as AI drove heat into a cool market, the cracks started to appear. 

    The venture capital industry has historically worked as a relay race where investors at one stage bring in the next stage of investors to fund and support companies as they scale. Right now, it feels like AI investing breaks this model in a few important ways.

    Charles Hudson, Managing Partner at Precursor Ventures

    This isn’t just about inflated valuations, overcapitalisation and ZIRP. It’s about the effort of a few firms to outgrow the boom-and-bust nature of venture capital through a new model for investing. 

    Consider the well documented rivalry between Andreessen Horowitz and Benchmark, with their opposing views on capital abundance in venture. 

    Bill Gurley declined my requests for comment, but he has publicly bemoaned all the money that firms such as a16z are pumping into the system at a time when he and many other V.C.s worry that the tech sector is experiencing another bubble. So many investors from outside the Valley want in on the startup world that valuations have been soaring: last year, thirty-eight U.S. startups received billion-dollar valuations, twenty-three more than in 2013. Many V.C.s have told their companies to raise as much money as possible now, to have a buffer against a crash.

    “The Mind of Marc Andreessen“, by Tad Friend

    Benchmark has set the standard for “traditional” venture capital, managing small and efficient funds, close relationships with founders, and exemplary returns. In contrast, Andreessen Horowitz represents the era of ‘capital of a competitive advantage’; a confluence of hyper-scalable SaaS, digital growth channels, and historically low interest rates. Not merely a competing venture strategy, but the emergence of a fundamentally new product that trades efficiency for scale.

    The divergence of Andreessen Horowitz (and a few similar firms) from the traditional playbook has created some confusion, as greener GPs attempted to emulate aspects of both. Lacking either the AUM of Andreessen Horowitz or the discipline of Benchmark, they inevitably hit the slippery slope of badly managed risk. This can also be seen in the muddled logic on topics like entry multiples, valuation and concentration, where the ‘common wisdom’ has often made little practical sense. 

    Fortunately, there is light at the end of the tunnel: As this new product is better understood by LPs, GPs and founders, the chaos is better controlled: LPs will have more realistic return expectations, benchmarks and timelines. GPs will be able to identify coherent strategies and compatible advice. Founders will have a clearer choice between traditional venture capital, and the new model of “venture bank”, and the expectations associated with each.

    Too Big to Fail

    In 2009, with the launch of their first fund, Ben Horowitz and Marc Andreessen slipped into the role of VC brilliantly. They had strong opinions about capital efficiency and portfolio structure, a level of sophistication that surpasses many of today’s GPs.

    Despite the success of that first outing, capturing huge outcomes like Skype, the market soon presented a new opportunity. In 2010, rebounding from the GFC, incumbent firms were raising funds in excess of $1B—a throwback to the dotcom exuberance that Benchmark had criticised. Institutional money was once again on the hunt for new opportunities and software had begun ‘eating the world’.

    As entrepreneurs (another contrast to Gurley) Horowitz and Andreessen felt no loyalty to the traditional playbook. They saw an opportunity to innovate; the chance to build a scalable model for venture capital and take a dominant position. So, they invested heavily in media to build a ‘household name’ brand. They sought network effects by launching scouts programs and accelerators. Platform teams were assembled to handle their growing portfolio. Rather than an investment team, they built something more like a sales force. Essentially, they exploited a “boom loop”: raising money to maximise exposure, manufacture category winners, distribute healthy markups and raise more capital. In this environment, venture capital became a cutthroat zero-sum game with very little discipline. 

    Some of the VCs who funded predation succeeded spectacularly, and the basic incentives of venture investing that tempt VCs to employ this strategy persist. The goal of antitrust law is to push businesses away from socially costly anticompetitive behavior and towards developing socially valuable efficiencies and innovations. We think that Silicon Valley could use a nudge in that direction.

    “Venture Predation“, by Matthew T. Wansley & Samuel N. Weinstein

    Jump to mid-2022, and the post-ZIRP hangover experienced by the venture market. Almost everyone had gotten caught-up during the decade of near-zero interest rates capped off by pandemic-driven irrationality (except Gurley, who had stepped back from Benchmark in 2020). The critics of the Andreessen Horowitz model began to emerge, pointing to their inflated check sizes and irrational pursuit of “Web3.0”. Now, like everyone else, Andreessen Horowitz would surely suffer from falling returns and backlash from LPs? 

    It happens that Andreessen Horowitz raised more than $14B in new funds during the first half of 2022, close to the combined total of their funds raised in the preceding three years. They were well prepared for the fundraising winter of 2023, a year which many large firms used to rebalance their portfolios through secondary transactions—playing on the extreme heat around companies like OpenAI, SpaceX and Anduril. 

    In 2024, as the slump continued (down roughly 50% from the funds raised by VCs in 2020 and 2021) Andreessen Horowitz came back and raised another $7B. In fact, the 10 largest venture banks raised more than half of all venture dollars last year. The firms that had contributed most to the collapse were suffering from it the least. How could this be? The outcome frustrated many smaller GPs who had been squeezed out of deals and were having a harder time closing funds. 

    Plans Measured in Decades

    If you want to build a venture bank, you need billions of dollars on a regular basis, and it needs to be truly patient capital. Not high-net-worths, not family offices, perhaps only the largest pension funds and endowments. Most of all, you need sovereign wealth. 

    This transition away from venture capital’s traditional LP base was the central gambit of venture banks. With performance stabilised by the scale of their AUM, the ability to extend into other assets and an internationally recognised brand, they are able to court some of the world’s largest pools of capital. Rather than hunting alpha, they farm ‘innovation beta’—indexing across as much of the fast-growing tech market as possible, with exposure to every hot theme, at every stage, both private, public and tokenised. 

    A lot of the detail is lost when you’re operating at that altitude. Valuation, consensus, discipline, markets
 they all matter less if you’ve escaped the typical venture capital fund cycles. Liquidity can be delivered through the secondary market, continuation funds, distributions of public stock or sales of crypto holdings. If it’s a tough market for IPOs, you have the scope to wait it out. When there’s a ‘liquidity window’, like 2021, you can jettison as much as possible—as quickly as possible. 

    If success in venture capital is primarily about being in the right place, at the right time, the proposition of venture banks is to be everywhere, all the time.

    Our results suggest that the early success of VC firms depends almost entirely on having been “in the right place at the right time”—that is, investing in industries and in regions that did particularly well in a given year.

    “The persistent effect of initial success“, by Ramana Nanda, Sampsa Samila and Olav Sorenson

    There is also opportunity in this bifurcation for GPs at traditional firms. While venture banks smother consensus themes, they do so on behalf of this new class of supersized LP. For everyone else, there should be less competition in the traditional LP base—once they recover their enthusiasm for the asset class. 

    The shift obliges smaller firms to focus on non-consensus themes—which they should have been doing all along. Access to ‘hot deals’ is no longer a core proposition for LPs, as it becomes clearer that the juice isn’t worth the squeeze. Instead, GPs should focus on evergreen investing theory. This includes portfolios structured to optimise for outliers and manage risk and a sourcing process that minimises bias, or what Joe Milam calls “Process Alpha”.

    The conventional venture model relied on the power law to rationalize overly concentrated portfolios and “hot deal chasing” when basic portfolio management theory emphasizes (non-systematic) risk management through diversification.

    “How to Construct and Manage Optimized Venture Portfolios“, by Joe Milam

    Essentially, venture capital goes back to being a positive-sum game that effectively finances innovation, and looks less like a ponzi scheme.

    A number of other positive consequences could emerge from this correction: 

    In moving away from consensus themes, VCs will be forced to develop an approach to understanding the value in novel propositions.

    So much funding in the valley has rushed towards the consumer, at the expense of what we would argue are more significant projects.

    Nicholas Zamiska, Office of the CEO, Palantir

    Some of that shift has been cultural, but much is simply that the pervasive practice of pricing with ARR multiples has favoured well-understood segments over the last decade. Deep tech and hardware suffered because VCs simply weren’t sure how to put a price tag on those companies. 

    Calculating or qualifying potential valuation using the simplistic and crude tool of a revenue multiple (also known as the price/revenue or price/sales ratio) was quite trendy back during the Internet bubble of the late 1990s. Perhaps it is not peculiar that our good friend the price/revenue ratio is back in vogue. But investors and analysts beware; this is a remarkably dangerous technique, because all revenues are not created equal.

    Bill Gurley, GP at Benchmark

    As VCs develop a keener and more independent sense for value, the exit pipeline begins to look a lot healthier. Metrics that look at financial health in addition to sheer growth of revenue will more effectively align companies with the expectations of both public market investors and prospective acquirers. Like the old days of venture, much of growth and prosperity will be available for mass market retail participation — which is great for the ‘soft power’ of tech. 

    Some of the world’s most highly valued public tech companies entered the public markets with quite modest valuations, at least by today’s standards. Microsoft, Amazon, Oracle and Cisco all debuted with market caps south of $1 billion. Of those, only Microsoft topped $500 million. This translated to relatively modest gains for their private market investors, compared to the massive value appreciation they have all experienced post-IPO.

    “Who’s reaping the gains from the rise of unicorns?“, by Adley Bowden

    On the other hand, venture banks who have developed monster companies like OpenAI, SpaceX and Anduril, will be able to keep tapping into this expanded pool of private market capital via secondaries. Where companies are strategically sensitive, particularly in the realms of AI or defense, this may be seen as preferable to public company disclosure requirements or the threat from activist shareholders. 

    Secondary markets also represent an opportunity for traditional venture capital. As managers pick up better standards for valuation, providing greater transparency and explainability, activity is bound to increase. This means more options for liquidity, more consistent buy-sell tension to keep prices under control, and participation from a more diverse base of investors. Finally, it also offers greater cap table flexibility to founders and early investors.

    Let’s be realistic here; you’re better off in the fullness of time if certain players are in your cap table, and not a seed fund. 

    Mike Maples, Jr, Co-Founding Partner at Floodgate

    Clarity Precedes Success

    2025 should mark the end of confusion about venture banks and their role in the ecosystem. After a decade of increasingly confused standards and benchmarks for traditional VC, the two products have been neatly split by the wedge of AI.

    This is an opportunity for rehabilitation, as the confusion around the change has created more issues than the change itself. Both sides are now able to lean into their strengths, consensus vs non-consensus, alpha vs beta, and deliver the commensurate returns to LPs with matched expectations. 

    Make no mistake, 2025 will be a crucible for venture capital. The divergence of exits from public market performance is worse than ever, and confidence is low. Despite this, there seems to be a resurgence of smaller firms raising capital—matching historical patterns where incumbents lead the rest of the market by a year. 

    Critically, how will smaller GPs execute in the new environment? Will they learn the lessons from the last five years and play to their strengths? Or will they keep trying to span the chasm of small fund risk tolerance and large fund risk appetite? 

  • Venture Banks

    Venture Banks

    In venture capital circles, the most widely discussed trend of 2024 (outside of AI) has been the concentration of capital into “venture banks” like Andreessen Horowitz, General Catalyst and Thrive Capital. The household names of venture capital have had a blockbuster year, while others carefully ration the tail-end of their last fund. 

    The first quarter opened with Andreessen Horowitz and General Catalyst scooping up 44% of the available capital. 2024 is closing on a similar note, with 9 firms having captured more than half of all funds raised so far. The 30 most capitalized firms this year collectively represent three quarters of the pool raised by at least 380 funds. 

    However, the real anomaly is not how much the large funds have raised, but rather how poorly everyone else has done. Why has the bottom fallen out of the market for smaller funds, if the giant firms are still able to vacuum up capital? 

    There are a range of opinions on this question: Consider the insights shared by Sam Lessin, in The Venture Capital Regatta; Yoni Rechtman in Return, Bifurcation or Megafund Dominance; or Charles Hudson, in Three Future States of the Early-Stage VC Ecosystem. Both are respected investors with valuable perspectives but a slightly different set of base assumptions, so triangulating on objectivity is difficult. 

    Ask a hundred GPs or LPs where they draw the line between small funds and large funds, or how they define multi-stage and multi-sector strategies, and you will get a hundred different answers. The lack of standard definitions and common understandings has dramatically hindered productive discourse about venture capital over the years. 

    Importantly, it has obscured the manner in which multi-stage venture capital has diverged from the rest of the market. Today, it operates a novel model for startup investment, targeting a new class of LPs with a very different premise.

    A Rapacious Playbook

    In 2011, Jay Levy of Zelkova Ventures wrote an article about the conflicting interests involved in insider pricing. His point was simple: when investors led rounds for existing portfolio companies, their desire for greater ownership would be outweighed by their need to show performance. 

    Two things are striking about this article:

    1. Jay’s concern probably seems alien or overly-dramatic to anyone who entered venture capital within the last decade. Today, it’s just the game on the table. 
    1. It is also likely to be the single largest contributing factor to the pricing bubble that grew during ZIRP and exploded in 2022, if you follow the incentives created. 

    In a rational market, where VCs are all stage-specific, each round of investment has a different lead investor. That means, at regular intervals in the company’s development, it will be valued by a neutral third-party. Outside investors that want to maximize ownership will go up against founders that want to limit dilution. From that tension, we expect a generally fair outcome to emerge. 

    Venture capital relies on this tension, and the increasing financial savvy of investors as the investment moves downstream, stewarding companies toward exits. From qualitative analysis at the earliest stages to the quality of cash flow at maturity; you move the dial from founder strength to financial performance as you go from pre-seed to IPO, and so the expertise of investors evolves in parallel.

    Multi-stage firms have a different (and fairly rapacious) view on this process. Instead of inviting scrutiny of the value of their portfolio companies, their strategy is to keep that in-house, or within a network of associated firms. Rather than rational pricing through the tension of buyer and seller, they have embraced the jagged edge of what Jay Levy described: why worry about valuation if pricing can be a competitive advantage? 

    Want 3-4x markups on investments to show LPs? Just do subsequent rounds at 3-4x and get them rubber-stamped by networked investors. With “performance” taken care of, it’s easier to raise more capital to feed portfolio companies, fuelling aggressive growth to grow into those markups. It’s putting the cart before the horse, compared to conventional venture thinking, but it has a certain brutal charm.

    So, we’re beginning to see that the ‘capital as a competitive advantage’ playbook didn’t expire with ZIRP. A decade of cheap capital was what it took to prove the model, and today it just needs a different class of LP to back it. Indeed, multi-stage GPs appear to have spent 2023 with their heads down, consolidating around the best-looking secondary opportunities (SpaceX, OpenAI, Anthropic, Anduril) ahead of a grand tour in the Middle East. Sovereign wealth, with giant pools of capital and no great pressure on liquidity, are complementary to the traditional large institutional LPs for this strategy. 

    Exploiting Venture Capital’s Flaws

    As multi-stage firms have expanded their funds under management, they’ve had to similarly scale their ability to capture market share. This has been solved through a fairly innovative list of features, each of which exploits a different dynamic of venture markets: 

    Platform Teams: Leaning into size as an advantage, multi-stage VCs have built platform teams with the advertised intent to offer support and resources to portfolio companies. In reality, portfolio teams are the serfs of the venture world, managing the burden of a large portfolio for a relatively small team of partners while generally adding little value for founders. 

    Signalling Risk: VCs are wildly vulnerable to herd behavior. An example of this is “signalling risk”: concern about the signal of how other investors respond to a startup. Despite being obviously silly, this essentially means “tier 1” firms get prima nocta on every founder they touch, so they scoop them up en masse with scout programs and EIR initiatives. 

    Backing GPs: While the rest of the market struggles, multi-stage funds can raise additional vehicles through which they become LPs in emerging managers. They look like the good guys, supporting the underdogs, broadening the market and encouraging competition. In fact, they are entrenching centralized positions in the relationship model of venture capital.

    Operator Investors: In the last decade, there has been an ideological shift towards the idea of ‘operator investors’. Former founders are seen as the ideal archetype for venture capital, having first-hand experience building companies. As it turns out, they don’t really make for better investors, they’re just extremely well networked and have credibility with founders.

    Procyclical Pricing: A huge amount of valuation wisdom has been discarded over the last decade, as the industry as a whole adapted to deal velocity with cruder pricing models—e.g. revenue multiples, raise/ownership, etc. These common practices lack critical specificity and amplify volatility in the market, a problem for venture firms that rely on rational pricing.

    The Value of Venture Beta

    The product of this multi-stage approach to startup investment is “venture beta”: returns will broadly track the market, while they expand in network, assets, and market share. For the largest institutional LPs, like sovereign wealth, this is fine: acceptable returns with minimal volatility, and they can brag about funding innovation with the support of the most prestigious firms.

    Further out, this model’s success depends on whether it can produce companies that are attractive to public market investors or private market acquirers. Up to now, large infusions of capital with crude pricing have produced sloppy, undisciplined businesses. The IPO market is still reeling from being force-fed companies with poor financial health in 2021. Whether this misalignment can be fixed, or is inherent to the strategy, has yet to be seen.  

    Some early stage VCs have commented that multi-stage VCs still rely on small, contrarian firms to identify opportunities before they are ‘legible’. It seems more accurate to say that small firms are just another source of signal about new market opportunities for the mutli-stage strategy, rather than a crucial part of the chain. Scout programs, hackathons and accelerators all create redundancy for the competence of small firms in this capacity. 

    For Those Seeking Alpha

    While historical patterns would indicate that the funding will bounce back for everyone else next year, it is worth some urgent reflection on how the growing share of multi-stage capital influences the market. 

    In the short term, multi-stage firms tapping into a new LP base shouldn’t have a huge impact on smaller funds, although many of their usual LPs will be spooked by the shift. GPs should have a good answer for how they adapt to this reality. How can they compete against the capital, network and brand strength of multi-stage firms in future? With increasing skepticism about the “value add” from venture capitalists, what do they offer founders that the multi-stage firms can’t?

    For GPs with high domain expertise in hard sciences, there is enough evidence of outperformance to differentiate them from large generalists. For everyone else, the burden of proof is going to be higher than ever, and will require becoming disciples of venture theory: Read everything there is about portfolio construction, historical performance, decision making, biases and strategy, and build a rock-solid case for LPs that you deliver on the two critical fronts:

    1. The potential to deliver excellent returns, in contrast to the mediocre performance of the largest firms. Not by swinging for the fences on every hit, but with properly optimized portfolio, price discipline, and solid understanding of the underlying theory. 
    1. Backing the best founders with the most important ideas. However good a multi-stage fund gets at identifying early stage opportunities, their model will always bias towards consensus themes and capital-intensive ideas. It is a limitation. 

    Essentially, GPs of smaller funds need to meet divergence with divergence, and embrace the strengths of their size and strategy: contrarianism and discipline, which amount to a form of value investing for early stage companies. Finding the easily overlooked. The alpha. 

    The Fork in the Road

    Multi-stage GPs spent the last decade cosplaying as VCs, despite their practices being opposed to the conventional rationale of venture capital. You can’t make good judgements about price vs value or question consensus themes if your existence is predicated on assigning arbitrary markups and chasing the hottest companies.

    Over the last decade, many VCs have sought to emulate “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. It has also produced some extremely poor practices, and bad attitudes.

    The more VCs study the history, theory and current reality of private market activity, the more conviction they can develop about their own mindset as investors. The more confidence they have, the better they will fare as individuals in a discipline where peer-validation is poison and the herd is always wrong.

    If that’s not for you, then there is a lucrative future to be had working at a venture bank. 

    But you need to decide which path to take. 

  • Venture Capital Abandoned Deep Tech and Is Paying the Price

    Venture Capital Abandoned Deep Tech and Is Paying the Price

    The venture capital industry, once lauded for its role in fostering innovation and technological breakthroughs, has lost its way. The pursuit of hyperscalable software companies, fueled by incentives tied to management fees and opaque valuation practices, has led VCs to prioritize short-term gains over long-term value creation.

    This shift has effectively sidelined deep tech startups in favor of software ventures that, while initially promising high margins, often end up as structurally unsustainable and unattractive in the eyes of public markets and acquirers.

    The liquidity crisis and the collapse of valuations post-2022 are, to a large extent, the result of this myopic focus.

    Markups, Management Fees, and Misaligned Incentives

    The core problem lies in how venture capital funds are structured. Many VCs earn their income primarily through management fees, which are a percentage of the assets under management (AUM). In this framework, VCs are incentivized to raise as much capital as possible and deploy it rapidly, not necessarily into companies with the strongest long-term potential, but into those that will generate high markups quickly. The logic is simple: markups create the illusion of success, which can then be showcased to Limited Partners (LPs) as evidence of strong fund performance, enabling VCs to raise subsequent funds and further increase their management fees.

    However, the criteria for these markups are alarmingly arbitrary. Without standardized metrics for valuing private companies or clear data collection methods, VCs have significant leeway to set valuations that align with their own interests. The result is an ecosystem that disproportionately rewards companies that raise as much capital as possible, at the highest valuation they can achieve, regardless of their underlying business fundamentals.

    This creates a vicious cycle where capital-intensive, rapidly scaling software startups are favored over deep tech ventures. The latter, which often require years of research and development before reaching commercial viability, do not fit neatly into this model. They lack the frequent fundraising rounds that VCs rely on for quick markups and cannot be easily measured using ARR multiples which have become the venture capital industry’s (moronic) North Star.

    A Crisis of Venture Capital’s Own Making

    The 2022 downturn in venture-backed company valuations, especially in the SaaS sector, was a long time coming. For years, VCs funneled billions into software companies with the promise of high margins, rapid user growth, and scalable business models. But as these companies matured, the flaws in this strategy became apparent. Many of these SaaS businesses, initially rewarded for their revenue growth, began to reveal cracks in their unit economics and competitive moats.

    In the public markets, where profitability, defensibility, and cash flow become the ultimate measures of value, these companies failed to meet expectations. The high-growth software playbook that worked so well in the private markets could not withstand the scrutiny of IPOs or M&A, leading to today’s slowdown in both exits and later stage valuations.

    The outcome? VCs are now sitting on portfolios filled with overvalued, underperforming software companies. The lack of attractive exit opportunities has created a liquidity crisis, trapping capital in companies that may never deliver the returns expected.

    The Opportunity Cost

    Amidst this frenzy for rapid scaling and quick markups, deep tech has been left behind. Yet, ironically, it is these deep tech companies—whether in biotech, space tech, or hardware—that have the potential to deliver outsized returns and societal impact. Unlike SaaS companies that can be replicated with relative ease, deep tech ventures are built on defensible intellectual property, technological breakthroughs, and years of research. Their competitive moats, while difficult to establish, are significantly harder to erode.

    Deep tech is fundamentally misaligned with the current VC incentive structure.1 These startups will take much longer to mature. They may not need to raise subsequent rounds until they have proven their solution, which may mean lengthy R&D cycles without easily measurable increase in value. This means fewer markups, less frequent fundraising, and, consequently, less “performance” to show to LPs.

    The paradox is that while deep tech may not deliver immediate returns, its potential for outsized impact—both in terms of financial returns and societal benefits—is far greater than the current crop of SaaS investments. If successful, deep tech companies can redefine industries, create entirely new markets, and generate returns that are an order of magnitude higher than those seen in the overfunded software space.

    The Return to Venture Capital’s Roots

    The original mission of venture capital was to take on the risk of funding transformative technologies that traditional finance would not touch. Semiconductors, biotech, and early internet technologies were all enabled by patient capital willing to bet on the future. However, over the past decade, this ethos has been replaced by a focus on capital velocity, management fees, and the illusion of quick wins.

    The solution to the current crisis is not simply more capital or better timing. It requires a fundamental realignment of venture capital with its original purpose. This means rethinking how funds are structured, how incentives are aligned, and how performance is measured. VCs need to shift away from the obsession with ARR multiples and markups toward a focus on genuine value creation, technological defensibility, and long-term impact.

    In essence, the liquidity crunch facing the VC industry today is self-inflicted. By prioritizing short-term returns over sustainable value, VCs have created portfolios filled with fragile businesses ill-equipped for the demands of public markets. A return to deep tech, with its focus on defensible, transformative technologies, offers a path forward—not just for the VC industry but for the broader economy.

    The future of venture capital should not be in chasing the next SaaS unicorn but in rediscovering the roots that built the industry: funding the innovations that will shape the next century. The hard pivot toward deep tech is not just a strategic necessity—it is a return to the true purpose of venture capital.

    1. While there are welcome signs of a hard tech rennaisance in places like El Segundo, it remains an uphill battle and is largely misaligned with venture capital incentives. Indeed, the fact that companies like SpaceX and Anduril had to be started by billionaires is evidence of venture capital’s failure. [↩]