“[AI] is the wave of our lives. I feel that way, by the way. If I look at internet, SaaS, mobile, AI, nothing looks the same. And will there be roadkill from this wave? Oh my God, there’s going to be a lot.” – David Frankel – Founder Collective

AI is compressing product cycles, inflating capital intensity and widening the gap between companies that can turn capability into distribution and those that cannot. That is why the current wave can be both transformational and destructive at the same time: the technology improves quickly, but the market clears far more brutally, leaving a long tail of funded businesses unable to justify the prices paid for them ^1,2,7.

Why the boom feels bigger than prior waves

Frankel places AI ahead of the internet, SaaS and mobile as a force in his investing lifetime, not because every company in the category will be a winner, but because the ceiling on the best outcomes is higher and the pace of change is faster ^1,2. The historical comparison matters. Over the past 25 years, he argues, there have been fewer than 100 sustainably large technology companies above roughly $10 billion in value, and the median of the top 500 companies created in that period is around $2.6 billion ¹. That means the modal outcome in every great wave has still been middling relative to the rare giants. AI is likely to repeat that pattern, only with greater speed and more capital chasing the same small set of large wins ^1,7.

The implication is uncomfortable for founders and investors alike. A great technology shift does not guarantee broad success for the companies formed around it. It can instead create a narrow funnel in which a handful of businesses become generational, while the rest become what Frankel calls roadkill ^1,2,7. The phrase is stark, but the underlying mechanism is familiar: when a category becomes the focus of intense consensus, valuations rise faster than the underlying certainty of durable differentiation. The market begins to treat participation in the wave as a substitute for competitive edge, even though participation alone is not enough ^1,4,35.

The seed market problem is really a maths problem

Frankel’s scepticism about frothy seed pricing is not emotional; it is arithmetic. If the entry valuation rises sharply, the eventual winner must be far larger, or much faster, for the fund maths to work. He pushes back directly against the idea that price no longer matters in AI. In his view, price always matters, because the required exit multiple changes with the starting point ¹. An uncapped note, a lofty cap, or a seed round priced as if it were a later stage all compress the margin of safety and make even a good company a less attractive venture investment ^1,7.

This explains why he is wary of the current fashion for large seed rounds led by multi-stage funds. Those firms can write big cheques, but their involvement often changes the power dynamics for everyone else. Frankel describes a recurring pattern in which a startup takes money from a prominent platform, only to discover later that the internal champion has left, the company is no longer strategic, and the founder is left looking for a second source of support ^1,7. That is why he sees Founder Collective’s smaller cheques as useful not because they dominate the round, but because they remain relevant after momentum investors move on ¹.

Discipline is becoming a strategic differentiator

The venture market increasingly rewards fund size, access and speed, yet Frankel still argues for discipline at seed. He does not deny that larger platforms can win, especially when a company is already on the path to being one of the very biggest ^1,7. What he disputes is the notion that scale alone is enough to produce durable venture outperformance. Mega-funds need the very largest outcomes because their fixed costs and cheque sizes require huge capital returns, whereas a boutique seed fund can still generate excellent returns from a more modest, but genuinely elite, company ^1,7.

That distinction gives context to his comments on Founder Collective’s strategy. The firm has always been willing to sacrifice some upside by staying small, avoiding management-fee maximisation and keeping a framework that favours founder quality over momentum ^1,3,7. He admits that this discipline has caused the firm to miss certain businesses that later became obvious successes, but he treats those misses as the cost of not turning the firm into a size-driven machine ^1,7. The argument is not that larger funds are wrong in all cases. It is that a small fund can still be a rational, coherent business when it is built around seed economics rather than later-stage asset gathering ^1,16.

Why founder quality still outranks category fashion

Even in an AI cycle, Frankel insists that the real edge lies in founder quality, complementary teams and deep domain knowledge ^1,7. The most interesting startups are not necessarily the ones that declare themselves AI companies first; they are often the ones where people with long industry experience can now use modern tools to attack a persistent pain point. A veteran SAP consultant, for example, who has spent years inside a broken workflow may suddenly be able to build a platform that was previously too expensive or too difficult to create ^1,7. The capability is new, but the insight is old. That combination is more durable than theme-chasing ^13,40.

Frankel also gives unusually high priority to the relationship between founder and market. He wants a founder who can sell, a technical co-founder who can do what he calls magic, and a partnership marked by trust rather than sameness ^1,7. That emphasis matters because AI lowers the barrier to building, but not the barrier to convincing, distributing and retaining customers. A model can be copied, but trust, timing and judgement remain hard to replicate. In that sense, AI may actually increase the value of operators who understand a niche deeply enough to see what the market has missed ^1,18,40.

AI changes the size of the company, not just the size of the market

One of the most important strategic shifts in the discussion is the claim that AI enables meaningful businesses to be built by very small teams ^1,7. That is not just a story about lower headcount; it is a story about a new operating baseline. With coding, support, research, analytics and even some creative tasks partially automated, a startup can begin with more leverage than earlier generations could access ^1,7. This resembles the effect cloud computing had on infrastructure, but higher up the stack: the product can be launched without assembling the same volume of human labour first ¹.

The same logic helps explain why Frankel does not expect mass unemployment, despite expecting significant labour disruption. He anticipates a widening divide between those who learn to use AI well and those who do not ^1,7. That divide may be especially sharp between younger workers who are already using voice, model-based tools and code assistants, and older workers whose workflows are more fixed ^1,7. The labour market consequence is not a simple replacement story. It is a reallocation story in which routine tasks disappear, service expectations rise and high-trust human judgement becomes more valuable in the situations where mistakes are expensive ^1,7.

The next battleground is not only models

Frankel is also explicit that today’s apparent winners are not guaranteed to remain so. He argues that technology platforms rarely stay on top forever, and that the next disruption could come from China, from open models, or from a computing shift that is not yet mainstream ^1,7. His photonic-computing comments are especially revealing because they show how he thinks about the stack: the model layer is important, but chips, energy and data-centre architecture may be equally decisive over time ¹. If optical or photonic chips materially reduce energy cost, then current assumptions about what constitutes an unassailable advantage could change quickly ^1,7.

That broader view also explains his concern about US research spending and regulation. He sees public R and D, universities and agencies such as DARPA as the upstream source of future commercial breakthroughs, and worries that underinvestment could erode American advantage ^1,7. At the same time, he views China’s faster experimentation and lighter practical constraints as a serious competitive force, especially in areas that require rapid iteration ^1,7. The strategic tension is clear: if the best ideas come from open-ended research but the fastest deployment happens elsewhere, then national advantage will depend on whether the US can preserve both invention and implementation ^1,7.

Why the most useful venture metric is liquidity, not paper value

Frankel’s attention to secondary sales and DPI is another sign of how pragmatic his framework has become ^1,7. In a market where companies can remain private for years while still changing shape underneath investors, paper gains can become misleading ^1,7. He is more interested in real cash returned than in theoretical value, and that is why he is open to partial liquidity when a company is clearly successful ^1,7. The point is not to be anti-growth. It is to recognise that waiting for a perfect exit may be inferior to realising some return while preserving upside ^1,7.

This concern dovetails with his comments on the speed of innovation cycles. If products can be made obsolete before liquidity arrives, then the venture business becomes more dependent on timing, optionality and fund construction ^1,7. Smaller funds can still work, but only if they remain disciplined about entry, selective about ownership and realistic about how much of the portfolio will actually become meaningful ^1,16. Frankel’s position is therefore not nostalgic. It is adaptive. He is willing to accept that AI will create enormous wealth, but he refuses to confuse that fact with an assumption that every investor, fund or startup in the category will share it ^1,2,7.

The long view: transformation and destruction are linked

The deepest thread in Frankel’s view is that technological revolutions always produce both creation and elimination. AI will likely make software more abundant, teams smaller, scientific work faster and services more accessible ^1,7. It will also compress the life expectancy of companies, intensify competitive pressure and expose valuations that were justified more by narrative than by economics ^1,2,7. That is why he can sound both enthusiastic and severe in the same breath. The opportunity is real, but so is the waste ^1,2.

For founders, the practical lesson is to build something with genuine edge rather than merely joining the category. For investors, the lesson is to respect price, preserve discipline and treat current leaders as temporary rather than permanent ^1,7. For the broader market, the lesson is simpler still: AI is not just a growth story. It is a sorting mechanism. The companies that understand their customers, own a real workflow and adapt faster than the model cycle can become extraordinary. The rest may help prove just how ruthless a great wave can be ^1,2,7.

 

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