The Illusion of Abundance
Not too much paid for the best, but too many treated as if they might be
Every venture cycle invents a theory of abundance.
In the 2010s, it was that every vertical could produce a platform. A clean interface and enough capital could turn almost any service into a network-effect machine. That theory gave us food delivery apps, mobility startups, and a wave of VC-subsidised convenience.
In today’s AI cycle, the illusion has changed form. This time, it lives at the application layer: in the wrappers, copilots, and workflow tools that grow quickly but stand on borrowed ground.
Most of the real winners will come from the hard and expensive work or changing user behavior and building real data moats. But the mistake is not overpaying for the best, it’s assuming there are more of them than there really are.
The Shape of Scarcity
It is a strange feature of financial history that markets, which are said to be composed of intelligent participants, routinely forget the same things. Chief among them: the unevenness of return.
We have had the data for a long time. In public equities, just four percent of stocks have accounted for nearly all net wealth creation over the last century. The median stock does not outperform short-duration bonds. The average only rises because the best are so good that they compensate for everything else.
This a structural fact rather than a statistical curiosity. A fact that holds even more strongly in venture. Private markets carry fewer signals, offer no exit path for most participants, and endure long stretches without price discovery. The outcome is predictable: one or two companies determine the fate of an entire portfolio; the rest fade, quietly or otherwise.
And yet, in every cycle, we behave as if greatness is not just possible but abundant. Capital flows not only into the company that is working, but into anything that looks sufficiently like it. The illusion takes hold that there are many more winners to be found; that the original was not exceptional, but representative.
This is the defining feature of a venture bubble. Not that valuations exceed reason, but that conviction exceeds scarcity. Not too much paid for the best, but too many treated as if they might be the best.
The mistake is not in the pricing, it is in the count. And it is happening as much in the current AI wave as in any cycle to date.
Last Time: The Illusion Was Breadth
The nountech boom of the 2010s was built on a powerful intuition: that mobile interfaces would reorder large parts of the economy. If the smartphone could intermediate human attention and action, then surely any service delivered offline could be reimagined as software.
This gave rise to a simple theory: any category, transportation, food, cleaning, housing, insurance, could produce its own Uber. The only requirements were a sleek interface, a well-subsidised experience, and enough time to capture demand. Everything from laundromats to car leases briefly appeared to be a platform in waiting.
At a surface level, the model worked: users flowed in; adoption was rapid; and in many cases capital kept the flywheel spinning until growth could be read as validation. But most of these companies were not changing consumer behavior. They were smoothing it. They relied on habits that already existed. The subsidy did not create a new market, it simply paid for an old one to appear modern.
Beneath it all was the real driver: a capital stack pushed out of shape. Interest rates sat below their natural level. Allocators with fixed liabilities could not meet return targets in traditional places. The result was not exuberance but compulsion.
Too much capital chased too many copies of a winner that was never really reproducible. Uber may well have been an outlier, a company that escaped by force of leadership and an almost reckless willingness to deploy capital before incumbents could react. It was not a template. It was, in a way, a fluke.
But it was mistaken for a pattern. And so the illusion took hold: that breadth, the extension of a model across many verticals, would yield a generation of giants. It did not.
This Time: The Illusion Is Surface
The AI boom is different. The technology is stronger and the gains, in some domains, are compounding. It is more plausible, at least on the surface, that we are living through a foundational shift.
But the illusion has returned. It has simply changed form. Where nountech mistook vertical proliferation for inevitability, AI risks mistaking surface traction for durable value.
Every week brings news of another AI-native company with explosive growth: vertical copilots, workflow wrappers, decision assistants. They grow quickly because they do not ask users to change much. The interface improves. The friction lessens. But the underlying behavior is familiar. In some cases, the user is simply being given autocomplete for an existing task.
The metrics are impressive. Revenue arrives early. But the advantage is borrowed. These companies build atop foundation models they do not control. They rely on cost structures that are artificially low. They exist in ecosystems where the model providers are not dormant infrastructure, but active competitors.
The Windsurf acquisition highlights this. The model layer will not remain passive. It will not watch as applications siphon margin and loyalty. It will acquire, integrate, and internalise the best ideas. Not out of spite, but because it can, and because, increasingly, it must.
What this means for the current generation of application-layer AI startups is uncomfortable. Many will not fail outright. They will simply be absorbed. They will be reinterpreted as product exploration exercises, not companies in their own right.
Just as nountech startups mistook app delivery for defensibility, many AI companies today are mistaking distribution for moat. The abundance is again an illusion. The surface looks wide. But the layer is thin.
Where the Real Opportunity Lies
If we are honest about what the power law demands, then we must also be honest about how little of today’s startup landscape will matter. Not because it is unserious, or poorly run, or even overvalued. But because it is not sufficiently different.
To create a company that escapes the gravitational pull of the model stack will require more than a clean interface or a faster sales cycle. It will require either a meaningful change in user behavior, or the creation of differentiated data that feeds back into product performance.
The former is hard. Users do not change easily. But those that do, those who form new habits, accept new workflows, or learn to trust machine judgment in previously human domains, can create lock-in that outlasts the initial feature advantage.
The latter may be harder still. Proprietary data, especially in enterprise or scientific contexts, is rarely just "found." It is cultivated. Reinforcement learning offers one path. Closed-loop systems, where usage refines prediction, offer another. But these are not shortcuts. They are commitments.
In both cases, what matters is the feedback. Not just from users, but from the system itself. A company that gets better as it grows, not only in revenue, but in precision, efficiency, and decision quality, is one that can survive the consolidation to come.
That is where the opportunity lies. Not in abundance, but in rarity. And not in what the market is excited about now, but in what it will still need when the illusion clears.
The temptation, especially if capital loosens again, will be to forget this. To believe once more that escape velocity is common and that scarcity can be scaled.
But it never has been, and it won’t be this time either.