80% of Investment Went to OpenAI and Anthropic: The Market Looks Broad, but the Center of the New Economy Is Already Very Narrow
Why is 80% of investment concentrated in OpenAI and Anthropic even though the AI market appears broad and diverse?
Separate the investment signal and the impressive demonstration from the real business in the case of OpenAI and Anthropic; the assessment must check who pays, which indispensable part of the chain the product controls, and whether the economics survive scale.
What to watch for
Key takeaways
The decision in “The Optical Illusion of the AI Market” depends on one criterion: this section clarifies the mechanism behind the topic and preserves a constraint that would otherwise be lost in an overly simple conclusion.
The “Why is it not just a rating, but a map of the next phase AI market?” scene leads to a working conclusion: the important signal is not one funding number: the next round, available runway, and closure rate show whether a company can survive the new cost of capital.
The practical meaning of “OpenAI and Anthropic as the centre of the new AI economy” is that the important signal is not one funding number: the next round, available runway, and closure rate show whether a company can survive the new cost of capital.
For the “What will happen to the programming market” scene, the decisive point is this: this section clarifies the mechanism behind the topic and preserves a constraint that would otherwise be lost in an overly simple conclusion.
For the “What will happen to applied professions” scene, the decisive point is this: this section clarifies the mechanism behind the topic and preserves a constraint that would otherwise be lost in an overly simple conclusion.
In the context of “Physical AI: robots, cars, devices,” this criterion applies: the issue turns on whether the rule can be enforced and who carries responsibility, not merely on the existence of a new requirement.
The discussion of “Creative AI: video, audio, images” yields a practical test: the issue turns on whether the rule can be enforced and who carries responsibility, not merely on the existence of a new requirement.
The decision in “Early Forbes AI companies: who could lead the next wave?” depends on one criterion: the conclusion for a profession is the same. Do not compete with a model at the operation it performs best. Occupy the place where the result has to enter a real market and someone has to answer for it.
What this episode is about
Two companies captured most of the capital, programming is changing faster than other professions, and physical and creative AI require supply chains of their own. It is dangerous to look only at the giants: the next wave may grow in applied markets where the model is not yet the product.
The AI market creates an optical illusion. Hundreds of startups appear every week, but most of the money concentrates in OpenAI and Anthropic. Investors finance models and compute because the rest of the ecosystem cannot exist without foundation intelligence.
The concentration is especially visible in programming. Codex and Claude Code can take over more and more work, while small tools risk becoming a feature inside a platform. The developer does not disappear, but the market pays less for manual production of standard code and more for architecture, data, and responsibility.
Applied professions are changing at different speeds. A lawyer, physician, salesperson, or analyst gets an assistant that accelerates individual operations. Full replacement is harder because the process involves trust, regulation, and ambiguous decisions. This is exactly where new companies arise by connecting a model to industry logic.
Physical AI—robots, vehicles, and devices—requires manufacturing, sensors, and service. Creative AI—video, audio, and images—depends on rights, brands, and distribution. OpenAI and Anthropic cannot automatically capture all the value in those markets even if they provide the foundation model.
Early companies on Forbes lists show where the next wave may appear: not in another general chat, but in a product with proprietary data and a customer process. The conclusion for a profession is the same. Do not compete with a model at the operation it performs best.
Occupy the place where the result has to enter a real market and someone has to answer for it.
An AI company should be judged not by the word AI in its pitch deck, but by its data, implementation cost, customer retention, and control of an important part of the process.
Episode transcript
The episode is in Russian; below is an English reading guide to the transcript (the full EN transcript is a machine translation). Voice matching applied to 5 segments: 3 identified, 0 mixed, 0 marked with ✓, and 2 unresolved.
Loading…