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An enormous portion of revenue for cloud providers and other AI infrastructure players comes from just OpenAI and Anthropic. This extreme customer concentration creates a fragile system where the failure of one or two key players could trigger a widespread collapse.

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Data reveals an extreme power law where model labs OpenAI and Anthropic capture nearly all AI startup revenue, and their share is growing. This indicates value is accruing to the foundational layer, posing an existential threat to the long-term viability of application-focused startups.

A massive portion of cloud providers' growth comes from just two AI companies, OpenAI and Anthropic. Since these same providers (e.g., Microsoft, Google) are also major investors in those startups, it creates a circular economy where investment capital flows directly back as revenue for compute.

OpenAI's massive, long-term contracts with key infrastructure players mean its success is deeply intertwined with the market. If OpenAI falters, the ripple effect could crash stocks like NVIDIA, Oracle, and Microsoft, potentially bursting the AI bubble.

An outsized portion of U.S. GDP growth is now driven by AI-related capital expenditures from a small number of tech giants. This concentration creates systemic risk. A pullback in AI spending or a correction in these over-inflated valuations could trigger a significant economic downturn.

A true platform enables its users to generate more revenue than the platform itself captures. AI companies like Anthropic are currently failing this test, as their revenue from token sales far exceeds the revenue generated by the startups building on them, creating an unsustainable circular economy.

Despite a booming AI startup ecosystem, revenue is intensely concentrated. Foundational model providers OpenAI and Anthropic capture nearly 90% of the market, and their share is growing, squeezing out application-layer companies.

As demand for AI far outpaces compute supply, costs will rise. Only labs with the most lucrative algorithms, like OpenAI and Anthropic, can afford it. They reinvest massive revenues into the next training run, creating a self-reinforcing loop that raises the barrier to entry for any potential competitor, solidifying their duopoly.

Analyst Gavin Baker argues a few dominant AI labs create a monopsony (a dominant buyer) for compute, suppressing margins for everyone else. The rise of competitive open-source models decentralizes this power, shifting value back to other layers of the AI stack, from chips to software and cloud providers.

The vast majority of spending and market capitalization in AI today is in the infrastructure layer—compute (NVIDIA), foundation models (OpenAI), and data services. The entire application layer's revenue combined is a rounding error in comparison, highlighting a massive, though likely temporary, imbalance in where value is currently being captured.

The vast majority of AI data center compute revenue, which backs billions in debt, depends on the continued, exponential growth of OpenAI and Anthropic. If these two "dual points of failure" falter, it could trigger a cascading financial crisis across the private credit and banking sectors.