Call it the nerd mindset. Silicon Valley has spent the last few years believing that whoever built the smartest artificial-intelligence system would rule the industry. Today, that calculation is starting to look misguided.

By Parmy Olson The cost of building AI models is going up, and the price of using them is going down. AI companies’ revenue growth has been impressive, but it’s also slowing. That doesn’t necessarily mean a bursting bubble, but it does look like the real power increasingly lies with the companies that distribute and power the technology: cloud giants like Microsoft Corp., Alphabet Inc. and Amazon.com Inc.

This trio of the Big Tech establishment, whose shares have risen an average of 10% since the start of 2026, controls the infrastructure, customer relationships and distribution channels through which AI is sold. They can profit whether a business uses Claude, or an older version of ChatGPT, or a cheaper open-weight model from China. That’s not the case for OpenAI or Anthropic.

The latter is on the runway to an initial public offering in November; it’s aiming for a $2 trillion valuation in what will be the biggest test of continued investor appetite for the AI boom. The problem: Anthropic recently told investors that its third-quarter revenue was $16.8 billion, a quarter-on-quarter growth rate of around 50%, compared with 140% earlier this year.

Go on X and you’ll find hysteria over this news. A popular financial commenter calls Anthropic’s numbers a “crazy sharp” deceleration. “It’s over,” they add, ominously.

Actually, it’s hardly a disaster. At 50% quarterly growth, sales would increase roughly five times in a year and 130 times in three years. The hard part is sustaining it, as each additional quarter requires billions more in new revenue.

Compounding the problem is the fact that the most powerful frontier models cost a fortune to build. A chart published by Citadel Securities in August highlighted the painful economic trap faced by OpenAI and Anthropic: The computing power needed to run AI is becoming more expensive, while so-called token prices have fallen, making it much cheaper to use.

The authors of the Citadel chart were optimistic, saying that cheaper AI was leading companies to use the technology more, enough to sustain demand for all that expensive, underlying compute — AI boosters often point to this phenomenon, known as Jevons paradox.

But it gets harder to be optimistic about Anthropic and OpenAI sustaining that pace of growth when more businesses are opting for AI that is just good enough, rather than the best. Cheaper, open-weight models now account for more than a third of enterprise AI token usage, up from 23% a year ago, according to Enterprise Technology Research, which surveyed 200 companies in September. Respondents expected that share to reach 41% in the next 12 months.

And over the past year, two major Anthropic customers — Microsoft and Meta Platforms Inc. — have cut back on their internal use of Claude in favor of their own proprietary solutions in an attempt to bring down astronomical token costs.

That all makes the future look more hazy for the leading AI companies, particularly in the face of a yawning gap between the money they’re bringing in, and the collective $1.3 trillion that OpenAI and Anthropic are expected to spend on data centers, chips and computing power through the end of 2030.

Does that spell a catastrophic sequence of falling dominoes for the rest of Silicon Valley? Some sort of correction seems inevitable when investors have committed such enormous sums to AI infrastructure and the debt financing it — which is already having second-order effects on the bond market — before anyone can reliably calculate the returns.

And if further growth struggles at Anthropic and OpenAI lead to a collapse in confidence, the cloud companies won’t be entirely immune either. They’ve invested billions of dollars in frontier AI firms and data centers to run their services. The use of cheaper models will also squeeze their margins.

But when it comes to weathering a coming storm, the established cloud giants are alsofar better placed than the newcomers. They have profitable businesses outside AI, longstanding relationships with corporate customers and the ability to make money whichever model comes out on top.

Recall that in the dot-com era, many investors lost their shirts because companies couldn’t quickly monetize expensive fiber-optic cables that had been installed to support the boom. That infrastructure just took longer than many expected to be utilized.

If the AI bull run follows a similar path, the biggest winners may be the companies with the most resilience — not the Sam Altmans or Dario Amodeis trying to build a machine god, but the ones who can afford to wait for it.