Can AI answer the $3 trillion question?

Three years ago, Sequoia partner David Cahn was one of the first people to do the math and put a number on the implications of Silicon Valley’s titanic spend on AI infrastructure.

In 2023 , he was reacting to Nvidia’s reported annual GPU revenue of $50 billion. Starting with that figure, and adding in the implied costs of operating the data centers and the margins for their operators, he deduced that $200 billion in revenue would be required to pay back the up-front investment.

He took it as a challenge, asking entrepreneurs to come up with AI products and services to make use of, and generate revenue from, all that infrastructure. Fast-forward to today, adding up three years of hyperscaling, and Cahn’s got a new number on AI infrastructure spending for 2026: $1.5 trillion.

All told, he calculates that the AI industry will have to earn $3 trillion to justify all those chips and other data center expenditures. And that’s probably an underestimate — the rising costs of memory and the increasing use of exotic or inference-specific chips will drive that number up. “Recently,” he writes, “the required revenue per GW of CapEx has sharply increased due to these bottleneck dynamics and rising costs of construction.”

On the other side of the ledger, Anthropic is thought to have hit $60 billion in ARR , while OpenAI reportedly earned $13 billion in 2025 (although in November 2025, it said it was at $20 billion ARR ) and is presumably making more this year. But there’s clearly a large gap to be closed.

Someone minding that gap is Torsten Slok, the chief economist at Apollo, the giant asset manager. In a recent note , he points out that the hyperscalers — Google, Meta, Microsoft, and Amazon — are all predicting massive accelerations in their free-cash flow in 2028. That is, they expect to see the payback from all those chips they bought.

What if they don’t? Slok notes a risk we’re currently seeing across AI usage: More organizations turning to cheaper open weight models, often Chinese, not those built by the frontier labs, and overall token prices falling. OpenAI’s latest model, per CEO Sam Altman, is 54% more token efficient on coding tasks. That’s good for users fretting about the cost of their AI agents, but it may be bad for companies building token factories should users not wildly increase their overall token usage with them.

Slok worries that if hyperscalers don’t meet their cash-flow goals, the market reaction could be severe — “with so much riding on so few names,” he writes, “a slower payoff wouldn’t just be a sector problem, it would risk tipping the economy into recession and the S&P 500 into a correction.”

Just something to keep in mind as you’re herding your AI agents toward cheaper tokens.

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