Federal Reserve Chair Kevin Warsh devoted a full section of his Jackson Hole keynote to artificial intelligence, describing the current moment as a "hinge point in history." Warsh said annualized sales at the two leading AI labs now exceed $100 billion, an increase of more than 500% from a year earlier, and confirmed that the Fed now treats AI as "potentially a new factor of production." He also flagged that he is watching AI's "second derivative"—whether capital-spending growth is still accelerating.

Kevin Warsh's first Jackson Hole keynote as Federal Reserve chair drew headlines largely for what he declined to say about interest rates. The annual symposium, hosted by the Kansas City Fed in Jackson Hole, Wyoming, has historically been the venue where Fed chairs signal shifts in policy thinking. But in the middle of the speech, under the header "Preparing for Future Policy Conjunctures," sits the section most relevant to anyone tracking where AI money is actually flowing—and it extends a thread Fed officials have been pulling publicly since at least 2024, when then-Chair Jerome Powell used the same podium to warn that AI stocks could be in a bubble.

Warsh opened that section by describing the mood before AI took off. Economists spent the years after the 2008 crisis warning of "secular stagnation"—a world with too much capital chasing too few good investments because, as Warsh put it, "all the good stuff had been invented." That thesis, he said, no longer holds.

He backed the claim with numbers. Business capital expenditures—what he called "the seed corn of future economic growth"—are rising at their fastest pace since 2021, up roughly 9% over the past four quarters, with more than half of that growth tied to AI buildout. What he will watch from here, he said, is not the level of that spending but "the second derivative"—whether the growth rate itself keeps accelerating or starts to fade. That framing matters because the AI buildout has increasingly been compared to the 1990s telecom-fiber boom, where massive capital spending eventually outran demand.

Those figures help contextualize what Warsh said about AI specifically.

1. AI progress has outpaced even its biggest believers

"Progress in artificial intelligence—the 80-year-old name for the newest technology—has been faster even than its evangelists predicted a couple of years ago," Warsh said. "The potential for substantially higher growth is on the rise."

He went further: "Ever-expanding pools of capital are pouring into AI-related infrastructure of all sorts. A kind of hyper–Moore's law seems to be playing out."

Moore's law is the decades-old observation that computing power roughly doubles every two years. Warsh's "hyper" version claims AI capability is compounding even faster than that—a striking framing from a historically hawkish central banker rather than a Silicon Valley pitch deck, and the closest he came to arguing that AI alone justifies the market's massive capital inflows.

2. The AI economy now runs on a $100 billion token market

"Capital and labor have combined to create the large language models at the heart of AI," Warsh said. "Users buy tokens to gain access to the models."

He then put a number on it: "Reports put annualized token sales for the two leading labs alone at more than $100 billion—an increase of 500-plus percent from a year ago."

A token, in this context, is the basic unit by which AI companies sell access—roughly a chunk of text a model reads or generates. That Warsh cited a specific dollar figure, rather than speaking abstractly about "the AI boom," signals that the Fed now treats token revenue as a trackable line item in the economy, not a niche tech metric. It also gives policymakers something analogous to watch that earlier productivity waves lacked: unit economics that can be measured quarter by quarter, much as railroads were once tracked through freight tonnage.

3. The Fed now treats AI as a factor of production

Warsh said the Fed is closely following the market dynamics of the AI ecosystem. "We recognize that AI is a new variable—potentially a new factor of production—that will have consequences for both the economy and the conduct of monetary policy."

Factors of production are labor, capital, and land. Placing AI in that category is not flattery. It means the Fed is treating token consumption as something that could change how much the economy can produce without overheating—the potential growth rate that determines where interest rates have to sit. Get that wrong and every rate decision inherits the error.

Warsh asked whether AI will drive "a significant, sustained rise in productivity across the economy? And if so, when?" He also asked whether "token usage" will be "complementary or competitive to labor." He answered neither question. A Fed task force on productivity and jobs is studying the issue, and he was blunt that its findings "have no bearing on decisions we make in the current policy conjuncture."

That gap is the real disclosure. The Fed has decided AI is macro-relevant while simultaneously admitting it has no framework for it yet—while cutting or holding rates on models built before any of this spending existed. Bill Gates has pushed the labor half of that question further, arguing for a robot tax and jobs humans cannot be fired from.

4. Nobody knows who captures the value

Warsh did not pretend to have answers. He noted that it is hard to predict whether AI will actually increase productivity in the global economy, or when that shift will begin.

He also raised the question of whether this business will produce global or merely sectoral growth. "How much of the surplus goes to owners of scarce assets—AI labs, chipmakers, energy producers, and cloud providers?"

He said a Fed task force on "productivity and jobs" is studying the question, though its conclusions, he stressed, "have no bearing on decisions we make in the current policy conjuncture."

Warsh's own question about scarce assets received a real-world answer two days before he took the podium. Nvidia posted record quarterly revenue of $96.2 billion and disclosed $366 billion in future AI infrastructure commitments, while separately moving to acquire Hugging Face, the open-source AI hub, for roughly $12.9 billion. Conversely, a report from a year ago shows that 95% of generative AI companies are failing.

So the value may be concentrating rather than distributing. If a single chipmaker can absorb that much of the AI buildout's surplus—along with its own compute customers—Warsh's open question about market structure may already have a leading answer. What to watch from here is the task force's output, the trajectory of the capex "second derivative" in coming quarters, and whether Warsh's framing of AI as a factor of production migrates from Jackson Hole rhetoric into the Fed's formal forecasting models.

Source: Federal Reserve speech