What’s at stake in AI’s trillion-dollar gamble
Jessica Wachter, a finance professor at the University of Pennsylvania’s Wharton School, wanted to assess AI’s impact on the economy over the next few years but faced many business and technical uncertainties. She started instead with what she calls a “remarkable fact” that is not in question: a handful of so-called hyperscalers are investing huge amounts of money to build AI data centers. Rather than predict how useful or widely deployed AI models will be, she and a collaborator asked how fast hyperscaler earnings must grow to justify their spending through 2027, when they estimate expenditures will reach nearly $1.1 trillion.
The results of that no-nonsense accounting approach are eye-opening: the AI companies will need to increase their own productivity by a factor of 2.7 to break even by 2030, accounting for the cost of capital, a 15% return, and depreciation of the assets. Wachter says this is not impossible, and that the result would lead to the kind of economic growth seen during the US IT boom over about 10 years starting in the mid-1990s. But for it to happen by 2030, she says, “that’s a lot of growth compressed into a few years.” If the hyperscalers cannot meet such profit goals, “then they will fall behind on their interest payments, and that risks bankruptcy,” says Wachter, previously the SEC’s chief economist and director of its division of economic and risk analysis. If a productivity boom “fails to materialize,” she and her coauthor conclude in their research paper, “the current buildout will be the largest misallocation of capital in history.”
The article notes that today’s large AI infrastructure investments come with huge risks. Hyperscalers will spend about $750 billion this year building massive data centers scattered across the country, and the spending spree shows no signs of slowing. According to some projections, total AI capital investments from hyperscaler companies—Alphabet, Microsoft, Amazon, Meta, and Oracle, which partners with OpenAI—could be more than $5 trillion over the next four years. It is one of the largest capital investments by any industry in history.
But there is an obvious problem, according to Gary Gensler, who ran the SEC during the Biden administration and is now a professor at MIT’s Sloan School: while hyperscalers plan to spend trillions, total AI revenues will be around $150 billion to $200 billion this year. “The challenge is that the spending does not have commensurate revenues yet. That’s a fact,” he says. “And then the question is, is that an investment that will be paid off in the future?” At stake in that trillion-dollar question is the financial health of the giant AI companies and the overall US economy—the investments could soon balloon to around 3% of GDP.