What’s at stake in AI’s trillion-dollar gamble

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David Rotman writes, What's at stake in AI's trillion-dollar gamble?

When 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, she faced a long list of business and technical uncertainties. So, she started with what she calls a remarkable fact that is not in question. A handful of so-called hyperscalers are investing huge sums of money to build AI datacenters. Instead of trying to predict how useful and widely deployed AI models will be, she simply asked how fast the hyperscalers' earnings will need to grow to justify their spending through 2027 When she and her collaborator estimate, expenditures will reach nearly $1.1 trillion.

It's a no-nonsense accounting approach to making sense of today's historical AI buildout.

The results 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 and a 15% return and appreciation of the assets. Not impossible, says Wachter. The result would lead to the kind of economic growth that we saw during the US IT boom over a period of about 10 years starting in the mid 1990s. But she says for it to happen by 2030, that's a lot of growth compressed into a few years. And if the hyperscalers cannot meet such profit goals, then they will fall behind on their interest payments and that risks bankruptcy, says Wachter, who was 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 co-author conclude in their research paper, the current buildout will be the largest misallocation of capital in history.

It doesn't take superintelligence to realize that today's large investments in the infrastructure for artificial intelligence come with huge risks. The hyperscalers will spend about $750 billion in 2026, 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 the hyperscaler companies, Alphabet, Microsoft, Amazon, Beta, and Oracle, which partners with OpenAI, could be more than $5 trillion over the next four years. It's one of the largest capital investments by any industry in history. But there's a problem that's obvious to anyone paying attention.

While the hyperscalers plan to spend trillions, total AI revenues will be around $150 billion to $200 billion in 2026, says Gary Gensler, who ran the SEC during the Biden administration and is now a professor at MIT's Sloan School. 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. The answer could also determine the fate of the hugely expensive data centers themselves.

No one really knows how profitable and useful these multi-billion-dollar behemoths will be down the road. Though AI models have made dazzling progress over the last few years, it's anyone's guess how much compute capacity we will need. The technology could become more efficient and therefore less dependent on raw computational power. Or demand for AI products could slow where customers could turn to cheaper models. The risks, both to investors and to the economy, have become even greater in 2026 as these AI companies have begun borrowing large amounts of money to build more and more data centers. Free cash flow, operating cash flow minus capital expenditures, is expected to soon dip into negative territory for the group. Even Alphabet, known for generating and hoarding huge amounts of cash, reports in the latest quarter that its impressive revenues of nearly $120 billion were devoured by AI infrastructure spending, leaving it with a free cash deficit of some $5.9 billion, its first shortfall since Google went public in 2004

In the near term, it's not a big financial worry for most of the companies. They make a lot of money and have very deep pockets, but debt is expensive, and some investors are losing patience. If future demand for the data center's computation power drops, the companies will still be on the hook to pay back the borrowed money. What's more, the risks are spreading to the rest of the economy as the loans get passed along via various financial mechanisms. It won't be enough to simply cover the enormous price tags of the new data centers. Hyperscalers will also have to pay for the rising costs of capital as they borrow more money.