KKR, a leading U.S. investment firm, warns about the growing pile of AI debt

Credit market experts at KKR & Co. Inc. KKR-N, the American investment firm that specializes in private assets, are warning investors about the growing pile of artificial intelligence-related debt, adding their voices to the debate about the current health of financial markets.

Issuing hundreds of billions or even trillions of dollars worth of debt to build out AI infrastructure isn’t worrisome on its own, but investors must be aware of the interconnectedness between all of the companies raising money, Christopher Sheldon, KKR’s co-head of credit and markets, and Tal Reback, a managing director in the same group, said in a report issued this week.

“Diversification is harder than it looks when the same short list of counterparties sit behind the equity book, the debt book, and increasingly the infrastructure supporting both,” they wrote. “Power, chips, cooling, land, leases and financing often route back to the same handful of economic actors.”

As of August, 31 companies accounted for more than US$500-billion worth of AI-related bonds, with five issuers representing more than half of that total, according to research from JP Morgan Chase & Co. and KKR.

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KKR’s credit market experts also cautioned that forecasts for AI infrastructure may be justified based on projections of AI use, but building the infrastructure to support it is a complicated task. Examples of risks that may not be accounted for include: AI chips losing value quickly as newer models are released; data centres taking years to build, delaying revenues; and power grids being unable to keep up with the demand, limiting AI usage for years.

If one or more of these variables become a reality, borrowers could struggle to pay their back debt. “When a mistake occurs in something this capital-intensive, you cannot always cut back to health if the cash burn is too far ahead of the operating reality,” KKR’s credit experts wrote.

And with so much money on the line, the troubles could spread far and wide.

Already, the sum of AI-related credit issued is staggering, with debt outstanding now sitting around US$600-billion. That amounts to roughly 6 per cent of the U.S. investment grade debt market, KKR said. (Investment grade bonds are a form of debt issued by companies deemed to be ‘investment grade,’ or of good financial quality, by credit rating agencies.)

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In total, capital expenditures for data centres and related AI infrastructure could swell to US$10-trillion between 2025 and 2032, Stijn van Nieuwerburgh, a Columbia Business School professor, estimated in a recent report for the think tank Brookings.

“The projected buildout would be larger relative to the economy than the major U.S. canal, railroad, electrification, highway, and telecommunications investment booms,” he wrote.

A good chunk of that is likely to be funded by credit, and KKR estimates the debt issued could ultimately total 20 per cent of the investment grade market.

If so, the concentration of risk in credit markets could rival what is playing out in equity markets, where the likes of Meta, Alphabet and Amazon have an outsized impact on the S&P 500.

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Throughout their report, KKR’s credit market experts repeatedly stressed there is enormous opportunity in the build out of AI infrastructure. But they also cautioned that past cycles of rapid investment across sectors show there is likely to be pain.

“Capital is flowing toward the AI theme with a force that is rarely seen in a single sector, and we believe the buildout represents a once in a generation industrial transformation. But the next two to three years will also begin to separate winners from losers in ways that may be difficult to unwind later,” they wrote.

Even if AI proves to be a powerful force in the future, over the next five to 10 years some loans and bonds could struggle, they warned. It pays, then, to remain disciplined and may even make sense for lenders to wait out some of the hype.

“The secular thesis can remain intact even as individual credits, structures, and entry points diverge,” they wrote.