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Science and technology
Chaos ahead? Grand investments in AI going bad might set off a painful chain of events
Massive loans taken to finance enormous investments in data centres and other AI-related fields could go bad if demand for AI fails expectations. From high net-worth individuals to banks, pension funds and even university endowments, many stand to suffer harsh blows if a bubble bursts.
For months, if not years, artificial intelligence (AI) has dominated speculation about the future, which has fuelled speculation in the present. AI-related ventures are largely responsible for the rapid increase of the S&P 500 stock index. Investment in data centres is a major contributor to the rise in US interest rates, as borrowing for construction competes for credit with a deficit-prone federal government.
With so much investment riding on a fast-developing new technology, what happens if the cash flow and profits of the hyperscalers building data centres, and of AI firms renting that infrastructure (two categories that overlap), turn out to disappoint? What would this disappointment mean for financial markets?
Would a crisis be in the offing?
This scenario could materialize for any number of reasons. The productivity benefits of AI could be overhyped, in which case firms contemplating the adoption of frontier models will curtail their demand for tokens, the basic units of data processed by such models.
Or the availability of open-weight models, which enable users to tailor a model’s parameters to their needs, may allow those users to build small language models that run on their own infrastructure or even on laptops like the one I am using to write this commentary. Either way, the income accruing to investors in data centres will fall short of expectations.
The most immediate and visible impact would be a sharp drop in the lofty stock prices of the ‘Magnificent 7,’ the seven ultra-large-cap tech and tech-adjacent firms that have driven the S&P 500. But stock-market corrections are no guarantee of crises or even of recessions.
If the price of Meta shares falls, its chief Mark Zuckerberg will buy fewer castles in Ireland. There would be a slowdown in consumption and investment spending. There would be pain for investment funds, such as Situational Awareness, that specialize in AI-related ventures.
But a financial crisis would follow only if banks, insurance companies and other mainstream institutional investors were implicated. And there is little evidence that they are heavily exposed to AI equity risk.
Developments in debt markets are more worrisome. With the cost of data-centre construction exceeding their sponsors’ free cash flow, and with income from potential tenants still a distant prospect, much of this investment is being financed by borrowing from private credit funds (non-bank firms making privately negotiated loans).
If demand for data-centre services is lower than expected, these loans will fail to perform, which is a polite way of saying that borrowers will default. The question then becomes who is invested in private credit funds. The popular image is of the same family offices and high-net-worth individuals who invest in venture capital and can afford occasional losses.
But the reality is that the vast majority of private credit is provided by institutional investors: pension funds, insurance companies and, dare I say, university endowments.
Moreover, private credit funds have been taking steps to tap this institutional money by acquiring insurance companies, whose asset portfolios they can load up with loans.
This invites the spectre of an insurance industry crisis, which could play out in several ways. Insurance companies’ private credit parents could recapitalize them, assuming they have the funds.
Alternatively, state guarantee funds, to which healthy insurance companies contribute, could fund the recapitalization, although there will be no healthy companies if the entire industry comes tumbling down. Lastly, the federal government could step in with a bailout, as it did for American International Group in 2008.
In addition to borrowing from pension funds and insurance companies, private credit firms borrow from commercial banks, using their portfolio of loans as collateral, thereby leveraging their commitments and juicing their returns. Even if banks don’t lend to or invest in AI businesses and data centres directly, they may still be on the hook. We can only hope that their regulators are watching.
Finally, private credit firms originally built on a buy-and-hold model now increasingly securitize their loans. They package those loans through special purpose vehicles that issue bonds backed by the associated cash flows, where the bonds are divided into risk tiers or tranches claiming first, second and third dibs on debt-service payments.
The so-called ‘mezzanine’ tranche of medium-risk bonds is then sold on to other asset managers. To assess the immediacy of crisis risk, we would need to know more about the riskiness of this mezzanine tranche and who holds it.
We don’t. If you are reminded of the roles of securitization, special purpose vehicles, tranching and opacity in the subprime-mortgage crisis of 2007-08, you are not alone.
Just as debt markets fuelled the global financial near-meltdown that followed from that crisis, their exposure to AI will drive developments now. The problem is that we possess only very limited public information about those markets and regulators are not making that information any easier to obtain. ©2026/Project Syndicate
The author is professor of economics and political science at the University of California, Berkeley, and the author, most recently, of ‘Money Beyond Borders: Global Currencies From Croesus to Crypto.’