ROI-Private credit’s huge AI footprint obscures growing risks: Altaf Kassam

Much of ​today’s massive AI build-out is being financed with instruments that do not trade. So, the question for investors now is not only whether ​AI will pay off, but also how they will know if it is starting to come off ‌the ​rails. While hyperscalers like Meta Platforms, Alphabet and Amazon have tapped public debt markets over the past year to fund their record-breaking AI infrastructure plans, much of the sector's funding is taking place behind closed doors through private credit funds, infrastructure vehicles and joint ventures.

With no market price to go on, these holdings are valued by their managers, usually quarterly, using models of expected cash flows and comparisons with similar deals. That is normal practice and, if done well, ‌should provide a good first approximation. But it is only an approximation, and they tend to move more slowly than markets. This process can keep volatility artificially low, creating so-called "smoothed valuations," which can make private credit look like it’s generating higher returns with lower risk. But, in reality, the risk is usually still there; it is just not being accurately measured.

This issue is becoming increasingly relevant because the scale of AI private lending is no longer marginal. The Organisation for Economic Co-operation and Development reckons AI-related deals made up 34% of global private credit transactions in 2025, twice the five-year average. The Bank of England also estimates that roughly $700 billion of data-centre ‌spending over 2026-28 will be financed by private credit. That is a vast sum for a market that the Financial Stability Board (FSB), a global watchdog, estimated at $1.5 trillion to $2 trillion at the end of 2024.

In short, AI is fast becoming the asset class’s biggest single theme. MARK TO MODEL

Valuation models ‌used by private lenders in this space have enormous power, and thus need to be robust, even if, like all models, they will never be perfect. But for a valuation model to be useful, its assumptions need to be robust, and some of those in AI debt deals may be fragile.

For one, there’s the issue of collateral. An AI data centre’s physical structure does not represent the majority of the cost or expected value of the total asset. The building typically accounts for only around 30% of the cost, according to JPMorgan Asset Management. The chips and equipment represent the remaining 70%. Lenders increasingly accept those chips as security. Yet chip values are far from stable. The hourly rate big cloud providers charge to rent Nvidia’s H100, the workhorse AI chip ⁠of the most ​recent cycle, has fallen by 25% to 50% across regions since early 2025, according ⁠to data provider Silicon Data, and rates at smaller specialist providers are lower still. A model that assumes outdated values for the bulk of an asset’s value certainly won’t be useful.

A FUSS OVER NOTHING? You could make a case that this worry is overdone: The tenants behind the biggest AI projects are among the most creditworthy companies in the world. Moreover, private credit default ⁠rates remain low.

And for a patient investor who will hold a loan to maturity, private credit’s infrequent valuations are arguably a feature, not a bug. If the borrower keeps paying, why mark the loan down every time markets wobble? That logic works for closed-end, long-horizon capital. It works less well when investors can leave.

And we’re already starting to see ​that happen. Blue Owl Capital, one of the largest lenders to data centres, twice capped redemptions from its private credit funds at 5% this year after heavy withdrawal requests. Semi-liquid funds that offer regular redemptions at stated asset value create what the FSB calls a “first-mover incentive”. Suppose a fund ⁠values its loans at 100 cents on the dollar, but a buyer today would pay only 90 cents. Investors who redeem first are paid at par. The shortfall falls on those who stay once the marks catch up.

DON’T BE THE LAST TO KNOW This issue matters more now because two trends are converging. The AI build-out is leaning ever harder on private capital at exactly the ⁠moment ​private markets are being opened up to ordinary savers.

In Britain, the largest workplace pension providers have pledged under the Mansion House Accord to put 10% of their default funds into private markets by 2030. In the US, 401(k) retirement accounts are being opened up to alternatives. The FSB estimated retail investors already held around 13% of global private credit in 2024, up from almost a decade before. Most pension savers don’t actively choose their investments. In the US, 59% of participants in Vanguard-administered workplace plans held their entire account in a single target-date fund in 2024. In Britain, more than 90% of workplace pension savers are ⁠invested in their scheme’s default strategy, according to the Financial Conduct Authority. So the marginal buyer of AI’s least transparent debt could increasingly be savers who will never read a fund’s quarterly report.

In past tech booms, the late money came through public markets, from day traders buying dotcom stocks ⁠in 2000. If this one ends badly, the late money could arrive through pensions, ⁠at prices set by models. And unlike the dotcom investor, the saver will not see the loss as it happens. They will find out when the marks catch up, or when they try to leave. (The opinions expressed here are those of Altaf Kassam, CFA, the founder of Reference Portfolio Partners, an investment consultant, and former head of Investment Strategy at State Street Investment Management.)

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(Writing by Mike Peacock Editing by Marguerita Choy and Anna Szymanski)