Private Credit's Chip-Backed AI Data Center Loans Face a Depreciation Test
Private credit lenders finance AI data centers with chip-backed loans, but chips depreciate faster than the debt. See the collateral risk for neoclouds.
Private credit is financing the AI data center build-out with loans secured by processing chips, and that collateral loses value on a schedule shorter than the debt it backs. Apollo and Blackstone have anchored a $35 billion loan secured against AI chips and computing power. This analysis argues that the double-digit yields on offer have not been tested against measured chip resale prices or utilization rates, which leaves the risk with neoclouds and the lenders who hold their debt.
How the private credit collateral gap opens
Processing chips sit on a three- to six-year depreciation schedule. The debt is fixed and the developer owes repayments regardless of what the equipment is worth. A single gigawatt of computing power may require as much as $30 billion in spending, so the borrowing per site is large relative to the hardware's useful life.
Some private credit lenders have moved past double-digit-yield debt and into equity positions in these operators. If chip values fall faster than assumed, neoclouds may have to book impairments, which reduces reported earnings before any missed payment occurs.
The depreciation assumptions themselves are contested. Credit investors interviewed by Reuters use three- to four-year lives for advanced accelerators, while Nvidia argues that current systems stay economically productive for considerably longer. A loan that runs past the lender's own depreciation assumption outlives the collateral value that lender underwrote.
Who bears the risk when chips lose value
The exposure is not spread evenly. The developer carries the repayment obligation, the lender carries the collateral risk, and the hyperscale customer signing the lease often carries the credit rating that makes the structure cheap to borrow against. One analysis of the sector notes that a small, money-losing company can borrow at low rates because the customer on its contract is creditworthy, not because the borrower is.
Banks are largely absent from the riskiest slices. Regulatory risk-weights penalize banks for holding fast-depreciating hardware as collateral, and that reluctance opened space for private credit funds to supply the capital. The result is that the lenders most willing to take chip risk are the ones with the least bank-style capital requirements, and the ones least able to exit quickly if values drop.
Concentration compounds the problem. Man Group points out that many deals reference a small pool of large technology tenants, so a change in one tenant's leasing plans or capital spending could affect several transactions at once, even without a default.
Three ways capital is reaching AI infrastructure
The sources show three distinct structures, each with a different exposure to chip value.
| Structure | Recent example | Exposure to chip value |
|---|---|---|
| Loans secured by chips and compute | Apollo and Blackstone anchor a $35 billion loan | Direct: the hardware is the collateral |
| Off-balance-sheet hardware vehicles | Hardware held in an SPV keeps it off Anthropic's balance sheet | Direct for the vehicle, indirect for the parent |
| Sale of leased data center buildings | Blackstone sold stakes in three fully leased Northern Virginia sites to Digital Realty for $3.5 billion | Low: the buildings carry long leases, not chip risk |
The first two structures place the depreciation gap directly on the balance sheet. The third shows that leased real estate can be sold for cash, with Blackstone taking $1.2 billion off the table in that transaction. Real estate is underwritten on 15- to 20-year cash flows, while computer hardware depreciates faster, a mismatch that Man Group says has to be priced into deals.
Scale adds weight to the off-balance-sheet route. Moody's Ratings estimates that the five largest US cloud companies hold about $662 billion of data center lease commitments off their balance sheets, equal to 113% of their most recent adjusted debt. Financing chips in separate vehicles extends that pattern from buildings to the hardware inside them.
Trade-offs and what remains untested
The lender case rests on contracts and tenants. Chip-backed loans rest on resale value and utilization, and the sources contain no measured resale or utilization results for these facilities. The yields lenders collect therefore reflect underwriting assumptions, not realized recoveries.
Liquidity is a second pressure point. Private credit managers faced redemption pressure in 2026 as investors sought exits, and many of those managers also run data center investment vehicles. A funding squeeze at the fund level would reach the same assets that secure the loans.
Carlyle has warned that private credit should not repeat in AI the mistakes it made in software lending. The arrangement depends on chips holding value for several years, and chips have historically depreciated quickly. Disruption Banking also notes that Oracle's reported syndication problem tests what happens when a bank cannot find buyers at the expected price, while the QTS bond sale shows that some structures still attract capital.
Public markets add a separate test. Morningstar reports that private capital firms behind the AI data center boom recently combined asset sales, a shelved data center mega-campus and a debt-repaying IPO. Those moves show sponsors reducing exposure and locking in gains, which matters for lenders who expect the same sponsors to keep refinancing their positions.
The Bank of England's Financial Policy Committee wrote in July 2026 that the main danger sits in three assumptions: future compute demand, power arriving on schedule, and the speed at which chips and buildings depreciate. Chip depreciation is the only one of the three that lenders can check against resale data during a loan's life, which makes it the variable most worth requiring from borrowers.
Reported syndication plans point to a larger test. Broadcom is reportedly arranging more than $50 billion for OpenAI's custom chip program, with Apollo and Blackstone counted among the firms currently in talks and the transaction expected to be finalized before the end of the year. Those talks are not confirmed, and a deal of that size would multiply the collateral exposure if it closes.
Verdict: what to watch
Lenders should require borrowers to show chip resale prices and utilization data against the depreciation schedule written into each loan, rather than accepting the three- to six-year range as given. Neoclouds should expect impairment questions from auditors and investors if chip values track the shorter lives that credit investors use. The next checkable milestones are the Broadcom-OpenAI financing close, targeted before year-end, and any impairment disclosures from neocloud operators.
Why this matters
Private credit now sits between AI hardware and the debt that funds it, so a faster fall in chip values moves losses onto the lenders and operators who signed for fixed repayments. The sources show that financing volume is growing while the resale evidence needed to price it is still missing.
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Researched and cross-referenced against primary sources by the Bytevyte editorial team. This article was generated with the assistance of artificial intelligence and reviewed by the Bytevyte editorial team.