AI Debt Issuance and Private Credit: The Financing Story Behind the Build

AI-related debt issuance is on track to reach roughly $570 billion in 2026, more than double the 2025 figure. The five largest hyperscalers issued $121 billion in US corporate bonds in 2025 alone, against an annual average near $28 billion between 2020 and 2024. The build is no longer being funded from operating cash flow, and that change is the most informative development in the entire cycle.

The funding shift is the story

For most of the past decade, the largest technology companies funded capital investment from operating cash flow. They generated more cash than they could deploy, which is why they accumulated enormous balances and returned capital to shareholders.

That has changed. The scale of the current build has outgrown what operations produce, and the gap is being filled with debt and equity issuance.

This matters because funding source is a revealed preference. A company borrowing to build is telling you that the opportunity is large enough to justify leverage, and simultaneously that the cash flow no longer covers it. Both statements are informative and only one is optimistic.

What the issuance looks like

Morgan Stanley projected in June 2026 that global AI-related debt issuance would reach nearly $570 billion in 2026, more than doubling the previous year.

That figure spans several instrument types, and the distinction matters because they carry very different risk. Investment-grade corporate bonds from highly profitable issuers sit at one end. Structured facilities secured against hardware sit at the other.

Where the leverage concentrates

Not all AI borrowers are the same, and averaging them together hides the interesting part.

The hyperscalers borrowing at investment grade have enormous operating cash flow behind the obligation. Their debt is large in absolute terms and modest relative to their earnings.

The specialist GPU cloud providers are a different proposition. These companies fund capacity through term loans and bond packages, secured against hardware, and service that debt from contracted revenue. One major provider secured an $8.5 billion term loan in March 2026 to fund GPU infrastructure scaling, alongside earlier loan and bond packages.

That model works when contracts hold for the life of the debt. It is exposed when they do not, because the collateral is depreciating hardware in a market where a newer generation arrives annually. The depreciation question underneath that is examined in the piece on GPU useful life assumptions.

Why the specialists borrow rather than raise equity

Equity is expensive when a business is capital intensive and growing fast, because each raise dilutes existing holders at a valuation the company believes is too low. Debt avoids that, provided the cash flow services it.

For a provider with contracted revenue from creditworthy customers, borrowing against those contracts is rational and cheap. The structure only strains when the contracts are shorter than the debt, or when the collateral loses value faster than the principal amortises.

Why the term structure matters more than the amount

Two companies can carry identical debt and face completely different risk depending on when it comes due and what it is secured against.

A ten-year bond issued by a company with predictable cash flow is barely a constraint. A three-year facility secured on hardware, refinanced into whatever conditions exist in 2029, is a very different obligation carrying the same headline number.

Most of the AI debt raised so far has been at the safer end. The concentration of maturities is the thing to watch, because refinancing risk is not about whether a company can pay. It is about whether it can borrow again at a price it can absorb, in whatever market exists on the day.

That is the mechanism by which credit conditions rather than business performance can create stress, and it operates independently of whether the underlying AI investment was a good idea.

The private credit channel

A meaningful share of data centre financing now runs through private credit rather than public markets. That channel has grown enormously and it is considerably less transparent.

The consequence is not that private credit is inherently riskier. It is that exposure is harder to observe. Public bond markets price risk continuously and visibly. Private credit marks are set periodically and disclosed selectively.

That opacity means an investor holding a diversified portfolio may have AI infrastructure exposure through funds that do not describe themselves as technology investments at all. The interdependence between hyperscalers, private credit funds and infrastructure investors is the specific channel through which stress at a few large players could propagate.

What actually creates risk here

Debt is not risk. Debt plus a mismatch is risk, and there are three potential mismatches worth naming.

The top row deserves emphasis because it is unusual. Most infrastructure debt finances assets with lives measured in decades. Financing hardware with a contested useful life over a multi-year term is a different proposition, and it is the feature of this cycle with the fewest historical precedents.

What the collateral question comes down to

Lending secured against hardware assumes the hardware retains value across the term. In most infrastructure lending that assumption is uncontroversial, because the asset is a building or a network with a life measured in decades.

Accelerators are different. Their resale value depends on whether a secondary market exists for the previous generation, and that market is thin and largely untested at scale. A lender foreclosing on a fleet of two-generation-old accelerators is holding an asset with no established price.

That does not make the lending unwise. It makes the recovery assumption underneath it an estimate rather than a known quantity, which is a meaningful distinction when the facilities are measured in billions.

The case that this is fine

Three serious counter-arguments.

Borrowing at investment grade against contracted revenue is ordinary corporate finance. Companies with strong credit profiles issuing bonds to fund capacity is what capital markets are for, and treating it as a warning sign confuses scale with imprudence.

Rates and terms have been available on reasonable terms, which suggests credit markets have assessed these borrowers and are comfortable. Bond investors are not naive about depreciating collateral.

And the demand underneath is real. Capacity is constrained, contracts are long, and utilisation is high. Financing an asset that is fully utilised on contracted terms is materially different from financing speculative capacity, which is the distinction that separates this from most historical build-out failures.

What to watch

The first row is the practical one for most readers. Credit markets reprice before equity markets do, and spreads on AI-linked issuance are public information that requires no special access to follow.

What a deterioration would actually look like

It would not begin with a default. It would begin with a refinancing that priced worse than the last one, followed by a provider extending contract terms to reassure lenders, followed by a slowdown in new capacity commitments.

By the time any of that reached headlines, the sequence would already be several quarters old. That is the argument for watching spreads rather than news, because spreads move first and are published continuously.

The counterpoint is that credit markets have been wrong before, and comfortably. Spreads on structured products were tight until they were not in 2007. Watching them is better than watching nothing, and it is not a substitute for understanding the underlying assets.

Frequently asked questions

Where to start this week

One thing to look up and one thing to check.

Look up the credit rating and recent issuance of whichever infrastructure provider your business depends on most. It takes five minutes and it tells you whether service continuity rests on a strong balance sheet or a financing structure.

Then check whether any of your pension or investment exposure runs through private credit funds. Most people discover they have infrastructure exposure they were not aware of, arriving through vehicles that describe themselves in entirely different terms.

Neither exercise requires a view on whether the build succeeds. Both simply establish what you are exposed to, which is the prerequisite for any view worth holding.

The one number worth tracking quarterly

If you only follow one thing from this post, follow issuance spreads on AI-linked debt. They are public, they update continuously, and they represent the aggregated judgement of the investors with the most to lose if the collateral assumptions are wrong.

Equity markets price growth expectations. Credit markets price the probability of getting paid back. On a question about whether a build is affordable, the second is the more relevant opinion, and it is the one almost nobody in technology reads.

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