Shifts in Credit Markets for the AI Buildout

AI’s enormous capital requirements are reshaping the way companies tap credit markets. Our Chief Fixed Income Strategist Vishy Tirupattur takes stock of this summer’s key financing developments.

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Vishy Tirupattur: Welcome to Thoughts on the Market. I am Vishy Tirupattur, Morgan Stanley’s Chief Fixed Income Strategist.

Today: Why the summer of 2026 is all about AI Financing and the evolution of credit markets.

It is Friday August 21st at 2pm in New York.

The summer of 2026 may ultimately be remembered not for a new model release or a breakthrough chip, but for developments in AI financing that highlighted how quickly capital markets are adapting to the demands of the AI buildout.

The starting point of our analysis remains unchanged: the demand for compute continues to outstrip supply of compute, resulting in upward revisions in AI infrastructure capex expectations as hyperscalers commit additional capital to secure future capacity.

Our equity research colleagues now estimate that the total capex for the four largest hyperscalers will rise 57 percent in 2027 versus 2026. These spending plans reflect growing conviction that such investments can generate 25 percent plus returns on invested capital.

At the same time, the lag between capex deployment and monetization continues to pressure near-term cash generation, with our analysts' 2027 free cash flow estimates for the four hyperscalers continuing to move lower.

To a credit analyst, what this means is that the result is a widening financing gap in 2027. That means AI-related credit issuance will remain substantial and may even need to increase further before cash flows from these investments begin to catch up.

Developments in credit spreads this summer have been equally telling. Credit spreads for hyperscalers have widened meaningfully. More notable even than the absolute level of widening is the divergence across financing channels.

For example, spread widening was most pronounced in unsecured bonds, where issuance volumes accelerated sharply and investors remained exposed to a broader range of risks tied to the AI investment cycle. By contrast, spread widening in data center ABS and CMBS was much more modest.

These structures are backed by operating assets that have already been constructed, powered, and leased, with contractual cash flows largely established. Combined with a more measured pace of issuance, these characteristics helped insulate securitized credit products from the volatility seen in unsecured credit markets.

The divergence across credit markets also reflects the differences in issuer incentives and sensitivity to funding costs, which will shape issuance volumes going forward. At the higher end of the quality spectrum, the major hyperscalers, with average ratings of roughly AA, combine substantial financing needs with significant ratings flexibility.

Given their ROIC expectations, these issuers are relatively insensitive to modest changes in borrowing costs. Higher funding costs alone are unlikely to materially slow capital raising by the highest-quality participants in the AI ecosystem.

The opposite is true further down the quality spectrum. Lower quality hyperscalers and data center developers, including former bitcoin miners and REITs, have less balance-sheet flexibility and lower tolerance for higher funding costs. For these borrowers, wider spreads represent a more meaningful constraint, making funding costs a natural stabilizer of future supply.

The next phase of AI financing is also likely to look quite a bit different as incremental capex shifts from data center shells toward compute equipment, particularly servers and chips, as well as energy assets. While some of these assets have already been financed through high-yield bonds and leveraged loans, compute infrastructure is particularly well-suited to asset-level financing, creating a larger role for private capital.

The emergence of large-scale component financing is likely to be enabled by the highest-quality issuers flexing their ratings as well as balance-sheet strength. We expect these issuers to increasingly provide backstops, credit support arrangements, and residual value guarantees, helping private capital underwrite ever-larger pools of AI infrastructure assets.

As AI scales from a technology cycle into a capital cycle, understanding the nuances of financing is becoming increasingly important. In the next phase of the AI buildout, understanding the flow of capital may prove nearly as important as understanding the flow of innovation itself. AI is no longer just a technology story. It is increasingly a capital markets story as well.

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