A Test for Capital Markets: Funding AI

This guide is based on the podcast episode 'A Test for Capital Markets: Funding AI' from the show 'Thoughts on the Market', published on July 16, 2026.

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   Credit markets are stepping in to fund the surging demand for AI. Our experts Lindsay Tyler and Anish Shah explore the opportunities and risks behind this record financing wave.

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   Read more insights from Morgan Stanley.

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   ----- Transcript -----

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   Lindsay Tyler: Welcome to Thoughts on the Market. I'm Lindsay Tyler, TMT Credit Research Analyst at Morgan Stanley.

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   Anish Shah: And I'm Anish Shah, Global Head of Debt Capital Markets at Morgan Stanley.

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   Lindsay Tyler: Today, how issuers and investors are approaching the rapidly evolving world of AI financing.

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   It's Thursday, July 16th at 10am in New York.

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   As AI demand accelerates, credit markets are being asked to finance infrastructure on a scale that used to be associated with utilities, telecom, or energy. That raises a central question for issuers and investors: How much debt can the AI ecosystem absorb? And at what price?

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   Anish, can you walk our listeners through the key products in your purview?

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    Anish Shah: Certainly, in my nearly twenty years at Morgan Stanley, this is probably the most incredible time period I've ever seen in the credit markets.

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    I've had the privilege of working across a number of different roles in capital markets and lending.

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    And a couple of years ago, we integrated the debt underwriting business across both investment-grade and leverage finance franchises in recognition of how interconnected the whole credit ecosystem has become.

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    In addition to our core activities helping clients raise capital for their strategic priorities, two of the big focus areas that we've had have been finding ways to harness the power of the private credit universe and also delivering best-in-class capabilities in funding this incredible growth in AI spend.

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    Lindsay Tyler: AI financing has certainly been a theme we've also been focused on in research.

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    Our equity research colleagues project that a handful of key players could add more than 30 gigawatts of capacity over a two-year timeframe, driving around [$]2 trillion of aggregate cash CapEx in that period.

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    And to put that into context, a single gigawatt of data center capacity can require roughly $12 billion for the shell, and then often more than double that for chips and racks.

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    So, from your vantage point, what inning are we in? And what gives you confidence that credit markets can continue funding this opportunity at scale?

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    Anish Shah: I mean, Lindsay, the numbers certainly are staggering, as you note. And if you just observe the CapEx estimates for the hyperscalers and broadly for AI infrastructure, we're certainly in the early innings.

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    Lindsay Tyler: Mm-hmm.

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    Anish Shah: The largest tech companies have historically, as you know, raised very little debt.

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    In fact, many of these companies have not even needed a credit facility.

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    As CapEx projections were materially increased in the second half of last year, we saw the beginning of scaled capital raises.

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    Hyperscaler issuance has quickly gone from less than one percent of the investment-grade market to more than 10 percent of the market.

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    You know, as I look ahead, based on what we're seeing on the ground, we think that AI-related funding, whether it's for data center development or financing compute capacity, could top 15 percent of the total issuance across all credit products.

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    This has been an unprecedented test for the capital markets, both in terms of the depth of capacity and the breadth of product. The teams have been on the forefront of deep investor dialogue and product innovation.

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    This spans corporate investment grade, first of their kind financings in high-yield and leveraged loan markets, and new takes on asset-backed financing. And each of these areas has seen material issuance both in public and private markets.

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    Lindsay Tyler: Great backdrop. Let's dig first into investment-grade corporate debt, an area you know well from your time previously leading the investment-grade team.

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    Can you help frame the scale and the significance of this financing bucket and how AI-related debt is scaling within it?

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    Anish Shah: Well, you know, as you know, the investment-grade bond market, specifically in dollars, is the deepest, most liquid pool of capital in the world. Volumes have grown materially over the last few years and are likely to eclipse $2 trillion in issuance this year.

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    Hyperscalers are among the very best credits in the world, and they have the ability to come in and out of markets with relatively quick twitch, little to no pre-marketing, and in fairly large size. You know, $20 billion-plus deals used to be rare in the investment-grade market, now happen multiple times a quarter.

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    This is why we've seen the predominance of AI-driven capital raising take place in the investment-grade market. For the most part, investors have digested that supply very well. While we've seen some modest widening credit spreads for hyperscalers and some of the other tech issuers, I'd say it's de minimis relative to their expected ROI.

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    Lindsay, I've talked a lot about supply dynamics and issuance. What other factors are you and investors considering when assessing fair value for investment-grade rated technology bonds?

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    It's prudent to really weigh a mix of technicals, fundamentals, and relative value.

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    You know, as you discussed on the technical side, and related to my discussions with debt and equity investors, I've been focused on scale of buildouts, market capacity, digestibility across currencies, positioning along the curve, implications of equity issuance, and whether AI financing could crowd out other areas of TMT credit.

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    But moving more to the fundamental side of things, you mentioned ROI, and for the players that are scaling compute capacity, there are a handful of key monetization and return questions that keep coming up. How quickly can these companies bring new capacity online? Once it's live, how does it translate into durable revenue and cash flow?

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    Is that capacity supporting internal products, proprietary models, broader cloud offerings, or compute leased to third parties? And then how fungible is the capacity across those use cases if demand or returns shift?

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    Further on the fundamental side, we've done some differentiated work around growing long-term commitments. We've seen that high-quality hyperscalers and a few of the semis companies are anchoring the AI ecosystem through leases, guarantees, other obligations. These commitments really extend beyond vanilla bond issuance.

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    So, I encourage investors to look beyond the funded debt and really understand the accounting and the ratings implications here of some of those commitments.

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    And this ties nicely into the next topic that I wanted to raise, which is project finance debt. I've noticed that, you know, a lot of the commitments that we're seeing from IG players support another layer of financing. Lease commitments can underpin project finance debt, an area of sizable issuance and innovation.

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    The public high-yield market has emerged as a new funding source in this way for data center construction, with more than 30 billion priced across 15 deals, since fall 2025. Can you walk us through, Anish, the innovation behind these structures, and how are these high yield deals different than other ways to, kind of, raise project finance debt?

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    Anish Shah: Yeah, it's incredibly interesting. I mean, the bulk of the issuance, as I noted has come in the investment grade market, but I would say the bulk of the innovation has come in the sub-investment grade market.

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    You know, historically, for very capital-intensive sectors like energy and power or real estate, the project loan market was the most efficient source of initial funding.

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    The developer would tap banks to underwrite a highly structured construction loan.

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    Once the project is up and running, you could then refinance that loan with the predictable cash flows into a more institutional financing, like the investment grade bond market or the term loan B or securitization ma