A Test for Capital Markets: Funding AI

**Lindsay Tyler** (0:00)
Welcome to Thoughts on the Market. I'm Lindsay Tyler, TMT Credit Research Analyst at Morgan Stanley.

**Anish Shah** (0:05)
And I'm Anish Shah, Global Head of Debt Capital Markets at Morgan Stanley.

**Lindsay Tyler** (0:09)
Today, how issuers and investors are approaching the rapidly evolving world of AI financing. It's Thursday, July 16th at 10 a.m. in New York.
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? Anish, can you walk our listeners through the key products in your purview?

**Anish Shah** (0:42)
Certainly in my nearly 20 years at Morgan Stanley, this is probably the most incredible time period I've ever seen in the credit markets.
I've had the privilege of working across a number of different roles in capital markets and lending, 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. 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.

**Lindsay Tyler** (1:22)
AI financing has certainly been a theme we've also been focused on in research.
Our equity research colleagues project that a handful of key players could add more than 30 gigawatts of capacity over a two-year time frame driving around 2 trillion of aggregate cash capex in that period. And to put that into context, a single gigawatt of data center capacity can require roughly $12 billion for the shell and often more than double that for chips and racks. 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?

**Anish Shah** (1:58)
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. The largest tech companies have historically, as you know, raised very little debt. In fact, many of these companies have not even needed a credit facility.
As capex projections were materially increased in the second half of last year, we saw the beginning of scaled capital raises. Hyperscaler issuance has quickly gone from less than 1% of the investment-grade market to more than 10% of the market.
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% of the total issuance across all credit products. 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. This spans corporate investment grade, first-of-their-kind financings in high-yield and leverage loan markets, and new takes on asset-back financing. And each of these areas has seen material issuance both in public and private markets.

**Lindsay Tyler** (3:11)
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. Can you help frame the scale and the significance of this financing bucket, and how AI-related debt is scaling within it?

**Anish Shah** (3:27)
Well, 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. 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. 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.
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?

**Lindsay Tyler** (4:29)
Sure, it's prudent to really weigh a mix of technicals, fundamentals and relative value. You know, as you discussed on the technical side and related to my discussions with debt and equity investors, I've been focused on the scale of build-outs, 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. But moving more to the fundamental side of things, you mentioned ROI.