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- [00:01]
A
Welcome to Thoughts on the Market. I'm Lindsey Tyler, TMT Credit Research Analyst at Morgan Stanley.
- [00:06]
B
And I'm Aneesh Shah, Global Head of Debt Capital Markets at Morgan Stanley.
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A
Today, how issuers and investors are approaching the rapidly evolving world of AI financing. It's Thursday, July 16th at 10am 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?
- [00:43]
B
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 leveraged 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 spending.
- [01:23]
A
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 that credit markets can continue funding this opportunity at scale?
- [01:58]
B
I mean Lindsey, 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. 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 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 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.
- [03:12]
A
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?
- [03:27]
B
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. Hyperscalers are among the very best credits in the world and they have the ability to come in and out of markets and 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. Lindsey, 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?
- [04:30]
A
Sure, it's prudent to really weigh a mix of technicals, fundamentals and relative value. As you discussed. On the technical side and related to my discussions with debt and equity investors, I've been focused on 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 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? Is that capacity supporting internal products, proprietary models, broader cloud Offerings or compute lease to third parties and then how fungible is the capacity across those use cases if demand or returns shift 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. 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. And this ties nicely into the next topic that I wanted to raise, which is project finance debt. I've noticed that 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. 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 Aneesh 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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B
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. 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. The developer would tap banks to underwrite a highly structured construction loan. Once the project is up and running, you could then refinance that loan and with the predictable cash flows into a more institutional financing like the investment grade bond market or the term loan, B or securitization markets. That product may still be very viable in many sectors, but we felt early on that bank provided construction loans would not meet the capacity needs of the AI investment cycle. The market really needed an institutional credit product that bypassed the need for construction loans. The key innovation came in the form of first of its kind high yield bonds that funded the development of a new data center complex. Given the relatively short construction period and the quote unquote offtake supported by some of the highest quality credits in the world, we felt like this financing structure would be incredibly well received in the high yield market. The win here is that the developer accesses fixed rate long term capital and maintains flexibility to call the bonds and refinance at a lower cost. Judging by how these financings have gone, there's a strong level of investor enthusiasm. I think that they've only scratched the surface and I would expect that we see much more of this and potentially even expanded to other products in the leveraged finance markets given the tremendous level of investor demand.
- [08:23]
A
Yeah, it's certainly been exciting to follow many of those deals. Beyond the public space, we're also seeing a wave of innovation in private credit and asset backed finance. Aneesh, how do companies decide whether capital is best raised in the public or the private markets?
- [08:38]
B
Well, I'm glad you raised the whole avenue of private markets because it may be the most significant change in the credit markets over the last few years, broadening the scope of private credit from directly lending into leveraged buyouts to now financing large investment grade projects. There are great examples in the world of GPU and TPU financing where we structure loans secured by the asset and the cash flows, or in data center development. Lindsey, from your perspective, what are investors focused on when these private structures intersect with public credits?
- [09:11]
A
Sure, many of these asset backed private financings have prompted investors to look more closely at any of the public companies involved, whether as issuers, tenants, customers or support providers. This ties back to the point I raised earlier. Where does the risk reside and who ultimately is on the hook? These financings have also sparked broader discussions around circularity, vendor financing and technology obsolescence risk even when amortizing structures are in place. I do think those are fair concerns to weigh and they really speak to how quickly the AI financing trend is evolving and how much credit work there is to do so. A niche with that balance in mind, relatively strong demand, rapid innovation, but also some real credit questions. Let's end with a quick lightning round.
- [09:56]
B
Lindsey. Let's do it.
- [09:57]
A
First, what is the biggest risk that could test investor appetite for AI related debt?
- [10:02]
B
I would say investors are acutely focused on construction delays. Don't underestimate the level of diligence being done by the breadth of capacity you're seeing in the markets. Investors are doing their homework and we're spending a lot of time trying to mitigate any of their concerns with structural protections.
- [10:18]
A
Got it. Second, beyond data center shells and chips, what is the next potential AI financing opportunity?
- [10:25]
B
It most certainly is energy and power. We're going to see a ton of capital being raised in utilities. It's going to be a little different than what the hyperscalers are doing just given the nature of their balance sheets. You're going to see more junior capital. We've seen a wave of junior subordinated debt issuance out of the utilities. We're also seeing a lot of activity from our Project Finance and Tax Equity team just given all things energy, infrastructure.
- [10:47]
A
Great. And third, if we're sitting here a year from now, what do you think could be the biggest AI financing story we're talking about?
- [10:53]
B
Well, we certainly underestimated the level of financing activity that we saw in the past year. I think when we look back a year from now, we will probably see that the AI labs were much more ready to finance on their own on a standalone basis. That's going to alleviate some of the pressures in the market, but I think it's going to create a whole new set of considerations in structural innovation.
- [11:14]
A
Well, it's certainly been remarkable to watch this financing theme take shape in real time, and the next chapter sounds like it could be even more interesting to follow. Aneesh, thanks for joining us and sharing your insights.
- [11:24]
B
Great to join.
- [11:25]
A
Lindsay, thanks and thank you for listening. If you enjoy thoughts on the market, please leave us a review wherever you listen and share the podcast with a friend or colleague today. The preceding content is informational only and based on information available when created. It is not an offer or solicitation, nor is it tax or legal advice. It does not consider your financial circumstances and objectives and may not be suitable for you.