In the second of our two-part panel discussion from Morgan Stanley’s TMT conference, our analysts break down the complexity of financing AI’s infrastructure and the technological disruption happening across industries. Read more insights from Morgan Stanley. ----- Transcript ----- Michelle Weaver: Welcome back to Thoughts on the Market, and welcome to part two of our conversation live from the Technology, Media and Telecom conference. I'm Michelle Weaver, U.S. Thematic and Equity Strategist at Morgan Stanley. Today we're continuing our conversation with Stephen Byrd, Josh Baer and Lindsay Tyler. This time looking at financing AI and some of the risks to the story. It's Friday, March 6th at 11am in San Francisco. So yesterday we spoke about AI adoption. And while there's a lot of excitement on this theme, there've also been some concerns bubbling up. Lindsay, I want to start with you around financing. That's another critical component of the AI build out. What's your latest on the magnitude of the data center financing gap, and what role [are] credit markets playing here? Lindsay Tyler: Yeah, in partnership with Thematic Research, Stephen and team, and colleagues across fixed income research last summer, we did put out a note, thinking about the data center financing gap, right? So, Stephen and team modeled a $3 trillion global data center CapEx need over a four-year timeframe. So, in partnership with fixed income across asset classes, we thought: okay, how will that really be funded? And we came to the conclusion that the hyperscalers, the high quality hyperscalers, generate a good amount of cash flow, right? So, there's cash from ops that can fund approximately half of that. But then we think that fixed income markets are critical to fund the rest of the funding gap. And really private credit is the leader in that and then aided by corporate credit and also securitized credit. What we've seen since is that yes, private credit has served a role. There is this difference between private credit 1.0, which is more of that middle market direct lending. And then private credit 2.0, which is more ABF – Asset Based Finance or Asset Backed Finance. And what we see there is an interest in leases of hyperscaler tenants, right? We've also seen in the market over the past nine months or so, investment grade bond issuance by hyperscalers. Obviously, a use of cash flow by hyperscalers. We've seen the construction loans with banks and also private credit per reports. We've also seen high yield bond issuance, which is kind of a new trend for construction financing. We've seen ABS and CMBS as well. And then something new that's emerging in focus for investors is more of a chip-backed or compute contract backed financings, like more creative solutions. We're really in early innings of the spend right now. And so, there is this shift. As we start to work through the construction early phases, the next focus is: okay, but what about the chips? And so, I think a big focus is that, you know, chips are more than 50 percent of the spend for if you're looking at a gigawatt site. And it depends what type of chips and kind of what generation. But that's the next leg of this too. So, it's kind of a focus, you know, for 2026. Michelle Weaver: And how do you view balance sheet leverage and financing when you think about hyperscaler debt raising magnitude and timelines? Lindsay Tyler: So just to bring it down to more of a basic level, if you need compute, you really might need two things, right? A powered shell and then the chips. And so, if you're looking for that compute, you could kind of go in three basic ways. You could look to build the shell and kind of build and buy the whole thing. You could lease the shell, from, you know, a developer, maybe a Bitcoin miner too – that is converted to HBC. And then you kind of buy the chips and you put them in yourselves. Or you could lease all the compute; quote unquote lease, it's more of a contract. In terms of the funding, if you're thinking about the cash flows of some of the big companies – think of that as primarily being put towards chip spend. If you're thinking about the construction that's kind of split between cash CapEx but also leases. And so, what we've seen is that there is more than [$]600 billion of un-commenced lease obligations that will commence over the next two to five years, across the big four or five players. And then my equity counterparts estimate around [$]700 billion of cash CapEx that needs this year for some of those players as well. So, these are big numbers. But that's kind of how, at a basic level, they're approaching some of the financing. It's a split approach. Michelle Weaver: And what have you learned around financing the past few days at the conference? Anything incremental to share there? Lindsay Tyler: Sure. Yeah. I think I found confirmation of some key themes here at the conference. The first being that numerous funding buckets are available. That was a big focus of our note last year is that you can kind of look at asset level financing. You can look at public bonds, you can look at some equity. There are these different funding buckets available. The second is that tenant quality matters for construction financing. I think I've seen this more in the markets than maybe at this conference over the past two to three weeks. But that has been a focus of pricing for the deals, but also market depth for the deals. A third confirmation of a key theme was around the neo clouds and also the GPU as a service business models. Thinking about those creative financings, right. Are they thinking about from their compute counterparties? Would they like upfront payments? Might they look to move financing off [the] balance sheet, if they have a very high-quality investment grade rated counterparty? So, there is some of this evolution around those solutions. And then a fourth key theme is just around the credit support. And Stephen has and I have talked about this around some of the Bitcoin miners – is that, you know, there can be these higher quality investment grade players that might look to lend their credit support. Maybe a lease backstop to other players in the ecosystem in order to get a better pricing on construction financing. And we are seeing some press pickup around how that might play out in chip financing down the road too. Michelle Weaver: Mm-hmm. AI driven risk and potential disruption has been a big feature of the price action we've seen year-to-date in this theme. Stephen, what are some asset classes or businesses you see as resistant to some of this disruption? Stephen Byrd: We spend a lot of time thinking about, sort of, asset classes that are resistant to deflation and disruption. And what's interesting is there's actually a handful of economists in the world that are doing remarkable work on this concept. That they would call it the economics of transformative AI. There are three Americans, two Canadians, two Brits, a number of others who are doing really, really interesting work. And essentially what they're looking at is what do economies look like? As we see very powerful AI enter many industries – cause price reductions, deflation… What does that do? They have a lot of interesting takeaways, but one is this idea that the relative value of assets that cannot be deflated by AI goes up. Very simple idea. But think of it this way, I mean, there's only, you know, one principle resort on Kauai. You know, there's a limited amount of metals. And so, what we go through is this list that's gotten a lot of investor attention of resistant asset classes or more of the resistant asset classes that can go up in value. So, there are obvious ones like land, though you have to be a little careful with real estate in the sense that like, office real estate probably wouldn't be where you would go. Nor would you potentially go sort of towards middle income, lower income housing. But more, you know, think of industrial REITs, higher-end real estate. But there are a lot of other categories that are interesting to me. All kinds of infrastructure should be quite resistant, all kinds of critical materials. Metals should do extremely well in this. But then when you go beyond that, it's actually kind of interesting that there; arguably there's a longer list than those classic sort of land and metals examples. Examples here would be compute… Michelle Weaver: Mm-hmm. Stephen Byrd: I thought Jensen put it, well, you know, if there's a limited amount of infrastructure available, you want to put the best compute. And ultimately, in some ways, intelligence becomes the new coin of the realm in the world, right? So, I would want to own the purveyors of intelligence. It could include high-end luxury. It could include unique human experiences. So, I don't know how many of y'all have children who are sort of college age. But my children are college age, and they absolutely hate what they would call AI slop. They want legit human content, and they seek it out. And they absolutely hate it when they see bad copies of human content. And so, I think there is a place in many parts of the economy for unique human experiences, unique human content, and it's interesting to kind of seek out where that might be in the economy. So those would be some examples of resistant assets. Michelle Weaver: Mm-hmm. Josh, software's been at really the center of this AI disruption debate. How would you compare the current pullback in software multiples to prior periods of peak uncertainty? And do you think any of these concerns are valid? Or how are you thinking about that? Josh Baer: Great question. I mean, software multiples on an EV to sales basis are down 30 – 35 percent just from the fall, I will say. And that's overall in the group. A lot of stocks, multiple handfuls, are down 60-70 percent
