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, US thematic and equity strategist at Morgan Stanley.
Today we're continuing our conversation with Steven Bird, 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 11 a.m. in San Francisco.
So yesterday, we spoke about AI adoption.
And while there's a lot of excitement on this theme, there have 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 is credit markets playing here?
Again in partnership with Thematic Research, Steven 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 Steven 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 10, which is more of that middle market direct lending, and then private credit 20, 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 CNBS as well.
And then something new that's emerging in focus for investors is more of 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, OK, but what about the chips?
And so I think a big focus is that you know, chips are more than 50 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.
And how do you view balance sheet leverage in financing when you think about hyperscaler debt raising magnitude and timelines?
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 a developer, maybe a Bitcoin miner that is converted to HPC, and then you kind of buy the chips and you 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 think 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 split between cash capex, but also leases.
What we've seen is that There is more than 600 billion of uncommenced 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 cap 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.
And what have you learned around financing the past few days of the conference?
Anything incremental to share there?
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 and 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-clowns 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 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 and I have talked about this around some of the Bitcoin miners is that 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 a construction financing.
And we are seeing some press pickup around how that might play out in chip financing down the road too.
And AI driven risk and potential disruption has been a big feature of the price action we've seen year to date in this theme.
Steven, what are some asset classes or businesses you see as resistant to some of this disruption?
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 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.
There's only one principal resort on Kauai.
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 than 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 of 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 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.
I mean, arguably, there's a longer list than those classic sort of land and metals examples.
Examples here would be compute.
I thought Jensen put it well.
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.
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 you 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.
Josh, software has been 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?
Great question.
I mean, software multiples on the EV to sales basis are down 30, 35% just from the fall.
I will say, and that's overall in the group, a lot of stocks, multiple handfuls, are down 60 70 over the last year.
And what's being priced in is really peak uncertainty, a lot of fear.
And these multiples, now four times sales, takes us all the way back about 10 years to to the shift to cloud.
And this time in many ways reminds us of that period of peak fear.
In this case, what's being priced in is terminal value risk.
We talked about this TAM yesterday, but who is going to win that share?
How is it divided from a competitive perspective, across these model providers, the LLMs, with new entrants, of course, the incumbents and this other idea of in-housing?
So there's competitive risk.
There's business model risk.
Are companies going to need to change their pricing models from seat based to consumption or hybrid?
And then last, margin risk.
Just thinking about the higher input costs and higher capital intensity.
And so, you know, all of those fears are being priced in right now.
And we, of course, though, had a bunch of these companies live with us at the conference.
How are they responding to some of these risks?
How are they addressing these investor concerns?
Most of the companies here from our coverage are the incumbent software vendors.
And I think that the leadership teams did a really nice job coming out and defending their competitive moats and really articulating the story of why they are in a great position to capitalize on the opportunity.
And the reasons can vary across different companies, but some of the commonalities are around enterprise grade trust security governance, acceptance from IT organizations.
The idea of vibe coding all apps in an organization get squashed when you actually talk to companies and chief information officers.
For some companies, there's proprietary data modes, network effects.
All of that's on top of existing customer relationships.
And so that was the message from the companies that we had, that we're the incumbents.
We get to use all of the same innovative AI technology in the same way that all these different competitive buckets do.
But we have that differentiation and that moat.
And so we're in a good place.
I want to wrap on a positive note.
Stephen, what did you hear at the conference that you're most excited about?
I'd say the life sciences.
A few investors pointed out that perhaps AI has a PR problem these days.
And I do think showing a significant benefit to humanity in terms of improved health outcomes, whether that's just better diagnosis away from this event, but I was in India the week before, and AI can have a powerful benefit to the people who suffer the most in terms of providing very powerful medical tools in a distributed manner.
So I'm a big, big fan there.
But in many ways curing the most challenging diseases plaguing humanity.
The kind of problems involved in providing those and developing those cures are perfect for AI.
So that, for me, stepping way back, that is by far the most exciting thing.
Thank you.
Josh, same to you.
What are you most excited about?
From my perspective, it's potentially the turning point for software the ability to showcase that we are at this inflection point and acceleration to actually see that it takes time for our software companies to develop new AI technologies technologies, put that into products that have been tested and proven and go through the enterprise adoption cycle.
And that we're at the cusp of more adoption.
That's what our survey work says.
And to see that inflection, I think, can help to re-rate this sector.
Lindsay, same question for you.
Maybe I'll tie it to markets.
I've already had a lot more conversations with equity investors over the past how many months.
There's a big fixed income focus right now, which is a great spot and really interesting opportunity in my seat.
And there's a lot of interesting structures coming to be right now in the credit space.
So I think it's an exciting time.
Lindsay Stephen Josh.
Thank you very much for joining to recap the event and let us know what you learned at the conference.
To our audience thank you for listening here live and to our audience tuning in.
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