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 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 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 you know, 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.
Michelle Weaver: Mm-hmm.
Josh Baer: 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.
Michelle Weaver: 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?
Josh Baer: 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 moats,
network effects. All of that's on top of existing customer
relationships.
And so, you know, 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, you know, that
differentiation in that moat. And so, we're in a good
place.
Michelle Weaver: I want to wrap on a
positive note. Stephen, what did you hear at the conference that
you're most excited about?
Stephen Byrd: 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, you know. Away from this event, but I was in India the
week before and, you know, 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 fan there.
But you know, 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.
Michelle Weaver: Josh, same to you. What
are you most excited about?
Josh Baer: 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. 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 rerate this sector.
Michelle Weaver: Lindsay, same question for
you…
Lindsay Tyler: Maybe I'll tie it to
markets. I've already had a lot of more conversations with equity
investors over the past, how many months? There's a
big fixed income focus right now, which is a great, you know,
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.
Michelle Weaver: 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,
thanks 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.
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