Live from Morgan Stanley’s TMT conference, our panel break down
where AI is already delivering real returns—and where rapid
advances are raising new risks.
Read more insights from Morgan Stanley.
----- Transcript -----
Michelle Weaver: Welcome to Thoughts on the
Market. I'm Michelle Weaver, U.S. Thematic and Equity Strategist
here at Morgan Stanley.
Today we've got a special episode on AI adoption. And this is a
first in a two-part conversation live from our Technology, Media
and Telecom conference.
It's Thursday, March 5th at 11am in San Francisco.
We're really excited to be here with all of you taping live. And
we've got on stage with me. Stephen Byrd, he's our Global Head of
Thematic and Sustainability Research; Josh Baer, Software
Analyst; and Lindsay Tyler, TMT Credit Research Analyst.
So, Stephen, I want to start with you, pretty broad, pretty high
level. We recently published our fifth AI Mapping Survey that
identifies how different companies are exposed to the broad AI
theme. Can you just share with us some insights from that piece
and how stocks are performing with this AI exposure?
Stephen Byrd: Yeah, it's interesting. I mean,
we've been doing this survey now, thanks to you, Michelle, and
your excellent work, for quite a while. And every six months it
is pretty telling to see the progression.
I would say a few things that got my attention from our most
recent mapping was the number of companies that are quantifying
the adoption benefits continues to go up quite a bit. And to me
that feels like that's going to be table stakes very soon as in
every industry you see two or three companies that are really
laying out quite specifically what they expect to be able to do
with AI and lay out the math. I think that really is going to
pull all the other companies to follow suit. So, we're seeing
that in a big way.
We do see adopters, with real tangible benefits performing well.
But a new thing that we're seeing now, of course, in the market
is concerns that in some cases adoption can lead to dramatic
deflation, disruption, et cetera. That's coming up as
well. So, we're seeing greater concerns around disruption as
well.
But broadly, I'd say a proliferation of adoption, that that
universe of companies continues to grow, increases in
quantification of the benefits. So, that is good. What's really
surprised me though, is the narrative among investors has so
quickly moved from those benefits which we've talked about into
flipping that to toggle all negative, which I know some of our
analysts have to deal with every day. The mapping work suggests
significant benefits. But the market is fast forwarding to very
powerful AI that is very disruptive in deflation. And that's been
a surprise to me.
Michelle Weaver: Mm-hmm. Josh, I want to bring
software into this. Your team has been arguing that AI is
actually good for software. And it's really something that you
need that application layer to then enable other companies to
adopt AI. Can you tell us a little bit about how much GenAI could
add to the broader enterprise software market? And how are you
thinking about monetization these days?
Josh Baer: Of course. I think the best starting
place is a reminder that AI is software, and so we see software
as a TAM expander. And in many ways, even though this is
extremely exciting innovation, it's following past innovation
trends where first you see value accrue and market cap accrue to
semiconductors, and then hardware and devices, and then
eventually software and services. And we do think that that
absolutely will occur just given [$]3 trillion in infrastructure
investment into data centers and GPUs.
There's got to be an application layer that brings all of these
productivity and efficiency gains to enterprises and advanced
capabilities to consumers as well. And so we see AI more as an
evolution for software than a revolution. An evolution of
capabilities and expansion of capabilities. LLMs and diffusion
engines absolutely unlocked all of these new features of what
software can do. But incumbents will play a key role in this
unlock.
And our CIO surveys really support that. Quarterly we ask chief
information officers about their spending intentions, and these
application vendors who we cover in the public markets are
increasingly selected as vendors that companies will go to, to
help deploy and apply AI and LLM technologies.
So, to answer your question, we estimate GenAI could unlock
[$]400 billion in incremental TAM for software; for enterprise
software by 2028. And this is based on looking at the type of
work able to be automated, the labor costs associated with that
work, the scope of automation, and then thinking about how much
of that value is captured typically by software vendors.
Michelle Weaver: And you have a bit of a
different lens on AI adoption. So, what are some of the ways
you're hearing software customers using these AI tools and
anything interesting that popped up at the conference?
Josh Baer: To echo what Stephen laid out, I
mean, all of our software companies are using AI internally, both
to drive efficiencies, but also to move faster. So thinking about
product. Innovation, you know, the incumbents are able to use all
of the same coding tools and, you know, …
Michelle Weaver: Mm-hmm.
Josh Bear: … products geared to developers to
move faster and more efficiently on R&D. So, they're doing
more. From a sales and marketing perspective, a G&A
perspective, every area of OpEx, our software companies are in a
great position to deploy the AI tools internally.
I think more important[ly], speaking to this TAM and expanded
opportunity, is our companies have skews that they're monetizing.
It might be a separate suite that incorporates advanced AI
functionality. It might be a standalone offering, or it might be
embedded into the core platform because the essence of software
is AI and it, you know, leading to better retention rates and
acceleration from here.
Michelle Weaver: Mm-hmm. And Stephen, going back
to you on the state of play for AI, we had the AI labs here and
we heard a lot about the developments and what's to come. So,
what's your view on the trajectory for LLM advancements and what
are some of the key signposts or catalysts you're watching here?
Stephen Byrd: Yeah, this is for me, maybe the
most important takeaway of the conference – is this continued
non-linear improvement of LLMs, which we've been writing about
for quite some time. And just to give you an example, we think
many of the labs have achieved a step change up in terms of the
compute that they have, in some cases 10 x the amount of compute
to train their LLMs. And that [if] the scaling laws hold – and we
see every sign that they will – a 10x increase in compute used to
train the models results in about a doubling of the model
capabilities.
Now just let that sink in for a moment. Let's just think about
that. A doubling from here in a relatively short period of time
is difficult to predict. It's obviously very
significant and I think several of the LLM execs at our event
sounded to me extremely bullish on what that will be. A lot of
that I think will be evident in greater agentic capabilities.
But also, I'd say greater creativity. It was about three weeks
ago, three of the best physics minds in the world worked with an
LLM to achieve a true breakthrough in physics – solving a problem
that had never been solved before. A couple of days ago, a math
team did the same thing. And so, what we're seeing is sort of
these breakthrough capabilities in creativity. This morning I
thought Sam speaking to, you know, incredible increases in what
these models can do – which also brings risk. You know, I think
it was interesting he spoke to, you know, the risk of
misalignment, the risk of what these models are doing.
But for me, that's the single biggest thing that I'm thinking
about, and that's going to be evident in the next several months.
Michelle Weaver: Mm-hmm.
Stephen Byrd: So, you know, on the positive
side, it leads to greater benefits from AI adoption. And to
Josh's point that, you know – more and more the economy can be
addressed by AI, I do get concerned about the risk that that kind
of step change will create greater concerns about disruption and
deflation.
That causes me to think a lot about that dynamic. Interestingly,
we think the Chinese labs will not be able to keep pace just for
one reason, which is compute. We think the Chinese labs have
everything else they need. They have the talent, the
infrastructure. They certainly have the energy, power. But they
don't have the chips.
If what we laid out with the American models turns out to be
true, I could see a chain reaction where the Chinese government
pushes the Trump administration for full transfer of the best
technology to China. And China could use their rare earth trade
position to ensure that. So, that's sort of the chain reaction
I've been thinking about.
Michelle Weaver: Mm-hmm. So, let's think about
then bottlenecks in the U.S. Power is still one of the main
bottlenecks. We had several of the solutions providers here at
the conference. So, what are you thinking in terms of the size of
the power bottleneck in the U.S. and how are we going to fix
that?
Stephen Byrd: Yeah, absolutely. I am bullish on
the companies that can de-bottleneck power, not just in the U.S.,
a few other places. Let's go through the math in terms of the
problem we face and then the solution.
So, we have this very cool – it is cool if you're a nerd – power
model that starts in the chip level up, from our semiconductor
teams. And from that, we build a global power demand model for
data centers. We then apply that to the U.S.
Through 2028 we need about 74 gigawatts of data centers, both AI
and non-AI to be built in the United States. I don't think we'll
be able to achieve that for lots of reasons. But starting from
that 74, we have sort of 10 gigs that have been recently built or
are under construction. We have 15 gigs of incremental grid
access, but after those two, we have to go to unconventional
solutions, meaning typically off-grid solutions, over 40
gigawatts of unconventional solutions.
So that will be repurposing Bitcoin sites, which could be sort of
10 to 15 gigawatts. That'll be big. Renewable energy, fuel cells
will be part of the solution. Gas turbines will be a big part of
the solution. Co-locating at a few nuclear plants. I'm less
bullish than I used to be on that. But when we net all that out,
we think the U.S. is likely to be 10 to 20 percent short of the
data center capacity that will need to be in.
It's not just a power grid access issue, though, that's a big
one. Labor is now showing up as a huge issue. Many of the
companies I speak to trying to develop data centers struggle with
availability of labor. Electricians being one very tangible
example. In the U.S. we need hundreds of thousands of additional
electricians.
So, for any of your children, like mine, thinking about careers,
you know, you'd be surprised [at] the amount of money that people
are making in the infrastructure business that does feel like
it's a labor shift that's going to have to happen, but it's going
to take years. So, in that context, we had a number of the
Bitcoin companies at our event here. And the economics of turning
a Bitcoin site into hosting a data center are extremely
attractive. I mean, extremely attractive.
To give you a sense of that. Before this opportunity presented
itself to these Bitcoin players, those stocks tended to trade at
an enterprise value per watt of about $1 to $2 a watt. Then we
started to see these deals in which the Bitcoin players build a
data center and lease them to hyperscalers. Those deals – depends
a lot on the deal but – have created between $10 and $18 a watt
of value. Let me repeat that. 10 to 18 – relative to where these
stocks were at 1 to 2.
Now many of these stocks have rerated, but not all of them. And
there's still quite a bit of upside. And what we've noticed is
the economics that the hyperscalers are paying are trending up
and up and up. Because of this power shortage that we're dealing
with. So, a lot of exciting opportunities are still in the power
space.
Michelle Weaver: Great. Well, I think that's a
good place to wrap this first part of our conversation around AI
adoption and the state of play. We'll be back again tomorrow with
Part Two, looking at financing and risks.
To our panelists, thank you for talking with me. And to our
audience, 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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