In the first of a two-part roundtable discussion, our Global Head
of Research joins our Global Head of Thematic Research and Head
of Firmwide AI to discuss how the economic and labor impacts of
AI adoption.
Read more insights from Morgan Stanley.
----- Transcript -----
Kathryn Huberty: Welcome to Thoughts on the
Market. I'm Katy Huberty, Morgan Stanley's Global Head of
Research, and I'm joined by Stephen Byrd, Global Head of Thematic
Research, and Jeff McMillan, Morgan Stanley's Head of Firm-wide
AI.
Today and tomorrow, we have a special two-part episode on the
number one question everyone is asking us: What does the future
of work look like as we scale AI?
It's Tuesday, November 4th at 10am in New York.
I wanted to talk to you both because Stephen, your groundbreaking
work provides a foundation for thinking through labor and
economic impacts of implementing AI across industries. And Jeff,
you're leading Morgan Stanley's efforts to implement AI across
our more than 80,000 employee firm, requiring critical change
management to unlock the full value of this technology.
Let's start big picture and look at this from the industry level.
And then tomorrow we'll dig into how AI is changing the nature of
work for individuals.
Stephen, one of the big questions in the news – and from
investors – is the size of AI adoption opportunity in terms of
earnings potential for S&P 500 companies and the economy as a
whole. What's the headline takeaway from your analysis?
Stephen Byrd: Yeah, this is the most popular
topic with my children when we talk about the work that I do. And
the impacts are so broad. So, let's start with the headline
numbers. We did a deep dive into the S&P 500 in terms of AI
adoption benefits. The net benefits based on where the technology
is now, would be about little over $900 billion. And that can
translate to well over 20 percent increased earnings power that
could generate over $13 trillion of market cap upon adoption. And
importantly, that's where the technology is now.
So, what's so interesting to me is the technology is evolving
very, very quickly. We've been writing a lot about the nonlinear
rate of improvement of AI. And what's especially exciting right
now is a number of the big American labs, the well-known
companies developing these LLMs, are now gathering about 10 times
the computational power to train their next model. If scaling
laws hold that would result in models that are about twice as
capable as they are today. So, I think 2026 is going to be a big
year in terms of thinking about where we're headed in terms of
adoption. So, it's frankly challenging to basically take a
snapshot because the picture is moving so quickly.
Kathryn Huberty: Stephen, you referenced just
the fast pace of change and the daily news flow. What's the view
of the timeline here? Are we measuring progress at the industry
level in months, in years?
Stephen Byrd: It's definitely in years. It's
fast and slow. Slow in the sense that, you know, it's taken some
companies a little while now and some over a year to really
prepare. But now what we're seeing in our CIO survey is many
companies are now moving into the first, I'd say, full fledged
adoption of AI, when you can start to really see this in numbers.
So, it sort of starts with a trickle, but then in 2026, it really
turns into something much, much bigger. And then I go back to
this point about non-linear improvement. So, what looks like,
areas where AI cannot perform a task six months from now will
look very different. And I think – I'm a former lawyer myself. In
the field of law, for example, this has changed so quickly as to
what AI can actually do. So, what I expect is it starts slow and
then suddenly we look at a wide variety of tasks and AI is fairly
suddenly able to do a lot more than we expect.
Kathryn Huberty: Which industries are likely to
be most impacted by the shift? And when you broke down the
analysis to the industry and job level, what were some of the
surprises?
Stephen Byrd: I thought what we would see would
be fairly high-tech oriented sectors – and including our own –
would be top of the list. What I found was very different. So,
think instead of sectors where there's fairly low profit per
employee, often low margin businesses, very labor-intensive
businesses. A number of areas in healthcare staples came to the
top. A few real estate management businesses. So, very different
than I expected.
The very high-tech sectors actually had some of the lowest
numbers, simply because those companies in high-tech tend to have
extremely high profit per employee. So, the impact is a lot less.
So that was surprising learning. A lot of clients have been
digging into that.
Kathryn Huberty: I could see why that would've
surprised you. But let's focus on banking for a moment since we
have the expert here. Jeff, what are some of the most exciting AI
use cases in banking right now?
Jeff McMillan: You know, I would start with
software development, which was probably the first Gen AI use
case out of the gate. And not only was it first, but it continues
to be the most rapidly advancing. And that's probably; mostly a
function of the software, you know, development community. I
mean, these are developers that are constantly fiddling and
making the technology better.
But productivity continues to advance at a linear pace. You know,
we have over 20,000 folks here at Morgan Stanley. That's 25
percent of our population. And, you know, the impact both in
terms of the size of that population and the efficiencies are
really, really significant.
So, I would start there. And then, you know, once you start
moving past that, it may not seem, you know, sexy. It's really
powerful around things like document processing. Financial
services firms move massive amounts of paper. We take paper in,
whether it be an account opening, whether it be a contract.
Somebody reads that information, they reason about it, and then
they type that information into a system. AI is really purpose
built for that.
And then finally, just document generation. I mean, the number of
presentations, portfolio reviews, you know, even in your world,
Katy, research reports that we create. Once again, AI is really
just – it's right down the middle in terms of its ability to
generate just content and help people reduce the time and effort
to do that.
Kathryn Huberty: There's a lot of excitement
around AI, but as Stephen mentioned, it's not a linear path. What
are the biggest challenges, Jeff, to AI adoption for a big global
enterprise like Morgan Stanley? What keeps you up at night?
Jeff McMillan: I've often made the analogy that
we own a Ferrari and we're driving around circles in a parking
lot. And what I mean by that is that the technology has so far
advanced beyond our own capacity to leverage it. And the biggest
issue is – it's our own capacity and awareness and education.
So, what keeps me up at night? it's the firm's understanding.
It's each person's and each leader's ability to understand what
this technology can do. Candidly, it's the basics of prompting.
We spend a lot of time here at the firm just teaching people how
to prompt, understanding how to speak to the machine because
until you know how to do that, you don't really understand the
art of the possible. I tell people, if you have $100 to spend,
you should start spending [$]90, on educating your employee base.
Because until you do that, you cannot effectively get the best
out of the technology.
Kathryn Huberty: And as we look out to 2026,
what AI trends are you watching closely and how are we preparing
the firm to take advantage of that?
Jeff McMillan: You and I were just out in
Silicon Valley a couple of weeks ago, and seemingly overnight,
every firm has become an agentic one. While much of that is
aspirational, I think it's actually going to be, in the long
term, a true narrative, right? And I think that step where we are
right now is really about experimentation, right? I think we have
to learn which tools work, what new governance processes we need
to put in place, where the lines are drawn. I think we're still
in the early stage, but we're leaning in really hard.
We've got about 20 use cases that we're experimenting with right
now. As things settle down and the vendor landscape really starts
to pan out, we'll be down position to fully take advantage of
that.
Kathryn Huberty: A key element of the agentic
solutions is linking to the data, the tools, the application that
we use every day in our workflow. And that ecosystem is
developing, and it feels that we're now on the cusp of those
agentic workflow applications taking hold.
Stephen Byrd: So, Katy, I want to jump in here
and ask you a question too. With your own background as an IT
hardware analyst, how does the AI era compare to past tech or
computing cycles? And what sort of lessons from those cycles
shape your view of the opportunities and challenges ahead?
Kathryn Huberty: The other big question in the
market right now is whether an AI bubble is forming. You hear
that in the press. It's one of the questions all three of us are
hearing regularly from clients. And implicit in that question is
a view that this doesn't look like past cycles, past trends. And
I just don't believe that to be the case.
We actually see the development of AI following a very similar
path. If you go back to mainframe and then minicomputer, the PC,
internet, mobile, cloud, and now AI. Each compute cycle is
roughly 10 times larger in terms of the amount of installed
compute.
The reality is we've gone from millions to billions to trillions,
and so it feels very different. But the reality is we have a
trillion dollars of installed CPU compute, and that means we
likely need $10 trillion of installed GPU compute. And so, we are
following the same pattern. Yes, the numbers are bigger because
we keep 10x-ing, but the pattern is the same. And so again, that
tells us we're in the early innings. You know, we're still at the
point of the semiconductor technology shipping out into
infrastructure. The applications will come.
The other pattern from past cycles is that exponential growth is
really difficult for humans to model. So, I think back to the
early days when Morgan Stanley's technology team was really
bullish, laying the groundwork for the PC era, the internet era,
the mobile era. When we go back and look at our forecasts, we
always underestimated the potential. And so that would suggest
that what we've seen with the upward earnings revisions for the
AI enablers and soon the AI adopters is likely to continue.
And so, I see many patterns, you know, that are thread across
computing cycles, and I would just encourage investors to realize
that AI so far is following similar patterns.
Jeff McMillan: Katy, you make the point that
much of the playbook is the same. But is there anything
fundamentally different about the AI cycle that investors should
be thinking about?
Kathryn Huberty: The breadth of impact to
industries and corporates, which speaks to Stephen's work. We
have now four times over mapped the 3,700 companies globally that
Morgan Stanley research covers to understand their role in this
theme.
Are they enabling AI? Are they adopting? Are they disrupted by
it? How important is it to the thesis? Do they have pricing
power? It's very valuable data to go and capture the alpha. But I
was looking at that dataset recently and a third of those nearly
4,000 companies we cover, our analysts are saying that AI has an
impact on the investment thesis. A third. And yet we're still in
the early innings. And so, what may be different, and make the
impact much bigger and broader is just the sheer number of
corporations that will be impacted by the theme.
Let's pause here and pick up tomorrow with more on workforce
transformation and the impact on individual workers.
Thank you to our listeners. Please join us tomorrow for part two
of our conversation. If you enjoy the show, please leave us a
review wherever you listen and share Thoughts on the Market with
a friend or colleague today.
Kommentare (0)
Melde dich an, um einen Kommentar zu schreiben.