In the first of a special series, Morgan Stanley’s U.S. Thematic
and Equity Strategist Michelle Weaver discusses new frontiers in
artificial intelligence with Keith Weiss, Head of U.S. Software
Research.
----- Transcript -----
Michelle: Welcome to Thoughts on the Market
I'm Michelle Weaver, Morgan Stanley's U.S. Thematic and Equity
Strategist.
Keith: And I'm Keith Weiss, Head of U.S.
Software Research.
Michelle: This episode is the first
episode of a special series we’re calling “Big Debates” –
where we dig deeper into some of the many hot topics of
conversation going on right now. Ideas that will shape
global markets in 2025. First up in the series: Artificial
Intelligence.
It's Friday, January 10th at 10am in New York.
When we look back at 2024, there were three major themes that
Morgan Stanley Research followed. And AI and tech diffusion were
among them. Throughout last year the market was largely focused
on AI enablers – we’re talking semiconductors, data centers,
and power companies. The companies that are really building
out the infrastructure of AI.
Now though, as we’re looking ahead, that story is starting to
change.
Keith, you cover enterprise software. Within your space, how will
the AI story morph in 2025?
Keith: I do think 2025 is going to be an
exciting year for software [be]cause a lot of these fundamental
capabilities that have come out from the training of these
models, of putting a lot of compute into the Large Language
Models, those capabilities are now being built into software
functionality. And that software functionality has been in the
market long enough that investors can expect to see more of it
come into results. That the product is there for people to
actually buy on a go forward basis.
One of the avenues of that product that we're most excited about
heading into 2025 is what we're calling agentic computing, where
we're moving beyond chatbots to a more automated proactive
type of interface into that software functionality that can
handle more complex problems, handle it more accurately and
really make use of that generative AI capability in a corporate
or in an enterprise software setting as we head into 2025.
Michelle: Could you give us an example of
what agentic AI is and how might an end user interact with it?
Keith: Sure. So, you and I have been
interacting with chatbots a lot to gain access to this generative
AI functionality. And if you think about the way you interact
with that chatbot, right, you have a prompt, you have a question.
You have to come up with the question. going to take that
question and it's going to, try to contextually understand the
nature of that question, and to the best of its ability it's
going to give you back an answer.
In agentic computing, what you're looking for is to add more
agency into that chatbot; meaning that it can reason more over
the overall question. It's not just one model that it's going to
be using to compose the answer. And it's not just the composition
of an answer where the functionality of that chatbot is going to
end. There's actually an ability to execute what that answer is.
So, it can handle more complex problems.
And it could actually automate the execution of the answer to
those problems.
Michelle: It sounds like this tech is going
to have a massive impact on the workplace. Have you estimated
what this could do to productivity?
Keith: Yeah, this is -- really aligns to
the work that we did actually back in 2023, where we did our AI
index, right. We came up with the conclusion that given the
current capabilities of Large Language Models, 25 per cent of
U.S. occupations are going to be impacted by these technologies.
As the capabilities evolve, we think that could go as high as 45
per cent of U.S. labor touched by these productivity enhancing.
Or, sort of, being replaced by these technologies. That equates
to, at the high end, $4 trillion of labor that's being augmented
or replaced on a go forward basis. The productivity gains still
yet to be seen; how much of a productivity gain you could see on
average. But the numbers are massive, right, in terms of the
potential because it touches so much labor.
Michelle: And finally on agentic, is the
market missing anything and how does your view differ from the
consensus?
Keith: I think part of what the market is
missing is that these agentic computing frameworks is not just
one model, right? There's typically a reasoning engine of some
sort that's organizing multiple models, multiple components of
the system that enable you to -- one, handle more complex
queries, more complex problems to be solved, lets you actually
execute to the answer. So, there's execution capabilities that
come along with that. And equally as important, put more error
correction into the system as well. So, you could have agents
that are actually ensuring you have a higher accuracy of the
answer.
It's the sugar that's going to make the medicine go down, if you
will. It's going to make a lot easier to adopt in enterprise
environments. I think that's why we're a little bit more
optimistic about the pace of adoption and the adoption curves we
could see with agentic computing despite the fact it's a
relatively early-stage technology.
Michelle: You just mentioned Large Language
Models, or LLMs; and one barrier there has been training these
models. It requires a ton of computing power, among other
constraints. How are companies addressing this, and what's in the
cards for next year?
Keith: So, if you think about the demand
for that compute in our mind comes from two fundamental sources.
And as a software analyst, I break this down into research versus
development, right? Research is investment that you make to find
core fundamental capabilities.
Development is when you take those capabilities and make the
investment to create product out of it. Thus far, again, the
primary focus has been on the training side of the equation.
I think that part of the equation looks to be asymptotic to a
certain extent. The – what people call the scaling laws, the
amount of incremental capability that you're getting from putting
more compute at the equation is starting to come down.
What people are overlooking is the amount of improvement that you
could see from the development side of the equation. So, whereas
the demand for GPUs, the demand for data center for that pure
training side of the equation might start to slow down a little
bit, I think what we're going to see expand greatly is the demand
for inference, the demand to utilize these models more fully to
solve real business problems.
In terms of where we're going to source this; there are
constraints in terms of data center capacity. The companies that
we cover, they've been thinking about these problems for the
past decade, right? And they have these decade long planning
cycles. They have good visibility in terms of being able to meet
that demand in the immediate future. But these questions on how
we are going to power these data centers is definitely top
of mind for our companies, and they're looking for new sources of
power and trying to get more creative there.
The pace with which data centers can be built out is a
fundamental constraint in terms of how quickly this demand can be
realized. So those supply constraints I don't think are going to
be a immediate limiter for any of our names when we're thinking
about calendar [20]25. But definitely, part of the planning
process and part of the longer-term forecasting for all of
these companies in terms of where are they going to find all this
fundamental resource – because whether it's training or
inference, still a lot of GPUs are going to be needed. A lot of
compute is going to be needed.
Michelle: Recently we've been hearing about
so called artificial general intelligence or AGI. What is it? And
do you think we're going to see it in 2025?
Keith: Yeah, so, AGI is the – it's
basically the holy grail of all of these development
efforts. Can we come up with models that can reason in the
human world as well as we can, right? That can understand the
inputs that we give it, understand the domains that we're trying
to operate in as well or better than we can, so it can solve
problems as effectively and as efficiently as we can.
The easiest way to solve that systems integration problem of
like, how can we get the software, how could we get the computers
to interact with the world in the way that we do? Or get all the
impact that we do is for it to replicate all those
functionalities. For it to be able to reason over unstructured
text the same way we do. To take visual stimuli the same way that
we do. And then we don't have to take data and put into a format
that's readable by the system anymore.
2025 is probably too early to be thinking about AGI, to be
honest. Most technologists think that there's more
breakthroughs needed before the algorithms are going to be that
good; before the models are going to be that good.
There's very few people who think Large Language Models and the
scaling of Large Language Models in themselves are going to get
us to that AGI. You're probably talking 10 to 20 years before we
truly see AGI emerge. So, 2025 is probably a little bit too
early.
Michelle: Well, great, Keith. Thank you for
taking the time to talk and helping us kick off big debates. It
looks like 2025 we'll see some major developments in AI.
And to our listeners, thanks for listening. If you enjoy Thoughts
on the Market, please leave us a review wherever you listen to
the show and share the podcast with a friend or colleague today.
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