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- [00:01]
Katie Huberty
Welcome to Thoughts on the Market. I'm Katie Huberty, Morgan Stanley's Global Head of Research and I'm joined by Steven Bird, 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 Steven, 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?
- [01:18]
Steven Bird
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 a little over $900 billion. And that can translate to well over 20% 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.
- [02:26]
Katie Huberty
Steven, 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?
- [02:39]
Steven Bird
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 nonlinear 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.
- [03:28]
Katie 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?
- [03:38]
Steven Bird
I thought what we would see would be fairly high tech oriented sectors, 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.
- [04:16]
Katie Huberty
I could see why that would have 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?
- [04:29]
Jeff McMillan
I would start with software development, which was probably the first genius case out of the gate. Not only was it first, but it continues to be the most rapidly advancing. And that's probably mostly a function of the software 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. We have over 20,000 folks here at Morgan Stanley. That's, that's 25% of our population. And 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 once you start moving past that, it may not seem 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, Katie 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.
- [06:04]
Katie Huberty
There's a lot of excitement around AI, but as Steven 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?
- [06:20]
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 you know 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.
- [07:21]
Katie 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?
- [07:31]
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. I think we're. 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 in a position to fully take advantage of that.
- [08:14]
Katie 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.
- [08:33]
Steven Bird
So, Katie, 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?
- [08:49]
Katie 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 think 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's 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 10xing, but the pattern is the same. And so again, that tells us we're in the early innings. 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 that are thread across computing cycles. And I would just encourage investors to realize that AI so far is following similar patterns.
- [11:06]
Jeff McMillan
Katie, 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?
- [11:14]
Katie Huberty
The breadth of impact to industries and corporates, which speaks to Steven's work. We have now four times over, mapped the 3,700 companies globally that Morgan Stanley Research covers to understand the 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 data set 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 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.
- [12:34]
Steven Bird
The preceding content is informational only and based on information available when created. It is not an offer or solicitation, nor is it tax or legal advice. It does not consider your financial circumstances and objectives and may not be suitable for you.