Future of Work: AI’s Impact on Industries

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.

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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