Stephen Byrd, Morgan Stanley global head of thematic research, joins CNBC's 'Squawk on the Street' to discuss how AI will shape the labor market, use cases for AI across job categories, and much more.

San Francisco

The release of Open AI's ChatGPT has accelerated the development of artificial intelligence and warnings about its impact on society.

Rise of AI

Stephen Byrd, global head of thematic and sustainability research at Morgan Stanley, stated that artificial intelligence is expected to impact virtually every job, with effects ranging from full automation of tasks to AI augmenting human work. He emphasized that sectors with lower income per employee, such as consumer and logistics industries, could see the most significant efficiency gains and bottom-line improvements from AI adoption.

Byrd noted that AI technology is advancing rapidly, with major U.S. labs set to apply significantly more computational power to new models, potentially resulting in substantial breakthroughs by 2026. He acknowledged uncertainties about the limits of AI progress but pointed to recent achievements, such as AI models excelling in challenging tests, as evidence of ongoing and accelerating improvements.

Morgan Stanley's Stephen Byrd: No job will be unaffected by AI

Joining us more to talk about more of that is the implications for AI for workers, Stephen Byrd, global head of thematic and sustainability research at Morgan Stanley, has done quite a bit of work in this area. Stephen, thanks for the time this morning. Thanks for having me on. I want to get to a couple of things. One is just the value creation that you see being made for the S&P, but when it does come to employment, we've been teasing this segment with this notion of 90% being impacted. Is that a fair number? Yes. And what impacts are positive versus negative? I think it is. I think virtually every job will be impacted by AI in some way. If you think about the range of that impact, I find it interesting. It's a range from automation, where certain parts of what we do will be completely completed by AI, versus augmenting what we do, where it's really going to serve as our assistant. And we see a wide range in between, but almost no job will be unaffected by AI. You do see, you sort of rank the sectors, business sectors, market sectors, by impact. Talk about the top and the bottom, the barbells of that. You know, honestly, I was surprised at the results. I was expecting very high-tech sectors to have the biggest impact. But what I really failed to appreciate, and our numbers really show this, is think more about sectors with fairly low income per employee. Those would be the sectors that would have bigger impacts, relatively speaking, in terms of the impact to the bottom line. So some consumer sectors, some logistics sectors, et cetera, where AI can really help improve efficiency a great deal. For those companies, in some cases, that can be more than 100% of their operating income over time. Now, that will take time, but I would say what's underappreciated is AI is not static. It's improving at a very rapid rate. So it seems like science fiction today, in terms of what AI can accomplish, I would expect in 2026 and beyond to not be science fiction. And an example of how this can really tangibly show up, Carl, is the top five U.S. labs developing frontier models are going to apply about 10 times the computational power to train the next model later this year and early next year. And researchers do expect that to result in not a little improvement, but a fairly massive improvement. So the analysis that we performed over the summer, I think, will look very different as we head into 2026. Yeah. If scaling laws hold, I guess, is a key uncertainty, isn't it, though? At this point, Stephen, I mean, do you expect that? Do we even know? Does that make so much of what you can predict uncertain? Great question. This is unknown territory. Even the best researchers in the AI community would admit that it's unknown territory. What I would say is when you look at the research being done by a number of teams, there was actually a great paper from a team from Meta last week on this in terms of scaling limits. It looks very promising. And you know, an example just in the last couple of weeks, a couple of models scored a gold medal on the International Math Olympiad. This past weekend, a couple of world-class physicists, mathematicians, computer scientists are now using AI to really enhance their thought process. Now, that's a sign of where we're heading in terms of further creativity that AI can accomplish. I do think back to the game of Go years ago, where AlphaGo achieved this breakthrough. I don't see a limit. But again, we don't know. And this is a key uncertainty, you're absolutely right. And so, you know, all right, is there a point at which we will know? Or, you know, can you give me some date here where you can come on and we can have a lot more certainty about the predictions and what we're seeing and how to approach it? Yeah, I really think we can in the sense that by the spring or early summer, the models that have been trained on about 10 times the compute will likely be revealed. And at that point, we will see whether or not all of this has resulted in improvements. Now, what gives me hope here is that when we look at the test results, that is these incredibly difficult tests that these models are put through, for much of this year, they were scoring fairly low. And then over the last couple of months, a handful of models achieved pretty big breakthroughs in those incredibly difficult tests. So I am optimistic that we'll see continued breakthroughs in terms of performance. Yeah, that is interesting, the idea that we're in the middle of a step function, literally in the coming months, according to some of your work, Stephen, I hope you have hope to have you back. I appreciate it very much. Stephen Burd over Morgan Stanley.

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Johannesburg

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Jerusalem

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Warsaw

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Paris

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Dhaka

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Morgan Stanley's Stephen Byrd: No job will be unaffected by AI

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Rise of AI