Episode Details

Original Release Date: April 28, 2026

Tom Wigg and Stephen Byrd discuss the accelerating pace of AI breakthroughs, the forces driving them and why the next phase of development may look very different from anything we’ve seen so far.

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Tom Wigg: Welcome to Thoughts on the Market. I’m Tom Wigg, Head of Specialty Sales in the Americas at Morgan Stanley, and a sector specialist in Technology, Media and Telecom.

We wake up every day to new AI product releases, so it’s easy to lose sight of the unprecedented non-linear improvement in AI capabilities. But things are about to get weird.

It’s Tuesday, April 28th at 8am in New York.

The market has been thinking about AI in linear terms. But we need to reframe that assumption of only incremental improvement and think about exponential improvement.

That was my takeaway from a conversation with Stephen Byrd, Global Head of Thematic and Sustainability Research at Morgan Stanley. In our conversation, we zeroed in on Stephen’s bull case for broader AI model improvements.

Tom Wigg: First, I want to talk about one obsession that you’ve been writing about for the last several months – is this idea that we’re going to see nonlinear improvements in the frontier models coming out this spring.

Stephen Byrd: Yes.

Tom Wigg: There’s been, you know, some big headlines around new models, benchmarks coming out publicly. Is this, you know, your bull case playing out on these models? And what are the implications?

Stephen Byrd: Yes! Absolutely, Tom. So we have, to your point, we are obsessed. And I know I’m not shy about that – with the nonlinear rate of AI improvement. It is the most important impact to so many stocks that I can think of in the sense that it can impact all industries, all business models. So, what we’ve been saying for some time is, if you look back over the last couple of years at the relationship between the amount of compute used to train these LLMs and the capabilities, we have a very clear scaling law.

And approximately the law is, if you increase the training compute by 10x, the capabilities of the models go up by 2x. Now, as you and I’ve talked about this a lot; just meditate on that for a moment. I think things are about to get weird in the sense that on the positive side, we’re going to see all kinds of underappreciated capabilities across many industries. So this disruption discussion, I think, is going to spread, but it’s also going to require investors to, kind of, be more thoughtful about what they do with that concept. Meaning you can’t sell everything. In the sense that AI will disrupt some businesses.

I actually think this is healthy in some ways because now it forces investors to really look at each business model and assess which is going to get disrupted, which can get supported and enabled by AI, which are immune. Because there are some business models that actually are immune.

But essentially from here, Tom, I’d say we are expecting through the spring and summer to see multiple models that are able to perform a much greater percentage of the economy at better levels of accuracy at incredibly low cost. Which I know you and I have talked a lot about the cost of actually doing this work from the LLMs.

This is massive. This is going to impact so many industries. I think this is all to the good for the AI infrastructure plays because it shows the importance of getting more intelligence out into the world.

Tom Wigg: So, you mentioned the constraints we’re seeing across compute, memory and power. It seems like most of the CEOs of the labs and hyperscalers are talking