AI’s Next Stress Test

**Thomas Wigg** (0:00)
Welcome to Thoughts on the Market. I'm Tom Wigg, Morgan Stanley's Head of America's Specialty Sales.

**Stephen Byrd** (0:05)
I'm Stephen Byrd, Morgan Stanley's Global Head of Thematic and Sustainability Research.

**Ariana Salvatore** (0:09)
And I'm Ariana Salvatore, Morgan Stanley's Head of Public Policy Research.

**Thomas Wigg** (0:13)
Today, the rally in AI CapEx beneficiaries has taken a breather in recent weeks on concerns of competition from open-source models, backlash to token maxing, and growing political opposition at data center builds. It's Tuesday, July 7th at 10 a.m. in New York.
Let's start with you, Stephen. There's a lot of discussion recently around a backlash to token maxing, essentially enterprises trying to curtail their high spending on AI tokens from the frontier labs, and in many cases shifting to cheaper open-source China models. Can you first offer some perspective here on the value of tokens for enterprises? I know you have a popular token factory model that walks through the economics of agents.

**Stephen Byrd** (0:53)
Yeah, Tom, we do have this model that really walks through token economics, both from the adopter side as well as the hyperscaler side. So let's do the adopter side. So there's a study out that shows a whole range of enterprise use cases of AI. And the average single use case that they identify would save a company about $55 or provide that much benefit. And while we don't know exactly how many tokens it will require, we can make some educated guesses as to a typical token usage to achieve that $55 outcome.
And we know that a typical American model of this varies a lot. You can think of as the cost per million tokens being in the range of $5 per million. Some will be lower, some will be higher. So for a few dollars of token costs, an enterprise can generate benefit of $55.
So that doesn't make me overly concerned about token spend and concerns about token maxing. We're going to get into that. But the foundation here is really good in the sense that enterprise use cases are very much in the money.

**Thomas Wigg** (1:56)
How do you think market share ultimately shakes out on tokens? Do the cheaper models overtake the frontier AI labs? Do tokens bifurcate based on the complexity of workloads? How do you think this plays out?

**Stephen Byrd** (2:07)
What we continue to see is this relentless pace of innovation and cost reduction. So the frontier keeps going out, meaning model capabilities continue to increase.
And with that, we see enterprise adoption growing quite a bit.
Long way to say there is a role for both the frontier as well as these open source models, and we'll continue to see both flourish. What I see is a lot of tokens will be spent on open source models. A lot of the value will be in the higher end models, because that's where enterprises are going to go. Let me give an example. I was speaking with one of our programmers about a recent project, and he used a very high-end coding tool, an American coding tool. And for him, that incremental cost of the tokens was very much worth it. And here's a very practical example as to why it makes sense for many enterprises to use the higher-end models. If a coding tool gets one of the thousands of lines of code wrong, the cost to remediate is very, very high. In other words, that incremental cost, if it's in this example I'm thinking of, it's a few dollars incremental cost, is so worth it because if the quality is not there, the cost to any enterprise to go back and remediate is so high. And that's true in a lot of enterprise use cases, but not in every use case. And what we are seeing is these open-source models that are cheaper will be very good for a variety of more mundane use cases that are still very valuable. That said, what we've seen in data from places like OpenRouter is dollar-weighted, meaning valued by enterprise spend. The vast majority is still the proprietary models, but even within proprietary models, we could have more expensive and less expensive models. You do not need to go to the frontier.
Where I come out on all this is that I'm very confident that the demand for compute is going to exceed the supply. What is difficult to exactly know is who are the winners? What is the exact mix? But the fundamentals of the demand for compute look extremely strong.

**Thomas Wigg** (4:09)
So I think you just gave me the answer, but I do want to bring this all back to AI CapEx. Now, last year when the market sold off on DeepSea Concerns, the concept of Jevons Paradox ultimately prevailed, where the cheaper pricing led to even greater demand and CapEx went higher. Do you think the same plays out here?