AI’s Next Stress Test

The biggest AI stocks have had a remarkable run – but questions still remain. Our Head of Americas Specialty Sales, Thomas Wigg, speaks with Global Head of Thematic and Sustainability Research Stephen Byrd and Global Head of Public Policy Research Ariana Salvatore about the competition and durability of the investment cycle.

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Thomas Wigg: Welcome to Thoughts on the Market. I'm Tom Wigg, Morgan Stanley's Head of Americas Specialty Sales.

Stephen Byrd: I'm Stephen Byrd, Morgan Stanley's Global Head of Thematic and Sustainability Research.

Ariana Salvatore: And I'm Ariana Salvatore, Morgan Stanley's Head of Public Policy Research.

Thomas Wigg: 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-maxxing, and growing political opposition to data center builds.

It's Tuesday, July 7th at 10am in New York.

Let's start with you, Stephen. There's a lot of discussion recently around a backlash at token-maxxing. 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: 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, though 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 cost, an enterprise can generate benefit of $55.

So that doesn't make me overly concerned about token spend and concerns about token-maxxing. I know 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: 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: 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 you 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 – 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: 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 Deep Seek 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?

Stephen Byrd: It does look that way very much. And the Jevons paradox dynamic is what we still see today in the sense that as the models get better, what we can do with the models increase, the cost of tokens will keep dropping, the cost of compute will keep dropping.

But let's talk about what might derail that, just to make sure we're thinking about all the risks. If somehow commoditized models could perform at the same level as proprietary models in all situations, then I would feel differently. But I don't see that. What I see is that these newer models really do have capabilities that are fairly breathtaking and that are worth that extra money.

But if somehow, we hit a wall where these models aren't getting better and therefore the sort of the open models are going to catch up, then I'd feel differently about that. This is where Ariana will, will come in in terms of policy and, you know, this comes up a lot when we think about U.S. versus China. How do we think about, you know, access to different models? How do we think about the cost of different models?

What about the risk of appropriation of capabilities by the Chinese firms, for example? That comes up a lot in policy circles. But the base case that I have is this just looks more like Jevons paradox, and there's going to be continued innovation, continued reduction in the cost of producing these services from these models. That looks like more of the same.

Thomas Wigg: Let's shift to Ariana to talk about the political angle here. The cover of Barron's over the weekend was a guy wearing a no data centers T-shirt. And this does seem to be one of the few bipartisan issues of agreement heading into the midterms.

The stat that the article gave was that 75 data center projects worth $130 billion were blocked or delayed in 1Q26, which is equal to the total number for 2025. This is according to Data Center Watch.

Now, most of this is in blue states like New York, Michigan, Illinois, Minnesota considering a statewide moratorium, but you're also seeing Pennsylvania, Arizona, Ohio, parts of Texas restricting tax incentives here.

So as this gets louder into the midterms, how do you think this plays out?

Ariana Salvatore: So, this is definitely one of the big wedge issues, not just for the midterm elections, but for 2028. And to your point, it's expanding into something that's got bipartisan momentum behind it.

Our view is that as long as the Trump administration is in power, something like a federal ban is unlikely to come to fruition. That's because we think the administration is still broadly supportive of the AI data center build-out. And I think even if you were to see a Democrat in office further down the road, that position is the same. And the reason is, it's just too difficult to imagine the U.S. giving up that strategic imperative relative to China.

So, while it is true that voters are against AI, while it is true that you are seeing these sorts of local efforts pick up steam, it's also the case that China is accelerating its own AI build-out – not just domestically, but around the rest of the world too. It's also the case that they are kind of tweaking some export restrictions on inputs for some of these data centers, and those geopolitical realities, I think, are hard to ignore.

So, at the end of the day, there is a broader strategic imperative here that both Democrats and Republicans kind of recognize and get behind. Now, what does that mean in the near term for the build-out? I think it's not that you're going to see a real pushback or moratorium so much as a conditional build-out.

That means you're going to see data centers have to incorporate things like grid modernization in their contracts, agree to longer term investments, for example. Do something that benefits the communities or give it back in some way. And I think that's kind of the policy trajectory in addition to the administration continuing to lean on tech companies to basically, you know, square the circle here and find some way to make this more affordable for, you know, local constituents.

Thomas Wigg: Stephen, let me get your take on this too, because I know you live in the D.C. area, and you have a lot of political conversations like you referenced earlier. How do you think this plays out? Is it a red state versus blue state dynamic?

And if what Ariana says comes to fruition, where it's a conditional build-out in terms of either giving back to the community or ensuring certain prices or certain technologies behind the meter, in front of the meter, does that have implications for certain areas of the market?

Stephen Byrd: Yeah. First, I think Ariana's points were all spot on. I just want to, kind of, build on that and, and dive into it a little more detail.

A few things. The politics are, from my perspective, not being the expert that Ariana is, I find them a little strange – in the sense that at the federal level, we have one dynamic, and at the state and local level, we have a bit of a different dynamic. And what I mean by that is, at the federal level, I think it's becoming increasingly clear just how geopolitically important AI supremacy is.

As these models get more capable, I think it's pretty clear that the Trump administration really sees just how potent these tools are from a geopolitical point of view. So that points in the direction of wanting to support AI and wanting to ensure that the United States has a leading and dominant position in terms of AI capabilities.

Pause there, and then go to your point about, sort of, the local and state level.

Building on what Ariana said, what I see are basically two approaches to data center development. In states where the utility is vertically integrated, meaning they control everything, like Louisiana, I do see a path where – in those kinds of states where the politics are a bit more favorable – you could develop a data center connected to the grid, where the data center developer is paying full freight and then some. Meaning that they are providing back to the community, they're providing sort of net benefits, and there should be plenty of capital to make that work and really support all constituents.

That can work – in a state where the politics work – because utilities are really weather vanes from a political point of view. So, if their state supports data center development, they will more likely support a data center development.

The other approach, though, in many states, whether it's deregulated or it's in a state where the politics are a little less favorable. Which, to your point on the cover of Barron’s, it's a lot of states, what I'm increasingly seeing is that the developers are going to go off grid. And they just don't want to show any impact to the community that could be considered negative.

So, no use of water, no use of power, and hopefully have a, you know, low or zero emissions profile to show no impact at all. Even then, you want to give back to the community. But the view there is, look, we want to sidestep all of these concerns that we might be causing impacts to the grid by just not being connected.

So, I think we're going to see a whole lot of off-grid data center projects. That's mostly natural gas turbines and fuel cells, that general approach. Energy storage will be required in a big way.

That's not easy to do. So, in the context of delays there, the Bitcoin players who do have grid access today are clearly seeing a lot of demand for their products.

So, I would say politics is now a huge issue that's showing up.

The other thing I'd flag is often local communities and states are rejecting projects and using permit requests as a way to do that. So, for example, if your data center needs an air permit because your turbines are going to emit some kind of an, you know, sulfur dioxide, et cetera, into the air, you can run into trouble there. If your data center requires water and you need a water permit, you can run into trouble.

So, that's causing these developers to try to find approaches that really minimize or eliminate the need for those kinds of permits.

Thomas Wigg: Stephen and Ariana, thank you for taking the time. And to our audience, thank you for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen to the show and share the podcast with a friend or colleague today.

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Tom Wigg is a member of Morgan Stanley’s Institutional Equity Division and is not a member of Morgan Stanley’s Research Department. Unless otherwise indicated, his views are his own and may differ from the views of the Morgan Stanley Research Department and from the views of others within Morgan Stanley.