Thoughts on the Market

AI as New Global Power?

February 27, 2026

AI as New Global Power?

February 27, 2026

Our Deputy Head of Global Research Michael Zezas and Stephen Byrd, Global Head of Thematic and Sustainability Research, discuss how the U.S. is positioning AI as a pillar of geopolitical influence and what that means for nations and investors.

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Transcript

Michael Zezas: Welcome to Thoughts on the Market. I'm Michael Zezas, Morgan Stanley's Deputy Head of Global Research.

 

Stephen Byrd: And I'm Stephen Byrd, Global Head of Thematic and Sustainability Research.

 

Michael Zezas: Today – is AI becoming the new anchor of geopolitical power?

 

It's Friday, February 27th at noon in New York.

 

So, Stephen, at the recent India AI Impact Summit, the U.S. laid out a vision to promote global AI adoption built around what it calls “real AI sovereignty.” Or strategic autonomy through integration with the American AI stack. But several nations from the global south and possibly parts of Europe – they appear skeptical of dependence on proprietary systems, citing concerns about control, explainability, and data ownership. And it appears that stake isn't just technology policy. It's the future structure of global power, economic stratification, and whether sovereign nations can realistically build competitive alternatives outside the U.S. and China.

 

So, Stephen, you were there and you've been describing a growing chasm in the AI world in terms of access to strategies between the U.S. and much of the global south, and possibly Europe. So, from what you heard at the summit, what are the core points of disagreement driving that divide?

 

Stephen Byrd: There definitely are areas of agreement; and we've seen a couple of high-profile agreements reached between the U.S. government and the Indian government just in the last several days. So there certainly is a lot of overlap. I point to the Pax Silica agreement that's so important to secure supply chains, to secure access to AI technology. I think the focus, for example, for India is, as you said; it is, you know, explainability, open access. I was really struck by Prime Minister Modi's focus on ensuring that all Indians have access to AI tools that can help them in their everyday life.

 

You know, a really tangible example that really stuck with me is – someone in a remote village in India who has a medical condition and there's no doctor or nurse nearby using AI to, you know, take a photo of the condition, receive diagnosis, receive support, figure out what the next steps should be. That's very powerful. So, I'd say, open access explainability is very important.

 

Now, the American hyperscalers are very much trying to serve the Indian market and serve the objectives really of the Indian government. And so, there are versions of their models that are open weights, that are being made freely available for health agencies in India, as an example; to the Indian government, as an example.

 

So, there is an attempt to really serve a number of objectives, but I think this key is around open access, explainability, that I do see that there's a tension.

 

Michael Zezas: So, let's talk about that a little bit more. Because it seems one of the concerns raised is this idea of being captive within proprietary Large Language Models. And maybe that includes the risk of having to pay more over time or losing control of citizen data. But, at the same time, you've described that there are some real benefits to AI that these countries want to adopt.

 

So, what is effectively the tension between being captive to a model or the trade off instead for pursuing open and free models? Is it that there's a major quality difference? And is that trade off acceptable?

 

Stephen Byrd: See, that's what's so fascinating, Mike, is, you know, what we need to be thinking about is not just where the technology is today, but where is it in six months, 12 months, 24 months? And from my perspective, it's very clear. That the proprietary American models are going to be much, much more capable.

 

So, let's put some numbers around that. The big five American firms have assembled about 10 times the compute to train their current LLMs compared to their prior LLMs, and that's a big deal. If the scaling laws hold, then a 10x increase in training compute to result in models are about twice as capable.

 

Now just let that sink in for a minute, twice as capable from here. That's a big deal. And so, when we think about the benefit of deploying these models, whether it's in the life sciences or any number of other disciplines, those benefits could start to get very large. And the challenge for the open models will be – will they be able to keep up in terms of access to compute, to training, access to data to train those models? That's a big question.

 

Now, again, there's room for both approaches and it's very possible for the Indian government to continue to experiment and really see which approach is going to serve their citizens the best. And I was really struck by just how focused the Indian government is on serving all of their citizens. Most notably, you know, the poorest of the poor in their nation. So, we'll just have to see.

 

But the pure technologist would say that these proprietary models are going to be increasing capability much faster than the open-source models.

 

So, Mike, let's pivot from the technology layer to the geopolitical layer because the U.S. strategy unveiled at the summit goes way beyond innovation.

 

Michael Zezas: Yeah, it's a good point. And within this discussion of whether or not other countries will choose to pursue open models or more closely adhere to U.S. based models is really a question about how the United States exercises power globally and how it creates alliances going forward.

 

Clearly some part of the strategy is that the U.S. assumes that if it has technology that's alluring to its partners, that they'll want to align with the U.S.’ broad goals globally. And that they'll want to be partners in supporting those goals, which of course are tied to AI development.

 

So, the Pax Silica [agreement], which you mentioned earlier, is an interesting point here because this is clearly part of the U.S. strategy to develop relationships with other countries – such that the other countries get access to U.S. models and access to U.S. AI in general. And what the U.S. gets in return is access to supply chain, critical resources, labor, all the things that you need to further the AI build out. Particularly as the U.S. is trying to disassociate more and more from China, and the resources that China might have been able to bring to bear in an AI build out.

 

Stephen Byrd: So, Mike, the U.S. framed “real AI sovereignty” as strategic autonomy rather than full self-sufficiency. So, essentially the. U.S. is encouraging nations to integrate components of the American AI stack. Now, from your perspective, Mike, from a macro and policy standpoint, how significant is that distinction?

 

Michael Zezas: Well, I think it's extremely important. And clearly the U.S. views its AI strategy as not just economic strategy, but national security strategy.

 

There are maybe some analogs to how the U.S. has been able to, over the past 80 years or so, use its dominance in military and military equipment to create a security umbrella that other countries want to be under. And do something similar with AI, which is if there is dominant technology and others want access to it for the societal or economic benefits, then that is going to help when you're negotiating with those countries on other things that you value – whether it be trade policy, foreign policy, sanctions versus another country. That type of thing.

 

So, in a lot of ways, it seems like the U.S. is talking about AI and developing AI as an anchor asset to its power, in a way that military power has been that anchor asset for much of the post World War II period.

 

Stephen Byrd: See, that's what's so interesting, Mike, [be]cause you've highlighted before to me that you believe AI could replace weaponry as really the anchor asset for U.S. global power. Almost a tech equivalent of a defense umbrella.

So how durable is that strategy, especially given that some countries are expressing unease about dependency?

 

Michael Zezas: Yeah, it's really hard to know, and I think the tension you and I talked about earlier, Stephen, about whether countries will be willing to make the trade off for access to superior AI models versus open and free models that might be inferior, that'll tell us if this is a viable strategy or not. And it appears like this is still playing out because, correct me if I'm wrong, it's not like we've received some very clear signals from India or other countries about their willingness to make that trade off.

 

Stephen Byrd: No, I think that's right. And just building on the concept of the trade-offs and, sort of, the standard for AI deployment, you know, the U.S. has explicitly rejected centralized global AI governance in favor of national control aligned with domestic values.

 

So, what does that signal about how global technology standards may evolve, particularly as in the U.S., the National Institute of Standards and Technology, or NIST, works to develop interoperable standards for agentic AI systems.

 

Michael Zezas: Yeah, Stephen, I think it's hard to know. It might be that the U.S. is okay with other countries having substantial degrees of freedom with how they use U.S.-based AI models because they could use U.S. law to, at a later date, change how those models are being used – if there's a use case that comes out of it that they find is against U.S. values. Similar in some way to how the U.S. dollar being the predominant currency and, therefore, being the predominant payment system globally, gives the U.S. degrees of freedom to impose sanctions and limit other types of economic transactions when it's in the U.S. interest.

 

So, I don't know that to be specifically true, but it's an interesting question to consider and a potential motivation behind why a laissez-faire approach might be, ultimately, still aligned with U.S. interests.

 

Stephen Byrd: So, Michael, it sounds like really AI is becoming the new strategic infrastructure globally.

 

Michael Zezas: Yeah, I think that's actually a great way to think about it. And so, Stephen, if that were the case, and we're talking about the potential for this to shape geopolitical competition, potentially economic differentials across the globe. And if that is correlated, at least, to some degree with the further development and computing power of these models, what do you think investors should be looking at for signals from here?

 

Stephen Byrd: Number one, by a mile for me, is really the pace of model progress. Not just American models, but Chinese models, open-source models. And there the big reveal for the United States should be somewhere between April and June – for the big five LLM players. That's a bit of speculation based on tracking their chip purchases, their power access, et cetera. But that appears to be the timeframe and a couple of execs have spoken to that approximate timeframe.

 

I would caution investors that I think we're going to be surprised in terms of just how powerful those models are. And we're already seeing in early 2026, these models that were not trained on that kind of volume of compute have really exceeded expectations, you know, quite dramatically in some cases. And I'll give you one example.

 

METR is a third-party that tracks the complexity, what these models can do. And METR has been highlining that every seven months, the complexity of what these models are able to do approximately doubles. It’s very fast. But what really got my attention was about a week ago, one of the LLMs broke that trend in a big way to the upside.

 

So, if the scaling laws would hold, based on what METR would've expected, they would expect a model to be able to act independently for about eight hours, a little over eight hours. And what we saw was, the best American model that was recently introduced was more like 15. That's a big deal. And so, I think we're seeing signs of non-linear improvement.

 

We're also going to see additional statements from these AI execs around recursive self-improvement of the models. One ex-AI executive spoke to that. Another LLM exec spoke to that recently as well. So, we're starting to see an acceleration. That means we then need to really consider the trade-offs between the open models and the proprietary. That's going to become really critical and that should happen really through the spring and summer.

Michael Zezas: Got it. Well, Stephen, thanks for taking the time to talk.

 

Stephen Byrd: Great speaking with you, Mike.

 

Michael Zezas: And thanks for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen. And share the podcast with a friend or colleague today.

 

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Transcript

Ariana Salvatore: Welcome to Thoughts on the Market. I'm Ariana Salvatore, Head of Public Policy Research for Morgan Stanley.

 

Today I'll be talking about our expectations for the upcoming USMCA review, and how the landscape has shifted from last year.

 

It's Wednesday, February 11th at 4pm in London.

 

As we highlighted last fall, the US-Mexico-Canada Agreement is approaching its first mandatory review in 2026. At the time, we argued that the risks were skewed modestly to the upside. Structural contingencies built into the agreement we think cap downside risk and tilt most outcomes toward preserving and over time deepening North American trade integration.

 

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We continue to expect an outcome that preserves the agreement and resolves several outstanding disputes – auto rules of origin, labor enforcement procedures, and select digital trade provisions.

 

On the China question, our view from last year also still holds. We expect incremental steps by Mexico to reduce trans-shipment risk and better align with U.S. trade priorities, though likely without a fully institutionalized enforcement mechanism by mid-2026. And remember, the USMCA’s 10-year escape clause keeps the agreement enforced at least through 2036, meaning the probability of a disruptive trade shock is structurally quite low.

 

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We still expect the formal review to conclude around mid-2026, albeit with a growing possibility that deeper institutional alignment happens further out or via parallel frameworks. It also is possible that into that deadline all three sides decide to extend negotiations out further into the future, extending the uncertainty for even longer.

 

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Thanks for listening. As a reminder, if you enjoy Thoughts on the Market, please take a moment to rate and review us wherever you listen. And share Thoughts on the Market with a friend or colleague today.

 

 

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Our Global Head of Thematic and Sustainability Research Stephen Byrd and U.S. Thematic and Equity...

Transcript

Stephen Byrd: Welcome to Thoughts on the Market. I'm Stephen Byrd, Global Head of Thematic and Sustainability Research.

 

Michelle Weaver: And I'm Michelle Weaver, U.S. Thematic and Equity Strategist.

 

Stephen Byrd: I was recently on the show to discuss Morgan Stanley's four key themes for 2026. Today, a look at how those themes could actually play out in the real world over the course of this year.

 

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

 

So one of the biggest challenges for investors right now is separating signal from noise. Markets are reacting to headlines by the minute, but the real drivers of long-term returns tend to move much more slowly and much more powerfully. That's why thematic analysis has been such an important part of how we think about markets, particularly during periods of high volatility.

 

For 2026, our framework is built around four key themes: AI and tech diffusion, the future of energy, the multipolar world, and societal shifts. In other words, three familiar themes and one meaningful evolution from last year. So Michelle, let's start at the top. When investors hear four key themes, what's different about the 2026 framework versus what we laid out in 2025?

 

Michelle Weaver: Well, like you mentioned before, three of our four key themes are the same as last year, so we're gonna continue to see important market impacts from AI and tech diffusion, the future of energy and the multipolar world.

But our fourth key theme, societal shifts, is really an expansion of our prior key theme longevity from last year. And while three of the four themes are the same broad categories, the way they impact the market is going to evolve. And these themes don't exist in isolation. They collide and they intersect with one another, having other important market implications. And we'll talk about many of those intersections today as they relate to multiple themes.

 

Let's start with AI. How does the AI and tech diffusion theme specifically evolve since last year?

 

Stephen Byrd: Yeah. You know, you mentioned earlier the evolution of all of our themes, and that was certainly the case with AI and tech diffusion.

 

What I think we'll see in 2026 is a few major evolutions. So, one is a concept that we think of as two worlds of LLM progress and AI adoption; and let me walk through what I mean by that. On LLM progress, we do think that the handful of American LLM developers that have 10 times the compute they had last year are going to be training and producing models of unprecedented capability.

 

We do not think the Chinese models will be able to keep up because they simply do not have the compute required for the training. And so we will see two worlds, very different approaches. That said, the Chinese models are quite excellent in terms of providing low cost solutions to a wide range of very practical business cases.

 

So that's one case of two worlds when we think about the world of AI and tech diffusion. Another is that essentially we could see a really big gap between what you can do with an LLM and what the average user is actually doing with LLMs. Now there're going to be outliers where really leaders will be able to fully utilize LLMs and achieve fairly substantial and breathtaking results. But on average, that won't be the case. And so you'll see a bit of a lag there. That said, I do think when investors see what those frontier capabilities are, I think that does eventually lead to bullishness.

 

So that's one dynamic. Another really big dynamic in 2026 is the mismatch between compute demand and compute supply. We dove very deeply into this in our note, and essentially where we come out is we believe, and our analysis supports this, that the demand for compute is going to be systematically much higher than the supply. That has all kinds of implications. Compute becomes a very precious resource, both at the company level, at the national level. So those are a couple of areas of evolution.

So Michelle, let's shift over to the future of energy, which does feel very different today than it did a year ago. Can you kind of walk through what's changed?

 

Michelle Weaver: Well, we absolutely still think that power is one of the key bottlenecks for data center growth. And our power modeling work shows around a 47 gigawatt shortfall before considering innovative time to power solutions. We get down to around a 10 to 20 percent shortfall in power needed in the U.S. though, even after considering those solutions. So power is still very much a bottleneck.

 

But the power picture is becoming even more challenged for data centers, and that's largely because of a major political overhang that's emerging. Consumers across the U.S. have seen their electricity bills rise and are increasingly pointing to data centers as the culprit behind this. I really want to emphasize though this is a nuanced issue and data center power demand is driving consumer bills higher in some areas like the Mid-Atlantic. But this isn't the case nationwide and really depends on a number of factors like data center density in the region and whether it's a regulated or unregulated utility market.

But public perception has really turned against data centers and local pushback is causing planned data centers to be canceled or delayed. And you're seeing similar opinions both across political affiliations and across different regional areas. So yes, in some areas data centers have impacted consumer power bills, but in other areas that hasn't been the case. But this is good news though, for companies that offer off-grid power generation, who are able to completely insulate consumers because they're not connecting to the grid.

Stephen, the multipolar theme was already strong last year. Why has it become even more central for 2026?

 

Stephen Byrd: Yeah, you're right. It was strong in 2025. In fact, of our 21 categories of stocks, the top three performing were really driven by multipolar world dynamics. Let me walk through three areas of focus that we have for multipolar world in 2026. Number one is an aggressive U.S. policy agenda, and that's going to show up in a number of ways. But examples here would be major efforts to reshore manufacturing, a real evolution in military spending towards a wide range of newer military technologies, reducing power prices and inflation more broadly. And also really focusing on trying to eliminate dependency on China for rare earths.

 

So that's the first big area of focus. The second is around AI technology transfer. And this is quite closely linked to rare earths. So here's the dynamic as we think about U.S. and China. China has a commanding position in rare earths. The United States has a leading position in access to computational resources. Those two are going to interplay quite a bit in 2026.

 

So, for example, we have a view that in 2026, when those American models, these LLMs achieve these step changes up in capabilities that China cannot match, we think that it's very likely that China may exert pressure in terms of rare earths access in order to force the transfer of technology, the best AI technology to China.

 

So that's an example of this linkage between AI and rare earths. And the last dynamic, I'd say broadly, would be the politics of energy, which you described quite well. I think that's going to be a big multipolar world dynamic everywhere around the world. A focus on how much of an impact our data centers are having – whether it's water access, price of power, et cetera. What are the impacts to jobs? And that's going to show up in a variety of policy actions in 2026.

 

Michelle Weaver: Mm-hmm.

 

Stephen Byrd: So Michelle, the last of our four key themes is societal shifts, and you walked through that briefly before. This expands on our prior longevity work. What does this broader framing capture?

 

Michelle Weaver: Societal shifts will include important topics from longevity still. So, things like preparing for an aging population and AI in healthcare. But the expansion really lets us look at the full age range of the demographic spectrum, and we can also now start thinking about what younger consumers want. It also allows us to look at other income based demographics, like what's been going on with the K-economy, which has been an important theme around the world.

 

And a really critical element, though, of this new theme is AI's impact on the labor market. Last year we did a big piece called The Future of Work. And in it we estimated that around 90 percent of jobs would be impacted by AI. I want to be clear: That's not to say that 90 percent of jobs would be lost by AI or automated by AI. But rather some task or some component of that job could be automated or augmented using AI.

 

And so you might have, you know, the jobs of today looking very different five years from now. Workers are adaptable and, and we do expect many to reskill as part of this evolving job landscape.

 

We've talked about the evolution of our key themes, but now let's focus a little on the results. So how have these themes actually performed from an investment standpoint?

 

Stephen Byrd: Yeah. I was very happy with the results in 2025. When we looked across our categories of thematic stocks; we have 21 categories of thematic stocks within our four big themes. On average in 2025, our thematic stock categories outperformed MSCI World by 16 percent and the S&P 500 by 27 percent respectively. So, I was very happy with that result.

 

When you look at the breakdown, it is interesting in terms of the categories, you did really well. As I mentioned, the top three were driven by multipolar world. That is Critical Minerals, AI Semis, and Defense. But after that you can see a lot of AI in Energy show up. Power in AI was a big winner. Nuclear Power did extremely well. So, we did see other categories, but I did find it really interesting that multipolar world really did top the charts in 2025.

 

Michelle Weaver: Mm-hmm.

 

Stephen Byrd: Michelle, thanks for taking the time to talk.

 

Michelle Weaver: Great speaking with you, Steven.

 

Stephen Byrd: And thanks for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen and share the podcast with a friend or colleague today. 

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