AI investment is reshaping the global outlook. In part one of this economic roundtable, our panel explores where the momentum is strongest — and where investment still needs to catch up.

Read more insights from Morgan Stanley.

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Seth Carpenter: Welcome to Thoughts on the Market. I'm Seth Carpenter, Morgan Stanley's Global Chief Economist and Head of Macro Research. 

Michael Gapen: And I'm Michael Gapen, Chief U.S. Economist. 

Chetan Ahya: And I'm Chetan Ahya, Chief Asia Economist. 

Jens Eisenschmidt: And I'm Jens Eisenschmidt, Chief Europe Economist. 

Seth Carpenter: And today is going to be our third quarter economic roundtable taking a wide-angle view on the global economy and all the key forces shaping our outlook and the economy. 

Seth Carpenter: It's Monday, July 20th at 10am in New York 

Jens Eisenschmidt: And 4pm in Frankfurt. 

Chetan Ahya: And 10pm in Hong Kong. 

Seth Carpenter: Since our last roundtable in April, the global economy has continued to face all sorts of shocks, a mix of resilience and friction. Inflation pressures have not disappeared. Energy and geopolitical risks have come up, they've receded, they've come back, they've receded all over the place 

But there is one underlying source of momentum that we have to talk about. And that is the AI-driven CapEx cycle. 

Michael, let me turn to you because the U.S. is a real focal point of all of this. Tell me a little bit about where Morgan Stanley Research is thinking about hyperscaler CapEx. How big it is? And then for you, when you think about the U.S. economy, just how big of a driver is it for what we're looking for in the U.S.? 

Michael Gapen: Yeah, we continue to revise higher our estimates for hyperscaler and AI-related CapEx in the U.S. economy. We were thinking a little over a trillion for 2027. Now we're more like 1.2 - 1.3 trillion, maybe as high as 1.4 trillion in 2028. So, the level of hyperscaler spending continues to keep rising. 

The growth rate and its effect on the economy is likely to slow. But as you noted, it's still a major driver of momentum in the U.S. You would look at that headline number and think, "Wow, that's, you know, 3.5 percent or so of GDP. Must be a massive source of momentum for GDP growth." But roughly about 60 percent of that hyperscaler CapEx spending goes to items like computers and peripherals, equipment spending categories that have a very, very high import content. 

We still get a significant number that AI CapEx is probably contributing around 40 basis points to growth this year. Be a similar-sized amount perhaps next year.

So, for an economy that's growing somewhere a little bit above 2 percent right now, maybe closer to 2.5 percent next year, that's a non-trivial amount. We just have to remember it's fueling growth around the world, just not here in the U.S. 

Seth Carpenter: Yeah, that's a really great point because I have seen some estimates where people say, "Well, if it wasn't for AI CapEx, the U.S. economy wouldn't have grown at all." And that's clearly wrong, as you point out. 

But U.S. imports are necessarily exports from somewhere else. And, Chetan, if I can pull you into the story then, U.S. firms are buying a lot of AI-related equipment from Asia. What does that mean in your part of the world? And in particular, I'm thinking about Korea, Taiwan, and maybe some other economies in Asia. 

What's the critical story there? 

Chetan Ahya: So, for Asia, this has definitely been a big boon. If you look at Asia's exports, they have been booming, and particularly for the ones which are exporting semiconductors to the U.S. They are seeing semiconductor exports growing by 90 percent. And when we go back in time and compare Asia's semiconductor exports, it's very tightly linked to the U.S. IT CapEx. And it's not surprising when Mike Gapen mentions about the imports going up. It's on the other side, helping Asia's exports quite meaningfully. 

So, so far, we've seen this benefiting Korea, number one, Taiwan, and also Japan. All these three are big beneficiaries of U.S. AI CapEx. And of course, also not just U.S., but the other countries which are doing any little amount of CapEx on AI front, that's also helping these three economies in the region. 

Seth Carpenter: You've been doing a lot of work, Chetan, recently about how much the story can actually broaden out, that the AI CapEx cycle has really contributed to Asian growth, but it doesn't tell the whole story that there's a broader industrial cycle. 

Can you give us a little bit of a flavor of that story? 

Chetan Ahya: That's right, Seth. So, we are actually highlighting that there is a CapEx and industrial super cycle that is underway in Asia, and there are four components to this story. AI and semiconductors CapEx, which we just briefly discussed. 

Number two is energy. Number three is defense. And number four is industrial supply chain onshoring related CapEx. I know that everybody still thinks that AI is the most important part of this story, but when I give you the numbers and the breakup of that... So, for Asia, AI and semiconductor companies CapEx is about $380 billion in 2026, but energy CapEx is going to be $900 billion. 

So, this is a far broader story than just AI for Asia. 

Seth Carpenter: Mike, let me come back to you and to the U.S. then. So, isn't the growth story also broader than that as well domestically? 

So, what's going on in terms of consumer spending in the U.S., and is there a broader CapEx story in the U.S. as well? 

Michael Gapen: I would say, is it broader than that? I think maybe you could argue also it's narrower than that. Here's what I mean by that. As I noted AI CapEx contributing about 40 basis points to growth, it's certainly underpinning equity valuations in the U.S. and underpinning strong wealth creation. 

So about [$]180 trillion in household net worth in the U.S. About [$]55 trillion of that has been created in just the last five years alone, underpinned in part by AI-related spending and optimism about future profitability. That's really supported spending by upper income households. So, I think it's both investment-led and consumer-led, but they're inextricably linked. 

So, the positive for the U.S. is that it's providing a lot of resilience. The negative component of that is it feels like momentum in the U.S. is narrowly driven. 

Jens Eisenschmidt: Let me maybe jump in here from Europe to provide some perspective from the other side. So, I think it's a fair summary to say that AI investment is not yet, or maybe will never get there, dominating the business cycle. 

What we do have instead is an unusually consumption-driven expansion. That has to do not so much with an extraordinary strength of consumption, but more of an absence of other factors. Now, prospectively looking forward, we think the fiscal expansion might help lifting us a little bit. And then it is really the debate how much AI investment can arrive in Europe. 

For now, I would say it's probably a factor of 20 that separates European investment plans from the plans we know that exist for the U.S. 

Seth Carpenter: Let me stick with you then in Europe because you brought up fiscal as one of the factors going on here and where it's going… You and your team recently wrote a blue paper talking about what the outlook is for fiscal policy in Europe, and in particular, we had this era of cheap debt. Interest rates in Europe were low, at times negative. It was super easy to borrow. Not as much happened then. 

There's been a shift towards more fiscal expansion at the same time that interest rates have gone up, causing the cost of debt to go up. Feels like there's a lot of push and pull going on. Can you unpack for us a little bit what was in that paper you wrote, what's going on with fiscal policy in Europe, especially in Germany? And what it might mean over time for Euro-area countries? 

Jens Eisenschmidt: Yeah, so I think fiscal policy in Europe really is looking at a regime shift. So, there is this very famous, probably in the U.S. even more so than here, notion that the Europeans have built a very comfortable welfare state. And that's true if you just look at the accounting from a GDP perspective. It's close to 50 percent that, you know, budgets are actually extended on welfare spending. 

And now you have three structural headwinds for any type of fiscal spend. So, one is aging related costs, you mentioned it already. Defense spending has to increase significantly, and the interest rate costs will also rise significantly. All of that means there will be very hard choices to be made. 

The one thing that actually could help here is growth. Growth is the one thing that's, for now at least, missing, at least in comparison to the U.S. It's probably half what we expect, what the U.S. colleagues think is in stake for the U.S., and a quarter or even less than that of what is there in Asia. 

So, growth is really the key, the solution, the answer to everything in Europe. More growth than just 1 percent, which is potential, would help solving that fiscal challenge. For now, it looks really, really like an uphill battle. Returning to Germany, it's the one country that has a very good fiscal starting position. 

They are pushing a lot but they're to some extent pushing a string. So, even with the German huge fiscal package, given that private sector investments so far are absent, doesn't get us a ton of growth. 

Seth Carpenter: Chetan, maybe I'll come back to you before we close part one of this roundtable. The AI CapEx cycle started with AI, broadened out further. How long do you expect this cycle to last? How durable can it be? And how might it compare to previous CapEx cycles? 

Chetan Ahya: Yeah, Seth. So, we think this will be a multi-year CapEx cycle. And when we are thinking about the duration of the cycle, there are two things that I would keep in mind. 

Number one is that most of the drivers that we just discussed – the CapEx on AI, energy, defense, and industrial supply chain onshoring related investments – these are all structural drivers. So, we think these are going to continue for some more time. At this point of time, we have the visibility for this cycle to be lasting for three-four more years. 

And then the second point of framework that I would keep in mind is that the corporate balance sheets are in a pretty good shape. So, when you are thinking about the leverage in the private sector, you can look at both households and the corporate sector balance sheet. But since the cycle is CapEx driven, we are looking at the corporate balance sheets, and they are in a pretty good shape. 

Across the region, corporate debt to GDP is below where it was in 2019. 

Seth Carpenter: Mike, let me, let me wrap up quickly with you. We talked about AI, AI CapEx. For now, that's a very strong demand story. 

When are we going to see a supply side of things coming from AI? Are you already seeing a big contribution to GDP and growth from productivity coming from AI? 

Michael Gapen: We are, but not outside of the high-tech sectors, and we're seeing limited, what I'll call labor market restructuring of tasks and occupations beyond high AI-exposed occupations. 

So right now, everything is still very isolated I think maybe as we get into 2029 and beyond, so as Chetan says, we probably have a three to four-year super cycle here around a build-out phase. Then we might see some of that broader-based diffusion to other non-tech sectors in the economy. 

Seth Carpenter: All right, Jens, for you, let's wrap up here. So, what is the state of play for the build-out in the CapEx cycle for AI in Europe? 

Jens Eisenschmidt: Yeah, it's very early stages. As I said before, we really; we connected to all the industry experts or analysts covering the sector and the total plans are a factor of 20 below what we see in the U.S. by just the seven hyperscalers. So, I would say very fragmented, very small, in general. Not only AI. 

I think the one thing I would be looking at for any type of sign of revival, sign of growth is investment. The second would be investment. And you can guess what the third would be… Investments in the core countries. That's really what we need to see, and we haven't seen much in Germany or France on this front. 

Seth Carpenter:

That's a great place for us to stop today. We talked about the real side of the economy, AI, CapEx, trade. Tomorrow we're going to come back, and we'll talk about how that growth outlook affects inflation. And once you start talking about growth and inflation, you got to talk about policy, and that's where we'll be tomorrow. 

Mike, Jens, and Chetan, thank you for joining today. And for the listeners, thank you for listening. Be sure to tune in tomorrow for Part 2 of our conversation. And I have to say, if you enjoy this show, please leave us a review wherever you listen, and share Thoughts on the Market with a friend or a colleague today.

Our analysts Andrew Sheets and Mark Schmidt unpack why U.S. healthcare feels so expensive and the potential impacts of rising hospital costs.

Read more insights from Morgan Stanley.

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Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley. 

Mark Schmidt: And I'm Mark Schmidt, Head of Municipal Strategy at Morgan Stanley. 

Andrew Sheets: And today on the program, a discussion into one of the biggest mysteries in one of the biggest sectors of the economy. We're talking about healthcare costs. 

It's Friday, July 17th at 2pm in London. 

Mark Schmidt: At 9am in New York. 

Andrew Sheets: So, we're talking today about healthcare, which represents roughly a fifth of the U.S. economy, the bulk of job creation over the last several years, and in my view, honestly, one of the biggest inflation paradoxes that we see in the market. 

On the one hand, the high cost of healthcare is taken as a given, and it's something that many Americans still struggle with financially. But if you look at the official inflation data in the U.S., healthcare costs have been lower than normal, and that's been true now for a number of years. 

So, what's going on? How do we tie this together? And Mark, you just wrote a report that tries to do exactly that. So, what did you hope to accomplish with this report? 

Mark Schmidt: You're absolutely right. It's hard to underline enough just how large healthcare is to the U.S. economy overall. Americans spend nearly $6 trillion on healthcare. That's more than the GDP of the entire country of Germany. And if we think about prices, Americans pay more. 

A knee replacement, for example, costs $25,000 in the United States. That same procedure costs just $6,000 in France. Common heart treatments that would cost $3,000 in Germany or $10,000 in Australia cost $34,000 in the U.S. 

It also matters for everyone's local community. Healthcare jobs have been growing twice as fast as the rate of job growth in the economy overall. And those are good jobs. They pay above average wages. For many Americans these days, the most secure path to the middle class is a career in healthcare. 

Now, this may seem a little bit arcane, but it probably hits close to your portfolio as well. Earlier in the year, when we took a look at how equity separately managed accounts invest, they typically have a core overweight to healthcare. And even though American prices may seem like an American issue, many of the largest and most profitable healthcare companies in the world are actually headquartered in Europe. 

So, whether you're sitting in New York or sitting in London, the price of American healthcare probably matters to you. 

But as you noted, Andrew, it does feel like a paradox because although Americans cite healthcare costs as one of their top concerns, and although healthcare spending is growing at 6 percent a year or more, the official inflation data says that healthcare prices are in check. And at one point earlier in the year, healthcare inflation, according to official data, even dipped below 3 percent. 

It just didn't make a lot of sense, and that's why we got together with our colleagues across equities, fixed income research, public policy, and economics to dig into what was actually going on. 

Andrew Sheets: So, Mark, let's dig right into that. I mean, it seems like a perfect encapsulation of the so-called Main Street versus Wall Street perception of the economy. 

So, what's going on? How does one kind of square those two numbers? 

Mark Schmidt: The easiest way to understand it is that you can't walk through a grocery store and figure out the price of a knee replacement. And that's true both for you and me. It's also true for the government. They have to survey hospitals and health insurance companies. 

The trouble is that the prices that health insurance companies pay hospitals, well, those are trade secrets. So, at any given point in time, even for the best government economists, it's not entirely clear what the price trends are. And that's why when you look at the official data, healthcare inflation typically has relatively lumpy jumps in the series. 

You could see several months of 0.1 or 0.2 percent official growth in healthcare inflation. Or as earlier this week, you could see certain categories jump to 0.4 or even 0.8. 

Andrew Sheets: Another element, Mark, that you talked about in the report is that people are also consuming more healthcare. So, talk a little bit about that. How that factors into this dynamic, and again, is that just going to be the new normal as the population ages and we tend to spend more on healthcare as we get older? 

Mark Schmidt: That's right. The good news is that we're living longer lives. The bad news is that means that we have more chronic healthcare conditions to deal with. The good news is that more procedures can be done in outpatient settings, and those, generally speaking, are cheaper. The bad news is that inpatient care, inpatient prices go up as the complexity of procedures that actually happen in a hospital setting increase significantly. 

When you balance it all out, it's a situation where, thankfully, the United States and most Americans have the means and the wealth to pay more for healthcare. The flip side of that is that they are paying more for healthcare, and that's why we think that the recent softness in healthcare inflation is actually too good to be true. 

Andrew Sheets: Something that jumped out at me from this report, Mark, was just how important hospitals are in this equation. And the experience of the patient and the experience of the hospital can be different economically. And that difference can also matter for how this shows up in official inflation and government statistics.

So, you know, it would be helpful maybe just to walk the listener through. If I go into the hospital and I need knee surgery. You know, how does that look like from my perspective in terms of paying for it, assuming I have health insurance through my employer? How could that look like to the hospital? And how could that look like coming out the other end into the official government statistics? 

Mark Schmidt: Well, of course, Andrew, the first thing that you do when you break your leg is you call six hospitals and shop around for the cheapest price, right? 

Andrew Sheets: [Laughs] Of course. 

Mark Schmidt: So that's actually the problem because when you get care, you're not in a place to ask about the price. And frankly, even if you asked your doctor or nurse what the price is, they probably wouldn't know. Not only is it not their job to know the price, but all of those negotiations happen after the fact – with the prices that the insurance companies negotiate with the hospitals. 

After COVID, hospitals had a lot more costs to spread out among the people who were coming in the door, and so they raised prices across the board, not just for procedures that were related to respiratory illness. Naturally, insurance companies noticed that, and they started to push back. 

So long after you get a cast for your broken leg – and by the way, I wish you a speedy recovery – insurance companies end up going back and forth negotiating with your doctors for exactly how much they should pay you. And although these prices were loosely set well before you walked in the door, the exact way it gets billed and coded? Well, let's just say there's a lot of back and forth. 

For a well-run hospital, the cost of talking to and ultimately getting reimbursement from your insurance company, that alone could be 2 to 4 percent of revenue. And in especially complex cases, that whole negotiation can eat up 5 to 7 percent of the total bill. 

You're also right to flag that hospitals really are still the central point of the U.S. healthcare system. Americans spend $2 trillion in a hospital setting. And hospitals overwhelmingly coordinate care for both primary, specialty, and pharmacy services. 

Andrew Sheets: Mark, another issue I wanted to ask you about was the Affordable Care Act, Medicare, Medicaid, and how those programs fit into the story? 

Mark Schmidt: The One Big Beautiful Bill Act included a variety of measures to slow the overall growth rate of healthcare. Now, for all the reasons we just discussed, that's probably warranted. The Affordable Care Act is another wrinkle. Enhanced subsidies, which were already set to expire – did in fact expire at the end of last year. And as a result, more Americans are now uninsured. 

It remains to be seen how that impacts overall costs. In the United States, when you have a health emergency, a hospital is legally obligated to treat you because of a 1990s law called EMTALA. Even if you can't pay, the system eventually does. 

Historically, uncompensated care costs have been passed on to individuals and companies with insurance. For now, however, it remains to be seen whether these changes in law and in the overall number of people with insurance will cause healthcare prices to rise or fall. 

Andrew Sheets: And Mark, just for the broad-based implications of this, right? It's fair to say that in any health insurance system, there are some people who consume a lot more healthcare. They're unhealthy or they're unlucky. And there are some who consume a lot less. 

And, you know, this is something where that overall coverage question matters. Because if you have things that reduce the number of otherwise healthy people who are in those healthcare pools, it can raise the cost for everybody else. Those people who were in some ways subsidizing the higher consumers of healthcare are no longer there. 

Is that a fair way to frame it, do you think? And are there potential changes given some of these legislative actions that could lead to changes of what the pool looks like – and what overall costs could look like? 

Mark Schmidt: That's a great point. And healthcare is probably the only part of our economy where you would say, "Thank goodness I did not get my money's worth." As we think about it… 

Andrew Sheets: [Laughs] Very true. Very true. 

Mark Schmidt: As we think about it, most young and healthy people are going to be paying more for their health insurance than they receive in healthcare. Again, that's a good thing. Because American healthcare prices are so much higher than anywhere else in the world, paying in more than you get back? Well, that hits the wallet harder in America than it does in other countries. 

And that's why for many people – choice – choosing how much health insurance to have and how much to pay for it, really is central to keeping the American economy dynamic. The flip side, however, is that as Americans get older, more people have Medicare. 

Now, Medicare is pretty good if you have it. But the catch is that Medicare prices, according to most independent estimates, do not fully reimburse for the cost of care. So, as more seniors take up more beds in a hospital, that means that commercial prices, the prices for people who have private insurance through their employer, are likely to rise even faster. 

Andrew Sheets: So, Mark, I think a good place to close it out and kind of bring this all together is a really important conclusion of this report – is that hospitals have been absorbing a number of these rising costs of healthcare through lower margins for the hospital. And that has resulted in lower ultimate inflation because the inflation is measured out the other side, out ultimately what the hospital earns. 

And if you could just maybe talk a little bit more about that. To what extent have those margins been compressed? And what that might mean for things going forward? 

Mark Schmidt: That's right. We dug into the finances for hundreds of not-for-profit hospitals in the United States. They are facing higher costs and shrinking margins. Historically, hospitals have partially passed on expense increases of this magnitude. 

Now, in their conversations with insurance companies, the biggest benchmark setting of prices happens once every two to three years. So, we're not going to see hospital prices show up in the inflation data overnight. But when we look at hospitals across the country, their budget information and their guidance is consistent with firming prices. 

Andrew Sheets: Great. Thank you so much, Mark. 

I've really enjoyed the conversation. 

Mark Schmidt: Thanks for having me, Andrew. 

Andrew Sheets: And thank you for listening. If you enjoy Thoughts on the Market, please share it with a friend or colleague today. And rate and review us on wherever you listen. It helps more people find the show.

Credit markets are stepping in to fund the surging demand for AI. Our experts Lindsay Tyler and Anish Shah explore the opportunities and risks behind this record financing wave.

Read more insights from Morgan Stanley.

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Lindsay Tyler: Welcome to Thoughts on the Market. I'm Lindsay Tyler, TMT Credit Research Analyst at Morgan Stanley. 

Anish Shah: And I'm Anish Shah, Global Head of Debt Capital Markets at Morgan Stanley. 

Lindsay Tyler: Today, how issuers and investors are approaching the rapidly evolving world of AI financing. 

It's Thursday, July 16th at 10am in New York. 

As AI demand accelerates, credit markets are being asked to finance infrastructure on a scale that used to be associated with utilities, telecom, or energy. That raises a central question for issuers and investors: How much debt can the AI ecosystem absorb? And at what price? 

Anish, can you walk our listeners through the key products in your purview? 

Anish Shah: Certainly, in my nearly twenty years at Morgan Stanley, this is probably the most incredible time period I've ever seen in the credit markets. I've had the privilege of working across a number of different roles in capital markets and lending. And a couple of years ago, we integrated the debt underwriting business across both investment-grade and leverage finance franchises in recognition of how interconnected the whole credit ecosystem has become. 

In addition to our core activities helping clients raise capital for their strategic priorities, two of the big focus areas that we've had have been finding ways to harness the power of the private credit universe and also delivering best-in-class capabilities in funding this incredible growth in AI spend. 

Lindsay Tyler: AI financing has certainly been a theme we've also been focused on in research. Our equity research colleagues project that a handful of key players could add more than 30 gigawatts of capacity over a two-year timeframe, driving around [$]2 trillion of aggregate cash CapEx in that period. And to put that into context, a single gigawatt of data center capacity can require roughly $12 billion for the shell, and then often more than double that for chips and racks. 

So, from your vantage point, what inning are we in? And what gives you confidence that credit markets can continue funding this opportunity at scale? 

Anish Shah: I mean, Lindsay, the numbers certainly are staggering, as you note. And if you just observe the CapEx estimates for the hyperscalers and broadly for AI infrastructure, we're certainly in the early innings. 

Lindsay Tyler: Mm-hmm. 

Anish Shah: The largest tech companies have historically, as you know, raised very little debt. In fact, many of these companies have not even needed a credit facility. As CapEx projections were materially increased in the second half of last year, we saw the beginning of scaled capital raises. Hyperscaler issuance has quickly gone from less than one percent of the investment-grade market to more than 10 percent of the market. 

You know, as I look ahead, based on what we're seeing on the ground, we think that AI-related funding, whether it's for data center development or financing compute capacity, could top 15 percent of the total issuance across all credit products. 

This has been an unprecedented test for the capital markets, both in terms of the depth of capacity and the breadth of product. The teams have been on the forefront of deep investor dialogue and product innovation. 

This spans corporate investment grade, first of their kind financings in high-yield and leveraged loan markets, and new takes on asset-backed financing. And each of these areas has seen material issuance both in public and private markets. 

Lindsay Tyler: Great backdrop. Let's dig first into investment-grade corporate debt, an area you know well from your time previously leading the investment-grade team. 

Can you help frame the scale and the significance of this financing bucket and how AI-related debt is scaling within it? 

Anish Shah: Well, you know, as you know, the investment-grade bond market, specifically in dollars, is the deepest, most liquid pool of capital in the world. Volumes have grown materially over the last few years and are likely to eclipse $2 trillion in issuance this year. 

Hyperscalers are among the very best credits in the world, and they have the ability to come in and out of markets with relatively quick twitch, little to no pre-marketing, and in fairly large size. You know, $20 billion-plus deals used to be rare in the investment-grade market, now happen multiple times a quarter. 

This is why we've seen the predominance of AI-driven capital raising take place in the investment-grade market. For the most part, investors have digested that supply very well. While we've seen some modest widening credit spreads for hyperscalers and some of the other tech issuers, I'd say it's de minimis relative to their expected ROI. 

Lindsay, I've talked a lot about supply dynamics and issuance. What other factors are you and investors considering when assessing fair value for investment-grade rated technology bonds? 

Lindsay Tyler: Sure. It's prudent to really weigh a mix of technicals, fundamentals, and relative value. You know, as you discussed on the technical side, and related to my discussions with debt and equity investors, I've been focused on scale of buildouts, market capacity, digestibility across currencies, positioning along the curve, implications of equity issuance, and whether AI financing could crowd out other areas of TMT credit. 

But moving more to the fundamental side of things, you mentioned ROI, and for the players that are scaling compute capacity, there are a handful of key monetization and return questions that keep coming up. How quickly can these companies bring new capacity online? Once it's live, how does it translate into durable revenue and cash flow? 

Is that capacity supporting internal products, proprietary models, broader cloud offerings, or compute leased to third parties? And then how fungible is the capacity across those use cases if demand or returns shift? 

Further on the fundamental side, we've done some differentiated work around growing long-term commitments. We've seen that high-quality hyperscalers and a few of the semis companies are anchoring the AI ecosystem through leases, guarantees, other obligations. These commitments really extend beyond vanilla bond issuance. 

So, I encourage investors to look beyond the funded debt and really understand the accounting and the ratings implications here of some of those commitments. 

And this ties nicely into the next topic that I wanted to raise, which is project finance debt. I've noticed that, you know, a lot of the commitments that we're seeing from IG players support another layer of financing. Lease commitments can underpin project finance debt, an area of sizable issuance and innovation. 

The public high-yield market has emerged as a new funding source in this way for data center construction, with more than 30 billion priced across 15 deals, since fall 2025. Can you walk us through, Anish, the innovation behind these structures, and how are these high yield deals different than other ways to, kind of, raise project finance debt? 

Anish Shah: Yeah, it's incredibly interesting. I mean, the bulk of the issuance, as I noted has come in the investment grade market, but I would say the bulk of the innovation has come in the sub-investment grade market. 

You know, historically, for very capital-intensive sectors like energy and power or real estate, the project loan market was the most efficient source of initial funding. The developer would tap banks to underwrite a highly structured construction loan. Once the project is up and running, you could then refinance that loan with the predictable cash flows into a more institutional financing, like the investment grade bond market or the term loan B or securitization markets.

That product may still be very viable in many sectors, but we felt early on that bank-provided construction loans would not meet the capacity needs of the AI investment cycle. The market really needed an institutional credit product that bypassed the need for construction loans. 

The key innovation came in the form of first-of-its-kind high-yield bonds that funded the development of a new data center complex. Given the relatively short construction period and the "offtake" supported by some of the highest quality credits in the world, we felt like this financing structure would be incredibly well-received in the high-yield market. 

The win here is that the developer accesses fixed rate long-term capital and maintains flexibility to call the bonds and refinance at a lower cost. Judging by how these financings have gone, there's a strong level of investor enthusiasm. 

I think that they've only scratched the surface, and I would expect that we see much more of this. And potentially even expand it to other products in the leverage finance markets given the tremendous level of investor demand. 

Lindsay Tyler: Yeah. It's certainly been exciting to follow many of those deals. Beyond the public space, we're also seeing a wave of innovation in private credit and asset-backed finance. Anish, how do companies decide whether capital is best raised in the public or the private markets? 

Anish Shah: Well, I'm glad you raised the whole avenue of private markets because it may be the most significant change in the credit markets over the last few years, broadening the scope of private credit from directly lending into leverage buyouts to now financing large investment-grade projects. 

There are great examples in the world of GPU and TPU financing, where we structure loans secured by the asset and the cash flows, or in data center development.

Lindsay, from your perspective, what are investors focused on when these private structures intersect with public credits? 

Lindsay Tyler: Sure. Many of these asset-backed private financings have prompted investors to look more closely at any of the public companies involved, whether as issuers, tenants, customers, or support providers. This ties back to the point I raised earlier. Where does the risk reside, and who ultimately is on the hook? 

These financings have also sparked broader discussions around circularity, vendor financing, and technology obsolescence risk, even when amortizing structures are in place. I do think those are fair concerns to weigh, and they really speak to how quickly the AI financing trend is evolving and how much credit work there is to do. 

So, Anish, with that balance in mind, relatively strong demand, rapid innovation, but also some real credit questions, let's end with a quick lightning round. 

Anish Shah: Lindsay, let's do it. 

Lindsay Tyler: First, what is the biggest risk that could test investor appetite for AI-related debt? 

Anish Shah: I would say investors are acutely focused on construction delays. Don't underestimate the level of diligence being done by the breadth of capacity you're seeing in the markets. Investors are doing their homework, and we're spending a lot of time trying to mitigate any of their concerns with structural protections. 

Lindsay Tyler: Got it. Second, beyond data center shells and chips, what is the next potential AI financing opportunity? 

Anish Shah: It most certainly is energy and power. We're going to see a ton of capital being raised in utilities. It's going to be a little different than what the hyperscalers are doing, just given the nature of their balance sheets. You're going to see more junior capital. We've seen a wave of junior subordinated debt issuance out of the utilities. 

We're also seeing a lot of activity from our project finance and tax equity team, just given all things energy infrastructure. 

Lindsay Tyler: Great. And third, if we're sitting here a year from now, what do you think could be the biggest AI financing story we're talking about? 

Anish Shah: Well, we certainly underestimated the level of financing activity that we saw in the past year. I think when we look back a year from now, we will probably see that the AI labs were much more ready to finance on their own on a standalone basis. That's going to alleviate some of the pressures in the market, but I think it's going to create a whole new set of considerations and structural innovation. 

Lindsay Tyler: Well, it's certainly been remarkable to watch this financing theme take shape in real time, and the next chapter sounds like it could be even more interesting to follow. Anish, thanks for joining us and sharing your insights. 

Anish Shah: Great to join, Lindsay. Thanks. 

Lindsay Tyler: And thank you 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.

*****

Anish Shah is a member of Morgan Stanley’s Global Capital Markets 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.

AI has become a strategic policy priority as governments race to secure their technological future. Our Head of U.S. Public Policy Research Ariana Salvatore explores what’s driving the shift and the implications for markets.

Read more insights from Morgan Stanley.

----- Transcript -----

Welcome to Thoughts on the Market. I’m Ariana Salvatore, Head of U.S. Public Policy Research at Morgan Stanley.

Today: Why sovereign AI is becoming a policy priority around the world.

It’s Wednesday, July 15th, at 10am in New York.

The AI controls debate used to be focused on chips. Cutting edge semiconductors are essential to train large AI models, after all. But over the past year, the debate has moved well beyond that narrow focus. The policy conversation has broadened beyond things like which advanced semis can be sold to China.

The bigger question now is who controls the full AI stack — chips, cloud infrastructure, frontier models, data centers, cybersecurity standards, and the energy systems that support all of it.

That’s what we mean when we talk about sovereign AI. At the simplest level, it's a country’s ability to develop and deploy artificial intelligence using its own infrastructure, data, workforce, and technology ecosystem. But sovereign AI is also about reducing strategic dependence on foreign platforms and foreign-controlled supply chains.

That echoes a trend toward multipolarity that we’ve been writing about since back in 2018. Countries around the world are prioritizing national security over economic efficiencies. We see that theme applying to AI as well.

So, what does this all mean for markets?

First, sovereign AI turns AI infrastructure into a matter of national industrial policy. Data centers, power availability, and grid reliability are just a few examples of components that are becoming strategic assets. That means governments are likely to play a larger role in deciding several aspects of the AI buildout. Where it’s built? Who finances it? And which countries get access to the most advanced parts of the stack?

Second, sovereign AI reinforces the shift toward derisking and a more fragmented international order. The U.S. is trying to promote the export of an American AI technology stack to allies and partners. At the same time, it’s preserving national security guardrails around the most sensitive capabilities. Meanwhile, we see China trying to indigenize as much of the technology as possible, from chips to cloud to model deployment. Other countries are navigating between the two.

Third, and importantly, sovereign AI is also an energy story. Who gets to build and benefit from AI increasingly depends on access to low-cost, reliable power. That makes energy availability a competitive advantage — and it also makes energy affordability a political constraint.

That dovetails with one of our thematic predictions heading into this year: the politics of energy. We see rising power costs as a more visible political issue. That’s led to backlash against data center development. There’s more local opposition to new projects, and greater pressure on policymakers and utilities to make sure that existing ratepayers are not subsidizing AI-driven grid investment.

We think that could push AI infrastructure in a few directions. One is toward a conditional build-out. Here, offsets like large-load tariffs and other cost-allocation mechanisms are designed to protect households and small businesses.

Another direction is policy support for the lowest-cost sources of energy, even where that might create tension with emissions objectives. And the third direction is more off-grid or behind-the-meter power solutions. That would include things like fuel cells, storage, and other time to power strategies — so data center developers can secure electricity without intensifying local affordability concerns.

The pursuit of sovereign AI comes with many questions around inflationary impacts: compute & power are both constrained, regulation remains uncertain, and there could be more limitations on things like tech transfers if the government sees a national security edge. So, to the extent that countries want to reduce their dependencies, it may cost more to get there. There are, however, companies that can benefit in this environment.

But there’s also a policy risk. We are left with a more reactive policy environment. Selective access in some areas, tighter controls in others, and ongoing uncertainty around how Washington will treat advanced chips, cloud infrastructure, and frontier model deployment. Now that uncertainty matters because it affects corporate planning, cross-border investment, and the shape of global AI alliances.

So what does this all mean for investors?

More and more, governments view AI capability as a source of economic power and geopolitical leverage. That means the AI race is moving from a question of who builds the best model to who controls the infrastructure, standards, supply chains, and energy systems that allow those models to scale.

In our view, that means sovereign AI is one of the most important themes to watch in the next phase of the AI buildout.

And we’ll be coming back to this topic soon. In the coming weeks, Stephen Byrd and I will talk about sovereign AI in more depth, particularly around what it means for power demand, data center investment, energy affordability, and the broader infrastructure required to support the next stage of AI adoption.

Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Our CIO and Chief U.S. Equity Strategist Mike Wilson discusses where investors may find opportunity beyond the AI sector and risks that could slow market gains.

Read more insights from Morgan Stanley.

----- Transcript -----

Welcome to Thoughts on the Market. I'm Mike Wilson, Morgan Stanley’s CIO and Chief U.S. Equity Strategist.  

Today on the podcast I’ll be discussing our broadening thesis and the near-term risks to monitor. 

It's Tuesday, July 14th at 11:30 am in New York.   

So, let’s get after it. 

The broadening trade is now playing out. It’s showing up in stock prices, relative performance and earnings revisions. It’s also making investors question the sustainability of the most crowded areas of the market, and consider other near-term risks.  

I first made the broadening call late last year based on my view that the economy had entered a new expansion after completing the rolling recession in April of 2025. In a new expansion, earnings growth tends to be much better than expected because revenue growth returns to companies that have already become more cost efficient. 

That’s classic operating leverage. The market began to anticipate that dynamic late last year, but then the Iran conflict interrupted the move. Oil surged, rate-cut expectations disappeared, and investors crowded back into the most obvious AI capex beneficiaries led by semiconductors and memory, in particular. 

Since mid May, that interruption has faded with oil prices falling sharply and the broadening trade has begun to work again. Importantly, the market is not abandoning AI. It is simply rotating within AI and beyond AI. And that distinction matters. 

Semiconductors have had a historic run, supported by earnings revisions. But even great stories get exhausted in the short term. When earnings revisions breadth is pressing against historical highs and the trade becomes one of the most crowded areas of the market, the bar for upside gets very high. At that point, the issue is not whether the story is good. The issue is whether the rate of change can keep improving. That is a very different question. 

The underperformance of the hyperscalers was probably the first warning sign. Semis depend on hyperscaler capex. So when the spenders start lagging the beneficiaries, that divergence usually resolves one way or another. And now we’re starting to see it. Meta’s decision to sell excess capacity to outside customers may not mean the AI capex cycle is over. But it does tell you the market is beginning to ask harder questions about the path and pace of that spending. 

Credit spreads and stock prices of these hyperscalers provide the feedback loop to managements that maybe they should curtail the pace of spend. We’ve had multiple corrections inside this AI cycle already. This looks like another one – not the end of the cycle, but a reset. 

That reset is what gives the rest of the market room to work. Our preferred ways to express the broadening remain Consumer Discretionary Goods, Transports, and Biotech. These are not the areas investors have been excited about. In fact, positioning and sentiment remain subdued. But that’s exactly why I like them. 

The risks to the story in the short term are two-fold. First, uncertainty about the full re-opening of the strait remains high, with pivots on both sides. This is keeping oil prices volatile in the short term even if the primary trend remains lower.  

Second, interest rate volatility is picking up again with the entire curve shifting higher in both nominal and real terms. If this doesn’t stabilize, it will have a negative impact on stocks both at the index level and even for stocks that should benefit from our broadening call. With the inflation data coming in today softer than expected, this should reduce some of the recent upward pressure on rates.  

However, the new Fed Chair and board remain resolute to make sure inflation doesn’t rear its head again. In the end, dealing with this risk up front is a good thing in my view even if it means uncertainty for markets. 

Bottom line, equity markets have been consolidating and correcting for the past several months. This is the result of the peak rate of change in earnings revisions and a reaction function shift at the Fed to focus more on the inflation mandate than growth.  

With the recent rollover in semiconductors, heavy supply of equity and credit issuance, and a transition of leadership at the Fed, expect more volatility and corrective activity in stocks before the next leg of the bull market resumes.  

Don’t chase momentum. Instead, add to risk on down days to areas that will benefit from a broadening in the economy and earnings growth.  

Thanks for tuning in; I hope you found it informative and useful. Let us know what you think by leaving us a review. And if you find Thoughts on the Market worthwhile, tell a friend or colleague to try it out!

Cheaper obesity medicines could unlock broader demand, while supply-chain bottlenecks and premium-drug innovation may also shape how the market evolves. Our analysts Terence Flynn and Thibault Boutherin break down the investor implications.

Read more insights from Morgan Stanley.

----- Transcript -----

Terence Flynn: Welcome to Thoughts on the Market. I'm Terence Flynn, Morgan Stanley's U.S. Pharma and Biotech Analyst. 

Thibault Boutherin: And I'm Thibault Boutherin, Morgan Stanley's Europe Pharmaceuticals Analyst. 

Terence Flynn: Today, how cheaper GLP-1 obesity medicines could reshape access, pricing, and supply chains; and what the first generic markets may signal for Europe and the U.S. 

It's Monday, July 13th at 10am in New York. 

Thibault Boutherin: And it's 3 pm in London. 

Terence Flynn: Around one billion people live with obesity worldwide, including over a 100 million in the U.S. Right now, the introduction of the first lower cost generics of semaglutide, a GLP-1 medicine, in some international markets, could have consequences on affordability and demand. 

Thibault, what are the first countries seeing the introduction of sema generics? What are the current dynamics, and why should global investors pay attention? 

Thibault Boutherin: Sure. So, so far generics are being introduced this year in three countries: in India, Canada and Brazil. And if we look at India, this is the first market where the generics are being introduced. The patent for semaglutide expired in March 2026, and 13 companies have launched 26 generics across different formulations: autoinjectors, vials, and pills, which price is lower than the branded drug. 

And because the India market was quite under-penetrated for GLP-1, we are seeing affordability driving volume expansion. In Canada, two generics have been launched so far. Four other generics are waiting for approval, and more are being filed. And finally, in Brazil, one generic was approved last month, and we are expecting these generics to be launched in Brazil in July. And 17 other generics are in different stage of regulatory review in Brazil, and we would expect more to enter the market by the end of this year. 

And the reason why we focus on these markets is because we believe they could provide a blueprint for what could happen later in the U.S. and in Europe; in particular for Canada, which shares some characteristics with Europe and the U.S. And the patent for semaglutide will expire in Europe in 2031 and in the U.S. from 2032. 

Terence Flynn: Great. Maybe on the India front, I know that's at the leading edge. What happened with patient demand when price came down? 

Thibault Boutherin: Sure. So, what we saw in India is a surge in volume when generics were launched, and the volume in April 2026 were already six times higher than the volume in February. And that expansion has been driven mostly by these generics launch, which captured 80 percent of semaglutide volume in April. And our India team expect that the GLP-1 market in India will actually expand in value from $125 million in [20]25 to more than $1 billion by 2030, despite lower prices as we see better, you know, greater volume and greater adoption of GLP-1s in India. 

Terence Flynn: The other thing, you know, you and I have discussed is the supply chain, and one of the questions is the ability of some of the generic manufacturers to scale semaglutide. So, maybe talk to us about the current capabilities. And could we see bottlenecks in the supply chain formation here? 

Thibault Boutherin: Yeah, sure. So, there are three key elements to watch on the supply chain. The first is the active pharmaceutical ingredient or API, and that's the semaglutide molecule itself. The second element is the device and the device components, and the third element is the fill and finish, which is basically putting all of these things together. 

On the API side, so semaglutide molecule, we believe there will be no bottleneck in supplying for generics as we see a handful of large Chinese companies, out of China, building multi-ton capacity for semaglutide. So, we believe there will be no shortage of API to supply the generic supply chain for injectables. 

On the device, these are the same device companies that are supplying the branded version of semaglutide, and other GLP-1s for the device that are also supplying the generic makers. And we are seeing meaningful investments being made, so we don't believe there will be a bottleneck here. 

Where we could see a bottleneck emerging is on the fill and finish side. Fill and finish requires highly controlled clean room space to minimize contamination. It requires regulatory approval, and it takes up to three years to build fill and finish capacity. And so, that's where if there is not more investment being made over the next few years, there could potentially [be] a bottleneck emerging for the generic companies. 

Terence, while semaglutide generics will definitely represent a challenge for the existing branded version of this GLP-1, there are some insights in these emerging dynamics that suggest that tirzepatide, the other GLP-1, could be less at risk. Can you touch a bit on some of these dynamics? 

Terence Flynn: Absolutely. So, just to remind listeners that semaglutide targets a pathway called GLP-1. Tirzepatide actually targets two pathways. The first is GLP-1, and the second is GIP. And there are some data comparing these molecules, both in Type 2 diabetes and obesity. And tirzepatide gives not only better efficacy but also improved tolerability. 

And so, what you're seeing in some of the ex-U.S. markets is segmentation, where there are some consumers that are willing to pay a premium price for tirzepatide. Our team in Brazil has done a lot of work on this front looking at this dynamic and, you know, we expect that to play out in many geographies. 

So, despite the entry of lower-cost generic versions, we think you will still see segmentation of the market between differentiated brand and the lower-cost generics. And that as a result, you will continue to see branded growth.

In the U.S. right now, market share is about 60 percent in favor of tirzepatide. And so again, you're seeing a differentiation between these two molecules. 

Thibault Boutherin: And beyond the introduction of generics GLP-1s, there are other dynamics in the industry that are driving this market. And the introduction of oral drugs this year has been a big topic. Terence, what are your views on the role that orals could play on the market? 

Terence Flynn: Yes, as a lot of people are probably aware, the many of the existing GLP-1 medicines are injectable. And so those are delivered once a week with a needle. But there are now additional oral options of these GLP-1 medicines. They started off first for Type 2 diabetes, but they have now broadened into obesity as well, following some recent FDA approvals. 

And what we're seeing is that the introduction in the U.S. so far is expanding the market. So, the majority of people that are taking the oral versions of these medicines are new users to GLP-1s. So again, you're getting market expansion. 

When you think about the orals as well, one of the other questions is capacity. I know, Thibault, you were talking about the supply chain. There are similar questions for these oral medicines because not all of the oral medicines are the same. Some are easier to manufacture than others, and as a result, that's another variable to consider. 

So, some of these are what's called peptide-based orals, and some of these are non-peptide-based orals. And the non-peptide-based orals are much easier to scale, for a larger global market. And so that's definitely another variable that we're monitoring and that I think investors need to consider. 

Thibault Boutherin: And beyond the pill versions of these GLP-1s, we are seeing more innovation in the drug pipeline of the industry, which could be a key driver of differentiation against the competition from the generics. So, what are we seeing emerging today from diabetes and obesity pipelines, which could be exciting for the future of the category? 

Terence Flynn: So, as we see time and time again in pharmaceutical markets, the key players continue to innovate to try to improve profiles of the existing medications. So, there are, you know, kind of two areas. One would be efficacy; another would be safety tolerability. 

And so, there are a number of players that are working first to develop longer acting medication. So, as I mentioned, the existing injectable drugs are dosed once weekly. But there are a number of companies that are working to develop potentially monthly or less frequent injections. So, that's one area that we're monitoring closely. 

And then the second, and again, this plays into what I discussed on tirzepatide, is additional pathways that are involved here in diabetes and obesity, and a number of players are working to target additional pathways beyond GLP-1 and GIP. And so, some of the leading pathways that are being studied are something called amylin and glucagon, and there are a number of medications that are in the late-stage pipeline that are coming along, which have some pretty interesting data. And so that's another area that we're watching. And again, the goal there would be to either improve efficacy and/or improve tolerability versus the existing medications. 

Thibault Boutherin: Great. And maybe we can also take this opportunity to talk about some of the short-term drivers in the market that are not facing generic today, like the U.S. So, what could be, you know, the key drivers for growth of GLP-1s and the overall obesity and diabetes category over the next five years? 

Terence Flynn: Yeah, obviously the key one is seeing additional uptake of these medicines. I think right now we estimate, again, obesity in particular, there's about low double-digit percent uptake. And so obviously seeing increasing uptake of these medicines. 

The orals, as I mentioned, are already driving market expansion. 

And then the third is access. So obviously in any market, that's very important. In the U.S., I think about 50 percent of employers cover these medications right now. We expect that to increase in the years ahead as the data continues to build. 

But then this year starting very shortly, the patients in the Medicare program in the U.S., so those people over the age of 65, will be able to access these medicines for $50 per month. And so, we think that is another driver of growth – is this will broaden access to about an additional 18 million people, starting this summer. 

So, the next phase of the diabesity market comes down to execution, lower cost and scaled supply in the mass market, and innovation and differentiation to compete in the premium segment. Thibault, thanks so much for taking the time to talk. 

Thibault Boutherin: Great speaking with you, Terence. 

Terence Flynn: 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.

The USMCA review is underway, with implications beyond tariffs. Our Head of U.S. Public Policy Research Ariana Salvatore breaks down the key issues shaping the road ahead.

Read more insights from Morgan Stanley.

----- Transcript -----

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

Today, I'll be talking about the USMCA review – what happened on July 1st, what it means for North American trade, and how investors should be thinking about the road ahead. 

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

Last week, the six-year review deadline for the USMCA came and went. And as we'd anticipated, the U.S. declined to extend the agreement for another sixteen-year term. U.S. Trade Representative Greer stated that the U.S. did not agree to renew the USMCA in its current form, pointing to shortcomings and trade deficits with both Canada and Mexico, much of which echoed his testimony in front of Congress in December of last year. 

So, what happens next? 

This decision triggers an annual review process that could continue until the agreement's scheduled expiration in 2036. So, that means effectively the new deadline for negotiations is now July of 2027. And if we get to that point and see a similar outcome, this procedure repeats until the deal is terminated in 2036. 

Now, importantly, the agreement itself remains fully in force during this period. The current tariff regime, rules of origin, investment protections, and dispute settlement mechanisms are all unaffected for now. That's actually in line with the expectation that we laid out earlier this year. In short, we anticipated an outcome in which negotiations stall and the deal moves to annual reviews. We thought that was becoming more likely than an ambitious expansion of the agreement in its current form. 

That being said, there are some important implications of this outcome. 

First, we think North American trade is being reshaped by a transition from a rules-based framework – where tariff schedules and preferential access anchored trade decisions – toward a more discretionary, sector-specific approach tied to industrial policy objectives. That, of course, increases uncertainty around exemptions, sector treatment, and consequently investment decisions for corporates. 

Second, we think two bilateral deals may not be off the table. While it's still our base case that the trilateral framework remains intact, reporting seems to suggest that negotiations are progressing much more substantively with Mexico than with Canada. A third round of U.S.-Mexico negotiations is scheduled for the week of July 20th, while substantive text-based negotiations between Canada and the U.S. have not yet begun. 

That asymmetry could mean that bilateral issues between the U.S. and Mexico are resolved more easily, while outstanding frictions like Canada's dairy market quota system could prove to be an overhang in those bilateral talks. 

Third, the structural divergence between Mexico and Canada is accelerating, which is something my colleagues have highlighted in their recent work. If we think about Canada's manufacturing export base – autos, metals, machinery, energy, and transportation equipment – that actually overlaps with the areas that the U.S. government is increasingly defining as strategic. And therefore, necessitating more government involvement through, in things like Section 232 tariffs. 

Canada accounts for only a negligible share of U.S. imports across computers, semiconductors, communications equipment, and advanced electronics. Those are actually the sectors where Mexico has become deeply integrated, particularly through assembly and re-export activity linked to AI servers, electronics, and industrial hardware. 

Mexico now supplies roughly 35 percent of U.S. IT hardware imports and nearly 50 percent of U.S. server imports. And the North in particular has emerged as a vital interconnection hub between Latin America and the U.S. That's been driven by nearshoring trends, AI adoption, and multi-cloud strategies, as my colleagues Nik Lippmann and Fernando Sedano highlight. That means the scope and the objectives of the bilateral talks between the U.S. and Mexico and the U.S. and Canada may diverge even more from here. 

So where does that leave us? 

The USMCA is still intact, but the annual review process means North American trade policy is now a recurring negotiation, not yet a settled framework. And that will likely remain the case if policymakers agree next July to punt the issue yet another year. 

The primary risk, in our view, stems less from the possibility of a full USMCA collapse and more from the prolonged uncertainty around implementation details, sector-specific trade measures, and Section 232 tariffs. 

Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Robert Feldman and Michael Gapen discuss how AI could reshape growth, labor markets and productivity in the U.S. and Japan.

Read more insights from Morgan Stanley.

----- Transcript -----

Robert Feldman: Welcome to Thoughts on the Market. I'm Robert Feldman, Senior Advisor at Morgan Stanley MUFG Securities in Tokyo. 

Michael Gapen: And I'm Michael Gapen, Morgan Stanley's Chief U.S. Economist. 

Robert Feldman: Today, we'll discuss why the U.S. and Japanese economies may react differently to the AI productivity test. 

It's Thursday, July 9th at 8 pm in Tokyo. 

Michael Gapen: And 9 am in New York. 

Robert Feldman: AI is the biggest theme around the world right now, but AI will play out differently in different economies. Take the cases of the U.S. and Japan. In the U.S., it's already a catalyst in investment, imports, productivity, and the labor market outlook. 

But here in Japan, it's seen as a savior for an economy with an intense labor shortage, low unemployment, and very little room to raise labor force participation. 

Mike, in the U.S., AI's contribution to real GDP growth will rise from about 0.05 percentage points in 2024 to an estimated 0.43 percentage points in 2027. 

What does that mean for markets? 

Michael Gapen: Well, Robby, I think it, it means a number of things, but, you know, I'm an economist, so the answer is always, "It depends." I think the real crux of the issue over time in the U.S., and therefore what it means for financial markets, is ultimately whether AI is labor replacing – and pushes the unemployment rate higher. Or it acts like a more traditional general-purpose technology that's labor augmenting. 

So, if, that's the case, meaning it looks similar to the internet and digital era, then it would mean faster output growth, stronger productivity growth, but still an economy that's running at or near full employment. That would be very beneficial in our estimation for risk assets, equity markets, credit markets, and it would probably mean that we stay in an interest rate environment that's certainly higher than it was during the post GFC period. 

But if – AI is a very different technology than we've seen in the past, and it displaces labor, and we get increases in the unemployment rate as AI diffuses through the economy. Then it could be very different for markets. Maybe returns to capital and equity markets are supported, but that might be more narrowly for technology stocks and not broader, say, consumer discretionary stocks. 

So, the answer, of course, is it depends. We don't know. And I think, ultimately, we come down on the side of thinking that AI will not create dystopian outcomes in the labor markets, that employment will hold up. 

So, we have a fairly constructive view, perhaps an optimistic view. And we think, ultimately it'll benefit markets greatly, similar to what we saw from the mid-90s to the early 2000’s. 

Robert Feldman: Well, in your model, you have a particular variable that captures the speed of diffusion. But your baseline has AI spreading twice as fast as the internet did. But without that rise of employment. Is that really manageable? And if it's not, what economic indicators would warn us, if we're crossing into the danger zone? 

Michael Gapen: This is really the tricky part as, as you know. We have a new technology. We have to model how it diffuses through the economy. And I would say I think there's an argument here that penetration rates and usage rates are very different than what economists think about diffusion, which is how the production process is reshaped because of this new technology. 

And so most economists look at the internet and digital era and think it took 20-25 years to fully diffuse. Mass penetration in maybe 10 years, but full diffusion in more like 20-25 years. And so, each innovation cycle tends to happen more rapidly. 

So, I do think AI will spread more rapidly. And even by saying it spreads twice as fast as the internet did still means that it'll take roughly a decade, maybe 10-12 years for this to fully diffuse. So, our argument here would be that that is enough time for a flexible economy and a flexible labor market, like we have in the U.S., to rebalance labor. 

But if we're wrong, then Robby, what I think you will see is that as AI rolls through, it diffuses faster. And what we would see then is increases in rates of job separation and layoffs that would overwhelm the labor market's ability to reallocate workers. 

So, I think we would see two things – or three things: scale layoffs, a rise in the unemployment rate, and probably a significant amount of underemployment. Those who get rebalanced may be rebalanced into work that's not, say, consistent with the skill of that worker. So, I think we would see a very disrupted labor market in the process. 

But if it takes a decade, maybe 10-12 years, we think ultimately the U.S. economy is flexible enough to rebalance labor without large scale layoffs. 

Robert Feldman: Now, people are afraid of a lot of things, but one other thing is that AI might create new kinds of jobs, new kinds of tasks, have different impacts on people's wealth, and different responses from policymakers as well. 

How do these knock-on effects change the AI labor story? 

Michael Gapen: Yeah. That's right. I think you make a very good point there that I think it's easy to fall into what an economist would call a partial equilibrium trap. So, for example, we look at occupations exposed to AI task replacement, and we say, "Wow, if all these tasks are replaced, we might lose 10 million workers or 20 million workers." 

But that's too simplistic, in our view. Because as you note, AI may destroy some tasks or replace some tasks, but it's also going to create new ones. So, it may eliminate some types of occupations but create others. 

And in addition, if people are, say, laid off because of AI, you get a loss in labor market income for the economy. But AI will likely create returns to capital, say, stronger equity performance, and that's an indirect wealth effect. 

So, our model kind of, looks at, say, three wedges or three horse races in the economy then. It's about the speed of diffusion of AI against the ability of the labor market to rebalance. It's task destruction or task replacement versus new task creation. And then third, it's we might have weakness in labor market income in the short run, but there are indirect wealth effects. 

So, thinking about it this way in a richer general equilibrium context, these feedback effects matter a lot. So, the combination of if the labor market's disrupted, we get easing in monetary policy, maybe a fiscal response. There are new tasks, new jobs that are created for workers to rebalance to over time. And overall demand in the economy gets held up because wealth effects can offset some lost income. 

All of that is extremely important in our view that ultimately the U.S. economy can rebalance and handle the AI diffusion in a manageable way. 

We could be wrong, of course, but our main point here is you have to think about this in a richer context. You can't just simply, say, stack up workers and occupations and say, "Oh, we're going to lose a lot of employment." That's not the way innovation waves have worked in the past. We don't think they're going to work that way in the future. 

Robert Feldman: Mm-hmm. That's fascinating because the situation in the United States is so different from that in Japan, largely because of the demographic situation. 

Here in Japan, the key element is how much AI can ease the labor shortage. In fact, in some labor-intensive jobs now, we're seeing 6 percent wage increases, and that's great. As long as productivity rises fast enough that price hikes aren't necessary. 

Michael Gapen: So Robby, in your scenarios for Japan, the same 10 percent productivity gain can lead to very different outcomes. Deflation and weaker employment in one case. More inflation, higher wages, and more employment in another. 

What do you think drives the difference? 

Robert Feldman: Mm-hmm. Well, the crucial element really is the flexibility of goods and labor markets. With high flexibility, you get higher GDP, higher employment, and moderate inflation. With low flexibility, you may get a bit higher GDP, but employment plunges, and there's deflation of both prices and wages – more in wages. 

Now, in Japan, over the last two decades, we've seen monopoly power in key markets go down. For example, agriculture and energy. Labor markets are more flexible too, but lifetime employment system still applies to about two-thirds of the economy. And that deters people from trying to find better jobs and even from acquiring the skills needed for a new job. 

Michael Gapen: What conditions are needed for AI to be additive to Japan's economy? 

Robert Feldman: We need more reskilling. Japan is lucky because people are healthy, and they want to work into their 70s and beyond. But acquiring the skills to remain productive is a challenge, even though Japan's workforce is well-educated and still has a strong work ethic. 

So, to sum up, in the U.S., the race is between diffusion and absorption. But in Japan it's between labor scarcity and productivity. Is that fair? 

Michael Gapen: It is fair, and we come down on the side of optimism. We think diffusion will happen fast, but it'll happen at a pace that the U.S. economy can handle. 

So, we come down having a positive view overall. We do not lean in the direction of dystopian labor market outcomes. 

Robert Feldman: Mm-hmm. I agree with that as well for Japan. So, Mike, thanks for taking the time to talk. 

Michael Gapen: Great speaking with you, Robby-san. 

Robert Feldman: And thanks for listening, everyone. 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.

Our Global Head of Fixed Income Research Andrew Sheets outlines what could potentially go wrong and disrupt markets’ optimism this summer.

Read more insights from Morgan Stanley.

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Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley. 

Today, discussing three things that could disrupt a quiet summer. 

It’s Wednesday, July 8th at noon in New York. 

As markets turn the page toward the second half of the year, there are lots of reasons for optimism. Global growth remains solid. Earnings growth is strong, and broadening across more companies. Capital markets remain open and deal activity is robust. We continue to think that the best analogy for current conditions is something like 1997 through 1998 or 2005 through 2006 – periods where corporate aggression was increasing, and had further to go, leading to equities outperforming credit. 

Even more immediately, July also happens to be one of the best months of the year for markets. And while one should never base their entire investment strategy on how far the earth has travelled around the sun, this month has been the best month for the U.S. High Yield returns, by far, over the last 15 years. The last time the S&P 500 fell in the month of July was 2014. 

So given all that, what could go wrong? Well, here are three things that are on our mind. 

First, a key part of our most optimistic view is that U.S. inflation will be lower than the Federal Reserve expects in the second half of this year, leading them to leave interest rates unchanged, rather than raise rates as the market expects. 

The risk is that this assumption is just wrong, perhaps soon. There is certainly an argument that, if the Fed is worried about inflation, it shouldn’t wait to act, and the market is currently placing roughly 1-in-3 chance that the Fed hikes rates on July 29th. If that happens – and again, our base case is it does not – it could drive volatility. 

Second is earnings season, which kicks off next week. While the general trend of earnings is important, the bigger focus is likely to be on the results of large U.S. tech companies, and in particular, how much they plan to spend building out AI infrastructure. 

Over the last several quarters, almost like clockwork, these spending estimates have been revised higher and higher. And that has helped boost confidence in AI – as the spending is a sign that the technology holds promise – as well as boosting the broader earnings outlook; since all of this spending is becoming other company’s revenue. 

Our base-case remains that this AI spending cycle has further to run, with capex from the major U.S. hyperscalers rising from over $800bn of spending this year to roughly $1.2 trillion of spending next year. 

But the risk would be that second quarter earnings now show more hesitation to spend, maybe because the share prices of some of these big spenders have been recent underperformers. And given how much the current growth and earnings story is linked to AI, and how popular AI exposure is with investors, that would create a risk. 

Finally, there’s Iran. Our base case assumes a gradual renormalization of flows through the Strait of Hormuz, and we forecast Brent oil at about $75/bbl in 12 months time, which is pretty similar to current levels. But as of this recording there were reports of renewed hostilities, and the ceasefire may be fragile. 

The U.S. has already drawn down its Strategic Petroleum Reserve to its lowest-ever levels, potentially reducing some ability to absorb shocks if the conflict re-escalates. 

Historically, July tends to be strong, and markets have a number of helpful tailwinds at their back. But an unexpected rate hike, an unexpected reduction in Hyperscaler Capex, and a resumption of the Iran conflict are three factors that are not in our base-case – and could disrupt that. 

Thank you, as always, for your time. If you find Thoughts on the Market useful, let us know by leaving a review wherever you listen. Also tell a friend or colleague about us today.

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.

Read more insights from Morgan Stanley.

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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.