Thoughts on the Market

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.

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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 Officer 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.

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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.

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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.

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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.

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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.