Lower Prices, Bigger Market: The Next Phase of GLP-1 Drugs

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 am 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, Robbie, 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 Robbie, 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 Robbie, 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, Robbie-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.

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 29^th. 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.