Thoughts on the Market

From short-term interest rates to long-term bond yields, the Fed's credibility is being tested. Global Head of Fixed Income Research Andrew Sheets discussed inflation, Federal Reserve Chair Kevin Warsh's outlook, and the options ahead.
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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.
Today: Can the Fed hold the line?
It's Wednesday, August 5th at 2pm in London.
The Federal Reserve has a difficult job.
The U.S. economy is a complex and varied ecosystem that covers everything from brain surgery to your burger order. The Fed is asked to keep prices stable and people employed using, for the most part, just one simple tool. A short-term interest rate, and without any control over what government policy or global events might bring.
Currently, the Fed probably feels pretty good about its success with one half of this – in the job market, given that the unemployment rate is near historical lows. But it probably feels less successful about price stability. Over the last five years, overall prices in the U.S. economy have risen over 20 percent based on the Fed's preferred inflation measure. That's roughly double the increase that a goal of 2 percent annual inflation would otherwise bring.
Into this complexity steps a new Fed chair, Kevin Warsh.
He has emphasized two changes for his tenure. First, that inflation is too high and needs to come down. And second, that the Fed has historically communicated too much with the market, which Chair Warshkeep thinks has helped contribute to investors potentially taking too much risk while also restricting the Fed's options to act.
What markets are now processing is a potential tension between these two goals.
After all, high inflation is an immediate issue. In a world where the Fed is hoping to keep price increases at about 2 percent per year, their preferred measure, PCE inflation, is rising more than 3 percent on an annualized basis over the last three, six, and 12 months. In the latest ISM Manufacturing Survey, [the] measure of price increases among manufacturers is well above normal.
In the face of that, one option for the Fed to combat this inflation would have been to raise interest rates. It didn't do that. Another would be to suggest that it was very close to taking action and likely to move soon. It didn't do that either.
Indeed, our economists think that the market took Chair Warsh's lack of guidance and action at the most recent Fed's meeting to suggest a pretty high bar for rate hikes; and even the potential to redefine the Fed's 2 percent inflation target in favor of something more general and unspecified.
The result was a market reaction that would suggest less focus on inflation. The prospects for rate hikes were reduced, the yield curve steepened, led by a sell-off of long-end yields, measures of expected inflation rose, and the U.S. dollar weakened.
In the days since, markets have settled a bit. But the result is going to be a market that is now going to be much more sensitive to incoming inflation data.
If that inflation data moderates in the second half of this year, as we at Morgan Stanley expect, then the Fed's approach could look justified – as the data suggests that neither action nor more communication about what they're going to do is necessary.
But if inflation doesn't cooperate, the challenge becomes immediate. Christopher Waller, another member of the Fed, recently said that "Sternly staring at inflation until it melts before our withering gaze is not an option."
The market will expect action and expect a framework explaining that action. Until that point, our rate strategists think that yield curves will continue to steepen.
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, and also tell a friend or colleague about us today.

Head of US Public Policy Strategy Ariana Salvatore and US Thematic Strategist Michelle Weaver, alongside Senior Economist and Strategist in Morgan Stanley’s Private Wealth Management Sarah Wolfe, examine the economics of the AI datacentre boom, the pushback and the policy implications.
Read more insights from Morgan Stanley.

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Ariana Salvatore: Welcome to Thoughts on the Market. I'm Ariana Salvatore, Head of Public Policy Research at Morgan Stanley.
Michelle Weaver: I'm Michelle Weaver, U.S. Thematic and Equity Strategist.
Sarah Wolfe: And I'm Sarah Wolfe, Senior Economist and Strategist with Morgan Stanley Wealth Management.
Ariana Salvatore: Today: the politics, economics, and market implications of America's AI data center build-out.
It's Tuesday, August 4th at 10am in New York.
AI infrastructure spending is becoming a major force in the U.S. investment cycle. But as you've heard on this podcast in recent weeks, local resistance to data centers is growing, and projects worth hundreds of billions of dollars are being canceled or delayed.
More than 300 local moratoria have passed since 2023, and restrictions now touch 40 states. Now, most are temporary pauses, not outright bans, but the community opposition is tangible.
For investors, the key question is how these local pressures shape the broader build-out. So, I wanted to talk to you both because, Sarah, you've looked at this on the local level, and Michelle, you've been leading some of our thematic work on this topic.
So, Sarah, maybe we'll start with what happens when a data center comes to town. How does a large project ripple through a local economy, especially when so much of the expensive hardware is imported?
Sarah Wolfe: I think we need to look at the data center build-out from two lenses. First, at the national level, and then what's really happening at the local level, county by county.
So, at the national level, the headline investment can actually overstate the contribution to GDP because a lot of the components that go into data centers – think chips, servers, networking equipment – most of that is imported. So, it's actually an offset in the GDP accounting.
But when we analyze the AI build-out at a local level, we see that the town experiences the project very differently. A data center still needs a physical shell, concrete, steel, electricians, construction workers, and then the restaurants that feed the construction workers.
So, the local multiplier depends on how much of that spending around the data center stays nearby. Workers are going to get paid, local suppliers win contracts, and nearby businesses will see more demand. And then importantly, governments may collect more property and business tax revenue. When we look at county-level research on the AI data center build-out, we do see positive effects on employment, business formation, wages, income, and tax returns.
So, these data centers are significant. They do have significant multipliers. But we need to dig a little bit deeper and look at how it affects different counties.
Ariana Salvatore: So, it sounds like there are some local economic benefits. How durable do you think those are?
Sarah Wolfe: Some of the effects are durable and some aren't. The largest and most important effects come through employment in the near term. If we look at the construction phase of these projects, let's look at a data center that's 250,000 square foot, in Virginia. That supports more than 1,500 workers during construction.
But then, if we look at what happens after construction is done, there's only about 50 full-time workers once it's operating. And I will say I think that's a high-end estimate. If you look at how many workers these data centers employ state by state, some numbers are 10, some numbers are 20, and some are 30 employees. So, 50 is maybe on the higher end.
So, the bottom line is that the labor market multiplier actually fades after the facility comes online. What does persist, are the smaller share of data center processing jobs, ongoing supplier and service activity, and then importantly, of course, the property tax base.
But even that fiscal benefit depends on how the incentive package is designed. If a locality, for example, grants a very large, long-lived sales or property tax exemption, it may give away much of the revenue that made the project attractive in the first place.
So, the job story is real, but it's much more front-loaded. And then the tax revenue story is real too, but it really matters on how the locality negotiated the incentive package.
Ariana Salvatore: So, it sounds like there are some benefits and some potential drawbacks. How do you think communities should judge whether a trade-off like that is worth it?
Sarah Wolfe: I think communities should be asking this question of how much spending and tax revenue actually stays local after all the incentives? How many jobs remain after construction? Who pays for new generation transmission, water system, and roads? And who bears the spillovers through utility bills, housing costs, or land use?
The evidence does suggest that data center growth can lift incomes and expand the tax base. But it also raises home prices. And as we know, it raises electricity prices as well. A typical AI data center may use as much electricity as 100,000 homes, so cost allocation is critical. The strongest agreements make benefits durable and costs explicit through transparent reporting, sunset dates or claw backs on incentives, infrastructure cost-sharing, and protections that keep the household from subsidizing this build-out.
The test is really whether the community captures enough lasting value to justify the demands on land, power, water, housing, and public finances.
Ariana Salvatore: Michelle, I want to bring you in here. The local picture that Sarah describes helped explain why the politics can be so uneven.
How are moratoria and other local restrictions changing the pace and the location of the build-out, maybe on a national scale?
Michelle Weaver: I think you have to think about just the different type of moratoria themselves even. So, we're not seeing them uniform across different states in what's been proposed.
However, the majority of moratoria are a pause, not a[n] outright ban on construction. So, they might say, "Okay, we want one year," or "We want three years to do local impact studies and, and think about the way these data centers are going to impact communities."
So, the primary risk is really to the pace of the build-out, and as more and more of these moratoria pop up, you have to start to think about how that could shift the geography and the location of where these data centers will ultimately be built.
We are seeing a shift towards more data centers being placed in rural locations. This also has implications for the international data center build-out. You're seeing more and more of these data centers go up in Canada and in Australia to serve U.S. needs.
Ariana Salvatore: The polling data show us that voters are increasingly skeptical of AI. Specifically, they're worried about electricity prices and local costs. How should investors read that concern?
Michelle Weaver: Well, there's a couple things we have to unpack here. First is really around perception. So, in certain areas where you have both high data center activity as well as unregulated utility markets; yes, it's true, there is some of this raised cost ending up on consumer power bills from data center activity.
But in other areas with unregulated utility markets and lower data center activity, you don't see the same link between consumer power bills and what's going on with data center electricity consumption. But perception is what really drives politics and given that this perception is becoming spread across different states with both regulated and unregulated utility markets, politicians are reacting to it.
And the second thing this gets at is affordability. Consumers have been stressed by inflation for years now and elevated prices. And given that they think that data center costs are now winding up on their power bills, it's not surprising that you're seeing this big reaction, and that anything having to do with affordability has become a huge issue for voters.
Ariana Salvatore: Translating that into how we think things evolve from here, what industry and financing trends do you think matter most going forward?
Michelle Weaver: We recently identified the three main bottlenecks for the data center build-out as power, people, and politics. This whole episode has been about that third P, politics, but let's unpack power and people. On power, we still think there's a potential shortfall of around 38 gigawatts needed through 2028.
So, power is going to remain a huge bottleneck, and as the politics layer gets placed on top of the power layer, you're seeing more and more of an issue there. And so, what that really argues for is for data centers to be off grid. That way they can say, "Okay, there's no way we can potentially impact consumer power bills if we're not even connected to the grid."
The second P, people, is another big bottleneck, and we're seeing a very tough time for data centers to get skilled laborers. It's very hard to find electricians right now and other skilled laborers needed to set up these data centers.
Ariana, that brings us to the policy debate. Why is data center opposition moving from town halls into state houses and Congress? And what does this mean for a conditional build-out?
Ariana Salvatore: Yes, I think the points that you both touched on really explain why we're seeing this sort of pushback evolve, right?
Local communities are concerned about their electricity prices. Again, we see that on more a regional than a national basis. They're concerned about quality-of-life concerns. They're concerned about the environmental impacts. And so, all of that has caused these efforts to sort of cross state lines. We see it in both Democrat-held state legislatures as well as Republican-held.
So, it's definitely resonating with voters, and this is an issue that we think is going to be a key wedge issue into the midterm elections. It started to move into Congress rhetorically, but we still think something like a federal ban or a federal moratorium is very unlikely. And that's because we see a different incentive structure for lawmakers in Congress from the state and local level.
Principally, I'm talking about the U.S.-China relationship. So, when you look at the geopolitical backdrop to this debate, there are certain things that you can't ignore. And one of those things is that the U.S. and China are locked in this race for AI supremacy at the moment. And I think federal lawmakers have more of an incentive to respond to those policy demands and those policy needs, meaning they want to keep facilitating the build-out.
So that's why you're seeing the national level still relatively supportive of this build-out. We're seeing permitting reform. We're seeing Defense Production Act being leveraged by the president. We're seeing still an overall very favorable environment trying to unlock, sort of, that power bottleneck, for example.
So that's kind of what brings us to this conditional build-out.
Now, what does that mean? We think that the hyperscalers in these companies are going to have to offer some concession to local communities to facilitate the build-out. And that could be a number of things. I think it depends on the state's concern or the local community's concern principally, but we see a few different options.
One of those things is behind-the-meter power generation. So on-site power is one of the clear kind of offsets to this debate. Another thing would be improving utilization rates. So, our sustainability analysts found that the capacity utilization rates are actually quite low at some of these data centers in the range of 30 to 40 percent.
So maybe that can be increased. We've got some potential new regulations or transparency requirements around water usage. So, the short of it is, there's not going to be a one-size-fits-all solution here. But we think there's enough on the policy side that these companies can do or offer essentially to local communities. So that the entire build-out doesn't get delayed or doesn't get stopped.
And, and that's kind of why we still expect elevated AI CapEx, not just this year, but next year as well. We think that the risks are skewed to the upside for those numbers.
Sarah Wolfe: Ariana, I want to touch back to the comment you made on low odds of a nationwide ban on AI data center build-out – and tie it to this broader competition between the U.S. and China, with global supremacy in AI.
Can you talk a little bit more about how competition with China is going to prevent a nationwide ban and some of the national security concerns around that?
Ariana Salvatore: This ties into the theme of sovereign AI, which is something that we've been focused on recently, especially with all these discussions of more tech restrictions and controls between the U.S. and China.
And specifically, it's one of the reasons that we think the geographical build-out will be constrained to either just the U.S. domestically or countries that we are closely aligned with. And really the point I want to make here is that there's three geopolitical realities that are going to form, we think, the incentive structure for federal lawmakers and that are slightly different from the things that state and local policymakers tend to focus on.
The first is that we're seeing China leverage its supply chain position to pressure the physical inputs required for AI infrastructure, right? So, we're seeing that tit-for-tat escalation in the context of a broader strategic détente, but there's still a competitive aspect there.
The second is that we know China's accelerating its own physical AI build-out. Reporting indicates they're spending something like $300 billion over five years on its own domestic network, so very much full steam ahead in terms of its own domestic potential.
And the third is that research has identified China-linked influence operations that use an American frontier model to generate social media posts, comments, and political cartoons linking the data center construction to rising energy prices.
So, there's still a little bit of uncertainty as to whether or not those campaigns actually influence public opinion at scale. But in our view, it really underscores the linkage between national security and geopolitics and the AI data center build-out.
All these developments together we think underscore the physical component of the AI race and make something like a national data center ban or federal legislation toward those ends really difficult to reconcile with the growing bipartisan strategic imperative around AI, which is something that we think persists past the midterms as well.
But at the end of the day, the pace of the AI build-out will depend not just on demand, but on how well projects address the concerns of the communities hosting them. That's why we think this conditional build-out is probably the right base case for now.
Michelle and Sarah, thanks so much for taking the time to talk.
Michelle Weaver: Great speaking with you both.
Sarah Wolfe: Thank you, Ariana.
Ariana Salvatore: 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.
*****
Sarah Wolfe is a member of Morgan Stanley's Wealth Management Division and is not a member of Morgan Stanley’s Research Department. Unless otherwise indicated, her views are her own and may differ from the views of the Morgan Stanley Research Department and from the views of others within Morgan Stanley.

Our CIO and Chief U.S. Equity Strategist Mike Wilson explains why investors should favor quality as the market moves from early-cycle momentum to more disciplined, mid-cycle leadership.
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Mike Wilson: 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 the ongoing transition in the economic recovery from early to mid-cycle.
It's Monday, August 3rd at 11:30 a.m. in New York. So, let’s get after it.
Following on from my podcasts the past few weeks, I want to reiterate our key call that the economy and the market are moving from early to mid-cycle. That may sound like strategist jargon, but it has very real implications for leadership, positioning, and how one should think about the next phase of this bull market.
For much of the past year, the market was rewarding early-cycle characteristics and behavior. Lower-quality, higher beta stocks, and the most explosive earnings revision stories led the way. That made sense. We were coming out of a rolling recession, operating leverage was improving rapidly, and earnings revisions were accelerating off of depressed levels. But as the business cycle matures, the market typically becomes more discerning. It starts to ask a harder question: not just who can grow, but who can sustain that growth with stable earnings, strong margins, and free cash flow generation.
In other words, quality starts to matter again.
That’s exactly where we are now. The rotation towards quality has begun, and I don’t view that as a bearish development for the broader market even if it’s bad for some of the former leaders. The S&P 500 is a very high-quality, large cap index. So, while the market may continue to consolidate in the near term, the quality rotation should ultimately support index resilience and help the S&P 500 work its way toward our 8000 year-end target.
The big market event last week was the capitulation in the historic momentum unwind. Momentum sold off hard, and semiconductors were at the center of it. That shouldn’t surprise anyone who has followed our work over the past several months. We’ve been using the Silver stock analog to think about semis, and remarkably, the semi index bottomed almost exactly where that analog suggested.
That argues for a tradable bounce in semiconductors over the next few weeks. However, the more important point is that semis may struggle to reclaim leadership for the rest of the year. Semis are a classic early-cycle group, and this is increasingly becoming a mid-cycle, quality-led market. The Silver stock analog would support the same conclusion.
The provocative way to say it is this: the AI cycle is not over, but the easy money in the most crowded AI beneficiaries may be. The AI investment cycle still has plenty of runway, but the market is no longer rewarding capex blindly. It’s asking for evidence of return on invested capital, adoption, monetization, and operational discipline. Last week’s performance gap between Microsoft and Meta was a perfect example. It wasn’t random. It was about capex discipline. The market is rewarding more prudent spending, and that could translate into a real overhang for the capex beneficiaries, in line with my views for the past several months.
That is why I still prefer hyperscalers over semis, with one important caveat: dispersion within the hyperscalers is rising. The group has already outperformed semis by 30% over the past four weeks, and I think it can continue over the next several months. Hyperscalers have resilient core businesses, exposure to the AI application layer, and an underappreciated ability to use AI to reduce operating expenses if needed. They’re both enablers and adopters. But the market will no longer treat them all the same. The winners will be the companies that can show return on investment, communicate capex discipline, and preserve earnings quality.
This is also why AI adoption is becoming so important. The next leg of the story is not just about who builds the infrastructure. It’s about who can use it more effectively. Our work shows that companies where AI is material to the investment thesis and pricing power is neutral to strong, are already seeing margin expectations improve. Relative net margins for that group have expanded by 50 basis points in just three months, and they now sit nearly 400 basis points above the broader market. That’s not hype. That’s operating leverage with a new engine.
The Fed is the other major piece of the puzzle. Chair Warsh stayed on hold last week, but he remains tight-lipped about his reaction function. Markets are still adjusting to a Fed that wants to rely less on forward guidance and more on unfiltered market signals. I think that’s a healthy development over the longer term, but transitions are rarely smooth. The biggest risk to this consolidation turning into a correction is if the 10-year yields rise above 5%. Such a rise could weigh on equity multiples and force the Fed to either revert to its old ways of guiding the markets or provide more liquidity to calm rate markets.
Bottom line, the bull market is not over, but it is changing. As we move from early to mid-cycle in this recovery, the equity market wants higher quality. Semis may bounce, but they are unlikely to be the leader again. Meanwhile, hyperscalers will likely continue to trade better, with the best ones exhibiting more capital discipline. More importantly, AI adoption is moving from promise to measurable margin benefit. This is what mid-cycle looks like: less forgiving, more discerning, but still constructive for investors who follow the rotation rather than fight it.
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!

Our Strategist Michelle Weaver talks to Michael Zezas and Jessica Alsford, Co-Directors of the Morgan Stanley Institute, about how AI, energy resilience and industrial policy are changing investment decisions.
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Michelle Weaver: Welcome to Thoughts on the Market. I'm Michelle Weaver, Morgan Stanley's U.S. Thematic and Equity Strategist.
Michael Zezas: I'm Michael Zezas, co-director of the Morgan Stanley Institute and Deputy Global Head of Morgan Stanley Research.
Jessica Alsford: And I'm Jessica Alsford, Morgan Stanley's Chief Sustainability Officer, and also co-director of the Morgan Stanley Institute.
Michelle Weaver: Today: how AI, energy, geopolitics, and industrial investment are competing for scarce resources – and what that competition could mean for markets.
It's Friday, July 31st at 10am in New York.
Jessica Alsford: And 3 pm in London.
Michelle Weaver: Mike and Jess, as co-directors, you speak with people across the firm to identify the biggest questions facing companies and investors, especially the important ones that may not have clear answers yet. And to understand how those questions are shaping client conversations.
Mike, what's one of the questions that you think investors are wrestling with the most right now?
Michael Zezas: So, one of the biggest questions is how several major investment cycles can happen at the same time. AI, energy infrastructure, manufacturing, and defense may all be competing for the same power, the same skilled labor, equipment, and capital.
So, investors need to look beyond each theme in isolation and ask where constraints could delay projects, raise costs, or redirect spending, and which companies are best positioned to manage all of that.
Michelle Weaver: Since the institute began, you've examined a number of topics, including AI, energy resilience, and geopolitical fragmentation, just to name a few. Jess, which topic has been the most compelling to you?
Jessica Alsford: It's difficult to pick one because, to be honest, for me, it's really the way that AI, energy resilience, and geopolitics have all really become one story. If you think about the energy transition, which has been playing out for a number of years. But now we also have the AI build-out, and that depends on reliable and affordable power. And then geopolitical shocks, which are demonstrating the need for countries to have energy security.
So, if you put all of this together and you can really see that there is a huge need to scale the global energy system, but using all types of power available to us, including renewables and nuclear.
Michelle Weaver: Mike, how is that intersection that Jess spoke about between AI, energy, and geopolitics altering the way that companies are thinking about investing?
Michael Zezas: So, geopolitical shocks, they're more norm than exception now. The situations in Iran, Ukraine, Venezuela, they all reflect an evolving international order where the U.S. is less interested than it used to be in preserving global security and trade standards.
And that's a particular problem in a world where companies and governments spent much of the last 50 years optimizing to benefit from globalization. So basically, looking for the lowest cost way to produce things, sourcing materials and labor in the most efficient way possible, presuming that the frictions in international goods and services trade would just keep getting lower.
That's obviously not the case now, and whether it's a good idea or not, the trend is toward governments leaning into industrial policy to prioritize supply chain security and protect whatever it sees as their national competitive advantages. And really that's correlated with higher trade barriers.
So, that means that while companies are still focused on efficiency, they have to build resilience through more regional supply chains, greater redundancy, and investment in strategically important capacity. So, the practical message from our teams is to map critical dependencies, diversify where possible, and be realistic about the extra cost of resilience rather than assuming the old globalization model will simply return.
Michelle Weaver:  One of the clearest constraints on the AI build-out is energy. Our thematic research team is estimating a nearly 40-gigawatt shortfall in power needed for data centers. For context, this is multiple New Yorks worth of power.
Jess, how significant of a limiting factor is power becoming?
Jessica Alsford: Power is definitely becoming a strategic constraint. If you think about grid connections, these can take years to set up. And so, access to power really is going to determine where facilities are built and how quickly they're able to come online. And it looks like there won't be one universal solution.
You've got natural gas, nuclear, renewables, storage, microgrids. They're all going to need to play a role. And for companies, that means that they really are going to have to be planning power alongside the site and financing. For investors, it means focusing on reliability, affordability, and permitting, not just headline demand.
Michelle Weaver: So, AI, energy, and geopolitics can no longer be considered in isolation. As countries and companies rethink where they source, build, and invest, where do you see the biggest opportunities emerging?
Jessica Alsford: The opportunity is likely to be broader than any single sector, to be honest. and the institute has shown that capital really needs to be flowing towards more resilient supply chains as well as new productive capacity and also the infrastructure that supports both of these. And this covers power, grids, automation, logistics, as well as data.
I'd also say that location matters, too. And companies need to be able to weigh political stability as well as skilled labor, reliable energy, and policy support. And investors should be looking for markets and businesses that can turn those advantages into durable returns.
Michelle Weaver: The institute has also looked at founders as a source of economic information. Jess, what can their decisions reveal before those changes appear in traditional economic data?
Jessica Alsford: So, founders are often making decisions at the leading edge of growth and capital formation, and so their behavior can provide an early read on both at-risk appetite and also financing conditions.
If we take the current macro environment as an example of this, the institute has shown that many founders are adapting rather than simply waiting, and this means extending fundraising timelines, spawning investor conversations, and considering private credit, structured equity or tender offers.
For companies, the takeaway really is to preserve financing flexibility. And for investors, it's to watch how those choices can reshape private market liquidity.
Michelle Weaver: Mike, to bring this back to where we started, if power, labor, and capital are all becoming more constrained, what should investors be watching most closely?
Michael Zezas: Yeah. I'd watch whether capital spending plans are being delayed or resized or redirected in some way, and I think importantly, what the reasons would be for any of those things happening.
Is there a constraint around power or labor or equipment permitting or financing? Those details help distinguish whether you'd be looking at temporary setbacks or a structural shift. So, something that would signal that we've built too much capacity in AI or manufacturing relative to demand. And that's the type of thing that would be a real headwind to the economic outlook and potentially create problems in the credit markets.
But to be clear, we don't see demand flagging anytime soon. And so, for investors, it's less about whether to be bullish or bearish on the outlook for the markets and the economy, and it's more about looking for companies that are durable beneficiaries of these trends. So those are ones with secure inputs, flexible balance sheets, and realistic return thresholds.
Michelle Weaver:  Absolutely. As Mike said, we don't see demand slowing, and we're seeing a lot of encouraging data points around AI adoption. One analysis we did recently shows that around 25 percent of S&P companies are now quantifying the benefits they're seeing from AI adoption. And this diffusion story is only going to continue to grow.
Mike, Jess, thanks for joining me.
Michael Zezas: Thanks Michelle.
Jessica Alsford: It’s great speaking with you both.
Michelle Weaver: And to our listeners, thanks for tuning in. If this is all piquing your interest, you can find the institute's articles, roundtables, and future work on Morgan Stanley's website. And as always, if you enjoy Thoughts on the Market, please leave us a review and share the podcast with a friend or colleague.

Our Global Head of Thematic and Sustainability Research Stephen Byrd explains why the recent AI infrastructure selloff may reflect technical pressures, not weakening fundamentals.
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Stephen Byrd: Welcome to Thoughts on the Market. I’m Stephen Byrd, Morgan Stanley’s Global Head of Thematic and Sustainability Research.
Today: Are investors misreading the AI infrastructure selloff?
It’s Thursday, July 30th, at 10am in New York.
The recent selloff in AI infrastructure stocks has raised a familiar question: Is the buildout running ahead of real demand? The market is pulling back and we think that reflects profit-taking, crowded positioning, and forced selling by investors. This is not about weaker fundamentals. But the selloff has brought to light three key concerns, which we think the market is overplaying.
The first concern is how much enterprises are willing to pay for AI. The median enterprise employee currently generates less than $11 a month in token spending. That’s the fee paid when an AI model processes a request and generates a response.
We think there is room for that to increase. From the employer’s perspective the economics are compelling. Across workplace applications, the cost to execute the economic task would be $2-$5. And that could save an enterprise $55. That to us suggests companies are likely to spend more, not less, on AI over time.
The second debate centers on efficient models, including competitive models developed in China.  And here, policy responses both from the U.S. and China can have an impact as well. Some investors worry that better efficiency means less computing demand. But we see the opposite risk. This is a classic example of Jevons paradox: When something becomes cheaper or more efficient to use, people use more of it. In AI, lower costs can attract more users, encourage more frequent use, and make complicated applications more economical.
The scale is striking. Industry leaders estimate that compute demand could double every six months, which would amount to more than a thousand-fold increase in compute over five years. Hyperscalers could quadruple available power capacity to roughly 120 gigawatts by 2028, from about 30 gigawatts in 2025.
And that leads to the third debate – whether data centers can secure enough power to keep expanding. It’s a valid concern. In the U.S., facilities under construction and contracted grid capacity cover about 30 gigawatts. That’s less than half the 68 gigawatts of power that data centers are likely to need from 2026 through 2028. Grid connections can take five to seven years in some regions. Skilled electricians, welders, and pipefitters are in short supply. And local opposition is increasing as communities debate electricity bills, tax incentives, and who should pay for grid upgrades.
These are real obstacles, but we view them as delays rather than dead ends. Onsite generation, fuel cells, energy storage, natural gas turbines, and the conversion of existing high-power sites could close the gap, at least partially.
We believe much of the recent weakness in AI infrastructure has been driven by technical factors rather than a change in the underlying fundamentals. As AI becomes more capable and cheaper to use, demand for intelligence, compute, and power is likely to keep rising. The global market is fragmented as policy decisions in the U.S. and China shape how growth unfolds. But strong economics should support continued investment.
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