Thoughts on the Market

AI Meets the Physical Economy

September 25, 2026

AI Meets the Physical Economy

September 25, 2026

Morgan Stanley Research analysts Michelle Weaver, Ravi Shanker and Dave Arcaro discuss two industrial inflection points: how long it will be before autonomous trucking becomes a reality and why power infrastructure is racing to keep up with AI-driven demand.

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Transcript

Michelle Weaver: Welcome to Thoughts on the Market. I'm Michelle Weaver, Morgan Stanley's U.S. Thematic and Equity Strategist.

 

Ravi Shanker: I'm Ravi Shanker, Morgan Stanley's U.S. trade transportation analyst

 

Dave Arcaro: And I'm Dave Arcaro, Morgan Stanley's Utilities, Power & Clean Energy analyst.

 

Michelle Weaver: Today, what we learned at Morgan Stanley's Industrials Conference about the changing economics of autonomous trucking and the increasingly tight power market supporting the AI build-out.

 

It's Friday, September 25th at 10am in New York.

 

Now, I know we're all on the road taking meetings post-conference, so the audio might sound a little bit different. But we wanted to bring you the latest from our annual Industrials Conference that recently concluded in Laguna Beach, where two themes really stuck out. The growing physical infrastructure demands behind AI, particularly power. And the shift in autonomous trucking from proving the viability of the technology to commercializing it at scale.

 

Ravi, after roughly a decade of development, you've said autonomous trucking is entering a critical 12- to 18-month period ahead of serial commercial production.

 

What's changed, and why is the debate shifting from whether the technology works to whether it can be commercialized at scale?

 

Ravi Shanker: I think for 10 years the industry has been focused on making the technology work. But with players like Aurora now putting up almost half a million miles of fully driverless revenue-generating operations, on public highways in the U.S., day and night, rain and shine, for different customers. With people like Kodiak, also running several trucks, in revenue-generating service, for customers like Atlas. I don't think there is much debate on the technology itself.

 

And so, I think the debate is now moving from does this work to can this work for me? Where the next steps are going to be dotting i's and crossing t's on the path to actually pressing these trucks into commercial service rather than having to prove that it works in the first place.

 

Michelle Weaver: Your research suggests that autonomous trucking can deliver roughly a 20 percent lower cost per mile, while higher utilization could be an even bigger source of value. What are the key assumptions behind that math? And what still needs to happen operationally for fleets to capture those benefits?

 

Ravi Shanker: Yeah, so we recently updated our TCO math, on autonomous trucks and published a North American insight, where we revised and revisited our views on autonomous trucking with a lot of proprietary data, in there as well. And part of that new TCO math, again, I think revisited some of the changes in the split of operating costs of trucking over the last several years.

 

First of all, I'll kind of throw a huge disclaimer out there that your mileage may vary, right? Because, depending on who you are as a trucker, if you're public or private, small or large, dry van or reefer, heavy or asset light, long haul or short haul, your split of costs are going to be slightly different.

 

But we started out, by looking at the ATRI's national average. And labor accounts for 35 to 40 percent of the P&L of the average trucker. So, when you take the driver out and substitute that with an autonomous driver, if you will. Even after paying the autonomous technology company roughly 85 cents a mile, for the autonomous operation, you will still save a significant amount of money. Versus the 40 percent of the roughly $3 per mile that it costs for labor today.

 

In addition to that, fuel is another third of your cost structure. And there, an autonomous truck should be anywhere from 13 to 22 percent more fuel efficient. We have taken the low end of the scale to be conservative. And then you layer on insurance savings, maintenance savings on top of that. Even if you add some incremental costs, either for human drayage at both ends or for the truck itself being more expensive – we believe you will save about 20 percent per mile versus a human driver today.

 

And I'll point out that the unit economic savings are only about a-third of the total savings with the utilization benefit driving another two-third savings on top of that.

 

Michelle Weaver: But there, there still seems to be a notable disconnect between how much freight carriers and shippers think can be automated and how much of the network may actually be suitable to be automated. What's the industry potentially underestimating?

 

Ravi Shanker: Yeah. We have seen this in our conversations. Again, part of our report was conducting detailed surveys and in-depth interviews with a lot of our coverage companies. And I will say that there still needs to be a lot of education, of how these trucks work, where they work, what the unit economics are going to be out there.

 

There's still a lot of misinformation. For instance, there's this big perception that you still need human drivers at both ends of an autonomous truck move because these trucks can only operate on a highway. And here's where our AlphaWise analysis, comes in. I think it's the first of its kind analysis where we use geolocation data to pinpoint 10,000 plus of the largest commercial facilities belonging to the hundred largest commercial shippers in the U.S.

 

And we found out that the average [00:05:00] commercial facility is less than two miles away from the nearest ramp point. And these trucks can comfortably do seven to 10 miles, if not longer, off a highway on main roads to get to their end destinations. So, I think you just need a lot of education in the industry.

 

And that is part of the dotting of i's and crossing of t's that we think the industry needs to do in the next 12 months before we see the start of serial commercial production next year.

 

Weaver: Thanks, Ravi. I want to bring Dave into the conversation here, and that question of turning demand into real world capacity brings us naturally to power, where the challenge is also increasingly about physical infrastructure and execution.

 

Dave, coming out of Laguna, you describe management commentary across power equipment as notably positive. What surprised you most about what you heard on demand bookings and project activity?

 

Arcaro: Yeah, absolutely. What surprised me most was probably how consistent the commentary was across companies, across large frame turbine providers and the smaller, on-site power equipment players, the new entrants and the more mature companies in the market. Very consistent feedback. All very positive.

 

And I would say also what surprised me too was the lack of disruption across the board. You know, we all see the headlines about data center moratoriums, political pushback, community challenges that really, it seemed, to increase the risk of data center execution and delays out in the market.

 

But at least with the power equipment companies, they're just not seeing it. You know, in terms of the feedback that we heard from management teams across the board at Laguna, they review project timelines actively with their customers, and that's all still intact. We haven't seen any changes in bookings or slot reservations for equipment deliveries.

 

Still seems to be a very stable and very strong backdrop across the board.

 

Weaver: One of the broader conference themes was the availability of power is becoming a bottleneck for AI infrastructure. How are equipment shortages, longer wait times, and customers planning further ahead affecting pricing? And how far ahead can the industry see?

 

Arcaro: Yeah, we are seeing equipment companies booking out orders farther and farther. The large frame gas turbines, to give you a couple examples, from companies like GE Vernova, they're now in conversations to contract turbines for 2031 and 2032. Smaller equipment companies like INNIO, who make, smaller scale engines for data centers, they're in conversations with customers and taking reservations into 2029 and 2030.

 

So, what we heard from the conference as well was that utilities, which is a big customer for this equipment, they're looking out farther and farther now into the 2030s. That's new and that's a surprisingly long time in terms of how far they're looking out. And we're also hearing data centers looking out toward the end of the decade, you know, late 2020s in terms of trying to secure their power equipment in advance.

 

We would still consider it very much a seller's market. Pricing has been rising, and companies at the conference gave further indications that it's likely to keep rising, what looks like into the 2030s from here. We just haven't seen any signs of softening yet, really regardless of the company or the equipment type that they're selling into the market.

 

So still farther and farther out that we're seeing visibility into the order flow, and with that is also coming firm and even rising prices into the 2030s.

 

Weaver: Investors often frame the power debate as electricity from the grid versus smaller power sources built on-site at data centers. Based on what you heard at Laguna, how should investors think about the balance between those two approaches?

 

Arcaro: Yeah, it's an interesting dynamic. When you talk to utilities and some of the large frame turbine companies, they all say that all this data center demand is going to the grid. Eventually, it's all going to go to the grid. When you talk to the smaller equipment manufacturers and the power as a service providers, they say nobody wants the grid.

 

They see long-term opportunities to sell, on-site power equipment and contract it with their end customers for 15 to 20 years, and we're seeing evidence of that. So, I think, it'll stay It's an ongoing debate among, investors as well. On our end, we think the on-site power market is going to be an extremely large market as we get toward 2030, given limitations in how much power is likely to be accessible from the grid over time for the data center industry.

 

But I would say, my takeaway and my observation from the conference that I would highlight is that it's a really favorable market and favorable backdrop for both sides.

 

Michelle Weaver: From autonomous freight to the power needed to support AI, one message from Laguna was clear. The next phase of technology adoption increasingly depends on what the physical economy can actually build and scale.

 

Ravi and Dave, thanks for taking the time to talk.

 

Shanker: Thanks, Michelle.

 

Arcaro: Thanks for having me.

 

Weaver: And to our listeners, thanks 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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  • Michelle Weaver, Ravi Shanker and Dave Arcaro

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Central banks are turning more hawkish as inflation risks increase. Our Global Chief Economist and...

Transcript

Welcome to Thoughts on the Market. I’m Seth Carpenter, Morgan Stanley’s Global Chief Economist and Head of Macro Research. Today, I’m going to talk about all the movement we’ve seen in central banks and how it’s changing our forecasts.

 

It’s Tuesday, September 22, at 10 a.m. in New York.

 

Over the past two weeks, our economists here at Morgan Stanley have revised their outlooks for the Fed, the ECB, and the Bank of Japan to include more rate hikes.

 

Each economy faces different challenges, but all three central banks have arrived at roughly the same conclusion: growth has remained remarkably resilient despite all of the shocks hitting the global economy, and renewed energy-price pressures have increased the risk that inflation proves more persistent than they had previously expected.

 

The clearest example—and our biggest revision here—is the Fed.

 

Now for much of this year, we had actually thought the Fed might avoid hiking interest rates altogether. But in addition to this increase that we just saw at the September FOMC meeting, we now expect two additional rate hikes, in December and in March. That will bring the terminal rate up to 4.25 to 4.5 percent.

 

While Chair Warsh has highlighted the inflationary implications of higher energy and commodity prices, for me, the more important signal was the assessment that policy is not sufficiently restrictive.

 

So in our view, the Fed appears to be reassessing not just the inflation outlook, but the amount of restraint that is required to bring inflation sustainably back to target.

 

But even with all of that said, we’re still looking at this shift as more of a recalibration of policy for the Fed rather than a fundamental shift in policy. So the market may have—just may have—overestimated how much hiking is left.

 

But the shift does have clear and important market implications.

 

Our rate strategists expect investors to pull forward additional tightening expectations in the near term, while increasingly questioning how long policy can remain at restrictive levels before growth starts to slow.

But more broadly, the Fed now appears a bit more sensitive to energy-driven inflation pressures, and that strengthens the case for a firmer dollar.

 

Over recent months, rising energy prices have supported the euro because investors have seen the ECB respond more aggressively than the Fed. That maybe former asymmetry could be changing.

 

Our foreign-exchange strategists therefore continue to favor dollar strength, particularly against the yen.

 

Now Europe does face a similar inflation challenge to the Fed, though through a different mechanism.

 

The renewed rise in natural-gas and other energy prices has led our economists to revise up their inflation forecast materially and, therefore, to add in another ECB rate hike in December.

 

But we have got to keep in mind that it is not energy prices by themselves that have changed the outlook.

 

Economic activity in the euro area has also proven to be much more resilient than we had anticipated. And that reduces concerns that an additional modest tightening of policy would derail growth.

 

And so if you take it all together, the ECB is increasingly focused on preventing higher energy costs from feeding into broader inflationary dynamics.

 

Now Japan might seem different, but the underlying story is surprisingly similar.

 

For decades, the Bank of Japan’s challenge was generating inflation. But now policymakers are now increasingly concerned about the possibility that inflation will overshoot its target.

 

After the Bank of Japan’s hike last week, we expect it to raise rates to 1.5 percent in December and then raise rates further, to about 1.75 percent, in March.

 

Like the Fed and the ECB, the BoJ faces an economy that has absorbed tighter financial conditions much better than had been expected.

 

Yet, unlike the Fed and the ECB, our strategists believe that markets have become too aggressive in pricing the eventual destination of rates. And that creates scope for expectations to be revised lower over time.

 

As a result, while Japanese rates may continue to rise gradually, our foreign-exchange strategists still expect a broader trend of yen weakness to emerge once temporary positioning effects fade.

 

So the common thread across all three of the central banks that I’ve discussed is that, while the energy shock has changed the inflation conversation, the resilience in growth has further changed the policy conversation.

And so for investors, next year will probably be characterized by higher policy rates and a stronger dollar than markets expected at the beginning of the year.

 

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

Morgan Stanley Thoughts on the Market Podcast
Tighter AI safety requirements could reshape the pace of AI investment. Ariana Salvatore and Micha...

Transcript

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

 

Michael Zezas: And I'm Michael Zezas, Deputy Global Head of Research at Morgan Stanley.

 

Ariana Salvatore: Today, we'll be talking about AI safety and regulation.

 

It's Wednesday, September 23rd, at 10am in New York.

 

We put out a note last week on AI frontier capability gain and the associated safety risks.

 

Those have been in focus in recent weeks, and as a result, we've gotten a number of questions about the path forward for government regulation.

 

So today, Mike and I are going to get into some of the newest developments, where we think things are headed, and how the midterms could shape that path.

 

Michael Zezas: Yeah, and this is pretty important because the concern is that if AI safety scrutiny increases, it's going to slow everything down. You might have less CapEx, fewer model releases, and there's all sorts of downstream effects for the pace of U.S. growth and investment strategy in equities and throughout the AI investment theme.

 

But Ariana, you and the team landed in a bit of a different place and are arguing that a bigger focus on AI safety could end up being a tailwind to compute spend rather than a brake on it. Can you break that down for us?

 

Ariana Salvatore: Sure. So, the way we see this playing out, is there are five potential states of the world. Some include industry self-policing; some include the prospects for heavier government intervention. Across all of them, as you mentioned, we actually think this is a pretty big tailwind to compute spend and CapEx more broadly.

 

That's because as the labs integrate greater safety monitoring infrastructure, we think that spend is only going to accelerate, especially as LLM capabilities increases at a nonlinear rate. Similarly, on the regulation front, we think there are a few things that prevent something like a large comprehensive AI regulation bill from coming to fruition.

 

We think there's really three, kind of, key obstacles to something like that happening.

 

The first is the politics. So, the president himself has said he's against some sort of large-scale regulation. The second is the procedure. So mechanically speaking, there would need to be a legislative vehicle for this sort of thing to ride on. That's hard to see emerging in the very near term. And the third is precedent.

 

So, historical precedent here tells you that usually regulation is catalyzed by some sort of high salience event. That's why our framework for government reaction here hinges on two components: incident salience, as I just mentioned, and instrument availability. Instrument availability basically reflects the extent to which the government already has a tool that it can pull in this direction.

 

So, that's how we think about it going forward. That doesn't mean all policy action is off the table, but that supports our expectation for higher CapEx, higher compute spend over the coming years.

 

Michael Zezas: Right. So, the idea is that the spending continues and the things that would otherwise limit that spending, you don't see as real plausible policy options at the moment. And can you break this down a little bit more? Because I know there's a lot of different proposals floating around Washington, D.C. from policymakers right now.

 

What are you paying attention to?

 

Ariana Salvatore: We don't expect an overarching AI regulatory authority in the near term. Now, importantly, we also don't expect sweeping open weight model regulation. The reason for that is threefold. First of all, we think the U.S. is keen on maintaining this managed stability relationship with China.

 

We've written about the expectations around the U.S.-China summit. That's kind of a delicate balance that we think is likely to persist. So, overly restricting open weights models might throw a little bit of a wrench into that equilibrium that we see. So that's the first reason.

 

The second reason is diffusion. We think the U.S. administration wants to see the proliferation of open weights models. We know that companies are using some sort of hybrid of open and closed weight. So, to the extent that, you know, banning these models would slow adoption, we don't think that's in the interest of the administration.

 

And the third reason is purely mechanical. It's really hard to enforce these sorts of restrictions. Once a model weight is published online, it can be really hard to clamp down exactly who and where it's going to.

 

Obviously, companies can download them, customize them, et cetera. So, the enforcement picture here is also really challenging. That being said, we do think that the executive can continue to lean in and, sort of, make some incremental adjustments or changes on the regulatory front. But we think it's likely less severe than some of the proposals you're seeing in Congress right now. Things like the Kill Switch Act, for example, which basically mandate that companies can maintain an ability to shut down models at a moment's notice, right? If a certain threshold is crossed.

 

So, that's something that we see as less likely to come to fruition. But again, setting safety standards, guardrails, all of that from the administration we think is possible in the near term.

 

Michael Zezas: What about some of the pushback that would at least appear to be rising at the state and local level around construction of data centers?

 

Is that something that you think might materially slow the industrial build-out and the CapEx levels around AI?

 

Ariana Salvatore: So far, what we've seen is that AI safety risks are not the top of the priority list when it comes to data center pushback, right? So, things like environmental concerns, affordability – those tend to be the main vectors of the opposition.

 

That being said, we've gotten the question, right, to your point, of does this, sort of, risk focus mean that the data center backlash is likely to grow? We think that it could, but at the same time, we think this is a highly idiosyncratic issue, meaning that this is something to pay attention to on a very granular level.

 

Certain states and localities will be the ones to really administer these restrictions, and we think in the aggregate, hyperscalers are going to be able to continue to mitigate. We've already seen these mitigation measures employed. We're still constructive on AI CapEx this year and next, because overall, we see the build-out really becoming more of a conditional build-out.

 

So, that means contingent upon some of these concessions, maybe it's more expensive in certain areas. But overall, we don't think that the concerns around safety are going to derail that story.

 

Michael Zezas: So, then when it comes to data centers, the conditions that might be being put on their construction at the state and local level, for the most part – those building out the data centers have been willing to make those concessions, so it hasn't slowed that much. Is that fair?

 

Ariana Salvatore: That's right, and it really depends on where the pushback is coming from, right? So, in some cases, you're seeing communities push back on things like water usage, right? And we're seeing the hyperscalers come out and respond and say explicitly, you know, how much water they're using in some of these operations. Google is proposing a regulatory framework, so that's something that they're mitigating through that lens.

 

In another example, you've got local communities pushing back on just, sort of, disruptions to quality of life, and you're seeing companies like Meta announce a fund to engage more locally there.

So, it really is different. There's no one-size-fits-all solution here. But yes, I agree with you that overall, we don't think this is going to meaningfully constrain the build-out.

 

Michael Zezas: Got it. So, it seems like the idea here is that the secular trend around AI development is going to continue in your view. Is there any way that you think the midterm elections or the outcome around that might change your thinking?

 

Ariana Salvatore: So, I think the midterms will be important for sentiment, but when it comes to the actual policy path, we don't think they're the main driver, and there's two key reasons for that.

 

The first is obviously the president is not changing until 2029. So, the fact that President Trump still has to be involved in any capacity – if we were to see a bill emerge from Congress to us gives a little bit of clarity on what that bill could actually look like. And so ultimately, whatever comes to fruition will have to be a product of collaboration between Democrats, Republicans in Congress, and the president. So, that's a pretty much a constant.

 

The second reason I would say is because, as I kind of alluded to earlier, you tend to see government response when there's a high salience event. And in that case, it doesn't really matter what the government configuration is if it's reactionary.

 

When you think back to things like the pandemic, we saw the CARES Act. In 2008-2009, you saw the ARRA. Those are all efforts that were produced in a divided government. And so, in that vein, we basically think that you need to see some sort of event catalyze a response.

 

The key driver is not going to be government configuration. It's going to be the salience of that event specifically.

 

Michael Zezas: Okay, got it. So, the guidance to investors on the back of all of this is what?

 

Ariana Salvatore: So, the thematic recommendations from our team are intact, right? So, what we were talking about is basically we see these all converging towards a tailwind to CapEx and a tailwind to compute supply.

 

So, in that vein, we still think that you should own inference compute bottlenecks because of that excess demand relative to supply. We think that's going to persist regardless of most of the policy scenarios.

 

We also think own leaders in cybersecurity, as we mentioned. This should drive increased spend, especially as open weight models become much more capable. And then in the third piece, we think you should own AI adopters. AI models are already pretty capable to drive significant productivity. We think that that's just going to continue to unlock.

 

The last thing I would mention, we didn't really get into it in this podcast, but in this conversation we typically also talk about AI sovereignty and the U.S.-China restrictions here.

 

So, in that context, we would avoid negative exposure to rising U.S.-China technology transfer restrictions. We think that's an increasing probability because we do see the governments taking more of an active role, which we think drives bifurcation of the global market.

 

Michael Zezas: Well, Ariana, thanks for breaking it down. Appreciate talking to you today.

 

Ariana Salvatore: Always great speaking with you, Mike. And thank you 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. 

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