Thoughts on the Market

In the second of our two-part panel discussion from Morgan Stanley’s TMT conference, our analysts break down the complexity of financing AI’s infrastructure and the technological disruption happening across industries.
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Michelle Weaver: Welcome back to Thoughts on the Market, and welcome to part two of our conversation live from the Technology, Media and Telecom conference. I'm Michelle Weaver, U.S. Thematic and Equity Strategist at Morgan Stanley.
Today we're continuing our conversation with Stephen Byrd, Josh Baer and Lindsay Tyler. This time looking at financing AI and some of the risks to the story.
It's Friday, March 6th at 11am in San Francisco.
So yesterday we spoke about AI adoption. And while there's a lot of excitement on this theme, there've also been some concerns bubbling up.
Lindsay, I want to start with you around financing. That's another critical component of the AI build out. What's your latest on the magnitude of the data center financing gap, and what role [are] credit markets playing here?
Lindsay Tyler: Yeah, in partnership with Thematic Research, Stephen and team, and colleagues across fixed income research last summer, we did put out a note, thinking about the data center financing gap, right? So, Stephen and team modeled a $3 trillion global data center CapEx need over a four-year timeframe.
So, in partnership with fixed income across asset classes, we thought: okay, how will that really be funded? And we came to the conclusion that the hyperscalers, the high quality hyperscalers, generate a good amount of cash flow, right? So, there's cash from ops that can fund approximately half of that. But then we think that fixed income markets are critical to fund the rest of the funding gap. And really private credit is the leader in that and then aided by corporate credit and also securitized credit.
What we've seen since is that yes, private credit has served a role. There is this difference between private credit 1.0, which is more of that middle market direct lending. And then private credit 2.0, which is more ABF – Asset Based Finance or Asset Backed Finance. And what we see there is an interest in leases of hyperscaler tenants, right?
We've also seen in the market over the past nine months or so, investment grade bond issuance by hyperscalers. Obviously, a use of cash flow by hyperscalers. We've seen the construction loans with banks and also private credit per reports. We've also seen high yield bond issuance, which is kind of a new trend for construction financing. We've seen ABS and CMBS as well. And then something new that's emerging in focus for investors is more of a chip-backed or compute contract backed financings, like more creative solutions.
We're really in early innings of the spend right now. And so, there is this shift. As we start to work through the construction early phases, the next focus is: okay, but what about the chips? And so, I think a big focus is that, you know, chips are more than 50 percent of the spend for if you're looking at a gigawatt site. And it depends what type of chips and kind of what generation. But that's the next leg of this too.
So, it's kind of a focus, you know, for 2026.
Michelle Weaver: And how do you view balance sheet leverage and financing when you think about hyperscaler debt raising magnitude and timelines?
Lindsay Tyler: So just to bring it down to more of a basic level, if you need compute, you really might need two things, right? A powered shell and then the chips. And so, if you're looking for that compute, you could kind of go in three basic ways. You could look to build the shell and kind of build and buy the whole thing. You could lease the shell, from, you know, a developer, maybe a Bitcoin miner too – that is converted to HBC. And then you kind of buy the chips and you put them in yourselves. Or you could lease all the compute; quote unquote lease, it's more of a contract.
In terms of the funding, if you're thinking about the cash flows of some of the big companies – think of that as primarily being put towards chip spend. If you're thinking about the construction that's kind of split between cash CapEx but also leases. And so, what we've seen is that there is more than [$]600 billion of un-commenced lease obligations that will commence over the next two to five years, across the big four or five players.
And then my equity counterparts estimate around [$]700 billion of cash CapEx that needs this year for some of those players as well. So, these are big numbers. But that's kind of how, at a basic level, they're approaching some of the financing. It's a split approach.
Michelle Weaver: And what have you learned around financing the past few days at the conference? Anything incremental to share there?
Lindsay Tyler: Sure. Yeah. I think I found confirmation of some key themes here at the conference. The first being that numerous funding buckets are available. That was a big focus of our note last year is that you can kind of look at asset level financing. You can look at public bonds, you can look at some equity. There are these different funding buckets available.
The second is that tenant quality matters for construction financing. I think I've seen this more in the markets than maybe at this conference over the past two to three weeks. But that has been a focus of pricing for the deals, but also market depth for the deals.
A third confirmation of a key theme was around the neo clouds and also the GPU as a service business models. Thinking about those creative financings, right. Are they thinking about from their compute counterparties? Would they like upfront payments? Might they look to move financing off [the] balance sheet, if they have a very high-quality investment grade rated counterparty? So, there is some of this evolution around those solutions.
And then a fourth key theme is just around the credit support. And Stephen has and I have talked about this around some of the Bitcoin miners – is that, you know, there can be these higher quality investment grade players that might look to lend their credit support. Maybe a lease backstop to other players in the ecosystem in order to get a better pricing on construction financing. And we are seeing some press pickup around how that might play out in chip financing down the road too.
Michelle Weaver: Mm-hmm. AI driven risk and potential disruption has been a big feature of the price action we've seen year-to-date in this theme. Stephen, what are some asset classes or businesses you see as resistant to some of this disruption?
Stephen Byrd: We spend a lot of time thinking about, sort of, asset classes that are resistant to deflation and disruption. And what's interesting is there's actually a handful of economists in the world that are doing remarkable work on this concept. That they would call it the economics of transformative AI.
There are three Americans, two Canadians, two Brits, a number of others who are doing really, really interesting work. And essentially what they're looking at is what do economies look like? As we see very powerful AI enter many industries – cause price reductions, deflation… What does that do? They have a lot of interesting takeaways, but one is this idea that the relative value of assets that cannot be deflated by AI goes up.
Very simple idea. But think of it this way, I mean, there's only, you know, one principle resort on Kauai. You know, there's a limited amount of metals. And so, what we go through is this list that's gotten a lot of investor attention of resistant asset classes or more of the resistant asset classes that can go up in value.
So, there are obvious ones like land, though you have to be a little careful with real estate in the sense that like, office real estate probably wouldn't be where you would go. Nor would you potentially go sort of towards middle income, lower income housing. But more, you know, think of industrial REITs, higher-end real estate.
But there are a lot of other categories that are interesting to me. All kinds of infrastructure should be quite resistant, all kinds of critical materials. Metals should do extremely well in this. But then when you go beyond that, it's actually kind of interesting that there; arguably there's a longer list than those classic sort of land and metals examples.
Examples here would be compute…
Michelle Weaver: Mm-hmm.
Stephen Byrd: I thought Jensen put it, well, you know, if there's a limited amount of infrastructure available, you want to put the best compute. And ultimately, in some ways, intelligence becomes the new coin of the realm in the world, right? So, I would want to own the purveyors of intelligence.
It could include high-end luxury. It could include unique human experiences. So, I don't know how many of y'all have children who are sort of college age. But my children are college age, and they absolutely hate what they would call AI slop.
They want legit human content, and they seek it out. And they absolutely hate it when they see bad copies of human content. And so, I think there is a place in many parts of the economy for unique human experiences, unique human content, and it's interesting to kind of seek out where that might be in the economy. So those would be some examples of resistant assets.
Michelle Weaver: Mm-hmm. Josh, software's been at really the center of this AI disruption debate. How would you compare the current pullback in software multiples to prior periods of peak uncertainty? And do you think any of these concerns are valid? Or how are you thinking about that?
Josh Baer: Great question. I mean, software multiples on an EV to sales basis are down 30 – 35 percent just from the fall, I will say. And that's overall in the group. A lot of stocks, multiple handfuls, are down 60-70 percent over the last year. And what's being priced in is really peak uncertainty, a lot of fear. And these multiples, now four times sales – takes us all the way back about 10 years to the shift to cloud. And this time in many ways reminds us of that period of peak fear. In this case, what's being priced in is terminal value risk.
We talked about this TAM yesterday. But you know, who is going to win that share? How is it divided from a competitive perspective across these model providers? The LLMs with new entrants. Of course, the incumbents. And this other idea of in-housing.
Michelle Weaver: Mm-hmm.
Josh Baer: So, there's competitive risk, there's business model risk. Are companies going to need to change their pricing models from seat-based to consumption or hybrid. And then last margin risk. Just thinking about the higher input costs and higher capital intensity. And so, you know, all of those fears are being priced in right now.
Michelle Weaver: And we, of course though, had a bunch of these companies live with us at the conference. How are they responding to some of these risks? How are they addressing these investor concerns?
Josh Baer: Most of the companies here from our coverage are the incumbent software vendors. And I think that the leadership teams did a really nice job coming out and defending their competitive moats and really articulating the story of why they are in a great position to capitalize on the opportunity. And the reasons can vary across different companies. But some of the commonalities are around enterprise grade, trust, security, governance, acceptance from IT organizations.
The idea of vibe coding all apps in an organization get squashed when you actually talk to companies and chief information officers. For some companies there's proprietary data moats, network effects. All of that's on top of existing customer relationships.
And so, you know, that was the message from the companies that we had. That we’re the incumbents. We get to use all of the same innovative AI technology in the same way that all these different competitive buckets do. But we have, you know, that differentiation in that moat. And so, we're in a good place.
Michelle Weaver: I want to wrap on a positive note. Stephen, what did you hear at the conference that you're most excited about?
Stephen Byrd: I'd say the life sciences. A few investors pointed out that perhaps AI has a PR problem these days. And I do think showing a significant benefit to humanity in terms of improved health outcomes, whether that's just better diagnosis, you know. Away from this event, but I was in India the week before and, you know, AI can have a powerful benefit to the people who suffer the most in terms of providing very powerful medical tools in a distributed manner. So, I’m a big fan there.
But you know, in many ways, curing the most challenging diseases plaguing humanity. The kind of problems involved in providing those and developing those cures are perfect for AI. So that, for me – stepping way back – that is by far the most exciting thing.
Michelle Weaver: Josh, same to you. What are you most excited about?
Josh Baer: From my perspective, it's potentially the turning point for software. The ability to showcase that we are at this inflection point and acceleration. To actually see that it takes time for our software companies to develop new AI technologies. Put that into products that have been tested and proven and go through the enterprise adoption cycle. And that we're at the cusp of more adoption – that's what our survey work says. And to see that inflection, I think can help to rerate this sector.
Michelle Weaver: Lindsay, same question for you…
Lindsay Tyler: Maybe I'll tie it to markets. I've already had a lot of more conversations with equity investors over the past, how many months? There's a big fixed income focus right now, which is a great, you know, spot and really interesting opportunity in my seat. And there's a lot of interesting structures coming to be right now in the credit space. So, I think it's an exciting time.
Michelle Weaver: Lindsay, Stephen, Josh, thank you very much for joining to recap the event and let us know what you learned at the conference. To our audience, thank you for listening here live. And to our audience tuning in, 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.

Live from Morgan Stanley’s TMT conference, our panel break down where AI is already delivering real returns—and where rapid advances are raising new risks.
Read more insights from Morgan Stanley.

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Michelle Weaver: Welcome to Thoughts on the Market. I'm Michelle Weaver, U.S. Thematic and Equity Strategist here at Morgan Stanley.
Today we've got a special episode on AI adoption. And this is a first in a two-part conversation live from our Technology, Media and Telecom conference.
It's Thursday, March 5th at 11am in San Francisco.
We're really excited to be here with all of you taping live. And we've got on stage with me. Stephen Byrd, he's our Global Head of Thematic and Sustainability Research; Josh Baer, Software Analyst; and Lindsay Tyler, TMT Credit Research Analyst.
So, Stephen, I want to start with you, pretty broad, pretty high level. We recently published our fifth AI Mapping Survey that identifies how different companies are exposed to the broad AI theme. Can you just share with us some insights from that piece and how stocks are performing with this AI exposure?
Stephen Byrd: Yeah, it's interesting. I mean, we've been doing this survey now, thanks to you, Michelle, and your excellent work, for quite a while. And every six months it is pretty telling to see the progression.
I would say a few things that got my attention from our most recent mapping was the number of companies that are quantifying the adoption benefits continues to go up quite a bit. And to me that feels like that's going to be table stakes very soon as in every industry you see two or three companies that are really laying out quite specifically what they expect to be able to do with AI and lay out the math. I think that really is going to pull all the other companies to follow suit. So, we're seeing that in a big way.
We do see adopters, with real tangible benefits performing well. But a new thing that we're seeing now, of course, in the market is concerns that in some cases adoption can lead to dramatic deflation, disruption, et cetera. That's coming up as well. So, we're seeing greater concerns around disruption as well.
But broadly, I'd say a proliferation of adoption, that that universe of companies continues to grow, increases in quantification of the benefits. So, that is good. What's really surprised me though, is the narrative among investors has so quickly moved from those benefits which we've talked about into flipping that to toggle all negative, which I know some of our analysts have to deal with every day. The mapping work suggests significant benefits. But the market is fast forwarding to very powerful AI that is very disruptive in deflation. And that's been a surprise to me.
Michelle Weaver: Mm-hmm. Josh, I want to bring software into this. Your team has been arguing that AI is actually good for software. And it's really something that you need that application layer to then enable other companies to adopt AI. Can you tell us a little bit about how much GenAI could add to the broader enterprise software market? And how are you thinking about monetization these days?
Josh Baer: Of course. I think the best starting place is a reminder that AI is software, and so we see software as a TAM expander. And in many ways, even though this is extremely exciting innovation, it's following past innovation trends where first you see value accrue and market cap accrue to semiconductors, and then hardware and devices, and then eventually software and services. And we do think that that absolutely will occur just given [$]3 trillion in infrastructure investment into data centers and GPUs.
There's got to be an application layer that brings all of these productivity and efficiency gains to enterprises and advanced capabilities to consumers as well. And so we see AI more as an evolution for software than a revolution. An evolution of capabilities and expansion of capabilities. LLMs and diffusion engines absolutely unlocked all of these new features of what software can do. But incumbents will play a key role in this unlock.
And our CIO surveys really support that. Quarterly we ask chief information officers about their spending intentions, and these application vendors who we cover in the public markets are increasingly selected as vendors that companies will go to, to help deploy and apply AI and LLM technologies.
So, to answer your question, we estimate GenAI could unlock [$]400 billion in incremental TAM for software; for enterprise software by 2028. And this is based on looking at the type of work able to be automated, the labor costs associated with that work, the scope of automation, and then thinking about how much of that value is captured typically by software vendors.
Michelle Weaver: And you have a bit of a different lens on AI adoption. So, what are some of the ways you're hearing software customers using these AI tools and anything interesting that popped up at the conference?
Josh Baer: To echo what Stephen laid out, I mean, all of our software companies are using AI internally, both to drive efficiencies, but also to move faster. So thinking about product. Innovation, you know, the incumbents are able to use all of the same coding tools and, you know, …
Michelle Weaver: Mm-hmm.
Josh Bear: … products geared to developers to move faster and more efficiently on R&D. So, they're doing more. From a sales and marketing perspective, a G&A perspective, every area of OpEx, our software companies are in a great position to deploy the AI tools internally.
I think more important[ly], speaking to this TAM and expanded opportunity, is our companies have skews that they're monetizing. It might be a separate suite that incorporates advanced AI functionality. It might be a standalone offering, or it might be embedded into the core platform because the essence of software is AI and it, you know, leading to better retention rates and acceleration from here.
Michelle Weaver: Mm-hmm. And Stephen, going back to you on the state of play for AI, we had the AI labs here and we heard a lot about the developments and what's to come. So, what's your view on the trajectory for LLM advancements and what are some of the key signposts or catalysts you're watching here?
Stephen Byrd: Yeah, this is for me, maybe the most important takeaway of the conference – is this continued non-linear improvement of LLMs, which we've been writing about for quite some time. And just to give you an example, we think many of the labs have achieved a step change up in terms of the compute that they have, in some cases 10 x the amount of compute to train their LLMs. And that [if] the scaling laws hold – and we see every sign that they will – a 10x increase in compute used to train the models results in about a doubling of the model capabilities.
Now just let that sink in for a moment. Let's just think about that. A doubling from here in a relatively short period of time is difficult to predict. It's obviously very significant and I think several of the LLM execs at our event sounded to me extremely bullish on what that will be. A lot of that I think will be evident in greater agentic capabilities.
But also, I'd say greater creativity. It was about three weeks ago, three of the best physics minds in the world worked with an LLM to achieve a true breakthrough in physics – solving a problem that had never been solved before. A couple of days ago, a math team did the same thing. And so, what we're seeing is sort of these breakthrough capabilities in creativity. This morning I thought Sam speaking to, you know, incredible increases in what these models can do – which also brings risk. You know, I think it was interesting he spoke to, you know, the risk of misalignment, the risk of what these models are doing.
But for me, that's the single biggest thing that I'm thinking about, and that's going to be evident in the next several months.
Michelle Weaver: Mm-hmm.
Stephen Byrd: So, you know, on the positive side, it leads to greater benefits from AI adoption. And to Josh's point that, you know – more and more the economy can be addressed by AI, I do get concerned about the risk that that kind of step change will create greater concerns about disruption and deflation.
That causes me to think a lot about that dynamic. Interestingly, we think the Chinese labs will not be able to keep pace just for one reason, which is compute. We think the Chinese labs have everything else they need. They have the talent, the infrastructure. They certainly have the energy, power. But they don't have the chips.
If what we laid out with the American models turns out to be true, I could see a chain reaction where the Chinese government pushes the Trump administration for full transfer of the best technology to China. And China could use their rare earth trade position to ensure that. So, that's sort of the chain reaction I've been thinking about.
Michelle Weaver: Mm-hmm. So, let's think about then bottlenecks in the U.S. Power is still one of the main bottlenecks. We had several of the solutions providers here at the conference. So, what are you thinking in terms of the size of the power bottleneck in the U.S. and how are we going to fix that?
Stephen Byrd: Yeah, absolutely. I am bullish on the companies that can de-bottleneck power, not just in the U.S., a few other places. Let's go through the math in terms of the problem we face and then the solution.
So, we have this very cool – it is cool if you're a nerd – power model that starts in the chip level up, from our semiconductor teams. And from that, we build a global power demand model for data centers. We then apply that to the U.S.
Through 2028 we need about 74 gigawatts of data centers, both AI and non-AI to be built in the United States. I don't think we'll be able to achieve that for lots of reasons. But starting from that 74, we have sort of 10 gigs that have been recently built or are under construction. We have 15 gigs of incremental grid access, but after those two, we have to go to unconventional solutions, meaning typically off-grid solutions, over 40 gigawatts of unconventional solutions.
So that will be repurposing Bitcoin sites, which could be sort of 10 to 15 gigawatts. That'll be big. Renewable energy, fuel cells will be part of the solution. Gas turbines will be a big part of the solution. Co-locating at a few nuclear plants. I'm less bullish than I used to be on that. But when we net all that out, we think the U.S. is likely to be 10 to 20 percent short of the data center capacity that will need to be in.
It's not just a power grid access issue, though, that's a big one. Labor is now showing up as a huge issue. Many of the companies I speak to trying to develop data centers struggle with availability of labor. Electricians being one very tangible example. In the U.S. we need hundreds of thousands of additional electricians.
So, for any of your children, like mine, thinking about careers, you know, you'd be surprised [at] the amount of money that people are making in the infrastructure business that does feel like it's a labor shift that's going to have to happen, but it's going to take years. So, in that context, we had a number of the Bitcoin companies at our event here. And the economics of turning a Bitcoin site into hosting a data center are extremely attractive. I mean, extremely attractive.
To give you a sense of that. Before this opportunity presented itself to these Bitcoin players, those stocks tended to trade at an enterprise value per watt of about $1 to $2 a watt. Then we started to see these deals in which the Bitcoin players build a data center and lease them to hyperscalers. Those deals – depends a lot on the deal but – have created between $10 and $18 a watt of value. Let me repeat that. 10 to 18 – relative to where these stocks were at 1 to 2.
Now many of these stocks have rerated, but not all of them. And there's still quite a bit of upside. And what we've noticed is the economics that the hyperscalers are paying are trending up and up and up. Because of this power shortage that we're dealing with. So, a lot of exciting opportunities are still in the power space.
Michelle Weaver: Great. Well, I think that's a good place to wrap this first part of our conversation around AI adoption and the state of play. We'll be back again tomorrow with Part Two, looking at financing and risks.
To our panelists, thank you for talking with me. And to our audience, 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.

Our Deputy Global Head of Research Michael Zezas and Head of Public Policy Research Ariana Salvatore assess the potential market outcomes of the Middle East conflict, weighing its possible duration and economic impact.
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Michael Zezas: Welcome to Thoughts on the Market. I'm Michael Zezas, Morgan Stanley's Deputy Global Head of Research.
Ariana Salvatore: And I'm Ariana Salvatore, Head of Public Policy Research.
Michael Zezas: Today we're discussing the escalating U.S.-Iran conflict, the market reaction, and what investors should be watching for next.
It's Wednesday, March 4th at 7:30am in San Francisco.
Ariana Salvatore: And 10:30am in New York.
Michael Zezas: So, Ariana, I'm in San Francisco at Morgan Stanley's TMT Conference, but obviously events in the Middle East have captured everyone's attention. There's uncertainty around the conflict and really important questions about how it affects all of us. And of course, markets have to discount all sorts of future uncertainty about very specific impacts – to financial asset prices, to commodity prices – and really look at it through that narrow lens.
And so, Ariana, the administration has suggested that this conflict and this campaign could last a few weeks. But also it said it could continue as long as it takes. So, what are the clearest signals investors should watch for to gauge duration?
Ariana Salvatore: For now, we're focused on three main indicators. First, I would say, and most important, is clarity around the objectives. The president and others in the administration have referenced things like eliminating Iran's missile arsenal, its navy and limiting proxy activity. Those goals are broader than the earlier focus on just the nuclear programs. Each objective, of course, implies a different timeline. A narrower objective likely means a shorter engagement. Broader ambitions, conversely, would extend it. So that's the first thing.
Second, obviously extremely important is traffic through the Strait of Hormuz. We'd viewed a full closure as unlikely, given the economic consequences for Iran itself. But tanker flows have at least temporarily fallen close to zero, and that's significant because production across the region has not been impaired. This is not about oil fields going offline. It's about whether or not oil can actually move. If shipping lanes normalize within weeks, markets can recalibrate. However, if flows remain materially curtailed beyond five weeks, the risks rise meaningfully.
Third, the frequency of strikes and proxy activity. Sustained or escalating engagement would suggest a longer conflict. Signs of diplomacy, on the other hand, might indicate de-escalation.
Michael Zezas: Right. So, let's build on that and talk about oil. And our colleague, Martijn Rats has really laid this out with a lot of different scenarios. But what we're seeing right now is that when it comes to oil, this is really a shock to the transport of it, not necessarily a shock to its production. So, oil supply exists. The question is really – can it be delivered or not?
So, if tanker flows normalize and the geopolitical risk premium fades, what Martijn is saying is that global oil prices could move back towards $60 to $65 a barrel. If the logistical disruption lasts four to five weeks, then prices maybe trade in the $75 to $80 range. And if disruption extends beyond five weeks and flows are materially constrained, then you could see a situation where oil prices have to rise towards $120 or $130 a barrel. And at that level, demand destruction is what becomes the balancing mechanism in setting price for oil.
So, one signal to watch is longer dated oil prices. Early month contracts can spike during geopolitical stress, but a sustained move materially above $80 to $85 [per] barrel would likely require longer dated prices to move higher as well. And that might signal that markets believe the disruption is persistent and not temporary.
Ariana, what about natural gas here? How does gas situation fit into the energy story?
Ariana Salvatore: As of this recording, Qatar has halted liquified natural gas production putting roughly 20 percent of global supply at risk. Prices have, as you might expect, risen sharply, which likely reflects expectations of a relatively short disruption. If exports were to resume quickly, prices could retrace. But, of course, if the outage lasts longer, prices could move meaningfully higher. Again, duration of the conflict is really critical here.
Michael Zezas: So, let's bring this back to the U.S. Ariana, how does this conflict feed into the domestic, political and economic backdrop?
Ariana Salvatore: When we're thinking about the midterm elections later this year, the way we see it, the clearest transmission channel is gasoline prices. Polling shows a majority of Americans oppose military action related to Iran, but voters typically prioritize domestic issues: things like inflation, cost of living, affordability over foreign policy. However, there's a very clear caveat here. If oil prices stay elevated, gasoline prices rise, and that's where this becomes politically more salient.
Michael Zezas: Right, and so our economists and our chief U.S. Economist Michael Gapen has been all over this. And the way he assesses it is if oil prices remain about 10 percent higher than where they were before the conflict for several months, headline inflation would likely rise by 0.3 percent before dissipating. Historically, oil price shocks primarily affect headline inflation rather than underlying inflation. That's an important distinction that they point out.
So maybe that could delay Federal Reserve rate cuts, even if policymakers ultimately look through the move. But if oil prices rise enough to weaken economic activity, particularly in the labor market or consumer spending, then our economists say the Fed could pivot toward easing despite elevated inflation.
Ariana Salvatore: So, given that backdrop, what's the simple takeaway for investors in stocks or bonds?
Michael Zezas: Right. So, I think we have to think about this in terms of duration of conflict and economic impact. So, if tanker flows normalize within a few weeks and oil prices move back towards that $60 to $65 range, then our economists are saying economic damage would be limited. And historically geopolitical events alone have not led to sustained volatility for U.S. equities. So, in that environment, our cross-asset team points out that stocks would likely remain supported.
If instead, oil prices remain elevated long enough to push inflation higher and weigh on growth, the picture would change. A sharp and persistent rise in oil prices – that can pose a risk to the duration of the business cycle, and in that scenario, we'd expect stocks to struggle.
Importantly, bonds may not provide the same diversification benefit if inflation remains sticky as a consequence of all of this. We could see stock and bond prices move in the same direction. That could challenge traditional balanced portfolios.
Ariana Salvatore: And what are we seeing specifically in U.S. Treasury markets?
Michael Zezas: So, as Matt Hornbach and our global macro strategy team have pointed out here, you've got two competing forces in the U.S. Treasury market. There's been some demand for safety, but investors are also focused on the risk that higher oil prices would lift inflation. So far, inflation concerns have taken precedence over growth concerns. How long that balance holds – that might depend on incoming data, especially labor market data.
If you get weaker labor market data suggesting that growth could weaken, then you could see treasuries rally more meaningfully and yields come down. If you don't see that and inflation concerns dominate, then maybe you're not going to see yields come down as much. And bonds rally as much.
Ariana Salvatore: So, stepping back, it seems like the key variables remain tanker traffic, longer dated oil prices and duration of the conflict itself.
Michael Zezas: I think that's right. Ariana, thanks for speaking with me.
Ariana Salvatore: Always a pleasure, Mike.
Michael Zezas: And thanks to our listeners for joining us. We'll continue tracking developments and what they mean for markets. If you enjoy Thoughts on the Market, please take a moment to rate and review us wherever you listen and share the podcast with a friend or colleague.

Important note regarding economic sanctions. This report references jurisdictions which may be the subject of economic sanctions. Readers are solely responsible for ensuring that their investment activities are carried out in compliance with applicable laws.

Our Hong Kong/China Transportation & Infrastructure Analyst Qianlei Fan discusses how China’s travel industry is shifting from a post-pandemic rebound to a multi-year expansion.
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Welcome to Thoughts on the Market. I’m Qianlei Fan, Morgan Stanley’s Hong Kong / China Transportation Analyst. Today, I'll share my thoughts on why travel is quickly emerging as one of [the] key drivers of China's economic rebalancing.
It’s Tuesday, March the 3rd, at 2pm in Hong Kong.
I've just gotten back from my Lunar New Year trip to mainland China. With the longest Chinese New Year break in history, people were out roaming, exploring, laughing, and the whole country felt like it was buzzing with people on a mission to enjoy every minute.
According to the Ministry of Culture and Tourism, total domestic tourism spending recorded a robust 19 percent year-on-year growth during the holiday. In fact, China’s tourism industry isn’t just rebounding after the pandemic. It’s entering a structurally stronger phase, supported by policy tailwinds, demographic shifts, and a clear pivot toward experience-driven consumption.
By 2030, tourism revenue could reach RMB 12 trillion – equal to roughly USD $1.7 trillion – implying 11 percent annual growth from the mid-2020s. Over the next five years, cumulative domestic and inbound revenue may approach RMB 50 trillion, or USD $7.2 trillion.
That scale makes travel more than a cyclical recovery – it’s becoming a core pillar of China’s consumption-led growth. We expect tourism’s share of GDP to rise to about 6.7 percent by 2030, up from 4.8 percent in 2024.
Domestic travel remains the backbone. People aren’t just traveling again; they’re traveling more than before.
Policy is reinforcing demand. Extended public holidays, new school breaks, and event-driven tourism are boosting activity. In 2025 alone, around 3,000 large-scale performances attracted more than 43 million attendees. And spending reflects that shift. Domestic tourism spending reached RMB 6.3 trillion in 2025, about 11 percent above pre-COVID levels. Even with slightly lower spend per trip, more frequent travel is lifting overall revenue.
International travel is emerging as a second growth engine. By 2030, inbound travel could represent 16 percent of total tourism revenue. In late 2025, inbound visitor growth in major cities was up about 30–50 percent year-over-year, supported by expanded visa-free access, which now accounts for the majority of foreign arrivals. These visitors often stay longer and spend more. Outbound travel is strengthening too. International air traffic grew 22 percent in 2025, far outpacing domestic growth, and now contributes a meaningful share of airline revenue.
Demographics and technology are reinforcing the trend. Younger consumers prioritize travel, while older households – with substantial savings – are beginning to spend more as services improve. At the same time, smart hotels, virtual reality attractions, and data-driven operations are enhancing engagement and willingness to pay.
This isn’t just pent-up demand. It’s policy, demographics, technology, and supply aligning at once. – with travel at the center of China’s consumption story.
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Our Chief Fixed Income Strategist Vishy Tirupattur and U.S. Head of Credit Strategy Vishwas Patkar discuss the implications of private credit’s exposure to the software industry.
Read more insights from Morgan Stanley.

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Vishy Tirupattur: Welcome to Thoughts on the Market. I am Vishy Tirupattur, Morgan Stanley's Chief Fixed Income Strategist.
Vishwas Patkar: I'm Vishwas Patkar, Morgan Stanley's U.S. Head of Credit Strategy.
Vishy Tirupattur: While potential disruption from AI has been a key driver for markets [in the] last few weeks, the focus of investor agenda has been in the software sector. On today's podcast, we will talk about software in the credit markets and its implications.
It's Monday, March 2nd at 10am in New York.
Vishwas, let's start by understanding how the exposure in software manifests in the credit markets. How does it compare to software, say, in the equity market?
Vishwas Patkar: Yeah, so the software exposure in credit markets is large, and understandably that's why investors are closely watching what's happening with software in the equity market. But what's interesting and important for investors to note is the exposure in credit is very different from what it is in equities.
So, for instance, a good chunk of exposure in the credit market is around private issuers. So, we estimate about 80 percent of companies are private in the whole sample set that we looked at. And that's largely a function of the fact that software is not a big part of the more liquid spaces like Investment Grade and High Yield. But it is heavily represented in the more opaque parts of the market, like leveraged loans, CLOs, and, you know, BDCs.
So, our analysis found that about 25 percent of BDC portfolios are in software, closely followed by private credit CLOs. And leveraged loan market was about 16 percent. So, that's an important distinction to keep in mind versus the equity market.
The second thing I would flag is – because the software sector grew a lot in the loan market through the LBO wave of 2020 and 2021, it has a weaker credit quality skew to it than the overall market. So about 50 percent of borrowers in the sector are rated B - or lower. So, that's the lowest rungs of the rating spectrum.
Many of these software deals were underwritten with higher leverage than the broad market. And as a result of that you also have more front-loaded maturities in the sector, which brings the risks of refinancing, if some of this disruption persists.
But Vishy, that's a nice segue to you. Over the past couple of years, you looked at the private credit market in depth and that's where I think the exposure we found is the highest in BDCs, you know, which is the public face of private credit. So, in your assessment, what is the risk of software to private credit, given all of the headlines that are popping up?
Vishy Tirupattur: Public face of private credit – Vishwas, that's a great line.
BDCs – business development corporations for those who are not familiar – are companies that invest in the debt of small and medium sized companies, sourced through non-bank channels. BDCs fund themselves through equity and debt issuance. So, if you look at the portfolios of BDCs to look at their exposure to software, there's a wide variation across the various BDC portfolios.
What makes the assessment of these software risks in BDCs challenging is that many of these companies are private companies without the reporting obligations of public companies. So, no earnings reports, no 10-Ks or cues or broadly publicly available financials look at.
So, in effect, these companies need to be re underwritten to evaluate which of these companies would be disrupted from AI; and which companies could actually benefit from AI and see their margins expand. So, in the context of BDCs, liability spreads are something we are watching closely. BDC liability spreads have widened but we think more needs to happen there. The clearing levels need to wait for the full resolution of the companies that benefit and that get hurt by disruption that is still awaited. So, we expect credit spreads of BDCs to remain volatile for some time to come.
Vishwas Patkar: Okay. So, seems like this is a significant, or at least a non-trivial risk factor for credit markets, given the growth of the sector, leverage, the skew and quality. But Vishy, do you think this could be systemic for risk markets at large?
Vishy Tirupattur: So, I do think that this is a significant risk, but I don't think it's a systemic risk. The amount of leverage in BDC is fairly small. About 2x is the kind of leverage. You compare that to the kind of leverage that existed in the financial system before the financial crisis – that’s orders of magnitude smaller risk. And also the linkage to the banking system comes through the back leverage provided to the non-bank lenders. But this leverage is substantially risk remote with very high subordination levels. So, my conclusion here is this is a significant risk but not a systemic risk.
So let me turn the same question to you, Vishwas. Taking on a sort of historical perspective as well as a macro perspective, how do you see this risk manifesting in the broader credit space?
Vishwas Patkar: Yeah, so I would agree with you Vishy, that we need to see a valuation reset. We think spreads should go wider because of disruption concerns, even if they affect a relatively narrow part of the market. But a lot of that's happening against issuance that's rising. But I would say the risk of systemic concerns really emerging is relatively low. if you look at historical cycles where credit has been the weak link in the economy, those are typically characterized by a lot of corporate re-leveraging.
So, think about the late 1990s or from 2004 to 2007 or the early 2000-teens. These are all cycles where corporates were being very aggressive, adding a lot of debt. And you know, when the economy slowed, credit became the source of some default and downgrade concerns.
We haven't really seen that type of credit cycle play out at all in the past few years. If you look at corporate debt to GDP, for example, it's gone down each of the last five years. Balance sheet corporate leverage has been flat or actually gone lower in spots. M&A activity, which is usually a good indicator of corporate aggressiveness, still remains below trend. So, I think we have had a fairly restrained credit cycle where in place fundamentals are quite strong. And that's why I think the systemic contagion from any credit spread weakness, I think could be relatively muted.
Vishy Tirupattur: So, the key takeaway from us is that software and credit is a significant risk but is not quite systemic risk.
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