Thoughts on the Market

Can markets absorb this much new debt? Our panel examines strong demand for investment-grade bonds, stress in high-yield debt and the macro risks that could shape the next six to twelve months in credit markets.
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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.
Anish Shah: I'm Anish Shah, Global Head of Debt Capital Markets.
Rehan Latif: I am Rehan Latif, Global Head of Credit Trading.
Samer Oweida: And I'm Samer Oweida, Global Head of Foreign Exchange and Emerging Markets.
Andrew Sheets: Today, a discussion of historical levels of bond issuance and how it could affect the broader markets.
It's Wednesday, October 21st at 9am in New York.
Gentlemen, it's great to talk with you. I think what we're talking about is an issue that really cuts across all parts of Morgan Stanley's businesses, from issuance to credit trading to macro markets and beyond.
And I'd like to get into it, but just quickly to kind of frame where we are. Anish, we are in the midst of a historic investment cycle in AI CapEx. We're in the midst of a very large pickup in M&A activity that I also want to make sure we get into.
But you sit at the intersection of our capital markets businesses. Talk a little bit about what we're seeing in the bond issuance space.
Anish Shah: Sure, Andrew. I look after all of our new issue capital markets business in credit, and that's across the credit spectrum and across geographies. And I'm constantly looking to Rehan for the latest in investor sentiment and how we can deliver the right product for our issuing clients. And to Samer on macro views that impact our clients every day.
We are in the midst of a significant decommoditization of new issue credit markets and a decompression in credit. It's really driven by four important factors.
One is, as you note, unprecedented levels of issuance. Investment grade debt volumes are up almost 25 percent on the year. That's driven by the AI CapEx theme and a resurgence in M&A.
Number two is the rate backdrop has been, to date, pretty constructive for credit. We've seen $100 billion of new cash come into investment grade credit. We've seen new pools of investors drive tremendous new capacity into credit markets, whether it be insurance, capital fueled by annuity investments or other private credit.
We're also seeing an increase in complexity, and that's driven by the AI CapEx cycle and the interconnectedness between hyperscalers, the chip makers, the neo clouds, the data centers, and that's requiring an unprecedented level of innovation in structuring new solutions for issuers.
And the last is the market is grappling with supply-demand dynamics and winners and losers in the AI thematic.
Andrew Sheets: Rehan, that's actually a great place to turn to you because you're on the other side of this. You're focused on can markets absorb all of this supply? And I often kind of hear two narratives.
You know, one is everything is great. You've got record issuance and record supply into the credit markets. And the other is cracks are forming. You're seeing widening of CCC spreads. You're seeing some headlines around some issuers running into more trouble. From your perch, how do you see things?
Rehan Latif: So, Andrew, instead of two, I'll give you three narratives within public credit trading.
One, I agree with Anish. I think first of all, I've got to thank Anish because it's never been busier on the public credit side because of the amount of issuance coming through. But there are, really, three different themes coming through.
One, investment grade has been incredibly strong. The higher quality part of the credit market is benefiting from all this new investor money coming through at these higher rate levels. For the longest period of my 20-year career, 20-plus year career, we were in a zero-rate environment. At current yield levels, investment grade has never looked like a more attractive proposition.
You add in growth in the U.S. and across the rest of the world, the reality is that investment grade looks like a very interesting investment thesis. And I think spreads continue to be appropriately priced for the risk.
On the CCC side of the market, that's where we're seeing real dispersion.
Where we're seeing investors from the distressed community, from the high yield community, really have to figure out how to refinance some of these names. And that's been where the real challenge has been from a credit perspective.
And then obviously, you've got the hyperscalers that have come through in significant wave across many different parts of the capital market.
So, one thing Anish mentioned was the number of different financings that are available to the hyperscalers. So, I do think that given the cash flows that these hyperscalers actually have, you've managed to be able to attract a new investor base to our market as well.
Andrew Sheets: Samar, we just heard that, you know, Anish has never been busier. Rehan has never been busier. There's clearly a very credit-specific story here. But, you know, this is something that I imagine is also top of mind in the macro markets.
You've got all of this supply. You've got AI that's kind of driving the market, but you've also got all these other broader issues around energy and deficits. How do you see the historic wave of credit supply fitting into the macro piece?
Samer Oweida: Well, maybe I'll start by also thanking Anish because our corporate derivative business, where we hedge exposure, whether it's on a deal basis or on a normal course basis for these issuers, has never been busier. And part of that is because of the historic issuance that's been discussed.
But part of that, Andrew, is also about the uncertainty that you mentioned in these markets. And so, this epic wave that we're seeing around hyperscalers and AI is happening at a time where energy prices are high, inflation is sticky, fiscal concerns abound, and there's a lot of uncertainty in the forward.
And so, what we've seen from a behavioral standpoint is clients having much more appetite to engage on these discussions because of the uncertainty and hedge that exposure.
What we've also seen is a lot of re-racking of traditional macro models and how do we think about what is the rate level that the economy can handle? What does that rate level mean for winners and losers by jurisdiction or by sector, as Rehan mentioned earlier?
And we've seen that manifest itself in a lot of the invested decisions that our clients have made over the course of the year, where we've seen capital and engagement go up with names both corporate and sovereign. Where there's a positive commodity story and there's a positive AI hyperscaler story.
And we've seen less investment around the jurisdictions and the names where that story is not the case. And so we're seeing some amount of divergence, but I think also given that the economic background globally, and particularly in the U.S., continues to be strong. We've just seen very high levels of activity coming out of our corporate and sponsors-based businesses.
Andrew Sheets: So, Anish, that brings me to a question I wanted to ask you about – which is, you know, Samer, you mentioned this, really important macro question is: at what point do higher rates slow down activity?
And yet, so far, activity's still been very solid, and it's not just AI. We've also seen a lot of strategic activity, a lot of M&A activity. I would love for you to take us inside the conversations that you're having with these companies – and how they're thinking about the environment. How much these current levels of interest rates are impacting their decision-making?
Anish Shah: Andrew, you could probably break that question down into two pieces.
One is in the AI ecosystem, where, you know, certainly there's a lot of optimism on the return on these investments. And so, sitting in the shoes of a hyperscaler or a data center developer or a chip maker, there's a lot of optimism that – notwithstanding the current higher rate environment, that the long-term return on these investments is going to be very, very attractive.
And so, we're seeing a relative level of insensitivity on rates from the issuer's perspective in accessing capital. The first wave of all of the AI CapEx has predominantly been in the investment-grade bond market. That's the deepest, most liquid, fastest way to raise capital and speed.
Now you're seeing some of these companies diversify into different currencies and different products, and that's been a really healthy technical for the markets. From an M&A context, I think a lot of the resurgence in activity is driven by the fact that there's a meeting of the minds in terms of valuation.
So, for the first time in many years, we're seeing large-scaled sponsor private equity-owned assets come to market and be acquired by an investment-grade corporate strategic. Yes, they are borrowing at higher rates than they would have a few years ago. But the view is that they're able to acquire these assets at an appropriate valuation, and it makes long-term strategic sense, and we're seeing just a big pickup in that activity.
I think that also has another implication for credit markets, which is some of these assets that have been long held by private equity sponsors had multi-billion dollar debt complexes that are going to be repaid as part of these transactions.
That's a recycling of credit. That's going to free up some capacity in the leverage finance markets as well.
Andrew Sheets: And yeah, Rehan, how do you think about that? Because, you know, again, we have these big macro forces of AI CapEx, and then these very micro company-specific stories around M&A. How do you see that impacting your backdrop?
Rehan Latif: Within credit, the biggest driver is always growth. You pay the rate that you pay simply because there's an expectation towards growth. When Anish talks about some of these developments that are happening within the market, these are all, you know, big paradigm shift in growth.
And, AI investment the reality is that we are at the forefront of creating the capital for that. And the reality is that the yield that is being paid; or the coupon that is being paid is essentially the price for that investment.
Investors are seeing that across all these different markets. I think we have to be very deliberate about the difference between investment-grade, investments in newer sectors, and what is actually happening within the more distressed part of the credit market.
Simply because that market has been challenged for a long time. That market was held together to a certain degree by a zero-rate environment. And that has changed quite dramatically as rates went higher and as the global economy has evolved as well.
So, from my perspective, this is all very healthy.
Rehan Latif: Samar, I'll let you have the last word here. You know, Anish and Rehan have talked about these historic waves of credit issuance. Part of that's coming from AI, but part of it's coming from M&A and strategic transactions. You know, we're sitting here on the eve of our Morgan Stanley Credit Symposium in New York. It's tomorrow. We have over 1,000 investors attending. I think that indicates just the amount of interest and focus on this space.
And yet, as you hear Anish talk about kind of the almost insensitivity of rates for some of these key borrowers, how does this impact how you're thinking about macro markets and how they could trade over the next six to twelve months?
Samer Oweida: I think it speaks to what both Anish and Rehan said in that between the growth and the higher level of rates, there are going to be winners and losers.
And what we can't predict is what energy markets or political developments or fiscal developments might have in store. But generally speaking, macro markets are responsive to underlying growth, inflation, and policy discipline. And I think as we go into the depths of what is one of the most consequential CapEx cycles in post-Industrial Revolution history, the governments, the jurisdictions, and the corporates that have discipline and capacity around this growth will be the winners.
We think so. We see that as on the go forward. And those that don't have a clear policy will suffer the consequences. I think that is still a very early innings discussion to be had. Lots of data still has to come through.
I think what we know is what is on the docket for the next year or two in terms of how much capital needs to be raised and debt issued. But what we don't know is how does that affect underlying economic activity.
And I think as that data manifests in conjunction with policy initiatives globally, there's a lot to be excited about. And there's a lot to be quite concerned about. And looking at how policy mistakes one way or the other might affect a lot of the behavioral patterns in credit markets and from an issuer standpoint.
Andrew Sheets: Gentlemen, thank you for taking the time to talk, and see you tomorrow at the conference. And thank you for listening. If you enjoy Thoughts the Market, please take a moment to rate and review us wherever you listen and tell a friend or colleague about us today.

Anish Shah is a member of Morgan Stanley’s Global Capital Markets Division. Rehan Latif and Samer Oweida are members of Morgan Stanley’s Fixed Income Division. They are not members of Morgan Stanley’s Research Department. Unless otherwise indicated, their views are their own and may differ from the views of the Morgan Stanley Research Department and from the views of others within Morgan Stanley.

With AI startups scaling at record speed, our analysts Sabrina Hao, Adam Wood and Brian Nowak share takeaways from Morgan Stanley's Spark Conference on AI returns, competitive moats and implications for public markets.
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Sabrina Hao: Welcome to Thoughts on the Market. I'm Sabrina Hao, Morgan Stanley's AI and Tech Thematic Equity Strategist.
Adam Wood: And I'm Adam Wood, Head of U.S. Software Research at Morgan Stanley.
Brian Nowak: And I'm Brian Nowak, Head of U.S. Internet Research.
Sabrina Hao: Today, what private companies are telling us about the AI opportunity and what it means for public markets.
It's Tuesday, October 20th, at 10am in New York.
Last week, we were at our Spark Private Company conference in California, bringing together more than a hundred startups across software, robotics, AI infrastructure, and beyond. There was even a company helping to manufacture pharmaceuticals in space.
It was a really interesting week to be at the conference given everything that was happening in the market. But really, we came away feeling even more positive about the AI cycle.
While public market investors are waiting for returns to justify trillions of dollars in AI spending, we're already seeing that pay off in private markets. AI demand is real, companies are delivering tangible returns for customers, and startups are scaling faster than ever.
Adam, let's start with growth. We heard from startups scaling at extraordinary speeds. One was approaching [$] 4 billion in annual recurring revenue in two years, another reaching a billion in under a year.
What really stood out to you about the pace of AI adoption, and why aren't we necessarily seeing that same growth at established software companies?
Adam Wood: So yeah, absolutely, the pace was remarkable. And clearly one of the standouts from the conference when we used to discuss how quickly a company could get to a $1 billion so-called unicorn valuation. And now we're discussing how quickly they can get to a unicorn revenue run rate of a billion dollars, which for me is quite incredible.
But I think I wouldn't make the debate simply about private versus public companies. I'd make the distinction between two different types of software.
I think we have to make a distinction between largely coding and inference tools. There were a number of coding and inference software companies at the conference, and application software. And I think there's a big difference in terms of time and complexity to bring these products to market.
I think the coding and inference, which tends to be the building blocks, as I would call them, underneath the application layer, they can be constructed much more quickly. And I think because we can adopt when we're still building bespoke, which I think is a lot of the phase we're in today, we're still building bespoke software rather than buying off the shelf. They can be adopted earlier as well.
So, we've got faster time to build and faster time to adopt by companies. So, I think it's natural you can see very fast growth in those types of businesses very quickly.
I think in the application landscape, it's a little bit different because those applications have to deliver complete business solutions for customers. In finance and customer service, that means integrating data, understanding workflows, and putting controls in place.
There's much more to build and much more to implement, and generally, the adoption of those tools takes place a little bit later. After we've gone from the bespoke phase where companies are building themselves, we go to a phase where companies see that there are off-the-shelf packages that are available, that companies have built out.
So, I think it's both a complexity of build debate and also, a time in terms of where we are in the software adoption cycle.
The second thing I'd say is, you know, in public software companies that have a big incumbent existing business, sometimes there can be that big incumbent business that's not growing very quickly. And a very fast-growing AI business in there as well. And that very fast AI growth business can be hidden a little bit by that large public software company that exists.
So, I don't think it's as straightforward as just saying, you know, private's growing very quickly, public not. I think there's a couple of layers of complexity in there in terms of what type of software you're dealing with. And then how big the existing kind of business is in a software company and what is kind of existing software growing a little bit less quickly. And then the AI bit that could potentially be growing very fast.
Sabrina Hao: Absolutely. It makes a ton of sense. Maybe on this theme of full stack solutions, you know, Brian, we heard about the shift from AI assistants to agents that can actually execute work. You've done a lot of work on this, both across consumer and enterprise.
What did you learn about how quickly agents are advancing? And how does it shape the outlook for the internet names that you cover?
Brian Nowak: This is a very exciting time for agentic adoption on both the consumer side and the enterprise side. You know, we've sized potential addressable markets of $30 to $40 trillion of spend on both the consumer side and the enterprise side that we think can be addressed by agentic tools. On the consumer side, people are shopping, they are planning their travel, they're planning their holiday shopping. On the enterprise side, we're seeing both vertical specific agents start to emerge. So, agents within the legal area or agents within the healthcare area. And then we're also seeing broad-based agents from some of the larger private companies also start to bud through.
So, there is a smattering of agents that are popping through. On the consumer side, Muse is your early leader, and we do expect more to come in 2027. And then I think on the enterprise side, they're both vertical specific as well as broader based agents expected to grow.
Sabrina Hao: Agents really raise an important question around disruption. Maybe for Adam, you've written about the moat and the journey in software. It's not just about if a company has a competitive advantage, but how it navigates that transition to an AI-driven world.
Did Spark change your thinking about which software incumbents are best positioned to defend their businesses or capture new growth?
Adam Wood: So, I think Spark really confirmed a lot of our views on that moat and journey framework that we've put forward. The moat protecting the existing business that you have and the journey turning AI into new growth opportunities. And in some ways, the deeper the moat for businesses, the longer that journey can be.
An incumbent embedded in complex mission-critical workflows is hard to replace because of the data integrations and customer trust it has by definition. But those same requirements can make introducing AI into its platform more demanding. You can't compromise the reliability of that work.
So that's why we've separated the durability of the business from the speed of its AI transition.
For some of the coding inference businesses we heard from – again, these incredibly fast run rate, fast growth businesses, the monetization has obviously moved very, very quickly. And I think there the investor debate's going to be a little bit the opposite way around. How durable and how big a competitive advantage have these companies built on that very fast-growing revenue base?
And that's not to say these businesses can't have moats. They can. You know, performance, model customization, and integration can create real differentiation. But I think there is a big difference to be made between rapid growth and defensibility.
And, you know, to give a couple of examples here in terms of the moat that some of these AI native vertical and business software companies are leveraging. You know, there's a legal AI company that's hiring former lawyers to become forward deployed lawyers.
We had forward deployed engineers, now we've got forward deployed lawyers. But the reason there is they bring that understanding of legal workflows to the product and its implementation.
So, the value isn't just access to the model. It's making the model and the technology work in the customer's business and understanding in depth those business workflows.
Sabrina Hao: And we've talked about this theme of AI adopters, and really what we need to get this trade to work are those quantifiable benefits from AI. So open weights are a key part of that.
Maybe to pull on another thread, and you, kind of, alluded to this, the models themselves are increasingly becoming table stakes rather than moats. And the value is shifting to everything around the model – whether that's your data, your workflows, the tools that make AI useful.
So where did you see this most clearly at Spark? And does it change your view on where durable value will accrue in software?
Adam Wood: So again, I mean, I think I'd come back to the growing importance of open weight models. I mean, it was a recurring theme at the conference. Almost every company spoke to us and spoke to, you know, the implementation. And again, it reinforces that view of a multi-model world.
You’ve got the inference companies talking about bringing down the costs of using the models. Again, that diffusion concept we talk about. At the application layer, a legal software company said it benchmarks 18 models across 200 criteria to decide which model should run the task. So, you know, you're getting incredible gains in efficiency.
I think there are two implications for public software vendors. I think, you know, first of all, there's an opportunity here for software companies to help their customers with orchestration. So, there's going to be a layer around how do we get the right task to the right model at the right price. And I think a lot of the public software companies, you know, at the hyperscaler level, they can help at a broader enterprise adoption level.
And then down at the application layer, whether we're talking about CRM, ERP, HR, there's an opportunity for software companies to help with orchestration at that layer as well.
I think the second thing, which is probably a little bit less talked about, is maybe there is a gross margin opportunity here. I think in software, all we've discussed so far is the pressure that's going to come on gross margins from having to include the cost of running tokens in a software business model and offer that to customers as part of the package.
If we can bring down inference costs dramatically, and you can hold on to some of the pricing as a software company – maybe there could be a little talked about bull case in the medium term. That maybe software margins are not going to be as pressured as people think. And we can maybe see gross margins hold in a lot better than people think.
Sabrina Hao: Brian, I want to end with something that may seem counterintuitive. In a world where software's becoming easier to build, some of the strongest competitive advantages may actually be in the physical world.
The conference had many companies, whether that was logistics, manufacturing, real-world operations. You know, why is that intersection of the digital and physical world so defensible? And how does it influence your view of who ultimately wins in an agentic world?
Brian Nowak: I think it's important for people to remember that AI is not just software. AI is going to get physical through new types of robots that impact the entire globe. We're seeing it in multiple industries already. We're seeing it on the defense side with drones and other types of mechanisms that are going to change the way that we build out our overall defense tools. We're seeing it on the industrial side, in warehouses, on delivery. We're seeing it on the agriculture side, the way that we're going to the future of farming.
And so over the long term, while I think we will have humanoids and we will have robots in our homes, I think it's important for people to keep in mind there are going to be a lot of physical and robotic implementations of AI over the next two, three, five years that are going to really change the way we do a lot of business.
Sabrina Hao: Absolutely. Lots of exciting things happening. Adam, Brian, thanks for joining me. And thank you 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.

Higher rates can take time to filter through the economy, making their full impact difficult to judge. Arunima Sinha from our U.S. and Global Economics teams considers where the pressure may emerge next and how policymakers could navigate the competing risks.
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Welcome to Thoughts on the Market. I'm Arunima Sinha, from the Morgan Stanley’s U.S. and Global Economics teams.
Today, I'll talk about what rising rates mean for economic activity, inflation, and the policy path.
It’s Monday, October 19th, at 10am in New York.
The FOMC raised rates in September. But expectations for future hikes remain volatile. Within hours of the latest CPI print, markets round-tripped, pushing the probability to even for an October hike before retreating.
Now, consistent with our baseline forecast, the market assigns December the highest likelihood for the next hike. But moves further along the U.S. curve matter even more; notably the 10-year yield shifted.
One question that arises, taking a step back is – how much we should worry that broad-based inflation upside means tighter policy is required to push down inflation and, therefore, growth? Or have financial conditions tightened enough on their own that the central bank has less, not more work to do?
The calibration of monetary policy has always been difficult, and now it is becoming urgent.
Economists try to put the level of interest rates into the context of where they stand relative to the economy's natural rate. Stronger productivity growth and investment demand should mean a higher natural rate, so a given level of rates is less restrictive.
The rise in long-term treasury yields alongside still firm growth and earnings expectations is an indication that the neutral rate may indeed be higher. When yields rise because growth prospects are improving, expected returns rise and the demand for capital strengthens. In that setting, higher rates are not necessarily contractionary.
But there is a less benign alternative.
Yields have also risen because inflation has been hot, fiscal concerns are intensifying, and investors are demanding more compensation for holding duration. The question that Chair Warsh has posed – are financial conditions restrictive – is starting to get a different answer.
Calibration will be key to how the economy evolves. For the U.S. so far, even the most recent upside inflation surprise pointed to resilient demand rather than energy-driven stagflation. The simple explanation is that many firms still see returns well above their cost of capital. Recent work from our U.S. economics team supports such a view.
Where there may be a contrast is at the sector level, and that seems to be becoming more apparent. If activity is concentrated in sectors driven by innovation, productivity, and strategic investment, the economy may be less rate sensitive than in past cycles.
Calibrating policy amid these opposing forces is no easy task. The fact that we're seeing the biggest spread widening in the lowest quality part of the credit spectrum and the fact that housing activity is at recessionary levels are some tells in the U.S. that policy and financial conditions are restrictive.
The signs of tighter financial conditions are somewhat more visible in Europe, in part because the AI CapEx spend is less of a factor. Growth has been resilient, but not sufficiently so to offset rising fiscal concerns from higher rates.
The French OAT sell-off shows how quickly the balance of risks shifts. Globally, financial conditions are introducing tougher questions to the macro landscape.
Until recently, the economy's message had been clear.
Higher rates were restraining some sectors, but without meaningfully altering the expansion's trajectory. As long as expected returns rise alongside borrowing costs, then yields can remain high – without becoming decisively restrictive. But as our rate strategy colleagues have noted, inflationary pressures have pushed up real rates, not break evens.
So, the market is telling us that policy is getting tighter to restrain growth and eventually pull-down inflation. The latest inflation print in the U.S. reinforces this debate. Are financial conditions restrictive?
With several catalysts ahead, Q3 earnings, hyperscaler CapEx credit spread widening, and U.S. elections, the Fed meeting coming up, markets will need to shift to assessing growth risks as central banks try to recalibrate.
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.

Our ABS Strategist Carolyn Campbell explains how rising borrowing costs are slowing the AI data center boom and what could drive a rebound in financing.
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Carolyn Campbell: Welcome to Thoughts on the Market. I'm Carolyn Campbell, Morgan Stanley's Asset-Backed Securities Strategist.
Today, why the AI boom is running into an unexpected obstacle: the cost of borrowing money.
It's Friday, October 16th, at 10am in New York.
Demand for data center facilities is booming. Yet even as companies race to build more capacity, the financing for them is becoming increasingly expensive. And that tension is creating an interesting story in credit markets.
Since January, five-year Treasury rates have climbed more than one percentage point, and spreads for data center assets have also gone wider. At the beginning of 2026, the highest-rated data center bonds offered yields below 5 percent. Today, similar bonds are yielding closer to 7 percent, and that increase matters enormously for companies trying to finance multi-billion-dollar facilities.
Many operators turn to the securitized credit markets to raise money. Essentially, they issue bonds backed by the income generated by their data centers. By the time these bonds are issued, construction on the data center is complete, the power is secured, and the rent is already being paid.
So, on the one hand, cash flows are stabilized – but there is a catch. These facilities often sign long-term leases well before they seek financing in the securitized credit market.
By the time they're ready to issue these bonds, rental income is already locked in. And so, when interest rates rise quickly, as we've seen this year, and these borrowing costs increase, revenues aren't necessarily keeping pace.
Our analysis suggests that when borrowing costs approach 8 percent, issuing these bonds becomes significantly more challenging under these current financing terms. And we're already seeing the effects in the market.
So far in 2026, data center operators have issued around [$] 15 billion in ABS and another [$] 7 billion in the CMBS markets. That's roughly in line with last year's pace, but it's below what we'd expect given the extraordinary growth in data center demand this year. Still, we believe the slowdown in supply is temporary.
Our forecast suggests that U.S. data center power capacity could grow from roughly 60 gigawatts today to 150 gigawatts by 2028; although there are some constraints on power availability that could leave capacity 30 percent to 40 percent below these projections.
Regardless, much of the lease capacity that comes online will require debt financing, and these operators have ambitious growth plans. So, they have some options to navigate these higher rate environments.
They might borrow less against their properties. They might accept lower investment returns in the near term. Or they can turn to different credit channels that offer more flexible financing structures.
So, where does this leave the market? To take into account the higher rate environment, we've lowered our forecast slightly for 2026 and 2027 for securitized credit data center issuance. But the amount of lease capacity that's set to come online in 2028 is so high that we've actually held those numbers steady.
All told, we now expect around a [$] 120 to a [$] 150 billion of supply over the next couple of years. That's substantial financing activity, even with the higher interest rates creating near-term challenges.
The higher cost of borrowing may slow the pace of data center financing for now, but the scale of the build-out ahead suggests that this market still has plenty of room to grow. And there are pockets within the market that we do like.
We've talked a lot about the hyperscaler data centers, which are big campuses leased to one or two hyperscaler tenants, often with 10- or 15-year leases.
But the other part of the market, the colocation data center market, is where we see a lot of value today. These campuses have many different types of tenants, many different industries represented, and they often have shorter leases.
Given the sharp increase in data center rental rates and vacancy rates around 1 percent, these data center operators are more quickly able to monetize the changes that we're seeing in the industry.
That flexibility could prove particularly valuable when borrowing costs remain elevated.
Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Our Chief Latin America Equity Strategist Nikolaj Lippmann explains why Brazil’s election, the cost of capital, AI investment and geopolitics could help unlock a new era of growth across Latin America.
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Welcome to Thoughts on the Market. I'm Nikolaj Lippmann, Morgan Stanley's Chief Latin America Equity Strategist.
Today – why Brazil could be the missing piece of what we call LatAm Spring, and what AI has to do with it.
It’s Thursday, October 15th, at 10am in New York.
Brazilian equities rallied sharply last week after a surprising first-round presidential election result. Flávio Bolsonaro finished ahead of President Lula, setting up a runoff later this month. The outcome remains uncertain. But for investors, I think there’s a bigger question. Can Brazil approach a policy shift that unlocks something the country has lacked for years: investment.
We've long argued that Latin America is moving from a consumption-led economy towards an investment-led cycle, thanks to: 1. Changing geopolitics; 2. investment-friendly policies; and 3. The potential for lower interest rates. What's more, this fits into the framework of the AI CapEx cycle, driving the global and especially the U.S. economy that has moved from consumption to CapEx, as argued by Mike Gapen, our U.S. economist. We're already seeing signs of this LatAm Spring in Argentina, Chile, Peru and Colombia. Brazil could be the largest missing piece. And there are three reasons why.
First, the cost of capital. Brazil remains one of the world's most expensive places to invest. High government debt and elevated interest rates have made fixed income investment more attractive than equities or entrepreneurial risk-taking. But credible fiscal reform could change that equation. Lower government spending could reduce borrowing costs, strengthen the currency, and encourage private investment. Which means capital could shift from sitting in fixed-income assets toward funding growth.
Second, AI. AI might seem unrelated to Brazilian politics, but we see an important connection. Our global framework suggests that annual capital spending could rise from roughly $2.5 trillion to about $5 trillion - $6 trillion as companies build out global AI infrastructure. And Latin America has a critical role to play.
Data centers need electricity. Electricity requires energy infrastructure. And the infrastructure requires copper, lithium, and other critical minerals. Latin America as a whole supplies roughly 10 percent of the world's energy, holds about 20 percent of global energy reserves, about 30 percent of global copper, and approximately 60 percent of the world's lithium resources.
So, we've argued that Brazil and Argentina look increasingly like Texas, with exports of energy and of agriculture. And one other area around politics where Brazilians could perhaps learn or be inspired from peer Latin American countries and gather some inspiration from Texas could be around deregulation.
For Brazil, lower interest rates and greater policy credibility could make participating in this investment boom considerably easier.
And that brings me to the third factor: geopolitics. Brazil has strengthened its economic ties with the East, while remaining deeply connected to Western capital markets and U.S. technology. A more balanced relationship could improve access to capital markets and support long-term investments. Of course, an election result alone doesn't guarantee change. Fiscal discipline, policy execution, and global financing conditions remain critical. Higher U.S. Treasury yields are an especially important risk for a region carrying substantial debt.
But consider what's already happening elsewhere. Mexico contributes hardware to the AI supply chain. Chile is rebuilding its investment pipeline. And Peru is benefiting from mining activity. Argentina is attracting capital after significant policy changes.
As Latin America's largest economy, Brazil could have substantial regional implications if it moves in the same direction. That's ultimately our LatAm Spring thesis: a shift away from consumption toward investment, entrepreneurship, and capital formation.
There's still a long road ahead, but Brazil could be the most important test of whether that spring truly takes hold.
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