Our Insights

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. 

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.