U.S. Tech: The Future of Artificial Intelligence

Our CIO and Chief U.S. Equity Strategist Mike Wilson discusses the outlook for stocks after the preliminary U.S.-China trade agreement and ahead of the Fed meeting and big tech earnings.Read more insights from Morgan Stanley.----- Transcript -----Welcome to Thoughts on the Market. I'm Mike Wilson, Morgan Stanley’s CIO and Chief U.S. Equity Strategist. Today on the podcast I’ll be discussing the remaining hurdles for equities after what appears to be a preliminary trade deal with China.It's Monday, October 27th at 11:30am in New York. So, let’s get after it.Over the past few weeks, trade tensions between the U.S. and China escalated once again focused on rare earths and technology transfers with each country playing its strongest card. Over the weekend, it appears that we have at least a preliminary agreement to de-escalate these tensions which means avoiding prohibitively high tariffs that were scheduled to go on at the end of this month. While we don’t have many details on what has been agreed to, it appears that critical rare earths will continue to ship to the U.S. while technology transfer restrictions by the U.S. to China will ease. Presumably, Fentanyl tariffs of 20 percent on China are likely to be part of any broader agreement between Presidents Trump and Xi, if they end up meeting at the upcoming Asia Pacific Economic Cooperation forum.Given the sharp sell-off in stocks a few weeks ago on the news of trade tensions re-escalating, it’s not surprising that stocks are rallying sharply this morning on news of a possible deal from last week’s talks. Our attention now turns to the other big events this week. First, the Federal Reserve is meeting tomorrow and Wednesday to decide its next move on monetary policy. There is a broad consensus view that the Fed will cut another 25 basis points but there are very different views about how they will address its balance sheet run-off known as quantitative tightening, or QT. Based on my conversations, there is a growing consensus view for the Fed to announce the end of QT but uncertainty around the timing. Our house view is for the Fed to wait until the January meeting to make this official with an end of the program in February. Others believe the Fed could announce something as early as this week. That dispersion in expectations does create some room for disappointment from markets, especially given the recent increase in funding market spreads. More specifically, the widening in spreads suggests banking reserves may already be too low and restrictive for the pick-up in economic activity and capital spending that requires more liquidity. Second, earnings revision breadth has rolled over sharply the past few weeks. Most of this decline is due to normal seasonality and the fact that revisions breadth had reached unsustainably high levels since bottoming out in April. Therefore, a reset should be expected as we previewed over a month ago. Nevertheless, it needs to stabilize and push higher again for stocks to continue their advance in my view. Perhaps most importantly for the S&P 500 is the fact that all of the hyperscalers are reporting this week and will likely determine if revision breadth rebounds. It will also be important to see how those stocks react to what is likely to be continued aggressive guidance on AI capex plans. Since April, the hyperscaler stocks have rewarded higher guidance on spending. Should that change, we may see a different tone to how these companies discuss their spending plans. Bottom line, I remain bullish on my 12 month view for U.S. stocks based on what I believe will be better and broader growth in earnings next year. Nevertheless, the near term window remains a bit cloudy on trade, Fed policy shifts and earnings revisions breadth. Stay patient with new capital deployment and look to take advantage of downdrafts when they arise like a few weeks ago. Thanks for tuning in; I hope you found it informative and useful. Let us know what you think by leaving us a review. And if you find Thoughts on the Market worthwhile, tell a friend or colleague to try it out!

27 Loka 20253min

Our U.S. Software Analyst Sanjit Singh explains how AI is reshaping software development and why the future for the sector may be brighter – and busier – than ever.Read more insights from Morgan Stanley.----- Transcript -----Welcome to Thoughts on the Market. I’m Sanjit Singh, the U.S. Software Analyst at Morgan Stanley.Today: how AI is transforming software and what that means for developers.It’s Friday, October 24th, at 10am in New York.There's been a lot of news stories and anecdotal accounts about AI taking over jobs, especially in the software industry. You may have heard of vibe coding, where people can use natural language prompts, guiding AI to build software applications. So yes, AI is creating a world where software writes itself. But at the same time, the demand for human creativity only grows.The introduction of AI coding assistants has dramatically expanded what software can do, fueling a surge in both the volume of code and the complexity of projects. But instead of shrinking the developer workforce, AI is actually supporting continued growth in developer headcount, even as productivity soars.We’re estimating the software development market will grow at a 20 percent compound annual growth rate, reaching $61 billion by 2029. And that’s up from $24 billion in 2024. And in terms of the developer population, [research] firms like IDC expect it to jump from 30 million paid developers in 2024 to 50 million by 2029 – that’s a 10 percent annual growth rate. Even the most conservative estimates, like those from the U.S. Bureau of Labor Statistics, see developer jobs growing roughly 2 percent per year through 2033, outpacing overall employment growth.So, what does this mean for people behind the code? AI isn’t replacing developers. It’s redefining them. Routine tasks are increasingly handled by AI agents, and this frees up developers to become curators, reviewers, architects, and most important problem-solvers.The upshot? Companies may need fewer developers for repetitive work, but the overall demand for skilled engineers remains robust. As AI lowers the barrier to entry, the pool of people who can build software applications expands dramatically. But at the same time, the complexity and ambitions of projects rise, keeping experienced developers in high demand.No doubt, AI coding tools are delivering real productivity gains. Some teams are reporting nearly doubling their code capacity and cutting pull request times in half after adopting AI assistants. Test coverage has increased sharply, resulting in 20 percent fewer production incidents for some organizations. But there is a catch with all this AI-generated code. It’s creating significant new bottlenecks downstream.An example of this is code review, which is becoming a major pain point. Many organizations are experiencing pull request fatigue, with developers rubber-stamping changes just to keep up. Some teams now require three reviewers for AI-generated change, compared to just one before. And in terms of automated testing, systems are getting overwhelmed because every change made with AI sets off a complete round of test.Now we estimate productivity gains from AI in software engineering at about 15–20 percent. But in complex projects, the gains are much lower, as the volume of new code often means more bugs and more rework – and hence more human developers.So where do we go from here? In our view, the future isn’t about fully autonomous software development. Instead, large enterprises are likely to favor an integrated approach, where AI agents and human developers work side by side. AI will automate more of the software development lifecycle. And that not only includes coding – which, coding typically accounts for 10-20 percent of the software development effort – but other areas like testing, security, and deployment. But humans will remain in the loop for oversight, design, and decision-making. And as software gets cheaper and faster to build, organizations won’t just do the same work with fewer people – they likely will do more.In short, the need for skilled developers isn’t going away. But it’s definitely evolving. And in the age of AI, it’s not about man versus machine. It’s about man with machine. And so with more software, we see more developers.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.

24 Loka 20254min

Our Head of Corporate Credit Research Andrew Sheets wades into the debate around whether the boom in artificial intelligence investment is a warning sign for credit markets. Read more insights from Morgan Stanley.----- Transcript ----- Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Head of Corporate Credit Research at Morgan Stanley.Today – the debate about whether elevated capital expenditure and AI technology is showing classic warning signs of overbuilding and worries for credit.It's Thursday, October 23rd at 2pm in London.Two things are true. AI related investment will be one of the largest investment cycles of this generation. And there is a long history of major investment cycles causing major headaches to the credit market. From the railroads to electrification, to the internet to shale oil, there are a number of instances where heavy investment created credit weakness, even when the underlying technology was highly successful.So, let's dig into this and why we think this AI CapEx cycle actually has much further to run.First, Morgan Stanley has done a lot of good collaborative in-depth work on where the AI related spend is coming from and what's still in the pipeline. And importantly, most of the spending that we expect is still well ahead of us. It's only really ramping up starting now.Next, we think that AI is seen as the most important technology of the next decade by some of the biggest, most profitable companies on the planet. We think this increases their willingness to invest and stick with those investments, even if there's a lot of uncertainty around what the return on all of this expenditure will ultimately be.Third, unlike some other major recent capital expenditure cycles – be they the internet of the late 1990s or shale oil of the mid 2010s, both of which were challenging for credit – much of the spending that we're seeing today on AI is backed by companies with extremely strong balance sheets and significant additional debt capacity. That just wasn't the case with some of those other prior investment cycles and should help this one run for longer.And finally, if we think about really what went wrong with some of these prior capital expenditure cycles, it's often really about overcapacity. A new technology – be it the railroads or electricity or the internet – comes along and it is transformational.And because it's transformational, you build a lot of it. And then sometimes you build too much; you build ahead of the underlying demand. And that can lower returns on that investment and cause losses.We can understand why large levels of AI capital investment and the history of large investment cycles in the past causes understandable concern. But when tying these dynamics together, it's important to remember why large investment cycles have a checkered history. It's usually not about the technology not working per se, but rather a promising technology being built ahead of demand for it and resulting in excess capacity driving down returns in that investment, and the builders lacking the financial resources to bridge that gap.So far, that's not what we see. Data centers are still seeing strong underlying demand and are often backed by companies with exceptionally good resources. We need to watch if either of these change.But for now, we think the AI CapEx cycle has much further to go.Thank you as always for your time. If you find Thoughts on the Market useful, let us know by leaving a review wherever you listen. And also tell a friend or colleague about us today

23 Loka 20253min

Our analysts Brian Nowak, Keith Weiss and Matt Bombassei break down the most important tech insights from Morgan Stanley’s Spark Private Company Conference and industry shifts that will likely shape 2026 and beyond. Read more insights from Morgan Stanley.----- Transcript ----- Brian Nowak: Welcome to Thoughts on the Market. I'm Brian Nowak, Morgan Stanley's Head of U.S. Internet Research. I'm joined today by Keith Weiss, Head of U.S. Software Research and Matt Bombassei from my team.Today we're going to talk about private companies and technology – and how they're showing us the direction of travel for disruptive technologies and emerging investment opportunities.It's Wednesday, October 22nd at 10am in New York.Keith and Matt, we just returned from Morgan Stanley's Spark Private Company Conference last week in Los Angeles. It had over 85 private tech companies, 150 plus investor firms. There were a lot of themes that were discussed across the entire tech space impacting a lot of different sectors, including energy, healthcare, financial services, and cybersecurity.Keith, what were some of the biggest takeaways you took away from Spark this year?Keith Weiss: I'd say just to start off with, the Spark Conference is one of my favorite conferences of the year. It's a more intimate conference where you really get to spend time with both the private company executives and founders, as well as investors from the VC community and public company investors. And the conversations are more broad ranging; they're more about the thematics in the industry. They're more long term in nature.So, it's not just a conversation about what's next quarter going to look like, or what data points are you drumming up. You're having these thoughtful conversations about what's going on in the industry and how that's going to impact business models, how it's going to impact innovation cycles, how it's going to impact pricing models, within these companies. So, it tends to be a very interesting conference for me to attend.So, for me, some of the key takeaways. Typically, when we're in these innovation cycles, it feels like everybody's rowing in the same direction. We all understand where the technology's heading, we're all understanding how it's going to be delivered, and it's a race to get there. And you're having a conversation about who's doing best in that race, who's best positioned, who's got a better motor in their race car, if you will.So, to me, one of the big takeaways was we don't have that agreement today, right? There's different players that are looking at this market evolution differently. On one side of the equation, the application vendors – and a lot of this debate is in SaaS based applications. They see SaaS based applications having a very big role in taking these models that are inherently in-determinative and making them to be more determinative and useful within an enterprise context.Bringing them the data that they need to get the job done and the right data; bringing them the context of the business process being solved; bringing the governance that's necessary to use in an enterprise environment. But most importantly, to make it effective and efficient for the large enterprise.On the other side of the equation, you have venture capital investors and more early-stage investors who are looking at this as a huge phase shift, right? This is going to fundamentally change how we build software, how we utilize software, and they worry about a deprecation of that SaaS application layer. They think the model itself is going to start to encompass, it's going to start to subsume a lot more of that application functionality, a lot more of that analytics. And they see a lot more disruption going forward.So that debate within the marketplace, that's something that's interesting to me. It's something that we don't typically see in these innovation cycles. So that's takeaway number one.Takeaway number two, we're still really early days, and that's a little bit implied in in the first statement; I definitely hear a lot of it when I talk to the end customer. When I talk to CIOs. This wasn't necessarily at Spark, but earlier in the week, I was at a CIO conference, there was 150 CIOs in the room. One of the gentlemen on stage asked a question. ‘Who in the room has a good understanding of what we're talking about when we mean Agentic AI, when we mean agentic computing within our enterprise.’ Of the 150 CIOs, four raised their hands. Still very early days in understanding how this is going to evolve, how we're going to actually deliver these capabilities into the enterprise.And the last takeaway I would say is more excitement about the federal government becoming a better customer for software companies overall. People are more interested in new avenues into that federal government. There's been some very successful companies that have opened the door to getting into these federal government contracts without going through the primes, without doing the typical federal government procurement cycles.And that's very interesting to the startup community, which tends to move faster, which tends to drive on innovation versus relationship building; versus being in an existing kind of incumbent prime. So, I thought that opening was – it was pretty interesting as well.Brian Nowak: it sounds like it's still very early, there are a lot of different points of view and no real consensus as to where technologies could go next. However, one theme with an enterprise software – [it] does seem like cybersecurity has a little more of a unified view.So maybe walk us through what you learned from a cybersecurity perspective and what should we be focused on there?Keith Weiss: Yeah, absolutely. If there is a consensus, the consensus is that generative AI and these innovations and the fast pace of innovation is going to be a positive for cybersecurity spending, right? The reason being, there's three main factors that are driving that overall spending.One is expansion of surface area, right? Cybersecurity in one dimension, you can think of how much is there to be protected, right? And if we think about the major themes that we're talking about, we're going to be developing a lot more software, right? The code generation tools are improving software developer productivity. You have an expanding capability of what you can actually automate.We'll be building a lot more software. That software needs to be protected, right? We have new entities that are going to be operating inside of enterprises, and that's the agents. So, CIOs are thinking about this future state where you have tens, thousands, maybe hundreds of thousands of agents operating in the environment, doing work on behalf of end users, but having permissions and having ability to execute business processes. How do we secure that side of the equation? We're talking about outside of just the four walls of the large enterprise, going into more operational technologies, being able to automate more of that work. That needs to be secured as well.So, an expanding surface area is definitely good for the cybersecurity budget. You can almost think of cybersecurity as a tax on that surface area. We generally think about it; somewhere between 4 and 6 percent of IT spend is going to be spent on overall security. So, that's one big driver.The second big driver is the elevated threat environment. So, while we're excited to get our hands on these extended capabilities of generative AI, the bad guys are already there, right? They're taking advantage of this. The sophistication, the volume and the velocity of these attacks is all increasing. That makes a harder job for the existing infrastructure to keep up, and it's going to likely necessitate more spending on cybersecurity to tackle these newer challenges; the newer dynamism within the cybersecurity threat appropriately. So, you're going to have to use generative AI to counter the generative AI.And then the last component of it; the last driver would be the regulatory environment. Regulatory tends to have some cybersecurity angles. If we think about it here, we're seeing it in terms of data governance is probably the big one. Where does this data go when it goes into the model? Are we putting the right controls around it? Do we have the right governance on it? So that's a big area of concern.A lot of complaining going on at the conference about the lack of consistency in that regulatory environment. All these different initiatives coming up from the state – really creates a challenging environment to navigate. But that's all good-ness for cybersecurity vendors that can help you get into compliance with these new regulations that are coming up. So overall, a lot of positivity around cybersecurity spending and startups definitely look to take advantage of that.Brian Nowak: Matt, so Keith says there's lack of consensus and boats being rode in every direction on what should be adopted first. And only 3 percent of CIOs know what agentic AI means. What did you learn about early signal on adoption? And some of the barriers to adoption? And hurdles that companies are talking about that they need to overcome to really adopt some of these new tools?Matt Bombassei: Yeah. Well, to Keith's point, it is really early, right? And that was a consistent theme that we heard from our companies at the conference. They are seeing early signs of cost efficiency, making employees more productive as opposed to maybe broad scale layoffs. But it's the deployment of these model technologies into specific sub-verticals – so accounting, legal engineering – where that adoption is driving greater efficiency within the organization.These companies are also adopting models that are smaller and a bit more fine tuned to their specific work product. And so that comes at a lower cost. We heard companies talking about costs at 1/50 of the cost of the broader foundational models when they're deploying it within the organization. And so, cost efficiency is something that we're seeing.At the same time, to speak to how early it is, one of the biggest hurdles here is change management and actually adoption. Getting people to use these products, getting them to learn the new technologies, that is a big hurdle. You know, you can lead a horse to water, you can't make it drink, right? And so, getting people to actually deploy these technologies is something that organizations are thinking through. How do we approach [it]?Brian Nowak: And you make an autonomous car drive? I know you've been doing a lot of work on autonomous driving more broadly. There were some autonomous driving and autonomous driving technology companies at Spark. What were your takeaways on autonomous driving from last week?Matt Bombassei: Yeah, well, not only can you make an autonomous car drive, you can make a truck drive and a bunch of other physical equipment. I think that was one of the takeaways here was that these neural nets that are powering autonomous vehicles are actually becoming much more generalizable. The integration of the transformer architecture into these neural nets is allowing them to take the context from one sub-vertical and deploy it in another vertical.So, we heard that 80 to 90 percent of the software, the underlying neural net, is applicable across these verticals. So, think applicable from autonomous ride sharing to autonomous trucking, right? What that means from our point of view is that it's important to get the scale of total miles driven – to establish that kind of safety hurdle if you're these companies.But also, don't necessarily think of these companies as defined by the vertical that they're operating in. If these models truly are generalizable, a company that's successful and scaled and autonomous ride hailing can switch or navigate verticals to also become successful potentially in trucking and other industries as well. So, the generalization of these models is particularly interesting for scale, and long-term market position for these companies.Brian Nowak: It's fascinating. Well, from consumer and enterprise adoption, the future of agentic computing and autonomous driving, there will be a lot more themes we all have to stay on top of. Keith, Matt, thanks so much for taking the time today.Keith Weiss: Great speaking with you Brian.Matt Bombassei: Thanks for having us.Brian Nowak: And thanks for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen and share the podcast with a friend or colleague today.

22 Loka 202511min

Our Global Head of Fixed Income Research and Public Policy Michael Zezas discuss the latest developments in U.S.-China relations and how they could affect investors.Read more insights from Morgan Stanley.----- Transcript ----- Welcome to Thoughts on the Market. I’m Michael Zezas, Global Head of Fixed Income Research and Public Policy Strategy. Today, we’re talking about the U.S. and China—why the relationship remains complicated, and what it means for markets. It’s Tuesday, Oct 21st, at 12:30pm in New York. If you’ve been following headlines, you know that U.S.-China relations are rarely out of the news. But beneath the surface, the dynamics are more nuanced than the daily soundbytes suggest. Investors often ask: Are we headed for a decoupling of the two economies, or is there room for cooperation? The answer, as always, is—it’s complicated. Let’s start with the basics. The U.S. and China are deeply intertwined economically, but strategic competition has intensified. Recent years have seen tariffs, export controls, and restrictions on technology transfer. Yet, there’s still plenty of trade between the two countries, and both economies are dependent on each other for growth and innovation. So what’s going on now? In recent weeks, China has moved to tighten rare earth export controls and the U.S. has proposed 100 percent tariffs in return. If this came to pass, these events could mark a clear economic split. But given the interdependencies we just cited, neither Washington nor Beijing seems eager for a true split, at least not anytime soon. The economic costs would be staggering, and both sides know it. So, a truce seems more likely, perhaps with somewhat different terms than the narrow semis-for-rare earths agreement they made this spring. And longer term, this episode seems to be a part of a broader dynamic, where rolling negotiations and truces are more likely than either a durable trade peace or a hard economic decoupling. For fixed income investors, this drives some important considerations. First, U.S. industrial policy is ramping up, with clear implications for AI infrastructure. AI is an area where the U.S. views it as essential that they outcompete China. Supported by renewed CapEx incentives from the latest tax bill, it’s clear to us that U.S. companies will be pushing further into AI development, where my colleagues have identified $2.9 trillion of data center financing needs over the next three years, about half of which will come from various credit markets. And for credit investors, this presents an important opportunity. Another consideration is how markets will balance near-term growth risks with an array of medium term growth possibilities. As our U.S. economics team has pointed out, the evidence suggests that corporates haven’t yet been forced to make tough decisions about passing on or absorbing tariff costs, underscoring that trade-related growth pressures aren’t yet in the rearview. The ongoing U.S. government shutdown doesn’t help either. It’s all a good argument for why bond yields could move lower in the near term. But also, we should expect yield curves could steepen more, with higher relative yields in longer maturities. This would reflect greater uncertainties around higher fiscal deficits, inflation, and economic growth. Our economists have been calling out the mixed messages in economic data, as well as a U.S. fiscal sustainability picture that appears reliant on acceleration in corporate CapEx for a manufacturing and AI-driven growth burst. In sum, the U.S.-China relationship is evolving, with global implications that don’t lend themselves to easy narratives or quick fixes. Our challenge will continue to be crafting investment strategies that reflect durable policy undercurrents, the signal amid news headline noise. 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.

21 Loka 20253min

As the S&P 500 continues to rally, our CIO and Chief U.S. Equity Strategist Mike Wilson discusses three factors that could lead to a stock market correction in the near term.Read more insights from Morgan Stanley.----- Transcript ----- Welcome to Thoughts on the Market. I'm Mike Wilson, Morgan Stanley's CIO and Chief U.S. Equity Strategist. Today on the podcast I'll be discussing why we are still in a new bull market even if a correction is likely in the near term. It's Monday, October 20th at 1pm in New York. So, let's get after it. I continue to believe the sharp selloff in April following Liberation Day marked the trough of what was effectively a three-year rolling recession in the U.S. economy. We have written extensively about this view; but it still remains very much out of consensus. Since 2022 most sectors of the private economy have gone through their own individual recession but at different times. The final trough in the rate of change in economic activity came in April around the tariff announcements which came as a surprise to almost everyone, at least in terms of the magnitude and scope. In short, Liberation Day was really capitulation day on the last piece of bad news for the economic cycle which then bottomed. Stocks seem to agree which is why they have rallied in a straight line since then, much like they do after the trough in any economic cycle. The other proof we have for this claim is the v-shaped recovery in earnings revision breadth, something we have discussed for many months in our written research and on this podcast. Based on our numerous conversations with investors, this view remains very unpopular. Instead, most believe the economy and earnings growth for next year are at risk of being lower rather than higher than expected, as I do. Core to my view is that we are now firmly in an inflationary regime since COVID and the implementation of helicopter money to get us out of that crisis. The government has to run it hot to get us out of the massive debt and deficit problem created over the past 20 years. The end result is that investors need to expect hotter but shorter cycles rather than the elongated 10-year cycles we experienced between 1980-2020 when inflation was falling. That means two-year up cycles followed by one-year down cycles for U.S. equity markets, which is exactly what's happened since 2020. We are now in the midst of a new up cycle that began in April. The key thing to understand during this new regime is that inflation is not bad for stocks so long as it's accelerating and the Fed is on the sidelines or easing like in 2020-21, 2023 and now today. Higher inflation means higher earnings growth which is why price earnings multiples are high today. With inflation likely to accelerate next year, stocks are anticipating better earnings growth. In other words, stocks are a hedge against inflation. In fact, relative to gold, high quality stocks may offer a cheaper inflation hedge at this point given their dramatic underperformance to precious metals year-to-date and since 2021. Eventually, inflation will be a problem again for stocks like in 2022 when the Fed has to react by tightening policy, but that's a story for another day. Having said all this, the equity markets are a bit frothy at the moment and so a 10-15 percent correction in the S&P 500 is not only possible but would be normal at this stage of a new bull market. I see three primary reasons for why we could get that in the near term. First, China-U.S. trade relations have recently escalated again, and we are slowly marching toward a November 1st deadline for tariffs on China to go back to Liberation Day levels. While most investors don't want to get sucked into selling at the worst possible time like they did in April, this risk is real and will weigh on stocks if we don't see evidence of a de-escalation in the next few weeks. Second, funding markets have exhibited some signs of increased stress lately. This is likely due to the ongoing quantitative tightening program by the Fed which is draining bank reserves. Should these stresses increase, it could spill over into equities. Third, our earnings revision breadth metric is rolling over now after its historic rise since April. This could continue into earnings season as it's normal to see some retracement from such a high level and tariffs start to flow through from inventories to the income statement. Trade tensions might also weigh on company guidance in the short term. Bottom line, I believe a new bull market began in April with a new rolling economic and earnings recovery that is now quite nascent. However, even new bull markets have corrections along the way, and certain conditions argue we are at risk for the first tradable one since April. Keep your powder dry in the near term for what should be a great buying opportunity, if it arrives. Thanks for tuning in; I hope you found it informative and useful. Let us know what you think by leaving us a review. And if you find Thoughts on the Market worthwhile, tell a friend or colleague to try it out!

20 Loka 20255min

Our U.S. Public Policy Strategist Ariana Salvatore unpacks how China’s announced rare earth export controls and signals of sweeping U.S. tariffs could impact global supply chains, markets and economic growth.Read more insights from Morgan Stanley.----- Transcript ----- Ariana Salvatore: Welcome to Thoughts on the Market. I'm Ariana Salvatore, Morgan Stanley's U.S. Public Policy Strategist. Today I'll talk about a development keeping markets and investors on alert: a re-escalation of U.S. China trade tensions. It's Friday, October 17th at 10am in New York. Since April, the U.S. and China have been in what we've been calling a very delicate detente. Remember, President Trump paused the additional reciprocal tariffs after Liberation Day. Since then, we've been consistently skeptical that the pause was durable enough to actually allow the U.S. and China to come up with a full-fledged trade agreement. But now we're equally as skeptical that the current escalation will lead to a material disruption in the bilateral relationship. So, what happened last week? China announced stricter export controls on rare earths, which are really critical for manufacturing everything from electric vehicles to defense equipment and advanced electronics. So, in response, the Trump administration on Friday announced a proposed 100 percent tariff, said to go into effect November 1st across all Chinese exports to the U.S. That date matters because that's around the same time that Presidents Trump and Xi were scheduled to meet at the upcoming APEC Summit in South Korea. When we think about this most recent escalation, it's pretty significant because China accounts for about 70 percent of global rare earth mining, and 90 percent of processing and refining. A lot of countries around the world – the U.S. Japan, Korea, and Germany – all rely heavily on these imports from China. And so potential new export controls mean that every economy may have to start negotiating bilaterally with China to secure supplies, which raises the risk of supply chain disruption across Asia, Europe, and the U.S. Looking ahead, we're thinking about four potential scenarios for how the current U.S.-China trade tensions could play out. The most likely outcome, which is our base case, is a return to the recent status quo following a period of rhetorical escalation and likely a reset of expectations heading into this APEC meeting. That's because we think both the U.S. and China would prefer to maintain the existing equilibrium to an abrupt supply chain decoupling. That equilibrium is effectively chips for rare earths. So, the U.S. receives China's rare earths, and then in return the U.S. exports some of its chips to China. But that equilibrium doesn't necessarily mean that the temporary implementation of trade barriers like higher tariffs or more export controls are off the table. The broader trajectory we think will continue to point toward competitive confrontation, which is a bipartisan strategy that encompasses both these traditional trade tactics as well as unilateral domestic investment – either vis-a-vis direct federal spending, or the government taking more stakes in companies involved in these critical industries. So, think things like the IRA, the CHIPS Act, and other bipartisan pieces of legislation. So, in the near and medium term, expect to see these trade barriers persisting and a bipartisan push toward U.S. industrial policy, as the U.S. attempts to undergo selective de-risking from China. Our base case scenario anticipates further short-term tensions, but ultimately a limited agreement that avoids deep structural changes. We've also thought through some alternate scenarios. So, in one downside case, you could see temporary escalation past November 1st. Both sides could fully implement their proposed policies, but after doing so, come back to the status quo once the economic costs become apparent. A more severe downside scenario involves durable escalation. So, in this case, we would see both countries maintain trade barriers for an extended period. That outcome would see both the U.S. and China decide to change calculus on that equilibrium, so that no longer holds. And in that case, we could see a push toward decoupling and a significant strain on supply chains. Finally, our last scenario reflects a quick de-escalation in which heightened rhetoric actually acts as a catalyst for renewed negotiations and a potential framework agreement that could result in some tariffs, but most likely at lower levels than initially proposed. So, what does this all mean? In the base case, our economists expect China's GDP growth to slow to below 4.5 percent in the second half of 2025, with exports supported by robust non-U.S. shipments. Our equity strategists in this outcome see the volatility actually providing a dip buying opportunity, given that they see a rolling recovery that began earlier this year. However, a more durable escalation could possibly prolong China's deflation and necessitate further policy adjustments. Similarly, that outcome could negate the early cycle rolling recovery thesis here in the U.S. 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.

17 Loka 20255min

With Morgan Stanley’s European Leveraged Finance Conference underway, our Head of Corporate Credit Research Andrew Sheets joins Chief Fixed Income Strategist Vishy Tirupattur to discuss private credit, M&A activity and AI infrastructure.Read more insights from Morgan Stanley.----- Transcript ----- Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Head of Corporate Credit Research at Morgan StanleyVishy Tirupattur: And I'm Vishy Tirupattur, Morgan Stanley's Chief Fixed Income Strategist.Andrew Sheets: Today, as we're hosting the Morgan Stanley European Leveraged Finance Conference, a discussion of three of the biggest topics on the minds of credit investors worldwide.It's Thursday, October 16th at 4pm in London.Vishy, it's so great to catch up with you here in London. I know you've been running around the world, quite literally, talking to investors about some of the biggest debates in credit – and that's exactly what we wanted to talk. We're here at Morgan Stanley's European Leveraged Finance Conference. We're talking with investors about the biggest debates, the biggest developments in credit markets, and there are really kind of three topics that stand out.There's what's going on with private credit? What's going on with the merger and acquisition, the M&A cycle? And how are we going to fund all of this AI infrastructure?And so maybe I'll throw the first question to you. We hear a lot about private credit, and so maybe just for the listener who's looking at a lot of different things. First, how do you define it? What are we really talking about when we're talking about private credit?Vishy Tirupattur: So, Andrew, when we talk about private credit, the most common understanding of private credit is lending by non-banks to small and medium sized companies. And we probably will discuss a bit later that this definition is actually expanding much beyond this narrow definition. So, when you think about private credit and spend time understanding what is the credit in private credit, what it boils down to is on average, on a leveraged basis, the credit in private credit is comparable to, say CCC to B - on a coverage basis to the public markets.So, the credits in the private credit market are weaker. But on the other hand, the quality of covenants in these deals is significantly better compared to the public credit markets. So, that's the credit in private credit.Andrew Sheets: So, Vishy, with that in mind then, what is the concern in this market? Or conversely, where do people see the opportunity?Vishy Tirupattur: So, the concern in this market comes from the opaqueness in these deals. Many of these private credit borrowers are not public filers. So not much is well known about what the underlying details are. But in a sense, a good part of the public markets, whether it's in high yield bonds or in the public, broadly syndicated leveraged loans are also not public filers. So, there is information asymmetry in those markets as well.So, the issue is not the opaqueness of private markets, but opaqueness in credit in general. But that said, when you look at the metrics of leverage, coverage, cash on balance sheet…Andrew Sheets: Because we can get some kind of high-level sense of what is in these portfolios...Vishy Tirupattur: Yeah. And we look at all those metrics, and we look at a wide range of metrics. We don't get to the conclusion that we are at a precipice of some systemic risk exposure in credit. On the other hand, there are idiosyncratic issues. And these idiosyncratic issues have always been there and will remain there. And we would expect that the default rates are sticky around these levels, which are slightly above the long-term average levels, and we expect that to remain.Andrew Sheets: So, you may see more dispersion within these portfolios. These are weaker, more cyclical, more levered companies. But overall, this is not something that we think at the moment is going to interrupt the credit cycle or the broader markets dynamic.Vishy Tirupattur: Absolutely. That is exactly where we come down to.So, Andrew, let me throw another question back at you. There's a lot of talk of growing M&A, growing LBO activity. And that could potentially lead to some challenges on the credit front. How do you look at it?Andrew Sheets: So, I'd like to actually build upon your answer from private credit, right? Because I think a lot of the questions that we're getting from investors are around this question of how far along in this always, kind of, cyclical process; ebb and flow of lending aggressiveness are we? And, you know, this is a cycle that goes back a hundred years – of lenders becoming more conservative and tighter with lending. And then as times get good, they become somewhat looser. And initially that's fine. And then eventually something, something happens.And so, I think we've seen the development of new markets like private credit that have opened up new lending opportunities and then also new questions. And I think we've also seen this question come up around M&A and corporate activity.And as we start to see headlines of very large leveraged buyouts or LBOs, as we start to see more merger and acquisition – M&A – activity coming back; something we've at Morgan Stanley been believers in. Are we really starting to see the things that we saw in the year 2000, or in the year 2007, when you saw very active capital markets actually coinciding with kind of near the peak of equity markets near the top of major market cycles.And in short, we do not think we're there yet. If we look at the actual volumes that we're seeing, we're actually a little bit below average in terms of corporate activity. There's really been a dearth of corporate activity after COVID. We're still catching up. Secondly, the big transactions that we're seeing are still more conservatively structured, which isn't usually what you see right at the end. And so, I think between these two things with still a lot of supportive factors for more corporate activity, we think we have further to go.Vishy Tirupattur: On that point, Andrew, I think if you look at the LBOs that are happening today versus the LBOs that happened in the 2007 era, the equity contribution is dramatically different. You know, equity to debt, these LBOs that are happening today [are] of a substantially higher amount of equity contribution compared to the LBOs we saw pre-Financial Crisis…Andrew Sheets: That's such a great point. And the listener may not know this, but Vishy and I were working together at Morgan Stanley prior to the Financial Crisis, and we were working in credit research when a lot of these LBOs were happening, and…Vishy Tirupattur: And I used to be tall and good looking.Andrew Sheets: (laughs) And they were just very different. We're still not there. If you go back and pull the numbers, you're looking at transactions still that are far more conservative than what we saw then. So, you know, this activity is cyclical, and I think we do have to watch deregulation, right? You saw a lot of regulations come in after the Financial Crisis that led to more conservative lending. If those regulations get rolled back, we could really move back towards more aggressive lending. But we haven't quite seen that yet.Vishy Tirupattur: Absolutely not.Andrew Sheets: And Vishy, maybe the third question that comes up a lot. We've covered private credit, which is very topical. We've covered kind of corporate aggressiveness. But maybe the icing on the cake. The biggest question is AI – and is AI spending?And it just feels like every day you come into the office and there's another headline on CNBC or Bloomberg about another mega AI funding deal. And the question is, okay, where's all that money going to come from?And maybe some of it comes from these companies themselves. They’re very profitable, but credit might have to fill in some of the gaps. And you and some of our colleagues have done a lot of work on this. Where do you think kind of the lending story and the borrowing story fits into this broader AI theme?Vishy Tirupattur: Our estimate of simply data center related CapEx requirements are close to $3 trillion. You add the power required for the data centers and add another $300-400 billion. So, a lot of this CapEx will come from – roughly about half might come from the operating cash flows of the hyperscalers. But the rest, so [$]1.5 trillion plus, has to come through various channels of credit.So, unsecured corporate credit, we think will play a fairly small role in this. Of that [$]1.5 trillion plus, maybe [$]200 billion to come from unsecured credit issuance by these hyperscalers, and perhaps some of the securitized markets, such as ABS and CMBS that rely on stabilized cash flows may be another 1[$]50 billion. But a different version of private credit, what we will call ABF or asset based finance, will play a very big role. So north of [$]800 billion we think will come from that kind of a private credit version of investment grade, or a private credit markets developing. So, this market is very much in the developmental mode.So, one way or the other, for AI to go from where it is today to substantially improving productivity and the earnings of companies that has to go through CapEx; and that CapEx needs to go through credit markets.Andrew Sheets: And I think that is so fascinating because, right Vishy, so much of the spending is still ahead of us. It hasn't even really started, if you look at the numbers.Vishy Tirupattur: Absolutely. We are in the early stages of this CapEx cycle. We should expect to see a lot more CapEx and that CapEx train has to run through credit markets.Andrew Sheets: So, Vishy, there's obviously a lot of history in financial markets of larger CapEx booms, and some of them work out well, and some of them don't. I mean, if you are trying to think about some of the dynamics of this funding for AI and data centers more broadly versus some of these other CapEx cycles that investors might be familiar with. Are there some similar dynamics and some key differences that you try to keep in mind?Vishy Tirupattur: So, in terms of similarities, you know, they're big numbers, whichever way you cut it, these numbers are going to be big dollar numbers.But there are substantial differences between the most recent CapEx boom that we saw towards the end of the late 90s, early 2000s; we saw a massive telecom boom, telecom related CapEx. The big difference is that spending was done by – predominantly by companies that had put debt on their balance sheet. They were already very leveraged. They were just barely investment grade or some below investment grade companies with not much cash on their balance sheet.And you contrast that with today's world, much of this is being done by highly rated companies; the hyperscalers or between, you know, A+ to AAA rated companies, with a lot of cash on their balance sheets and with very little outstanding debt on their part.On top of that, the kind of channels that exist today, you know, data center, ABS and CMBS, asset-based finance, joint venture kind of financing. All of these channels were simply not available back then. And the fact that they all are available today means that this risk of CapEx is actually much more widely distributed.So that makes me feel a lot better about the evolution of this CapEx cycle compared to the most recent one we saw.Andrew Sheets: Private credit, a rise in M&A and a very active funding market for AI. Three big topics that are defining the credit debate today. Vishy, thanks for taking the time to talk.Vishy Tirupattur: Andrew, always fun to hang with youAndrew Sheets: And thank you for listening. If you enjoy Thoughts on the Market, please leave us review wherever you listen and tell a friend or colleague about us today.

16 Loka 202511min