U.S. Tech: The Future of Artificial Intelligence

U.S. Tech: The Future of Artificial Intelligence

As the advancement of generative AI takes off, how might this inflection point in technology impact markets, companies, and investors alike? Equity Analyst and Head of U.S. Internet Research Brian Nowak and Head of the U.S. Software Research Team Keith Weiss discuss.


----- Transcript -----


Brian Nowak: Welcome to Thoughts on the Market. I'm Brian Nowak, Equity Analyst and Head of U.S. Internet Research for Morgan Stanley.


Keith Weiss: And I'm Keith Weiss, Head of the U.S. Software Research Team.


Brian Nowak: Today, we're at Morgan Stanley's annual Tech, Media, and Telecom conference in downtown San Francisco. We've been here most of the week talking with industry leaders and emerging companies across the spectrum, and the topic on everyone's mind is clearly A.I. So today, we're going to share some of what we're hearing and our views on the rise of artificial intelligence tools. It's Thursday, March 9th at 2 p.m. here on the West Coast.


Brian Nowak: All week, Keith and I have been meeting with companies and speaking with new companies that are developing technologies in artificial intelligence. We've written research about how we think that artificial intelligence is reaching somewhat of an iPhone inflection moment with new people using new tools, and businesses starting to realize artificial intelligence is here to stay and can drive real change. Keith, talk to us about how we reached this moment of inflection and how do you think about some of the big picture changes across technology?


Keith Weiss: Well, thank you for having me, Brian. So we've been talking about artificial intelligence for some time now. Software companies have been infusing their solutions with machine learning driven type algorithms that optimize outcomes for quite some time. But I do think the iPhone analogy is apt, for two reasons. One, what we're talking about today with generative AI is more foundational technologies. You can almost think about that as the operating system on the mobile phone like the iOS operating system. And what we've heard all week long is companies are really seeing opportunity to create new apps on top of that operating system, new use cases for this generative AI. The other reason why this is such an apt analogy is, like the iPhone, this is really capturing the imagination of not just technology executives, not just investors like you and I, but everyday people. This is something that our kids are coming home from high school and saying, "Hey, dad, look at what I'm able to do or with chatGPT, isn't this incredible?" So you have that marketing moment of everybody realizes that this new capability, this new powerful technology is really available to everybody.


Keith Weiss: So, Brian, what do you think are going to be the impacts of this technology on the consumer internet companies that you cover?


Brian Nowak: We expect significant change. There is approximately $6 trillion of U.S. consumer expenditure that we think is going to be addressed by change. We see changes across search. We see more personalized search, more complete search. We see increasing uses of chatbots that can drive more accurate, personalized and complete answers in a faster manner across all types of categories. Think about improved e-commerce search helping you find products you would like to buy faster. Think about travel itinerary AI chatbots that create entire travel itineraries for your family. We see the capability for social media to change, better rank ordering and algorithms that determine what paid and organic content to show people at each moment. We see new creator tools, generative AI is going to enable people to make not only static images but more video based images across the entire economy. So people will be able to express themselves in more ways across social media, which will drive more engagement and ultimately more monetization for those social media platforms. We see e-commerce companies being able to better match inventory to people. Long tail inventory that previously perhaps could not find the right person or the right potential buyer will now better be able to be matched to buyers and to wallets. We see the shared economy across rideshare and food delivery also benefiting from this. Again, you're going to have more information to better match drivers to potential riders, restaurants to potential eaters. And down the line we go where we ultimately see artificial intelligence leading to an acceleration in digitization of consumers time, digitization of consumers wallets and all of that was going to bring more dollars online to the consumer internet companies.


Brian Nowak: Now that's the consumer side, how do you think about artificial intelligence impacting enterprise in the B2B side?


Keith Weiss: Yeah, I think there's a lot of commonalities into what you went through. On one level you talked about search, and what these generative AI technologies are able to do is put the questions that we're asking in context, and that enables a much better search functionality. And it's not just searching the Internet. Think about the searches that you do of your email inbox, and they're not very effective today and it's going to become a lot more effective. But that search can now extend across all the information within your organization that can be pretty powerful. When you talk about the generative capabilities in terms of writing content, we write content all day long, whether it's in emails, whether it's in text messages, and that can be automated and made more efficient and more effective. But also, the Excel formulas that we write in our Excel sheets, the reports that you and I write every day could be really augmented by this generative AI capability. And then there's a whole nother kind of class of capabilities that come in doing jobs better. So if we think about how this changes the landscape for software developers, one of the initial use cases we've seen of generative AI is making software developers much more productive by the models handling a lot of the rote software development, doing the easy stuff. So that software developer could focus his time on the hard problems to be solved in overall software development. So if you think about it holistically, what we've seen in technology trends really over the last two decades, we've seen the cost of computing coming way down, stuff like Public Cloud and the Hyperscalers have taken that compute cost down and that curve continues to come down. The cost of data is coming down, it's more accessible, there's more out of it because we've digitized so much of the economy. And then thirdly, now you're going to see the cost of software development come down as the software developers become more productive and the AI is doing more of that development. So those are all of your input cost in terms of what you do to automate business processes. And at the same time, the capabilities of the software is expanding. Fundamentally, that's what this AI is doing, is expanding the classes and types of work that can be automated with software. So if your input costs are coming way down and your capabilities are coming up, I think the amount of software that's being developed and where it's applied is really going to inflate a lot. It's going to accelerate and you're going to see an explosion of software development. I'm as bullish about the software industry right now as I've been over the past 20 years.


Keith Weiss: So one of the things that investors ask me a lot about is the cost side of the equation. These new capabilities are a lot more compute intensive, and is this going to impact the gross margins and the operating margins of the companies that need to deploy this. So, how do you think about that part of the equation, Brian?


Brian Nowak: There's likely to be some near-term impact, but we think the impacts are near-term in nature. It is true that the compute intensity and the capital intensity of a lot of these new models is higher than some of the current models that we're using across tech. The compute intensity of the large language models is higher than it is for search, it is higher than it is for a lot of the existing e-commerce or social media platforms that are used. So as we do think that the companies are going to need to invest more in capital expenditure, more in GPUs, which are some of the chips that enable a lot of these new large language models and capabilities to come. But these are more near-term cost headwinds because over the long term, as the companies work with the models, tune the models and train the models, we would expect these leading tech companies to put their efficiency teams in place and actually find ways to optimize the models to get the costs down over time. And when you layer that in with the new revenue opportunities, whether we're talking about incremental search revenue dollars, incremental e-commerce transactions, incremental B2B, SAS like revenue streams from some companies that will be paying more for these services that you spoke about, we think the ROI is going to be positive. So while there is going to likely be some near-term cost pressure across the space, we think it's near-term and to your point, this is a very exciting time within tech because these new capabilities are going to just expand the runway for top line growth for a lot of the companies across the space.


Brian Nowak: And this is all very exciting on the consumer side and the business side, but Keith talk to us about sort of some of the uncertainties and sort of some of the factors that need to be ironed out as we continue to push more AI tools across the economy.


Keith Weiss: Yeah, there's definitely uncertainties and definitely a risk out there when it comes to these technologies. So if we think about some of the broader risks that we see, these models are trained on the internet. So you have to think about all the data that's out there. Some of that data is good, some of that data is bad, some of that data could introduce biases into the search engines. And then the people using these search engines that are imbued with the AI, depending on how hard they're pushing on the search engines on the prompts, and that's the questions that they're asking the search engines, you could elicit some really strange behavior. And some of that behavior has elicited fears and scared some people, frankly, by what these search engines are bringing back to them. But there's also business model risk. From a software perspective, this is going to be the new user interface of how individual users access software functionality. If you're a software company that's not integrating this soon enough, you're going to be at a real disadvantage. So there's business has to be taken into account. And then there's broader economic risk. We're talking about all the capabilities that this generative AI can now do that these models can now take over. So for the software developer, does this mean there's job risk for software developers? For creative professionals who used to come up with the content on their own, does this mean less jobs for creative professionals? Or you and I? Are these models going to start writing our research reports on a go forward basis? So those are all kind of potential risks that we're thinking about on a go forward basis.


Keith Weiss: So, Brian, maybe to wrap up, how do you think about the milestones and sort of the key indicators that you're keeping an eye on for who are going to be the winners and losers as this AI technology pervades everything more fully?


Brian Nowak: It's a great question. I would break it into a couple different answers. First, because of the high compute intensity and costs of a lot of these models, we only see a handful of large tech companies likely being able to build these large language models and train them and fully deploy them. So the first thing I would say is look for new large language model applications from big tech being integrated into search, being integrated into e-commerce platforms, being integrated into social media platforms, being integrated into online video platforms. Watch for new large language tools to roll across all of big tech. Secondly, pay attention to your app stores because we expect developers to build a lot of new applications for both businesses and consumers using these large language models. And that is what we think is ultimately going to lead to a lot of these consumer behavior changes and spur a lot of the productivity that you talked about on the business side.


Keith Weiss: Outstanding.


Brian Nowak: Keith, thanks for taking the time.


Keith Weiss: Great speaking with you, Brian.


Brian Nowak: As a reminder, if you enjoy Thoughts on the Market, please take a moment to rate and review us on the Apple Podcasts app. It helps more people to find the show.

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A Turnaround in Sight for Healthcare?

A Turnaround in Sight for Healthcare?

Our U.S. Biotech and Biopharma analysts Sean Laaman and Terence Flynn discuss the latest developments that could be positioning the healthcare sector for strong outperformance.Read more insights from Morgan Stanley.----- Transcript -----Sean Laaman: Welcome to Thoughts on the Market. I'm Sean Laaman, Morgan Stanley's U.S. Small and Mid-Cap Biotech Analyst. Terence Flynn: And I'm Terence Flynn, Morgan Stanley's U.S. Biopharma Analyst. Sean Laaman: Today, we'll discuss how a rally in the healthcare sector is being driven by more favorable macro conditions. It's Tuesday, October 28th at 10am in New York. So, Terence, healthcare has lagged the broader market year-to-date, and valuations have been near historical lows. But recent weeks show strengthening performance. Policy headwinds have been front and center.What's changed in the regulatory environment and how is the biopharma sector adapting to these pricing and tariff dynamics? Terence Flynn: Sean, as you know, with many other sectors, tariffs were initially a focus earlier this year. But a number of companies in our space have subsequently announced significant U.S. manufacturing investments to reshore supply chains. And hence, the market's less focused on tariffs in our space right now. But the other policy dynamic and focus is what's called Most Favored Nation or MFN drug pricing. Now, this is where the President's been focused on aligning U.S. drug prices with those in other developed countries. And recently we've seen several companies announce agreements with the administration along these lines, which importantly has provided investors with more visibility here. And we're watching to see if additional agreements get announced. Sean Laaman: Got it. Another hurdle for Large-cap biopharma is a looming expiration of patents with [$]177 billion exposed by 2030. How is this shaping M&A trends and strategic priorities? Terence Flynn: For sure. I mean, as you know, Sean, patent expiry is our normal part of the life cycle of drug development. Every company goes through this at some point, but this does put the focus on company's internal pipelines to continue to progress while also being able to access external innovation via M&A. Recently we have started to see a pickup in deal activity, which could bode well for performance in SMID-cap biotech. Sean Laaman: At the same time, you believe relative valuations look compelling for Large-cap biopharma. Where are valuations versus where they've been historically? What's driving this and how should investors think about positioning? Terence Flynn: Absolutely. Look, on a price to earnings multiple, the sector's trading at about a 30 percent discount to the S&P 500 right now. Now that's in line with prior periods of policy uncertainty. But as policy visibility improves, we expect the focus will shift back to fundamentals. Now, positioning to me still feels light here, given some of the patent cliff dynamics we just discussed. Now, Sean, with the Fed moving toward rate cuts, how do you see this impacting your sector on the biotech side? Sean Laaman: Well, Terence, particularly in my space, which is Small- and Mid-cap biotech companies, they're typically capital consumers are not capital producers. They're particularly sensitive to the current rate environment.Therefore, they're sensitive to spending on pipeline. They're sensitive to M&A. So, as rates come down, we expect more spending on pipeline and more M&A activity, which is generally positive for the sector. Looking forward, biotech sector is generally the best performing sector on a six-to-12-month timeframe post the first rate cut. Terence Flynn: Great. You've also talked about this SMID to Big thesis on the biotech side. Can you explain what's driving that? Sean Laaman: Sure Terence. There’s three pieces to the SMID to Big thematic. So, we in SMID-cap biotech, we cover 80 to 90 companies. About a third of those are newly, kind of profitable companies. Those companies are turning from being capital consumers to capital producers. We see about $15 billion of cash on balance sheets for 2025, going to north of 130 billion by 2030. That's the first piece. The second piece is due to regulatory uncertainty at the USFDA. We're seeing more attractive valuations amongst clinical stage names. That's the second piece. And third piece relates to your coverage, Terence. I refer back to that [$]177 billion of LOE. So, we expect generally that M&A activity will be quite high amongst our sector. Terence Flynn: And let's not forget about AI, which has implications across the healthcare space. How much is this changing the dynamic in biotech, Sean? Sean Laaman: It is changing, but we're really at the beginning. I think there's three things to think about. The first one is faster trial recruitment. The second one is faster regulatory submissions. And the third one, which is the most interesting, but we're really at the beginning of, is faster time to appropriately targeted molecules. Terence Flynn: Great. And maybe lastly, what are the key risks and catalysts for SMID-cap biotech in the current environment? Sean Laaman: As always, we're focused on pipeline failures in terms of risk. Secondly, in terms of risk, we're looking at regulatory risk at the FDA. And thirdly, we're looking at the rise in China biotech and the competitive dynamic there.Whether you're watching large cap biopharma, M&A moves, or the rise of cash-rich, SMID-cap biotechs, the healthcare sector setup is unlike anything we've seen in years.Terence, thanks for speaking with me. Terence Flynn: Always a pleasure to be on the show. Thanks for having me, Sean. Sean Laaman: 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.

28 Okt 5min

Will the Stock Market Rally Continue?

Will the Stock Market Rally Continue?

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 Okt 3min

What Happens to Software Developers as AI Can Code?

What Happens to Software Developers as AI Can Code?

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 Okt 4min

Should AI Spending Worry Investors?

Should AI Spending Worry Investors?

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 Okt 3min

The Next Turning Points in Tech

The Next Turning Points in Tech

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 Okt 11min

How to Navigate U.S.-China Tensions

How to Navigate U.S.-China Tensions

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 Okt 3min

Time for a Bull Market Correction?

Time for a Bull Market Correction?

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 Okt 5min

U.S.-China Tensions: What Could Happen Next?

U.S.-China Tensions: What Could Happen Next?

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 Okt 5min

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