Thoughts on the Market

Our Head of Europe and Asia Technology Research Shawn Kim discusses AI’s move from passive chatbots to active agents—and how this influences tech supply chains.
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Welcome to Thoughts on the Market. I’m Shawn Kim, Head of Morgan Stanley’s Europe and Asia Technology Team.
Today: A foundational shift in the development of AI and its broad market implications.
It’s Tuesday, May 5th, at 3pm in London.
Think about the last time you asked a chatbot to write a summary or a draft. Or maybe answer a query. It was probably useful. But you were also still driving the interaction: asking, refining, copying, checking, and moving the work forward.
Now imagine a system that does not just respond, but acts. It remembers what you asked last week, understands your preferences, works across digital tools, plans a workflow, and adapts as circumstances change.
That is the shift from GenAI to agentic AI: from AI that helps with thinking to AI that helps with doing. GenAI is mostly passive. It takes a prompt and produces an answer. Agentic AI is active – less a copilot for one task but an autopilot for multi-step workflows.
The distinction is key because computing requirements are changing. In GenAI, large language models and GPUs handle much of the thinking. GPUs, or graphics processing units, process many calculations in parallel, making them central to modern AI models. In agentic AI, CPU becomes more important. CPUs, or central processing units, coordinate tasks and connect systems to the broader digital infrastructure.
Agentic AI also depends on three stacks: the brain, or the large language model; orchestration, where the CPU manages the doing; and knowledge, which is memory.
Memory may be the most important layer. An agent that knows your preferences, documents, tone, and task history becomes more useful over time. That creates a context flywheel. The more context it collects, the more personalized it becomes, and the harder it is to leave.
Typically, in computing, we think of memory as storage, mainly. We need to rethink this. Memory is also continuity. When an AI system can use past experiences, memory becomes a long-term state, shared knowledge, and behavioral grounding.
And that matters because LLMs have fixed context windows. Once a conversation exceeds that window, older content falls off. For simple questions, that may be fine. But for a coding agent working across a large codebase over days or weeks, it is a major limitation. Serious work requires persistent memory, short-term orientation, and active retrieval – remembering prior decisions, understanding changed files, and finding relevant codes without the user pointing to every dependency.
For investors, the implication is clear – agentic AI changes the bottlenecks. We see CPUs as the new bottleneck, with memory seeing the highest content increase. We estimate as much as 60 percent, or $60 billion of incremental CPU total addressable market by 2030, within a total CPU market of more than $100 billion. We also estimate up to 70 percent of incremental DRAM bit shipment tied to this theme.
That makes us more positive on supply chains including memory, foundry, substrates, CPU and memory interface, and capacitors and CPU sockets. These areas benefit from content growth, pricing power, and capacity constraints into 2027.
As AI moves from answering questions to taking actions, investors should watch the infrastructure behind the shift. Because in the agentic era, the next big AI leap may be less about the prompt, but more about the processor.
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.

Introducing a recent episode of Hard Lessons, featuring Rick Rieder, BlackRock’s CIO for Global Fixed Income and Head of the Global Allocation Investment Team, in conversation with Seth Carpenter, Global Chief Economist and Head of Macro Research at Morgan Stanley.

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Our CIO and Chief U.S. Equity Strategist Mike Wilson explains the factors behind stock gains across sectors.
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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 earnings remain the most important variable for equity markets.
It's Monday, May 4th at 2pm in New York.
So, let’s get after it.
The more I think about what’s been driving this market, and the more time I spend with the data, the more I keep coming back to the same conclusion: it’s earnings. Not the headlines, not even the Fed. Earnings are doing the heavy lifting right now.
When I look at this reporting season, what stands out isn’t just resilience, it’s strength that’s broader than most people appreciate. The typical company in the S&P 500 is growing earnings at about 16 percent, and the median earnings surprise is running around 6 percent. That’s the strongest we’ve seen in four years.
What’s really interesting to me is that this strength is no longer confined to just the biggest tech names. Yes, hyper scalers and semiconductors are still playing a leading role, but the story is expanding. We’re seeing earnings revisions move higher across Financials, Industrials, and Consumer Cyclicals, in particular. That kind of breadth tells me this isn’t just a narrow leadership story; it’s something more sustainable.
At the same time, many investors are focused on the geopolitical backdrop, particularly the Iran conflict and what it means for oil, inflation, and supply chains. To be fair, companies are feeling some of that pressure. When you listen to earnings calls, you hear about rising freight costs, tighter supply chains, and higher input prices across industries like chemicals and machinery.
But here’s the nuance: those impacts are uneven. They’re not hitting the entire market in the same way. In fact, at the index level, they’re being offset. Energy has become a positive contributor to earnings growth, and the higher-end consumer remains relatively strong. Even with higher fuel costs, we’re not seeing a meaningful pullback in overall consumption – at least not yet.
That tells me that we’re not dealing with a classic demand shock. We’re dealing with a redistribution of pressure, and companies are adapting. In many cases, they’re passing through higher costs. Revenue surprises are running above historical norms, which suggests pricing power is improving.
Now, of course, earnings aren’t the only piece of the puzzle. Policy still matters, and the shift in rate expectations this year has been meaningful. The Fed has clearly become more concerned about inflation, and the market has repriced expectations to fewer cuts, and maybe even a higher probability of hikes. That repricing is a big reason why valuations corrected so sharply over the past six months.
It’s notable that even with that headwind, equities have managed to stabilize, thanks to earnings. When earnings are growing at an above-trend pace, equities can deliver solid returns regardless of whether the Fed is cutting or not.
That said, I do think that there’s one area of risk that deserves further attention, and that’s liquidity. We’ve seen periods of funding stress over the past six months, and those moments have coincided with pressure on valuations. The Fed and the Treasury have stepped in at times to stabilize these conditions, helping to reduce bond volatility and support equity multiples.
Bottom line, we have already had a meaningful correction in valuations this year with price earnings multiples falling 18 percent from their peak last fall. That adjustment occurred as the market digested the many risks that we have been highlighting. Meanwhile, earnings are not only holding up, they’re accelerating and broadening across sectors. The risks that we’ve all all focused on – geopolitics, oil, supply chains – are real. But they’re being absorbed at the company level. As a result, the price declines were much more modest than the compression in valuations.
Meanwhile, monetary policy is providing some headwinds, but it’s not overwhelming the earnings story. Equity markets move on two things: earnings and liquidity. Right now, earnings are more than offsetting the lingering liquidity concerns. In short, earnings growth is greater than the valuation reset. This is classic bull market behavior and as long as that continues, I think the U.S. equity market will grind higher for the rest of the year with intermittent bouts of volatility.
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!

Our Global Chief Economist and Head of Macro Research Seth Carpenter discusses whether the economy can adapt fast enough to turn AI into a productivity boom rather than a labor market shock.
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Seth Carpenter: Welcome to Thoughts in the Market. I'm Seth Carpenter, Morgan Stanley's Global Chief Economist and Head of Macro Research.
Today we're going to try to look past the hype and the anxiety around AI and ask what will be the effect on the labor market.
It's Friday, May 1st at 10am in New York.
Now, odds are that you've used AI to draft an email or summarize a document, maybe learn about a new topic, help plan a trip. The new technology is clearly lowering the cost of certain tasks. And I think the research shows that there are plenty and an increasing number of tasks that AI can do better than most humans. But that's not really the question.
What I hear all the time is, ‘Well, if we can get the same amount of output with less labor, then surely millions of people will lose their job.’ I think the same logic also implies that we can just get a lot more output from the economy using all the labor that we have. And the difference between those two views really is at the heart of the debate.
So far, I would say the data allow for some cautious optimism. Despite rapid advances in AI capability and evidence that adoption is spreading, the broad labor market indicators still show remarkably little disruption. Economic growth is holding in there. The unemployment rate is not rising rapidly. If anything, it's ticked down recently. Job openings are not soaring, and separations do not suggest that there's systematic weakness in AI exposed industries.
Now, productivity data are beginning to show perhaps a bit of AI's positive effects, but they don't show the mass displacement that many people fear. According to our research, industries with higher AI exposures have recorded stronger labor productivity gains, driven mainly by faster output growth rather than fewer hours worked. And that distinction for me is critical. So far, the evidence looks like workers are producing more than firms are cutting back on labor.
There's also a physical constraint. AI adoption depends – and will continue to depend – on infrastructure that is still being built. Of the more than $3 trillion in expected data center and related infrastructure CapEx from 2025 through 2028, only about a quarter of that has been deployed so far.
The future remains opaque. No two ways about it. The biggest productivity gains from my perspective are likely still ahead of us, and some job losses are likely unavoidable. Earlier, innovation waves unfolded over decades, and AI is moving much faster, compressing the adjustment period. And that does create the central risk to the labor market; that job destruction happens faster than new job creation happens.
And so, what our research has been doing is to try to look beyond the immediate effects. Yes, some jobs and tasks will likely be disrupted. But higher productivity can also mean higher incomes. Higher wealth. With higher income and higher wealth can also mean higher spending, which, in turn, drives the economy faster.
Inside corporations, new tasks and new roles will likely emerge giving some of the displaced workers somewhere else to go. And even if employment does slow down for a while – and that could put downward pressure on inflation and maybe upward pressure on the unemployment rate – I don't really think policy makers are simply going to sit back on the sidelines. Central banks can respond by trying to stimulate the economy and bring it back towards full employment.
This is something that economists call General Equilibrium. We can't look simply at one side of the equation. We have to think about the system as a whole. And I have to say, if monetary policy runs out of room, fiscal policy makers can get into the game as well. Between automatic stabilizers like unemployment benefits and directed targeted government action, there's another way in which the economy could be pushed back to full employment.
So, the bigger point is this, AI clearly has a chance to create some labor market disruption, but the economy has all sorts of other systems and levers in place that can pull us back to full employment.
And with those buffers in place, any rise in the unemployment rate from AI is probably going to end up being smaller, shorter, and easier to manage – at least for the next couple of years than maybe some of the first pass analysis that I've seen suggests.
AI's labor market impact is not predetermined. The debate will almost certainly come down to speed. How fast is AI adoption relative to the economy's ability to adapt? History suggests that productivity ultimately wins. The economy gets bigger and people stay employed. History also tells us that not everyone benefits equally. And more importantly, not every transition is smooth.
So, what does that mean? Should we be just blithely optimistic? Absolutely not. For now, the early evidence is reassuring, but the story is still being written.
Thanks for listening, and if you enjoy this show, please leave us a review wherever you listen. And share Thoughts on the Market with a friend or a colleague today.

Capital spending usually signals how a company is positioning itself for the future. Our Global Head of Fixed Income Research Andrew Sheets explains why this metric is getting more attention from investors.
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Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley.
Today: Why capital expenditure is rapidly becoming one of the most important numbers in earning season across asset classes.
It's Thursday, April 30th at 2pm in London.
This is a high-risk episode in the sense that it may already be obsolete by the time that you hear it. But then again, maybe that's fitting for a discussion of record capital spending on cutting edge technology.
We are in the middle of the busiest part of earning season, and yesterday four of the largest companies in the world reported numbers. These companies – Alphabet, Amazon, Microsoft, and Meta – have a combined market cap of nearly $12 trillion.
Yet, while the focus of earning season is traditionally about earnings, another line item is rapidly rising in importance. Capital spending on AI infrastructure – the chips, power cooling, and connections that are required to build and run AI models is soaring. And the companies that reported yesterday are at the leading edge of this trend.
The first thing about all this spending is simply the scale. For this year alone, Morgan Stanley estimates that it will amount to over $600 billion across the largest U.S. hyperscalers. To put that in perspective, that means just a handful of U.S. tech companies are now set to spend almost as much on capital and equipment this year as every non-technology company in the S&P 500 did in 2025. And as big as that spending is, it's been accelerating.
That over 600 billion spending number that we forecast for 2026? Well, a year ago we thought it would be roughly half that, and that estimate was well above consensus at the time. U.S. companies have repeatedly guided their spending higher as they seek to capture the AI opportunity. And we think that continues.
By 2028, my Morgan Stanley colleagues estimate that this U.S. hyperscaler capital spending could hit an annual rate of $1 trillion. In other words, as big as these numbers may seem, much of the spending story still lies ahead.
All of that investment, both recently and in the future, has big implications. First, one company's spending is another company's revenue, and many of the stock markets recent winners have been directly tied to this historic buildout.
As of this recording, U.S. semiconductor stocks have risen over 30 percent this month alone.
Second, while these large U.S. tech companies have enormous financial resources, this spending is at a scale that still requires significant borrowing. Our credit strategy teams expect record bond issuance this year, with U.S. tech borrowing a big part of that.
And so far, it's playing out. The first quarter was the busiest quarter for U.S. investment grade bond issuance on record. Which brings us back to these recent earnings – and a dilemma that seems negatively skewed for credit relative to equities.
If these companies continue to sound confident about their capital spending plans or even raise expectations further, that could support AI suppliers and the broader equity market. But it would mean even more borrowing needs to be absorbed by the corporate bond market, a credit negative. The results we got yesterday certainly hint at a continuation of this trend.
On the other hand, if capital spending is guided down, that could undermine a key pillar of recent market strength and broader risk appetite, which could drag credit wider by association. In the near term, the risk reward seems better in other parts of fixed income, such as mortgage-backed securities.
The implications of yesterday's results may also extend to the Federal Reserve. As we discussed last week, Kevin Warsh, nominee to be the next Fed Chair, believes that large levels of investment can boost productivity, lowering inflation, and thus justifying lower interest rates.
And so, what these large spenders do, how confident they feel about the future, and what all of this spending can ultimately deliver – well, the implications of that may extend even into the monetary policy story.
Thank you as always, for your time. If you find Thoughts of the Market useful, let us know by leaving a review wherever you listen. And also tell a friend or colleague about us today.