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This podcast has been prepared exclusively for institutional, wholesale, professional clients and qualified investors only, as defined by local laws and regulations. Please read other important information which can be found on the link at the end of the podcast episode.
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Happy New Year, everybody. Welcome to the 2026 Eye in the Market Outlook podcast, recording this in the last week of December from Santa Fe, New Mexico. There are no fish at all here, but there's lots of quilts and incense everywhere. So that's different. So I wanted to think about different topics for this year, and I decided to take a deep dive into this question of whether this giant moat that's supporting the global equity markets is really as indefensible as it's sometimes described to be. And by that I'm referring to the giant moat of the big hyperscalers and the semiconductor companies that that support them. And that is the 80% of the outlook. And then we have a few pages on everything else. So in this podcast, I'm going to walk quickly through the outline of what we of what we look at in this year, and then some discussion about US Equity outperformance versus the rest of the world. So let's get into it. So is this MO really indestructible? The COVID art this year is Game of Thrones inspired, vaguely, loosely, and it's a dragon and he's sitting on top of a castle. And the flags of the four hyperscalers are there, and the bricks of this moat are made up of Nvidia, tsmc, AMD, and asml, which is the Dutch lithography company, and the dragon spitting a fiery stream of TSMC's 2 nanometer chips down on the people at the bottom of the moat. So that's kind of the sense you get when you read about this moat, that it's this giant indestructible thing. And I understand why people see it that way, and I partially see it that way. We're talking about just eight stocks here that were worth $3 trillion in 2018 and are now worth $18 trillion. So they've gone up by a factor of six in just a few years. And so here are some facts and figures about this moat. The four hyperscalers have alone spent 1.3 trillion in capex and R&D since the fourth quarter of 2022 when ChatGPT was launched. If we broaden the moat to the eight companies which are the four hyperscalers plus Nvidia, TSMC, AMD and ASML, they represent 20% of global equity market cap, which is kind of Incredible for made stocks. Then if we broaden it more to 42 AI related companies in the US equity markets, they account for 65 to 75% of S& P earnings, revenues and capital spending since Q4, 2022 and then in the GDP accounts. And this is based on the numbers that just came out at the end of December. Tech capital spending accounted for 40 to 45% of US GDP growth in the first three quarters of 2025. Not GDP GDP growth, but still those are kind of some amazing numbers. And just to put the tech capital spending numbers in context, there's a chart that we included. So tech capital spending was in the neighborhood of 2% of GDP in 2025. That's the sum of the peak year spending on the interstate highway system that was started by Eisenhower, the Apollo project, the moon landing, electrification of farming, the Manhattan Project, and several of the FDR public works projects from the 1930s including the Triborough Bridge, the Midtown Tunnel and LaGuardia Airport. So 2025 tech capital spending was equal to the sum of all those things in real relative to GDP at the time dollars. So that's kind of amazing. And the stakes are there's got to be some really substantial productivity and profits outcome from all this spending. So here's the good news, and you've heard me talk about this before. Most of the companies that are in our direct AI basket make a lot of money. When we look at the free cash flow to revenue ratios of these stocks, they're substantially above the average for the market. And really Oracle and Intel are the only two laggards at, at the end that, that don't now. But the hyperscalers are betting the ranch. And these numbers are sometimes even hard to believe until you triple check them. But the average S and P company spends about 10% of its revenues on CapEx and R& D. But okay, that includes a lot of companies that aren't necessarily very Capex intensive. Let's just look at the information technology space. That number is about 17%. So the average, the average overall tech company is about 17%. Capex and R& D as a share of revenues, the four hyperscalers are bunched up between 35 and 40%. And then the meta number is just nuts. It's almost 70% at this point. So these guys are definitely betting the ranch on this. Now so far this Capex boom we've been seeing has been financed mostly from internal cash flow. And that's what makes it different from, you know, casinos and airlines and gas pipelines. And things from prior decades and the broadband boom at the end of the 1990s. Most prior capex cycles in the US were financed by debt, at least by the end of it. This one, not so much. We have a chart in here that looks at the share of capex and dividends financed with debt rather than cash flow. And even though the capex to sales numbers are going up sharply right now, the amount that's being financed with debt is not. And that's in very stark contrast to the end of the 1990s. Now that said, in the fourth quarter of 2025, all of a sudden there was an explosion of hyperscaler debt financing. And by that I mean bonds, loans and leases to finance data centers and other AI related expenditures. And Oracle, Google, Meta and Amazon were the big borrowers that even so, you know, we started writing about Oracle in September. But if you strip out Oracle, most of the rest of the of these direct AI stocks that we're looking at here have net debt to EBITDA ratios that are even that are either negative because they've got more cash and equivalents than debt, or they have, like Amazon and Microsoft, very, very low positive debt ratios that are way below the S and P median. So Meta and Oracle are kind of pushing the needle right now on debt financing of all this, but in some ways, at least so far, they're outliers. And by the way, Oracle is paying the price. And Oracle is showing the market what happens when you push too hard to finance this stuff. Their credit default swap spreads have been widening pretty sharply, about 100 basis points just over the last couple of months in 2025. Now, when most people, or at least when a lot of people talk about this current situation we're in, people talk about valuation and they believe that we're in a valuation bubble. I'm not so sure the valuations are less extreme than you might think. And the way that we've been looking at that is to say, well, let's just not look at pes, right? Let's adjust PE for margins and profitability and earnings growth. And one way to do that is look at, we have a chart in here that looks at price to book ratios relative to return on earnings. And if you look at the sectors that way, all of a sudden there's an inherent sensibility to the way the market's being priced. So you may believe that investors are paying too much for growth right now, and maybe they are, but there's a lot more internal coherence to the way the markets are being priced than a lot of time, than a lot of things I read. And even when we add the individual stocks of those hyperscalers and the four big semiconductor companies, they lie along the same curve. In other words, there's a linear relationship between return on earnings and margins and what kind of valuation the markets are putting on. Another way, another way to think about it is when you can look at a PEG ratio, which is a PE ratio adjusted for expected earnings growth. And when you look at the PEG ratio for the tech sector globally, the PEG ratio is pretty much the same as the overall market. So investors are paying up for growth, but not in any kind of 2000ish type way, at least so far, as far as we can tell. Now that said, if you look at the big economy wide GDP accounts, private fixed investment in information processing equipment and software as a share of GDP has now reached a new all time high, just past the 2000 peak. So there's just another way of looking at this. A ton of money pouring into this space right now. So the lessons I've learned as an investor is when the markets are bombed out or after a sell off and when sentiment is negative, you're supposed to ask what could go right rather than obsessing over the things that led to the sell off in the first place. But when you're at all time highs and there's a lot of enthusiasm priced into markets, you're supposed to ask what could go wrong? And so that's what we do in this, in Most of the 2026 outlook, we ask what could go wrong? And we focus specifically on four medium term risks. Number one, a metaverse moment for the hyperscalers. I'll explain what that means in a couple of minutes. But basically, you know, we had a metaverse moment a couple of years ago and the Mag 7 stocks fell by 50% in 2022 simply because people lost confidence in the earnings projections. The second thing is a power generation constraint. The third thing is that China, the third risk is China somehow scales the moat on its own with its own lithography and semiconductor technology. I think that's a when, not an if. And then lastly, as Chinese dependence on Taiwan starts to go down, you have to start thinking more about risks to Taiwan and TSMC and Western access to those chips. And then we have a section at the end that looks at all of the other stuff going on in terms of the Fed and tariffs, labor supply, US equities, China, Japan and things like that. So we also have a section at the end that looks at the history of populism for investors. So I'm not going to go through too much of this, but let me just talk about a few things. The first risk, and I think the most important one to think about is this Metaverse moment. And as I mentioned, the MAG7 stocks fell by 50% in 2022 simply because of a lack of confidence in their ability to sustain the level of earnings growth that they had been posting. So what we do is we bucket the whole tsunami of information about AI into six things. We look at the improved technological capabilities of these models. We look at adoption rates of these models. We look at the impact of these models on corporate profits and employment. We look at hyperscaler revenues, free cash flow margins and cash balances. And then we look at the question around GPU and networking depreciation assumptions. Towards the end of the year there were some well known short sellers that started to focus on that, I think for good reasons. And so we take a deep dive into that. But I stacked up these Metaverse moment topics in order of importance. In other words, the stuff we end with in this section is more important than the stuff in the beginning. So yes, we all know about the improved technological capabilities of these models, but their impact on actual profits, revenues and labor productivity is really the more important thing for us. So I'm not going to go through too much of the details. It's all in there. I will say to me, the more robust the survey that we've seen and surveys are just surveys, right? But, but the more robust and detailed the survey, the less optimistic it is about the actual cost and revenue impact on companies adopting generative AI. And so I think it's important to keep that in mind. Now there are some real labor productivity benefits happening. There's a few individual companies that have reported, you know, Anthropic themselves and Autodesk and Adobe and Deloitte the Denset. I mean there, there are, of course there are a lot of examples, but on a broad market wide basis, so far this whole AI trade has been about the infrastructure of it rather than the benefits of it. And what do I mean by that? A lot of the sell side Wall street firms have created three buckets of stocks and we show ones here from, from one of them, but they're all pretty similar. And there's three buckets AI infrastructure, which are the semiconductors, the electrical equipment tech, hardware, power suppliers, and then that's one bucket. Another bucket is companies that are supposed to benefit from selling products and services related to all this. And the third bucket are the companies that are supposed to benefit from productivity gains because they have a high labor share of sales. So far, only the first bucket is really doing well. The other two buckets are kind of flat to the broad market since GPT was launched. And so this is so, so far this has been a stock pickers other than the, in the infrastructure trade. The rest of it so far has been a stock pickers game rather than a secular one. So what we're going to take a look at is how sustainable is this? Well, the hyperscaler free cash flow margins are starting to trend down, particularly for Meta, which is outspending everybody else. And their cash balances are going down. And that's important too. And the chart in here is kind of remarkable. Hyperscaler cash and cash equivalents were 40 to 50% of total assets a few years ago and now they're converging to, let's call it 10 to 20%. So the glide path that we're on is these companies are getting closer and closer to where they're either going to have to start borrowing a lot of money or they're going to have to start making a lot more money on their generative AI investments than they have so far. And then we also spend some time looking specifically at OpenAI. And to me, the best way to think about it is the good, the bad and the ugly, because there's a little bit of everything. The good is the, the projections the company is making about its future are the stuff is flying off the shelves. And what I mean by that is in the middle of 2025, they made a bunch of forecasts for 2027 revenues, users, paid subscribers, tokens, you know, all, all of the widgets that are involved in this whole thing. And then just three months later, in October of last year, they redid the forecast for 2027 and they had gone up dramatically in just three months. So the good news is, from OpenAI's perspective, its business prospects are booming. The bad is for them to reach their long term targets, they need 30 gigawatts of power by 2030. And I'll get into that more in a minute. And the ugly is, you know, OpenAI is arguably the biggest individual risk to the moat, even more than Nvidia. And despite that, the fact that OpenAI is still a private company, OpenAI is on track to make about 10 to 20 billion in revenue and they have commitments of 1.4 trillion to its corporate partners. And they currently survive almost exclusively on subscription fees and developer AI. Fees with little or no search, advertising revenue, cloud computing revenue, or hardware sales. And you know Altman's response to Brad Gerstner on his podcast of if, when Gerstner kept asking him questions about this. And Altman replies, look, if you want to sell your shares, I'll find you a buyer. You know, I think this is going to be a very interesting place to watch over the next 12 to 18 months. All right, risk number two is data centers and energy. And you've all seen some of these charts from us from before. Office building investment is collapsing for obvious reasons. Data center spending, electric power going up a lot. The question is, are we getting closer to a wall of power constraints? And as many of our clients know, I do probably six months of work a year on energy topics and every March I publish our annual energy paper. And so there'll be a big data center section in there. But you have to start asking yourself the question of whether or not some of these targets that these hyperscalers are throwing out there can be met. And I have found in my own personal experience, historically, some of the people at hyperscalers in charge of energy policy have not always been very realistic about the future. A few years ago, Google had this kind of net zero forecast thing that they shelved after a few years once they realized that it was infeasible. So there's a chart in here that I think is the most important one, which is how much capacity is the United States adding each year? Right. Because that'll tell us whether or not 30 gigawatts for open air is a reasonable number or not. But I have two lines on this chart. One is the nameplate capacity that gets added, and the other one is the nameplate capacity weighted by the effective load carrying capability of all this new capacity. In other words, adjusting nameplate capacity for its reliability and intermittency. Right. I mean, that's. And historically, from 1950 to around 2010, you didn't have to worry about that. But start when the renewables revolution started. A lot of the capacity you're getting, whether it's hydro or wind or solar or even batteries have certain intermittency and reliability issues. So once we adjust for that. The US didn't add 65 gigawatts last year. The US added 25 gigawatts last year. It's a very different picture. So a 30 gigawatt build for OpenAI looks like a lot of money. The other thing too is there's a data center backlash. And there's another important chart in here to help you understand why, we have a chart that shows that for 10 or 12 utilities, how much are they charging their regular customers for electricity? And even with the extra amount that they charge the data centers as an extra fee for all of the additional generation, transmission, distribution, and complexity that they bring to the table, almost none of these utilities are, are even with those extra tariffs, charging enough to cover the cost of new generation. And so that's the reason why there's a backlash against the data centers within the power generation community. Because the power generation community and the ISOs, which are the independent system operators, want these guys to pay full freight for the, for the cost of, for the cost related to what they're doing to the grid. I go into more detail in the piece. There are some strenuous objections to what I just said in papers, either academic papers or financed by Google, that argue that there's plenty of spare and slack natural gas capacity, et cetera, et cetera. The paper gets into more detail on that. But the bottom line is that there's just not enough production capacity of combined cycle turbines to meet demand. And it is what it is. And over the last 24 months, the cost of a new combined cycle turbine has risen from about 1,200 kilowatt kilowatt to 2,500. Delivery times are three to seven years. And the same thing is now impacting the simpler single cycle turbines, whose costs have also doubled and whose delivery times have also gone up a lot. So I feel like we're getting closer to some kind of power constraint wall. I spoke at a conference last year. People from Brookfield disagree with that. Time will tell. One thing that's important to remember is on the tariff question, while the moat companies, which are the semiconductors, computer parts and peripherals, are mostly exempt from tariffs right there, all of Those products are 70 to 80% exempt from tariffs. That's not the case with the power generation ecosystem. So the moat companies did a better job lobbying the White House and the Commerce Department to exempt a lot of their products from tariffs. The power generation industry had no such luck. So when we're talking about turbines, generators, transformers, electrical switches, solar panels, batteries, electric boards and things like that, motherboards, those things are subject to much fewer exemptions and therefore much higher terms. And then just to wrap up on the OpenAI question, 30 gigawatts of power over five years is roughly equal to the peak capacity added during the nuclear boom in the United states in the 1970s. And, and then the one that took place in the 1980s. So just for context, that's a lot of power. Now, one country that is not having trouble adding more generation is China. And so China is gearing up to compete with the US on both brain and brawn. And what I mean by that is there's a lot of innovation going on in China. But the part the gap they can't make up versus Nvidia based on pure innovation, they're going to outbrawn them by simply stringing more chips together, making bigger clusters. Normally you wouldn't do that in the west because of the cost of power, but in China, they're less sensitive about the cost of power and they're more focused on national security and building out domestic supply chains and reducing exposure on Taiwan and the US in the process. So the third risk we talk about in the piece is what if China scales this moat eventually on its own? Now, to be clear, China currently relies really heavily on the West. And when you look at the origin of the chips that are used to train Chinese models, the vast majority of those are Western chips. Only a handful of them rely on Chinese chips. And when you look at installed semiconductor capacity around the world, China has plenty. But the older chips that are 15 nanometers and above that are going to be used in automobiles, refrigerators, and simple mechanical devices. When you start talking about advanced chips, and specifically Those less than 14nm, China doesn't really have much production capacity at all. That's what they're now aiming to change. We go into a fair bit of technical detail in the piece because I thought it was interesting and I think it's important to understand if you're really trying to get at this China question. The bottom line is that the current generation of Huawei's chips are about a little more than two times have two times the power draw per unit of computation when compared to Nvidia's current offerings. And when you look in the future, those ratios go even higher. And so what's on the table is that Nvidia's power demands are expected to drop pretty substantially per unit of computation relative to Huawei. And so what Huawei is proposing instead is let's string together 8,000 or 15,000 individual processing units compared to the same cluster from Nvidia, that would be either 140 or around 600. And so that kind of daisy chain approach from China is very inefficient. But they're aiming to make up for it by with massive subsidies and a lot more investment in power generation and distribution. So the bottom line Is China has a long way to scale the moat if you're using a Western lens, because they're one to three generations behind on almost everything. But they have a different approach than the west and its Asian allies. As I mentioned, massive industry subsidies are willing to absorb higher costs in the interest of national security, a no holds barred build out of all forms of power generation, and also no small amount of industrial espionage, whether it's against TSMC or ASML or Nvidia or everybody else. What it feels like to me is that China is gradually reducing its technological reliance on Taiwan and TSMC specifically, just as it continues to ramp up its military hardware focused on Taiwan. And so the last of the four risks that we all need to think about is Taiwan may be the most blockade sensitive, advanced economy in the entire world. And that is by no means an exaggeration and might actually be an understatement because look at this chart we have in here. Taiwan imports 90% of its primary energy consumption. I mean, there's only a couple of countries in the world like Singapore and Cyprus and Morocco that are at similar levels. I mean that's so 90% of fossil fuels imported. It represents 90% of their entire primary energy consumption. And by the way, it's not just energy, it's also food. So Taiwan is In the top 10 of food imports as a percentage of the domestic food supply. And the thing that's interesting about this list of countries, it's the only country not in the Middle east that has this kind of food import sensitivity, right? And you can understand why Qatar and Kuwait and Saudi Arabia and Oman and the UAE import a lot of food based on the topography of where they live. But Taiwan has plenty of arable land and yet still is in the top 10 list of countries with that import a lot of the domestic food supply. And so, and I'm just going to close this out by talking about and showing this last chart. At the end of last year, China had somewhere between 60 and 100% of all of its military assets deployed in the Taiwan Strait. Whether we're talking about surface vessels, submarines, Air Force special mission aircraft, specifically destroyers, frigates, amphibious assault ships. 100% of the amphibious assault ships that China has are in the Taiwan Strait. So I don't think anybody has a crystal ball here. But when we start thinking about risks, we have to start thinking about this. And then what is the US trying to do? Well, the Secretary of Commerce Lutnick has talked about the US becoming 40% self sufficient in semiconductor production by the end of the Decade. It's ambitious. If you look at the roadmaps, they're a little imprecise. I wish we had a little bit more hard numbers from some of the companies involved. But we drew out the roadmap of what TSMC is pointing to building in Arizona and currently building. We look at existing intel production and then future production potentially in both Arizona and Oregon. And then we look at estimated future production from Samsung for Qualcomm and Tesla in Texas. And if you add all of that up and you make projections of US semiconductor advanced node demand, by the end of the decade, the US could get to 30 to 35%. But it's a stretch ambition and that would mean the US is still highly reliant on TSMC in Taiwan. And note that even today, all the chips made in Arizona are still sent back to Taiwan for packaging, dicing and testing. At least until Amcor builds this new facility they're working on which is targeted for 2028. So we're in an interesting vortex here where it looks like it'll be the end of the decade at the earliest when the US starts to achieve something that looks like partial self sufficiency in advanced semiconductors. So there's a ton more information in the Outlook. Take a look at those four sections. If you're interested in getting into the details, I have a few pages at the end of the Outlook on everything else. You know, the Fed tariffs, immigration, the US capital markets recovery, healthcare stocks, China, Japan, as I mentioned, a page on the history of populism for investors. And then for those of you that remember Robert McNamara and John Mitchell, we have some comments on them. Let me just mention a couple things and let me close this out and thank you all for listening. Let me close this out with a discussion of the US versus the rest of the world in equities. So the US lagged the rest of the world, or almost all of it other than India in 2025. If you look at US large cap, US small cap, it trailed almost every country. So I can understand somebody saying, wow, that was an opportunity missed. This year was a great year to have been diversified. The challenge has been the average investment strategist, the average sell side Wall street strategist has been saying that for the last 11 years. And over the last 11 years, the PE discount for non US stocks, which started at parity in 2009, kept going down and down and down and down. And it turns out that the beginning of last year was rock bottom when non US stocks traded at a 40% PE discount versus the US and so last year some of that got caught up. But I thought the right framework for thinking about this look, if you switch to the if you switched from the US to non US stocks at the beginning of last year, you're a genius, right? Because that was perfect timing. But look at this last chart I want to show you. Suppose you had switched at any point since 2010, you would have vastly underperformed, never switching at all. So the first bar on this chart shows that you would have made almost 900% on your money cumulatively by investing in the S&P 500 since January 2009. If you had swapped out of the S and P into non US stocks, which is this MSCI World X US Index, in all the years in between, you would have substantially underperformed. So you would needed to have avoided the siren song calls. And by the way, I'm really looking forward to the new Christopher Nolan Odyssey movie. I saw the trailer. You would have needed to avoid all the siren song calls to go into international stocks because they were cheaper every year for the last 11 years and just hit the bid at the beginning of last year in order to have navigated this properly. So for those of you that missed it last year but were invested mostly in the S and P, you're generally way ahead of everybody else. And I'm not sure how much room there is for the non US Stocks to continue to appreciate. They're currently trading at about a 30% discount to the U.S. i think 20% is about as good as it gets because of how much more profitable and the higher earnings growth that US Sectors tend to generate compared to Europe, Japan and China. So that is the Smothering Heights Podcast. Thank you very much for listening and take a look at the piece which is in your inbox this morning and I look forward to talking to you again soon and so long from incense.
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Ridden Santa Fe Michael Semblist's Eye on the Market offers a unique perspective on the economy, current events, markets and investment portfolios and is a production of JP Morgan Asset and Wealth Management. Michael Semblast is the Chairman of Market and investment strategy for J.P. morgan Asset Management and is one of our most renowned and provocative speakers. For more information, please subscribe to the Eye on the Market by contacting your JP Morgan representative. If you would like to hear more, please explore episodes on itunes or on our website. This podcast is intended for informational purposes only and is a communication on behalf of JP Morgan Institutional Investments, Inc. Views may not be suitable for all investors and are not intended as personal investment advice or a solicitation or recommendation. Outlooks and past performance are never guarantees of future results. This is not investment research. Please read other important information which can be found at www.jpmorgan.com disclaimer EOTM.