Michael Cembalest on Why AI Is the Stock Market Bet of the Century

25:27Yeah, and I usually get airlifted into things that nobody – that are a pain. I became, for the asset management business and even for the broader firm, I ended up being our COVID point person. And I put together a medical advisory committee of biophysicians and mathematical epidemiologists and people like that from La Jolla Institute of Immunology. And so I get airlifted into things. I would say, oddly enough, of all the sectors and sub-sectors I can think of, the one that drives me nuts the most is health care and managed care. because you know how they snuck this provision into the energy bill three years ago where the government can negotiate drug prices finally after never being able to do that on the face of it that should be negative for the big pharma companies but because they can play around with all sorts of discounting mechanisms and distribution arrangements a lot of which aren't super public the actual impact on their margins so far has been somewhat negligible so there's a There's an impenetrability to parts of the way the healthcare system function that drives me a little bananas.

26:35That said, large-cap pharma and biotech right now are among the cheapest sectors if you compare price to book to projected ROE or something like that. We have this giant map of all the sectors and subsectors and industries and where they trade relative to price to book and ROA and ROE. and healthcare is cheaper. But it would normally make me want to dive in and kind of come up with an asset allocation recommendation for our investment process. And it's just very difficult to do. This is how I feel about US health insurance, by the way, as someone who did not grow up that much in the US. I still struggle to wrap my head around how all of this works.

27:18It is truly impenetrable to me. I think many people would agree with that assessment. I want to go back to actually AI for a second because you're talking about the hyperscalers and this booming business for them. It's still actually a little bit hard for me to understand because for some companies, their AI revenue is very easy to understand, right? So Microsoft sells various things that go along with its suite that whatever, whether it's coding tools or so forth. OK, they're selling an AI service. Maybe Google will one day figure out how to do that too. For a company like Meta, their model is an open source model.

27:56They don't sell enterprise llama as far as I know, yet they spend like crazy on AI and they talk about how much they spend. Can you articulate for me what Meta's AI business model is? Because all I hear is about their spending and I don't even know what revenue they're deriving from. Yeah, when you try to understand that, what you learn is that Meta basically considers itself a generative AI company. Yeah. And that everything they do and the success of all of its algorithms and social media tools maintain their market dominance because of how well they function. And the way that they function is because they're driven by AI.

28:37And I agree. I think they thought that the open source LOM model would become an indirect profit center as people built tools and applications on top of it. And it seems like they're stepping away from that right now. Like a year ago, that was the kind of message you were hearing and not so much right now. But they – I think they would say – But somehow all of this spending, which they talk is AI spending specifically, even though they're not selling AI. But somehow it's accruing to their fundamental business of selling ads or selling content or selling time. And here's how I would articulate that.

29:16If you go back three, four years, there were these really interesting sell-side presentations that talked about, well, Google's going to try to compete with Apple here. Apple's going to try to compete with NVIDIA here. NVIDIA's going to try and compete with OpenAI here. And guess what? A lot of those moats are still in place. And a lot of the encroachment that people were looking for hasn't happened. So Meta's AI spend is primarily focused on maintaining an impenetrable moat for other companies that might otherwise want to compete with their core business. And that's how they see it, which means that you basically will want to look at top line free cash flow and other profitability metrics for the company as a whole to understand the benefits of all that spend.

30:04I think investors are right to be skeptical. It's only three years ago that they took a ton of money, put it in a pile and set it on fire with the metaverse, right? So they have had a history of misreading where things are going. Tracy, by the way, I actually really admire how quick they were willing to pivot off that. Because you think, you know, you make this big bet, it's embarrassing. You rename your company meta off for this. And then you're like, we're an AI company. Actually, that raised my admiration for Zuckerberg as a CEO that he didn't succumb to some big sunk cost fallacy there. Anyway.

30:40When the facts change, I change my business model. That's my version of the Keynes quote. He has demonstrated a mental agility to pivot as well earlier this year. And I'm not going to make a value judgment on this, but I was struck by him saying that he needed to re-inject a dose of masculinity. I think that's a sign of somebody that, this is audio, so you can't see my eyes rolling, but that's a sign of somebody that is willing to kind of change horses when necessary. Oh dear. I have thoughts on AI as a masculine endeavor. But anyway, one thing I wanted to ask is, okay, so at the beginning of this conversation, you described AI as the bet of the century.

31:24It is. And when I hear, you know, when we're talking about the dominance of the big U.S. tech players and the moats and the billions, if not trillions of dollars that people are spending on this now, it feels like almost certainly it's the big players that are going to emerge as the winners from this. It seems very, very hard for smaller entrants to get into this. Is that right? Is it just, you know, a fait accompli, I guess, that the big guys are going to win? At the hyperscaler level, yes, but in a whole bunch of other industries, not necessarily. There's plenty of room. But look, you're seeing these unicorns all the time that are chipping away at different aspects of machine learning and generative AI.

32:04One of the most fascinating ones is look at Palantir, right? I mean, Palantir is taking a run at the large defense contractors primarily through its nimbleness and better application and understanding of the intersection between generative AI and defense weaponry. And for everybody that thinks about data centers, the massive concentration of data centers in Virginia and in the PJM ISO region more broadly is a very clear indication of just how the national security and defense stuff is tied into generative AI and who their big users are. If that didn't matter, there would be a lot more data centers built in the Midwest where 20 to 30 % of the time, electricity prices are actually negative.

32:50Right? Think about this. You have certain applications that are so reliant on not there being a millisecond of lag that they'd be willing to pay more for power and proximity than to have cheaper power that has just a tiny bit of lag built into it.

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34:06The point is, you're engaged with your investments, and Public gets that. That's why they built an investing platform for those who take it seriously. On Public, you can put together a multi-asset portfolio for the long haul. Stocks, bonds, options, crypto, it's all there. plus an industry-leading 3.8 % APY high-yield cash account. Switch to the platform built for those who take investing seriously. Go to public.com and earn an uncapped 1 % bonus when you transfer your portfolio. That's public.com. Paid for by Public Investing. All investing involves the risk of loss, including loss of principal.

34:45Brokerage services for U.S.-listed registered securities, options and bonds, and a self-directed account are offered by Public Investing, Inc., member FINRA and SIPC. Crypto trading provided by Backed Crypto Solutions, LLC. Complete disclosure is available at public.com slash disclosure. One company where there's some question about the moat is Alphabet. And, you know, people are like, well, could OpenAI just become people's default homepage or the first page that people go to when they want to learn something? And you've seen Alphabet shares underperform in some of the rebound. I think from a multiple standpoint, it's trading at some of its cheapest levels in a very long time.

35:23there's a lot of questions about the degree to which it can productize its own models. Historically, it's not so good at that. It makes great tech. The models are very good. I'm still discovering new alphabet models that I never knew existed because they're buried under some URL. Is there a risk to their business model specifically that perhaps their distribution, their ownership of digital real estate risk to the rents that they can extract from it? Yeah, but this is the kind of thing where I would continue to just look at their market share. You know, for a while, Bing was taking a run at them and then nothing really happened.

36:01Okay, so in Europe, when you buy a device, Apple is no longer able to make Google the default engine. Oh, yeah, yeah. So you have to set it up yourself. People pick Google anyway. Yeah. So you could actually argue that Google is overpaying Apple for the default status on their devices. Oh, that's interesting. Because people would be willing to pick it anyway. I don't know. I think obviously the tools, OpenAI, I personally love perplexity. But I use them when necessary. And I wouldn't bet against Google's generic market share for search until you actually saw them beginning to lose market share.

36:35It reminds me for the last 15 years, people have been talking and screaming about the end of the dollar as the world's reserve currency. And every year. I love this analogy. We look at the data on the share of foreign exchange reserves invested in dollars, the share of corporate and equity debt issuance in dollars, particularly offshore, the share of SWIFT transaction payments, and the dollar share of all these things, BIS, intercompany bank loans, cross-border. The dollar share is kind of unchanged. So it's – you have to wait until you see stuff. Network effects are definitely a thing. One thing I wanted to ask you, or one of the reasons we wanted to have you on is, as you say, you read a lot.

37:17You talk to a lot of people, a lot of clients. And I'm curious, what's the sort of time split or the question split between, I guess, Trump and policy versus, you know, other market questions like AI? I mean, it's hard to say, right? I mean, the tariff, the whole tariff drama was, took an enormous amount of time. Because I would say that a lot of CEOs, and I've described this recently, is I went to college in the Boston area and every time it snowed, some of the local kids would come out. And when there was a lot of snow and ice on the road, they would hold onto the back of trucks and go sketching.

37:56And I had never heard of sketching until I saw people doing it. I never did it because like, after you stop sketching, you tend to slam face first into the ice. A lot of CEOs are sketching right now on this administration because they thought that what they were going to get was deregulation, oil and gas pipelines, and lower corporate tax rates. That's what they were supporting this guy for. And they come out of the gate with deportations and tariffs, and the productivity shock supply side agenda got put in the back seat for a while. They appear to be wanting to get started on some of that stuff.

38:29And I think we can talk about it. It'll be interesting to see if they can get the constitution pipeline reinvigorated, which would bring Marcella Shale gas to the northeast. But in the beginning, because so much of the first stuff was tariffs and deportations, a lot of focus on that. And tariffs in particular require a lot of work. There's 12 ,000 HTS codes across 200-something countries. And they came up with a patchwork of legislation that differentiated. And so there's just a lot of math involved in really understanding what the impacts are. By the way, I see you went to Tufts. And so I think you're the first person to ever say you went to a Boston area college and that not being code word for Harvard.

39:11You actually did go to a Boston area college, excellent university. There's lots of them. No, I know there's lots, but you weren't being coy when you said Boston area college. As you mentioned, another huge theme over the years, and you write a lot about it, is energy. You've learned a lot from Foclop's Mill. And we could talk hours just about energy. I would love to talk hours about energy at some point. But in the interest of time, how much has the AI story over the last few years changed how you thought about the future of North American energy needs and energy production and just the energy story in general?

39:52You know, not until very recently because it was a negligible part of overall electricity consumption, you know, sub 1 % until a couple of years ago and has been growing slowly. There are three big components to this electricity story. One's AI. Another one's electrification of transportation, you know, EVs. And the third one that people should focus on because it's just as big is the potential for electrification of winter heating in homes, office buildings, and industrial locations. One of the hardest things when you break down energy consumption, an enormous amount of it, an enormous amount of fossil fuel consumption is used for heat.

40:33Some of the heat, around a third of all the heat that's used is used at temperatures below 200 degrees centigrade. And that's where heat pumps are extremely efficient and measured by something called the coefficient of performance, which is the units of heat you get per unit of electricity you put in. And so there's three components roughly equal, right? So data centers, electrification of heating, electrification of transport. The wide-eyed kind of fairytale projections of the Rocky Mountain Institute and green tech media people are looking at 20 % to 25 % growth in electricity demand over the next 10 years.

41:15I think that's way too high, adding up those three components. The lower end is closer to like 5 % to 7%. BNEF, which I think does very good work on these topics, is somewhere in between. Thank you. Thank you for the plug. Yeah. Again, I know all of the sources and the rigor of their work. And so I think we will need 7 % or 8 % additional electricity capacity for generation over the next decade or so. Electricity consumption in the United States has been roughly flat for 20 years. But before that, it went up a lot. The issue back then was we were meeting it with nuclear and large natural gas plants.

41:51It's a lot harder today because most of the country is going to try to do it with renewables, which is just very difficult. And I'm also, for reasons I wrote about in our annual energy paper, I'm a skeptic of small modular reactors. Great idea on paper, very complicated in terms of execution. By the way, Tracy, it's interesting the heat as one of the big consumption drivers of energy. You know, there's a lot of Northeastern chauvinism where they say people shouldn't be living out in Arizona and all these like air-conditioned places. But it really is heat. Like really if we want to be more energy efficient, my understanding is we should all leave the Northeast and go to the desert and live in air conditioning instead of heating our home.

42:32No, for real. As someone with an old house in Connecticut, I will push back on that narrative. You know, I have a personal bias on this, but I was very excited about heat pumps. But then we looked into it for a home that was built in 1850. And it turns out you would have to like re-insulate the entire house for this thing to have any effect. So I – Oh, yeah. No, the biggest problem with heat pumps is the fact that while it's more efficient, you're buying electricity instead of natural gas. And per megajoule of energy, it can be – depending on the state you live in, three to four times more expensive.

43:05So it's great for the climate. It's just economically challenging. And that's why I think some of those Rocky Mountain Institute and Greentech media forecasts are way too aggressive, because I think these transitions are going to take place over a much longer period of time.

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44:16The point is, you're engaged with your investments, and Public gets that. That's why they built an investing platform for those who take it seriously. On Public, you can put together a multi-asset portfolio for the long haul. Stocks, bonds, options, crypto, it's all there. Plus an industry leading 3.8 % APY high yield cash account. Switch to the platform built for those who take investing seriously. Go to public.com and earn an uncapped 1 % bonus when you transfer your portfolio. That's public.com. Paid for by public investing. All investing involves the risk of loss, including loss of principal.

44:55Brokerage services for us listed registered securities, options and bonds in a self-directed account are offered by public investing Inc. Member FINRA and SIPC. Crypto trading provided by Backed Crypto Solutions, LLC. Complete disclosures available at public.com slash disclosure. Since we have you, since you've been in the business for almost four decades now, if I was reading one of your notes or one of your research pieces in, let's say, 1987 or 1988, I'm not sure what exactly you were doing back then. I was an analyst. Oh, OK. And I was sitting someplace behind a pile of pizza boxes and nobody was talking to me.

45:25I don't know. So how different would your research then be versus your research now? I'm definitely somebody that has taken advantage of massive quantities of information and research, right? It was a lot harder. I used to have to go to the Mid-Manhattan Research Library to find things. And I used to go and you'd pull out microfiche and then you would go and you would try to, you know, you put in a quarter and they would let you print it. I mean, it was hard to get historical data back then. And I'm a data hound. And so the way the world works today is much easier for me to draw parallels. I've also learned a lot of lessons in investing for all these years.

46:04I was the chief investment officer just in the private bank for a long time. And so I've learned a lot of lessons. And we're in the process of putting together a 20-year on the market retrospective. And it's amazing to go back and publish some of those pieces and see what we learned at the time. Oh, well, okay. Give us a preview. What were the big lessons? The number one thing, more than anything else, and we have 30 topics we're going to show, And some of them are in there kind of for humor or sarcasm. But the number one investment lesson was equities bottom before everything else. And so does credit and so does high yield and so does real estate.

46:39In other words, asset prices bottom so far in advance of the related fundamentals. For instance, during the financial crisis in 2009, bank stocks bottomed when only 10 % of the ultimate bank failures had taken place. So if you were listening to the really bearish voices at the time, they would say, too soon to invest. There's a giant tidal wave of bank failures coming. But the KBW or whatever index you want to look at bottomed in advance of all of that. And the same thing happened during the S &L crisis. Stocks bottomed way before all the defaults eventually took place. And if you then take the seven post-war recessions, in six out of the seven of them, equities bottomed before you even saw an upturn in payrolls, industrial production, housing, credit card delinquencies.

47:35And so as an investments person, I have to acclimate our clients and our investment committee to be willing to take risk when you look out the window and everything looks terrible. Yeah. Oh, this is a great lesson because people say right now, oh, the shortages from the tariffs haven't even hit. Maybe we won't have them because the tariffs have been dialed back so much. But the idea that maybe the market already bottomed for this cycle on everything related to tariffs. Sometimes I like to say hope is the only strategy because if you wait until all of the data confirms that things are back, it's clearly going to be priced in at that point.

48:11But I actually wanted to ask something else. I am an SMR skeptic too, but I form my opinions based on seeing five tweets that I like, okay, yeah, those sound good to me. I'm an SMR skeptic. You have a much more rigorous process. Can you explain to us why you are a skeptic of small modular reactors? Okay. So the easiest one is nobody's built one. That's a good answer. In the United States, one hasn't been built. Point number two, there are three or four of them, depending on how you want to define what small is, that have been built in China and Russia. And the costs have been multiples of the original projections and the timeframes have been much longer.

48:53So failure even in places that have success building regular – Here's what I figure. If they were so good, China would have built 100 by then. Third point, there's no industrial project on Earth that is more capital intensive than nuclear power. And so when nuclear power first started to become popular in the late 60s, early 70s, the goal was how big can we make them because we want to spread some of the sunk costs over the most megawatts we can build. The notion that you want to shrink a capital project to make it more efficient makes no sense to me unless you can completely commoditize them.

49:29The mistake I think people are making is that there has been an incredible learning curve in solar polysilicon panels in to lesser extent in wind turbines, both onshore and offshore. And the best example is lithium ion batteries. Best example of a learning curve you've ever seen. But that's because millions and tens of millions of units are being produced. I don't believe that you can, from going from one reactor to four reactors or six reactors, develop the kind of learning curve that would result in exponential cost declines. And so I'll wait until I see it, and I don't think we will, but I would love to eat my hat if Oclo or some of the other public companies or private companies that are working on this can deliver a small modular reactor in anywhere of the zip code of the original projections.

50:20And just one last thing, as a reminder, the new scale originally said$3 million a megawatt. When it hit 20, the plug was pulled. So the most recent example of such efforts in the United States failed when the budget hit 6x. That was a fantastic answer, very intuitive answer too. Real quickly, what about geothermal? Do you think that could be a meaningful contributor to our portfolio? I do. It takes effort. Chris Wright, who's a secretary of energy, is a big fan. Yeah. And there's different kinds of geothermal. The most interesting kind is where you're going really deep and you're accessing like supercritical fluids whose heat is comparable to nuclear power.

51:04But, you know, that instrumentation has to be able to withstand very high heat. You have to use plasma drilling bits. I think there's opportunity for expansion of geothermal. But they're long-term industrial projects. It'll take a long time to do, not a quick fix. Yeah. You know? And the other thing too is – and this is amazing. I've been very focused on the rising costs of wind and solar power, and I should have been paying more attention to it. I turned my head the other day. The cost of a natural gas combined cycle turbine has gone from 1 ,200 per kilowatt to 2 ,500, like more than double just in the last two years.

51:39supply chains and mostly a reflection of the fact that ge vernova and siemens the other companies involved don't believe the long-term demand story enough to expand their production facilities so that's why classic energy phenomenon right so that's why you go to ge vernova today we're happy to deliver you a plant it's going to cost two times more than it did a couple of years ago and we'll give it to you in five years that's the ai bottleneck this is how falling demand can result in higher prices or expectations of falling. Right. Because the two to three entities, Mitsubishi as well, that are involved in this sector, don't feel comfortable enough to go out and do a mega capital raise to double and triple the size of their production facilities for these kind of turbines.

52:24And that I think is going to end up being a binding constraint for AI at some point. I just don't know when. So one of the reasons I'm a big fan of your research is because it is so wide ranging. And again, you have a very diverse diet when it comes to what you read and the information and data that you consume. Last question for me, but give us one under the radar thing that you are watching at the moment.

52:53Basically, what should we do an episode on? Yeah. What should we do an episode on? Let's see. I mean, not in any kind of order or importance. I'm just going to give you a stream of consciousness. Sometimes I heard you in the introduction talk about narratives that come and go. And I agree with that totally. And one of the narratives that came and went but might come back is there are still$500 to$700 billion of unrealized interest rate-related losses on bank balance sheets. That has not gone away. Now, it only goes away if you go back to 3 % or some 3 % on the 10-year. But we still have a banking system where many of the participants went hog wild extending duration on treasuries and agencies at a time of historically low interest rates because you had this deposit surge from all the Fed bazooka money.

53:49And that hasn't gone away. Now, the Fed has proven itself willing to provide whatever emergency facilities. I actually was personally against bailing out the VC depositors in Silicon Valley Bank. The average deposit balance in that bank was$2.5 million. I mean it wasn't really a bank. It was something else. But they decided for systemic reasons to do it. They'd probably do it again. But that issue hasn't gone away. And the higher 10-year rates go, the more those losses get pronounced. So that's an issue that's kind of still out there. I'm glad you brought that up because I remember we – Trace and I did a bunch of episodes in like 2021, 22.

54:24too. It's like, why isn't the rate rise having more of an effect? And everyone's like, oh, they got termed out. Everyone locked in low rates, et cetera. And then it's like, all right, but now rates are higher. At some point, they only locked them in for so long. I'm almost tempted to believe that the Fed essentially created this problem through financial repression, tempted some bad asset liability managers at these regional banks to load up at low rates and feel some sense of actual responsibility for this mess, because it was Fed monetary and congressional fiscal policy that led to this mess.

54:58The deposit explosion. Yeah. So I think they feel somewhat beholden to try to manage around some of the risks. Another thing I would say is I don't think the tariff story is done. Okay. You know, the average tariff rate at the end of last year was something like 2.5%. I'm using round numbers. Yeah. If you did all the math and you don't assume any import substitution, at the peak of the kind of, you know, Lutnik cardboard cutout, you know, presentation. We were looking at a 25 % all-in rate. Yeah. Again, assuming no import substitution and things like that. Now we're back down at like 13%. So the market's like, wow, we dodged a bullet.

55:36It went from 20-something to 14. But 14 is still very high compared to two. Isn't it like the highest since the 1930s or 40s or something? Yeah, definitely. Now, he may backtrack again. We'll see. But historically, whenever there's been a disconnect between hard data and soft data, it takes about 90 days for that to resolve itself. So some of the shocks that are seeping through the system are going to take until July or August to play out. One of the things I wrote earlier this year that resonated with a lot of people is the markets are the ones that you can't deport them. You can't intimidate them.

56:14You can't arrest them. At the end of the day, the markets are going to be a binding constraint on what this administration can do and can't do. And that's actually a plus for investors because it means at some point they're going to have to adhere to some generally acceptable level of policymaking. I just have one last quick question. Going back to AI, you hear a lot about inference versus training and people wondering, could there be some alternative to NVIDIA for some of these services? Because right now on the chip side, all the profits seem to be accruing to NVIDIA. Is that moat, you know, people talk about the CUDA ecosystem.

56:49Is that an impenetrable moat or are there opportunities for other chip companies to gain some real profits in the AI world? There are. In the piece that we did last year, we listed all the companies that are trying to pick away the NVIDIA moat. But in the asset management business, or when companies hire underwriters, if an underwriting goes bad and you picked JP Morgan, Goldman, and Morgan Stanley, nobody's going to question you. If you pick a ranked eight or 11 underwriter and something goes wrong, people will question you. So to some extent, the network effect and the moats that you guys are referring to, nobody's going to get blamed for spending extra money to buy NVIDIA products and wait for a long time until these other products have proven themselves.

57:38And so I think it's going to take a long time for that to play out. But yeah, eventually, if this AI story works, we should be looking at 80 % inference and 20 % training in two to three years, right? At that point, corporate adoption, which drives the training models, are supposed to be the overwhelming amount of what's going on. But again, Amazon claims to be working on a foundational model, haven't seen it yet. Microsoft has actually kind of hinted that, oh, we've created our own foundational model. There was this great story recently, if you like AI, the head of the Microsoft AI group was basically told to pound sand by the OpenAI people when he was asking them to fully disclose how some of the new reasoning models worked.

58:23So Microsoft is working on their own foundational model. Hasn't been released yet, even internally within Microsoft. So again, some of these moats, I wouldn't underestimate the amount of time that NVIDIA is spending defending that moat and improving its products and its energy efficiency so that they don't give a market share prematurely. All right. Well, by the time this episode comes out, NVIDIA earnings will have been published. So maybe we'll get a little bit more insight. But Michael Sembalist, thank you so much for coming on All Thoughts. Yeah, that was a real pleasure. This is a crime that we hadn't had you before.

58:57But I guess the fact that you couldn't. I think I've been put back in the cage after this. All right.

59:14Joe, that was a real treat to get Michael finally, finally on the podcast. It was so good. And it's like, I've always liked his notes. I mean, A, I think everyone who hasn't read them will now understand why he has sort of this cult following on Wall Street where everyone devours his note. But then even beyond that, hearing him, like in my mind, it's like, okay, these notes are great. But there's a difference between comfort at like being able to put together this big research product or this big note product, but also just being able to talk about all these things extemporaneously. Absolutely.

59:48It's really impressive. There's so much interesting stuff to pick out there. But one of the things that struck me was, I guess the importance of geographical location for data centers. Oh, yeah. Because I hadn't thought about that, but he's absolutely right. You know, you could locate a bunch of data centers in the middle of nowhere where maybe there's cheaper energy costs, but people so far have not chosen to do that. Yeah, I hadn't really thought about that either because it was like, you know, we all know that Northern Virginia is this huge data center cluster. But why the data centers really need to be that close to the government and defense industry and so forth is like, come on.

1:00:26It's like, is it really that big of a deal? Right. Your extra millisecond of latency or something. But I mean, you know, they're obviously there for a reason. I also just thought like this idea of the moats, you know, obviously that the big tech companies have built up and the amount of effort they have to sustain those moats and distribute through those moats and so forth. And like how powerful that is and how much that is the story of the fact that the biggest keep getting bigger at literally the expense of anything else. It's such an important perspective I have on all kinds of dimensions, whether you're thinking U.S.

1:01:00versus international, the price of U.S. equities and so forth. You just have to remember like how extraordinarily unusual these companies are. Absolutely. And the fact that they continue to just throw off cash, even while they're spending billions and billions of dollars on AI tech, which we're not entirely sure like the degree to which it's actually going to be able to be monetized. But they still managed to do it while generating cash. The clarity of the way he spoke about energy was really impressive. And I I really liked what he said about small modular reactors. You know, when we've talked to - No one's done it yet in the US?

1:01:33Yeah. And when we talked to Jigar Shah about it, he used an analogy, which I thought was fantastic. You know, he's more optimistic about them. He used an analogy that I thought was great, which is that the nuclear industry has to be in the business of building airplanes instead of airports. Because airports are all one-offs, but airplanes are obviously, you know, they're built on a very complex assembly line. But, you know, after you've built several, the cost gets cheaper. And this idea that there's so much capital intensivity in building nuclear and the idea that there is no insight prospect for that learning curve where you do it over and over and over again for the cost to fall dramatically, that they haven't even done that in China.

1:02:14maybe you're not going to ever really get the sort of learning curves that you get in solar or lithium ion batteries. Super, very clear, intuitive explanation of why the opportunity may not be there. Yeah, we have to have him back on. Yeah. Oh, so much more we could talk about. All right. Shall we leave it there? Let's leave it there. This has been another episode of the All Thoughts Podcast. I'm Traci Alloway. You can follow me at Traci Alloway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Check out all of Michael Sembalist's notes. They're all online. Search for them. All of them are a must-read.

1:02:47I'm going to go back and read a bunch of them. Follow our producers, Kerman Rodriguez at Kerman Armand, Dash O 'Bennett at Dashbot, and Kale Brooks at Kale Brooks. For more OddLots content, go to bloomberg.com slash oddlots. We have a daily newsletter and all of our episodes. And you can chat about all of these topics 24-7 in our Discord, discord.gg slash oddlots. And if you enjoy OddLots, If you like it when we talk to market veterans like Michael Sembalist, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free.

1:03:24All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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