What AI really means, as a first principle, is we're converting electricity into intelligence right now. That's exactly what's happening. And so if that's true, and the demand for intelligence is seemingly infinite, I think the demand for power is seemingly infinite. That's a very basic question that investors should ask themselves. Do you believe the management in these companies is competent at seeing the future? And if the answer is yes, then they should be rewarded for making these investments. And if the answer is no, then they should be penalized for that. I do believe we're going to see in the near term, we'll call it a five years, more acute knowledge work or unemployment than we saw around mobile or the internet. And but I ultimately, I think it does fix itself because people realize you got to get on board. About 700 different stocks across all these models. And I'll give you the drum roll to see any guesses on who's the top model right now before I reveal it. Doug Jean, thank you guys very much for coming back on excess returns. You are in high demand these days. So the fact that we can get you for 45 or 60 minutes is we really appreciate it in our audience, too, because you guys always have a lot of great things to say when it comes to technology. And I always appreciate your ability to explain these things in a way that our audience, I think, can get a lot from these. It's not you guys can go in depth when you need to, but at the same time, you can talk high level. So I think a lot of this conversation today will be high level, but then we'll get into some of the details, too. Our audience can learn more and follow Doug and G of Deep Waterassed Management and also learn how Doug is and his team are building and constructing investment strategies, benchmarks. And actually, we're going to, I think, have an opportunity to look at a pretty cool tool that you guys built over at IntelligentAltah.com. So a lot to get through today. Thank you very much for joining us. And we wanted to start, I want to start Doug with, and you wrote this tweet, which we talked about. I think last time you were on, and we'll put up on the screen here, but you know, you wrote, and this was back at the end of 2023. My highest three to five year conviction idea is that AI will culminate in a bubble bigger than the dot com bubble. It's the nature of major tech innovations to create bubbles. AI isn't close to a peak or a 1995. And I would think, you can correct me if I'm wrong. I mean, that's, that's kind of going according to plan. Wouldn't you say? And then I guess, you know, what do you, is anything sort of change your view on this or what's the current state from your perspective? I would say so far so good in terms of the prediction that, you know, AI will ultimately be a bubble. Maybe it's a weird thing to say when you're sort of predicting a bubble. But the thing I think that has changed for us is if, how the end of 23 was 1995, that would imply we're in, you know, 1998 now. I don't think we're quite in 1998. I think it might actually still be closer to 1995, 1996. I think there's probably still more room to go on the AI trade setting aside. When do we get to a bubble? I think there's probably still a few years left in the trade when we think about what are the bottlenecks in terms of building data centers in terms of powering data centers. I think it's probably the biggest bottleneck. But then also the demand that we're seeing from these services. I mean, cloud code, I think is totally unleashed the ability of AI to really be effective in enterprise and productivity. And we're just really starting to see the beginnings of that being adopted in enterprise. What are your thoughts on, and maybe you can explain what the cloud, Mithos sort of is in the technology behind it and sort of how big a jump some of these new models are in terms of the development, you know, as users of AI, we're kind of stuck in the current models that we have. But I mean, I know you guys know and test and look at some of these frontier models. And then is there anything to be said for that development tying back to some of the incredible performance we've seen out of the semiconductor stocks and sort of other related stocks in the market? I do think there is a tie there. And really if you try to find like what was the real catalyst to a lot of the run that we're seeing now, especially in the semi side, I think it was probably when anthropic released Opus 4.6. It was late last year. And there was something about that model where I think it made this idea of using a coding tool, a coding agent accessible to the mainstream. Like you didn't really have to know that much about programming to really build code at that point. The reason that's important in my mind is if you think about every sort of knowledge work that someone might do, I think it's all reducible to a computer program. And so being accessible, making that concept accessible, describing what you need to do, having it reduced to code and then just having a machine do it, I think that was a totally new paradigm that really happened in November. I think it started to then really spread to the masses, you know, the early people got it in November, December, January. I think it really started to catch fire and spread to the masses in roughly March. And that has coincided with this huge rally we've seen in semis where I think the light has just turned on for a lot of people that AI is truly powerful. We've had this question dogging AI for two years now since this really began of when will we see the productivity gains? When can it actually do something useful? And we are absolutely in the days of utility now. And I would even argue, you know, you kind of asked about the progress of the models. I think a year ago these models, you could compare them to like a high school graduate. I think now the models are probably equivalent to someone who has graduated college maybe two years in the workforce. And by the end of the year, we'll have models that are people who are well 10 year, five, 10 year employees, PhDs. That's how fast it's getting done. Does that mean we're going to be at general intelligence? Well, you know my, my, uh, quirks around the idea of general intelligence. Like I, you could make an argument that we're in general intelligence now. But so many of these debates about AGI super intelligence are very semantic because I don't think there's one uniform definition of like what is a GI? What is super intelligence? What I would tell you is if you go and use any of these models today, they are capable of probably answering or figuring out, you know, 90, five to 98% of whatever you would throw at it with pretty decent accuracy. And so I mean, is that general intelligence that seems like pretty intelligent to me? Yeah, I think it's a kind of silly conversation, but it's one that kind of orbits around the utility of these models is when we get to general intelligence or some, yes, they hallucinate humans make mistakes too. I want to pick up on another point you made Doug, you talked about that kind of explosive growth that happened with the clog code and the new model back in November. And of course, inthropics revenue going from a nine billion to 45 billion run rate over a four month period. That's like breathtaking. So you said, I think you said mass adoption or widespread adoption. Like the reality is is that we're still not, when it comes to vibe coding, like when you say mass adoption, you mean like within people who like experimenting with tech, it's not the average person has no clue how to even spin up and start clog code. Yeah, I think when I say mass adoption, I mean more at the enterprise level and you just reference those anthropic numbers, you know, going from nine to mid 40s in just a few months. I think that is the definition of sort of wider spread adoption at the enterprise because almost all that revenue is incrementally from enterprises that are deploying these models. And I mean, a few kind of just anecdotal data points there. I think it's really important. The CTOs of both Uber and ServiceNow have both said that they basically burned through their entire budget for inference this year in like the first four months of the year. Oh my goodness. And now they have to go back to the drawing board because their company is and their employees where they're giving these models to they're finding so much utility now in using cloud code or codex that the amount that they probably needed to budget was like two, three, four, five X, what they did. And so think about what that means for forward numbers and demand. I talked to I'm not going to name the company. I'm just going to give a range of a tech company that has a market cap somewhere between five and 25 billion. I want to give a nice comfortable range here, but it's a real company. And they mentioned that they think that automation could have a massive impact on their white color, their knowledge workers. And I guess the question is we think about these models getting smarter. Does it matter that there is the whole unemployment thing or the impact of jobs? Because I think that's what I hear in this conversation is like, what does it mean
for me, a lot of knowledge workers listening to this. How do you think people should view what some of what we've seen, some what we're picking up on, looking at how smart the models are? I think AI for any individual, it can either supercharge you or it can make you irrelevant. It's about that binary, in my opinion. And so anybody who is worried and they haven't yet really adopted and embraced these tools, I think you need to go as fast as you can in the direction of figuring out how to use them to do your job better. Because I mean, we've always had this thesis. I mean, you know, I've talked a lot about this at Deepwater and Telgin Alpha. It's 80/20. It's Pareto again. The 20% of employees who are super high performers who figure out how to use AI, they're still gonna be very valuable to companies. But it's the 80%, right? It's the marginal person. It's the person who's maybe afraid of AI. That someone who's just kind of skeptical, I think that those people are in danger, especially in the knowledge work side. And so there will be disruption. - But ultimately, they just get religion and then able to kind of keep their job or do their jobs go away and doesn't matter. - Some of them have to go away, I think. Yeah, I think some of them have to go away naturally. If AI is as good as we say it is, if AI is good as we all think it is, it will replace some jobs. But new jobs will come as they usually do for different tasks that the models can't do. I mean, we've talked about the idea of what data is useful and just kind of like conceptually. The most useful data in the world is data that the models don't have access to, just by definition. And so I think there will be jobs, we call them detectives, but people that go out in the world, can they find this useful unknown data that the models don't have and bring it back into the enterprise and give it to the models and then create them? - Segment of the workforce are detectives. - Maybe. - That's, by the way, that's the kind of question that people are asking a lot these days, which is if this is the most disruptive technology we've ever seen in a positive way, like how much is it gonna be disruptive in the short term to get there? And you know, with all other revolutions, the new jobs have come, but the question is, is the pain getting there gonna be a little bit more, or maybe a lot more than it's been in the past? Do you have any thoughts on that? - I'm Doug and I have debated this and I don't know where you're standing currently. My sense is that the next five years, there's gonna be more disruption than what we saw in other cycles. Of course, over the last 40 years, 40 years, 60% of the jobs did exist 40 years ago. So, like, this is how humanity works. You know, the detective MO starts to gain momentum. But my sense is there's gonna be some kind of a gap that will fix itself when education kind of changes, but it might be like a five year gap. And if I was gonna put some numbers around this, I think we see a step up and knowledge work on employment, I use that. I think that is important to look at because I think it's representative, how transformative and how useful these tools are. It's hard to say that because these numbers, we get numb talking about them, but they're like people's lives that are being disrupted and turned upside down. I do believe we're gonna see in the near term, we'll call it a five years, more acute knowledge work or unemployment than we saw around mobile or the internet. And, but I ultimately, I think it does fix itself because people realize you gotta get on board, I gotta become the detective. I gotta become the salesperson, the taste maker, and they will kind of the free hand of the market will push them to develop the skills that are necessarative to survive. - Doug, I want to ask you, when you and Gene enter the debate ring, does he enter with the main gene handle? - I'm probably usually meaner than Gene. The main gene is ironic for Gene 'cause he's like the nicest guy ever. Out in the main guy. - Doug was talking about the enterprise and what's happened with the Anthropic and a question was, we've seen openly, I really push codex and you can talk about some of the things that you've observed in terms of how good that is, relative to cloud code. What's Google doing on this front? We got IO coming up next week. Feels like there's still more focused on making search better and Google Cloud and I just haven't heard, maybe I'm missing it. Like what's their response to what's happened with codex and cloud code? - I think it's been unfortunately slow and I would give you this perspective and I think a lot of different enterprises use these tools in different ways. At Intelligent Alpha, we think codex is the best tool for actually writing code. So when we're putting something into production, we're using codex to build that product. When we're doing like product development, when we're doing kind of earlier on stuff, when we're ideating, we actually, at least I do often, I use cloud 'cause I actually think it's a little bit better of a thought partner than codex or GPT-55 at the moment, although 5.5 is really good. So I think you can kind of use these models in tandem. I think that's the best way to optimize them currently. But we've also tested and played around with Gemini and Gemini CLI, which is basically their competitor to codex or cloud code. And it's just not there. And I think it's actually a good point, Gene, where I think Google has done a very good job of integrating Gemini into search because a lot of people still just, we default to search. I default to search still all the time. I'll ask, literally, I'll ask an LLM-type question in my search bar, and I'll get a decent answer, using it from Gemini. - Oh, no, I know. - It works. So they have a really great advantage there, but I think that they certainly of the three that we're talking about of Anthropic, OpenAI, and Google, there's certainly the slowest, I think, to really embrace the sort of coding revolution and really the agentic revolution. - It does seem like on codex, when you talk to the Elite programmers, they were all cloud code people, and it does seem like you're seeing movement towards Cloud, towards OpenAI codex, from those Elite-type programmer people. - Yeah, it's funny. And we've always said this, because we see it as we use the models to do portfolio management tasks with the tools we build at Intelligent Alpha. But the different models do have different personalities. Certain models are better at better things. That's why there's all these benchmarks out there, and you see different performance. But I do think that that is becoming kind of an open secret, really, is that if you want to write code, if you really want to build a useful product that's going into production that's going to serve users, I think a lot of programmers are defaulting to codex if they have a choice. And if you're really just trying to do more product dev, then I think people are defaulting to Cloud. What's actually interesting in that paradigm is there's like a higher-der question, which is, well, what's the bigger market? Is the bigger market to kind of do the higher order thing and ideate on product and imagine things and maybe build simple products? Or is the bigger market actually building production apps? I don't know. I think you could make an argument for either one. Certainly right now, seems like the bigger market is for Cloud, but we'll see you over time. - So Jean on the model war, what are your thoughts? I'm going to defer to Dog. He's like, deep into this. What do you think, that's-- I'll tell you the current rankings in my mind are GPT 5.5, Opus 4.7, Gemini 3.1 and GROC 4.3 are in my mind, basically tied, and then there's everybody else. We test a lot of these models. - So you put GPT at the top? - For me, GPT is the best right now, yes. And before 5.5 came out, I would have told you that Opus 4.7 was the best. Cloud. So it does change. I mean, the leaderboard does change almost every time a new model comes out because each incremental new model does seem to be a little better than the one before. And think about the game too. I mean, these model builders all know. They're all testing each other's models. They're all paying attention to the same benchmarks. And so when OpenAI releases a model or an anthropic releases a model, they want to be as sure as they can that everybody's gonna sort of feel the same way. Like, hey, this one is this is the best. I have to navigate to this one again. And 5.5 is the most recent model and so they're king of the hill right now. - So one thing caught my attention today was this lawsuit that OpenAI has against Apple, basically saying that they've breached their distribution agreement. Apple, of course, is using more Gemini with Google. And they're gonna be, we'll probably hear at the beginning of June about them being able, you mean developers being able to more easily plug into different models. And my question is, isn't this a negative read on OpenAI if they're out trying to take little action on Apple? Like if things were like really cruising forum, wouldn't they just be like, we don't even need this, like the demands through the roof, but you mentioned, I kind of caught my attention when you talked about GPT being at the top of the board because I've got this, I agree there's like fits and starts. And by the way, rising tide, I'm a big believer that OpenAI is in a great position. I think this is a trillion dollar plus public company, but just kind of reading at least the current score. It just seems odd that they would try to pick a fight with Apple. - Well, I mean, you look at,
You look at Elon Musk's suit and open AI. But there's a lot of litigiousness, I would say, amongst all these companies. And you never know what angle they're trying to play. But I would say this. I think that, I mean, Jack asked a question a minute ago about, is it kind of winner take all as in zero sum? And I actually think that is related to what you're talking about, Gene. There's this perspective in the market that the model where it's not really zero sum. It's actually that there's going to be so much demand that whoever has capacity will be able to sell their capacity and therefore be a winner. Right? So let's say you're, let's say you have the best model undeniable, like you've won the game and nobody will ever catch up to you. You'll sell all the capacity that you, you have, right? But if the demand for intelligence is as big as it seems to be, you're probably not going to be able to fill all that demand, given whatever your capacity is, because other people have agreements to use data centers elsewhere, right? They have capacity elsewhere. And so then the second best gets their capacity filled, and the third best and so on and so forth. And so I've kind of, I think that that view, and I've heard a few people kind of talk about that, I actually think that view makes a lot of sense, given what we know about the market right now, which is the demand for intelligence, it feels like it's basically infinite. You know, all these model builders are capacity constrained at this point. And so, you know, if you have a model that is, it's really hard to do it this way, but let's just say it's 0.5% worse than the top model, but you have capacity, you're probably going to fill as much capacity as you have. That's my guess. Does this play into the whole XAI and Throbbit deal? Because XAI was one that did have capacity, right? And they've sold a lot of that capacity, Dan Throbbit? I think that's exactly right. And you know, if they had so much demand on their side that they were using that capacity, I don't think they would have sold it. I think that they are rational economic actors though. And they said, look, we have all this extra infrastructure we built. We need to do something with it. And I think they also got the additional chip of opening up cloud models to be able to use to XAI. Now, I think it's SpaceX AI. Internally, so that they could use cloud code, which is previously shut off to them and shut off to some of the other model builders. Forgetting about the revenue part of it, though, on the model, like the models leaving each other all the time, like do we expect eventually, like one of these companies will jump way ahead? Or do we think they're all, you know, going to just be racing each other in there? Or do we stay pretty similar over time? I think for the foreseeable future, I think they're going to be pretty close. I think they'll stay pretty close. They're all too large. They're going to be close today. We've got. Yeah. There's four, five is metano in that camp. Yeah, they're new model on benchmarks. I haven't really been able to play with it yet. We're trying to get API access on benchmarks. Their new model looks really good. Looks pretty capable. So we'd have, remember the eminent as model, I should know this. Will or something. No, it's, yeah, no, I'm like, is that what you want? Lama was the old one. Let's pull it up here. So we've got spark, muse spark. So we got GBT Gemini. Yep. Claude spark, Grock. Muse, yeah. And then you got. And then the, and then on the other side of the planet, you've got. By do. Yeah, Quinn. That's different. That's open open source stuff. Yeah. But kind of Western world, we got basically five horses in the race. In the language model space, that's correct. Yeah. And then you've got call it five open source, big open source players, largely in China. And then five years, are there should give me five, so orbiting around the hoop? I would say in two to three years, there's still going to be the general same structure we have. Five's hard. It's so hard to predict because it's moving so fast. Yeah. And I think like to, to, well, to Jack's question, though, like, here's where I think things could separate, because basically right now, all these providers are approaching the problem in roughly the same way. You know, they, they all use transformer architecture. So the models are, are built essentially the same way. They're for the most part trying to acquire the same types of data. So they're being sort of trained the same way. The one thing that I think is different right now, where it feels like anthropics moving faster, is that they're using the model to improve itself. So they've got this recursive thing going. I think open AI is probably pretty close to getting there too. If not already there, they haven't really talked about it as much. And I think Google, it feels like is in jemen, I are probably further behind on that front. And so if there was a reason for one of these companies to get really far ahead of the other, I think that is the most likely reason is that somebody figures out a really powerful, you know, recursive loop, where the model is just training itself super efficiently. And the other providers don't figure that out because they're really not doing a whole lot that's different unlike the training or the data side. And talking about the horse race of the models, this is probably a good time to pivot to intelligent alpha because you've got your own little race. You're doing here in terms of this, but before we get into that, can you just talk about what intelligent alpha is and what you're trying to do there? Yeah, we started the project of intelligent alpha about three years ago. So it was mid 2023. It was a little after a chat GPT came out. And we had this thesis that we wanted to figure out if language models could be good investors, because they just beat the S&P 500. So we ran a bunch of tests. The tests looked very favorable and now kind of fast forward three years. We have two investment funds that we run using our language models, using our AI process, two analyst docs, pick stocks, manage the portfolio and end. And within that, add intelligent alpha, a ton of the work we do is actually in assessing these models, right? We want to know which ones are good at picking stocks and which ones aren't as good and why are they good? Why are they not good? And so we actually just launched a new product called the intelligent earnings benchmark, where we use 12 different models. So we're just talking about the 10. There's a couple more that we kind of fit in there. But it's all the big players we were just talking about like OpenAI and Claude. We also use a lot of the Chinese open source models to see how well they stack up. And we test them on the ability to predict a company's forward earnings, kind of the direction that those earnings are moving with the insight, hopefully being that if you get the earnings direction right, you probably get the stock right. It's really really cool what you've done here because you're basically looking at each model individually and you're looking at how good it is at predicting these forward estimates, right? It's exactly right. And so it's scrolled down here. So we have kind of our leaderboard if you if you visit our site and tell gen alpha.co. We have our leaderboard here where we've run this process for several different quarters. We've got it going back to Q3 of 2025. We'll publish some of that data very soon. But we test about 700 different stocks across all these models. >>GVT. >>Yeah, and Gene didn't even cheat. I know he didn't look at those before. GVT is the top. And so we test these models just directionally. Did they get if earnings are kind of moving up or down? And then we also test magnitude, small, medium, large. We have buckets that have, you know, bands of what percentage that might mean for the accuracy. But yeah, as you can see, and we've seen this, I'd say, across most of our testing, there is a pretty consistent run for GVT. They've consistently been kind of the best model at the top. And often we're also seeing that the closed source models, so the American models from OpenAI, Anthropic, Google, and XAI, they all seem to stand out above the closed source models. Gene think is a good thing. It's probably what you would expect given how much money is going to training these models. You would hope they'd be better at a general task like this. And so far, through our testing that has been true. So this is all financial statement type of data that's being done, doing natural language processing on earnings calls and stuff like that too. What do the inputs, I guess? That's right. So we have basically built what's called a harness. And the harness is essentially a system where the LLMs can access a packet of data that we've prepared. So the data includes some of the things that you just talked about Justin. The last transcript of earnings. What are some of the current estimates? Basically, what is the street expecting for revenue and EPS, historical financial statements, things like that. We package that all up into a consistent query that each of the models, they all get the same exact thing. So it's a fair test. And then we have them for each of the 700 stocks, make their guess of where we'll revenue and earnings both go over the next quarter. What do you attribute the outperformance? I mean, it's been a consistent outperformance actually getting
wider more recently. What do you attribute that to? It's, I think a few things and I'll give you a few also just observations as we've done this benchmark and use this internally. As we get these new model paradigms like kind of like we talked about earlier, you know, 5.5 seems to be better than 5.4 if we compare them head to head and 5.4 was better than 5.1, which is model before it. Same thing has been true for Anthropic with Opus 474645. And so I think part of the reason is the models are they're just literally getting better. They're just getting, you know, smarter, which is the most general term I could use. And that smarts that general intelligence, I think is reflecting and accepting this data and saying, okay, here's, here's the data that's been given to me. We're talking about base rates before we start of recording. Here are the base rates, right? What are the expectations both for this company and also for the universe of large cap stocks? And here's what seems to be most likely to happen. So they're getting better, I think just at that as kind of a general task. Do you think that fact that machines aren't emotional, you know, and the asset management business were in that business, you stride to be objective and unemotional. But when you do introduce an idea to a portfolio, there's a natural feeling of wanting it to succeed. And I'm curious, do are the models quicker to cut off of a company, cut bait sooner than you think a human would? Yes, is the short answer. Yeah, there's no sort of endowment effect that these models suffer from. They don't have any sort of bias because they did a bunch of work on something. Yeah, thinking of this more valuable, just because you own it already. As far as earnings though, you know, like there's an adjacent thought to that, which is these models aren't emotional. But there's a funny byproduct to that, which can be a negative, right? Not emotional. Emotional. They're not emotional. Yeah, and there can be a negative byproduct of that, which is when you need to make a really high conviction call. Like, do you think a company is going to crush earnings? Some of these recent semi-stocks that we were talking about earlier. There is a little bit of like a faith and an emotion in there, because again, I go back to our conversation about bass rates earlier. That's not going to be in the data. The model is going to feel like that's a risky call to say, you know, whatever, Lumentum is going to have an incredible quarter because the demand for optics is just off the charts right now. And so they might be earnings by, you know, 30%. The models are going to be really, really hesitant to make a call like that because it just happens so infrequently in the data. So that's kind of the other the other side of the sort is on average, these models are right very often. I think they're probably right more than the average human, but the average human might still have a really good like slugging ability. Like if they get one call really right, they can still make sense. So think of like GPT is more, it's not going to be up 40% in a year when the market's up five, but it's going to hopefully outperform kind of on a steady basis. Yep, that's right. So we're between it big swing. So we're between traditional quant and human. Yeah, it's what I kind of know. I think it's like the model. So the models that are best at like predicting the earnings revisions are those the same models that are the best at picking stocks. So do you see like different leaders in different areas? It's fun. It's actually it's it's really a great question because it is a little different. And so we look, we can kind of categorize that in two ways. Number one, the best two stock pickers and this is something we haven't published yet, but I'll give a little preview. The best two stock pickers since we started doing this are Claude and GPT in that order. And that goes back to 2023. A lot of different iterations of the models. And I would say Claude actually can't gain some more ground more recently when their models were more powerful in my opinion than GPT. So yes, there is a little bit of a difference. And then there's some things we do in a children awful. We take the earnings prediction as like one signal. And we put that into our process with a bunch of other signals and kind of marry it with other data. And so the way we kind of use the models to use this particular prediction is a little bit more like a human. You know, this is kind of one angle, right? Our earnings going to be good or bad. And then what is the relative valuation? I might look at momentum of the stock. Do I think some of it's already priced in? You know, maybe earnings are going to be great, but maybe everybody already knows it. We kind of try to create a framework for the models to be able to think about things like that. But this is the fun part to answer your question. If you actually take all that stuff away and just say, let's make a portfolio of the predictions for earnings, assuming that that is where stocks generally go. The best performing model was actually deep seek so far. Interesting. In our tests. Yeah. And they were actually, if you go back to our screen, if you visit our website, they were actually in kind of the bottom half of accuracy. So they had good slugging as we kind of think of it. They had some of the big calls really right. If we take a step back to like investing in AI overall right now, like, how are you guys thinking about, like, I guess you're looking at stuff across everything, but like, how are you thinking about like, where in the stack to invest? Like, many people have said, like, we'll move down from the infrastructure layer. We'll move to like applications and other stuff, but it seems like the infrastructure layer is still like on a massive tear. So like, how do you think about that? Yeah, I'll give my quick take into Gene. You sounds good. Yeah. Bill and I, because we have, we have, I think, lateral thoughts in it. I think about AI, like, the moment right now, what AI really means, as a first principle is we're converting electricity into intelligence right now. Like, that's exactly what's happening. And so if that's true and the demand for intelligence is seemingly infinite, I think the demand for power is seemingly infinite. And the thing that I feel most confident in still when we talk about this AI trade cycle is that we are woefully underbuilt for energy of almost all kinds, whether we talk about Nat gas, I think nuclear almost has to be a big part of the solution to power all these data centers that we're building that might mean small modular reactors. It might mean other things. I think alternative energy as well storing that is is a huge challenge. There's a company that we've invested in in our private funds at deep water and our venture side called Antora that does solve the state storage. I think that's going to be a huge theme. And so power to me is the thing that just, it makes the most sense that the demand is insatiable. It won't go away. Even if, you know, we start building data centers in different ways, if the model architecture has changed, if all these other things might evolve, the demand for energy probably doesn't. Mike, you know, there are a lot of different data points you can pull out on this topic of like, how much further do we have to go? A couple guide, or maybe guide posts along the way here. One is that the currently we're getting stopped out and using these models more frequently today than we did a year ago. So within intelligent alpha. So what that means is demand is dug into, talked about before, demand for the models is outpacing infrastructure. So we know we need more infrastructure. The second is that if you look at the kind of the key marker for this, it's cat backs by the hyper scalars cat backs growth. A year ago at this time, the expectations were that they would grow cat backs and count or 26 by 10% over 25. It's probably going to be up 70%. As it looks today next year, the streets looking for about 10% growth in cat backs next year. And our sense is it's probably going to be closer to 20 to 30. It's not going to be 70, but it's still going to be much higher than what people expect. In part because there's still as cash flow from these hyper scalars to continue to make these investments. On top of that, outside of the hyper scalars were seen industrial AI being built and sovereign AI. And so we kind of put all this together. The brain, we think of the data centers as the brain of AI. And like the apps are an inference is the thinking around it. But the brain still is going to expand more than what people expect. Quick finder point on that energy conversation, crash course on energy in the US. 1958 was the first nuclear power plant. And they basically ran a bunch of them. I think there was something like 50 of them or so were built till the mid 70s. And during that period, the average increase in output of energy in the US grew on average 7% a year. I mean, it is, that's like wicked increase in growth. From essentially 1985 till 2022, it was essentially flat. More people, but more efficient HVAC systems. And so we basically saw that flat lining over the next seven to 10 years. This is from a White House paper, also a Goldman report talks about that averaging increasing by about 3% a year, a little bit over 3% a year. And that might not sound like much. But 3% is a massive investment cycle. And so. Set a different way is that a lot of times the AI infrastructure conversation centers around GPUs and optical components cooling things like that, but this energy play is Even though it has had a move higher is still underappreciated by Wall Street Yeah, it was gonna. I was gonna revisit some of the Jeans predictions from the beginning of the year You've already you've mentioned something you got right here, which is one that a cat-backed growth was gonna be very strong Which I think we're we're definitely gonna be right on that one and then as that being up 10% or more at least so far You're you're in good shape on that one A third one we talked about though was IPOs And you would kind of at that point decide that I think that that one was not gonna be right because you figured these companies might come out But that's that's in the news all over the place right now space X and then maybe the you know, Anthropic and open AI like What are you guys thinking about that? I mean, do you think those are gonna IPO this year? I think they will and and we're recording this today on the day of cerebrus's IPO Which um last time I looks which was probably an hour ago. I think the stock was up 108% So they've had a good day Anybody who got in the IPO Good sign for future IPOs. Yeah, I think that's the bottom line is to me. I think that's a signal that Not that like a space X or an Anthropic or an open AI needed and all clear But I do think maybe the second tier of companies that might think about going public they have to feel pretty With their prospects at this point after seeing the demand for cerebrus So you think about a company like data bricks or maybe some of these other, you know, coding tool companies who have Meaningful revenue in any sphere which is cursor or cognition which has a product called Devon You know, these are companies that are valued in the tens of billions already Um, and I think if they went public I would have to imagine there'd be a lot of excitement around them just like there is around cerebrus Do you think these like do these IPOs have an impact on the overall market like we've never seen I assume these will be the three biggest IPOs of all time right when they come out um like How does that impact on the market when you IPO companies of three companies of that size? You mean like what does it mean for the mega caps? Yeah, we look to assume that source of funds right Could be and I think the indexes. I mean, it's a huge question for them and it just as one Reference data point uh, a ramco Saudi ramco. I think in terms of size and market cap was the biggest IPO Uh, ever. I think SpaceX will probably Uh, will probably eclipse that pun intended um, but you know ramco. I think it was like a trillion dollar plus IPO For reference the stock actually was up about 30% From the day it issued to about two weeks in and then kind of the market fell apart a little bit So even for these massive companies. It's not out of the question that you could have a pretty healthy move very early on Um at a trade sex is so much more exciting too Sir agreed. Yeah, bias, but agreed um, but you know, I think that what it means for the markets What it means for potentially the other mega caps is as they get included in the indexes and there's a lot of talk about how particularly for like the qqq the nasak 100 index Um, there will be an early inclusion 15 days in for SpaceX I would imagine that open AI and anthropic probably get a similar deal and I think you then do have probably a little bit of a source of funds coming from some of the other mega caps because those indexes are going to have to sell down and adjust their weightings across the various companies to get these new big guys in there What do you I wanted to ask kind of probably a boring question, but it's one that I've been thinking about sort of Up until like maybe a month or so ago I thought that the market was kind of maybe penalizing some of the mag seven And the hyperscaler for their investment into this and kind of questioning, you know, what is the payback going to be but then like I don't know if it was the earnings They're quarterly earnings that came out gene you might have even been on cnc that night on fast money or something like that because it was such a big earnings day And I feel like now it's it's the you know the at least the price performance has seemed to You know rotate back to the the mag seven is is that kind of like right and I guess what are your thoughts and And thinking through that I was like well, maybe Apple is the is the play here because They're really not going aggressive into their You know cap expending and I thought the market might actually like reward that but it seems to have flipped the other way So I don't know if you're you have well Google had a step up to and just to kind of set the stage is that if we look at tesla microsoft Amazon Google and meta those five and tesla is usually not included in the broader hyperscaler conversation, but is relevant to this topic is of those five tesla talked about their CapEx this year being more than 25 billion three months ago. They said it was more than 20 billion and Stock traded down on that comment like meaningfully it's three or four percent on that comment Metta bumped up. I think it was like from the hind of their range from 175 to 185 billion this year stock traded down on it Those two companies don't have cloud businesses google I believe they raised their bumped up with their expectations were like materially increased What they expect for CapEx this year and microsoft did too Amazon more or less was a watch But both those companies, you know This the stock if you look in after ours trading when those comments were made They take it took a few minute dip and then just came right back So there may be something around Investors feeling there's like a faster return on CapEx if you have a hyper if you have a cloud business that's but that's about the I think the through line just in terms of how it trades around the quarter You know the bigger picture is like the real takeaway here is that competent people believe that this is going to be more disruptive than what the market believes What the analysts believe that all street expectations are because they're putting their money with their mouth is And so I see that as you know, it's a very basic question that investors should ask themselves Do you believe the management? In these companies is competent at seeing the future and if the answer is yes, then They should be rewarded for making these investments and if the answer is no then they should be Penalized for that That I think you could actually even make an argument if you look at I'll leave a narrow our set to just the hyper scalers the cloud providers google amazon microsoft One month on their stocks basically back to when they reported earnings to today Microsoft is the worst performing then amazon and google google is the best performing And I think the part of the reason for that is going back to this excitement around anthropic anthropic obviously premiere partner early partner with amazon So if you're using a ws Arguably most likely if and you have an a i product you might be on anthropic tooling Google they've signed a deal they've made investments anthropic and so my my gut is Part of the answer to that question is sort of what I think jeans are looting to is All of them are building out this infrastructure. All of them are seeing this massive demand And two of them are seeing massive demand correlated directly to the hottest company in the space which is anthropic And I don't think it's an accident that their stocks are probably the two that the performed better than Microsoft which is really tied to opening eyes still What do you guys think about um the opportunity in Some of these second order space dot like the stocks that are currently on the market today that are You know have business lines or we'll have business lines in spaces or anything there that um Kind of gets you excited. I personally am excited. I'm very interested in like seeing And if you know what comes down the road with that and so I think You know, what's the investment opportunity? I guess now and in the future there and I think if you you know have a minute if there's a company's business model that You know you know of that is like really unique. I think it's it's it's it'd be a good discussion because I think there are things happening There's things coming down the pipeline that you know most of investors know nothing about so I'd be very interested in your thoughts on that Tell you want to talk about that early stage investment we made On and can I like imagery the satellite imagery company Yeah, yeah, well, I'll talk about a few different things um It I think like space to to us is exciting because um it opens up This potential for new avenues to create energy going back to kind of what is like one of the fundamental sources of things we need energy provides um the Sort of ballast to create so many things in our lives And also productivity right so it's like those are maybe two things people don't immediately think of when you talk about space Because it's like well, okay, we're just we're going to space is awesome. I was like no What is what is the purpose of going to space? I think those are two of the big things that you get from Yeah figuring out novel ways to extract energy from the universe And figuring out new ways to be productive those are the big things and so from an energy standpoint I'll go to the the Everybody's favorite topic to either really love or really hate but orbital data centers um
Google is rumored to be in talks with SpaceX to potentially create some orbital data centers. I think whether you believe in the physics of it or not is almost irrelevant at this point. I think the question is, is somebody going to try it and if somebody tries it, it's going to be SpaceX almost undoubtedly. And I think they will try it. And we should hope that they're successful because if they find that space is a place where we can put a lot of these data centers, one of the biggest issues with building a data center right now is getting local permitting done. It's brutal, no towns want to allow a data center in their backyard. And so if we can put them in space and if we can maybe even power them more efficiently in space in the atmosphere, that'd be a win for everybody. It'd be amazing. And so that I think as an overarching concept is probably the most exciting reason to go to space right now. The other one that I would give you is, and this has long been kind of this discussion about, if we go to space and we're working in these zero gravity environments, does that open the door to maybe creating products that we couldn't create on Earth where gravity is an issue? And I just saw this the other day, there's a company called Varda, which is a space company. They partnered with United Therapeutics to potentially develop and create drugs in space. I mean, I think that's really cool. Again, who knows if it works, but I think we should hope it works in that it creates something novel and new that we could never have done if we just maintain production on Earth. So it's those kinds of really, they're almost hard to conceive of things, but I think those are the things that we should be really excited about. We think about the space opportunity. Would you guys bet, yes or no, if we had to bet on like data centers in space like five years from now? I would say that there, yeah, I think there'll be prototypes that will be the performance will be pathetic, but they will be operational, which means that eventually we get there. It'll be effectively like 32 megabytes of internet. I bet there's more than five operational, but less than 25. I'll give you a range. I think that's about right. Jack, you and I should have an internal goal about doing the first podcast from space. What do you think? We'll be down around it. It'll be great. All right, guys, thank you very much. We always appreciate you coming on, sharing your thoughts with our audience, and we hope to see you soon. Can't wait. Thank you for tuning into this episode. If you found this discussion interesting and valuable, please subscribe on your favorite audio platform or on YouTube. You can also follow all the podcasts in the access returns network at accessforturnspod.com. If you have any feedback or questions, you can contact us at
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