He Predicted the AI Bubble in 2023 | Doug Clinton and Gene Munster on Why We're Still in 1996

Added: 2026-05-19

[00:00:00]what AI really means just 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 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 uh they should be rewarded for making these investments. And if the answer is no, then they should be uh 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 worker unemployment than we saw around mobile or the internet. And uh but I ultimately I think it does fix itself uh because people realize you got to get on board. We test about 700 different uh stocks across all these models and I'll give you the drum roll to see any guesses on on who's the top model right now before I re reveal it.

[00:01:08]Doug Jean, thank you guys very much for coming back on excess returns. You are in high demand these days. So, and >> the fact that we can get you for, you know, 45 or 60 minutes is we really appreciate it and our audience does too cuz you guys always have a lot of uh great things to say when it comes to technology and I always appreciate your ability to you know explain these things in a way that our audience I think can get you know a lot from these. It's not you guys can go in depth when you need to but at the same time you can kind of talk talk high level. So I think a lot of this conversation today will be high level but then we'll get into some some of the details too. Um our audience can learn more and follow Doug NG of Deep Water Asset 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 intelligentalfalpha.com.

[00:02:07]So um 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 um 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 3 to 5 year conviction idea is that AI will culminate in a bubble bigger than the dot bubble. It's the nature of major tech innovations to create bubbles. AI isn't close to a peak. We're in 1995.

[00:02:35]And and I I I would think um you you can correct me if I'm wrong. I mean that's that's kind of going according to plan.

[00:02:43]Um wouldn't you say? And then I guess you know what do you is has anything sort of changed your view on this or or what's the current state from your perspective?

[00:02:52]>> 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 um the thing I think that has changed for us is if kind of 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 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 is probably the biggest bottleneck but then also the demand that we're seeing from these services I mean claude code I think has totally unleashed the ability of AI to really be effective in enterprise and productivity and uh we're just really starting to see the beginnings of that being adopted in enterprise what are your thoughts on and maybe you you can explain what the Claude mythos sort of is and 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 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. Um, 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?

[00:04:33]I I do think there is a tie there and really if you 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 six. It was late last year. And there was something about that model where I think it made this idea of using a 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. And 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.

[00:05:51]We've had this question dogging AI for 2 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 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 uh 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 tenured 5 10 year employees PhDs that's how fast it's getting done. Does that mean, Doug, does that mean we're going to be a general intelligence?

[00:06:36]>> Genius, you know, my my uh quirks around the idea of general intelligence. Like I I you could make an argument that we're in general intelligence now. Like so 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 AGI? 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, 95 to 98% of whatever you would throw at it with pretty decent accuracy.

[00:07:15]>> And so, I mean, is that general intelligence? That seems like pretty intelligent to me.

[00:07:19]>> Yeah.

[00:07:19]>> Yeah. I think it's a 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.

[00:07:33]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, Enthropics revenue going from a 9 billion to a 45 billion run rate over a four-month period. That's like breathtaking. But you said uh the uh 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 uh spin up and start cloud code.

[00:08:15]>> Yeah. Yeah, I think when I say mass adoption, I mean more at the enterprise level and and you just referenced those anthropic numbers, you know, going from 9 to mid40s in just a few months. I think that is the the definition of sort of wider spread adoption at the enterprise cuz 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 are really important. The CTOs of both Uber and Service Now have both said that they basically burned through their entire budget for inference this year in like the first four months of the year.

[00:08:53]>> Oh my goodness.

[00:08:54]>> And now they have to go they have to go back to the drawing board because their companies and their employees who they're giving these models to, they're finding so much utility now in using Cloud Code or Codeex um that the amount that they probably needed to budget was like 2 3 4 5x what they did. And so think about what that means for forward numbers and demand.

[00:09:15]>> I I talked to uh 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 uh they mentioned that they think that automation um could have a massive impact on their white collar uh their knowledge workers.

[00:09:43]And I guess a question as we think about these models getting smarter, does it matter that there uh does 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.

[00:10:02]>> How do you think people should uh view what some of what we've seen, some of 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 it's about that binary in my opinion.

[00:10:20]And so anybody who uh 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 I we've always had this thesis. I mean G and I have talked a lot about this at Deep Water and Intelligent Alpha. Um, it's 8020. It's it's paro again. The 20% of employees who are super high performers who figure out how to use AI, they're still going to be very valuable to companies, but it's the 80%, right? It's the marginal person.

[00:10:56]It's the person who's maybe afraid of AI. It's 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. Ultimately, do they just get religion and then able to kind of keep their job or do their jobs go away and does it matter?

[00:11:14]>> 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, uh it will replace some jobs, but new jobs will come as they usually do uh for different different tasks that the models can't do. I mean we've talked about the idea of what what data is useful 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 that the models don't have and bring it back into the enterprise give it to the models and then create >> 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 going to 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 going to 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 Doug and I have debated this and I don't know where you're where you're standing uh currently my my sense is that the next five years there's going to be more disruption than what we saw in other cycles. Of course, over the last 40 years, 40 years, 60% of the jobs didn't exist 40 years ago. So, like this is how humanity works. You know, the detective MMO starts to gain momentum, but my sense is there's going to be some kind of a gap that will fix itself when education kind of changes, but it might be like a fiveyear gap. And if I was going to put some numbers around this, I think we see a step up in knowledge worker unemployment. 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.

[00:13:21]I do believe we're going to see in the near term, we'll call it a five years, more acute knowledge worker unemployment than we saw around mobile or the internet. And uh but I ultimately I think it does fix itself uh because people realize you got to get on board. I got to become the detective. I got to become the salesperson, the taste maker. and they will uh kind of the free hand of the market will push them to develop the skills that are necessary to survive.

[00:13:51]>> Doug, I want to ask you when you and uh Gan enter the debate ring, does he enter with the mean gene handle?

[00:13:56]>> I'm probably usually meaner than Jean.

[00:13:58]Mean Jean's ironic for Gene because he's like the nicest guy ever. I'm the mean guy. Doug was talking about the enterprise and what's happened with Enthropic and a question was we've seen open AI really push codeex and you can talk about the 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.

[00:14:23]you know, feels like they're still more focused on the making search better and and Google cloud and I just haven't heard maybe I'm missing it like what's their response to what's happened with codecs and cloud code.

[00:14:37]>> You know, it's I think it's been um unfortunately slow and I would give you this perspective and I think a lot of different enterprises use these tools in different ways. Um, at Intelligent Alpha, we think Codeex is the best tool for actually writing code. So, when we're putting something into production, we're using codec to build that product.

[00:14:59]When we're doing like product development, when we're doing kind of earlier on stuff, when we're ideulating, we actually, at least I do often, I use Claude uh because I actually think it's it's a little bit better of a thought partner than uh Codeex or GPT55 at the moment, although 55 is really good. Um, so I think you can kind of use these models in tandem. I think 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 codeex or cloud code, and it's just not there. And I think it's actually a good point, Gene, where, you know, 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.

[00:15:44]I default to search still all the time.

[00:15:46]I'll ask like literally I'll ask an LLM type question in my search bar and I'll get a decent answer usually from Gemini >> by the way that >> it works. So, so they have a really great advantage there, but I think that they certainly of the three that we're talking about of uh anthropic, open AI and Google. They're certainly the slowest I think to really embrace um the the sort of coding revolution and really the agentic revolution. It does seem like on on codeex like when you talk to the elite programmers like they were all clawed code people and it does seem like you're seeing like movement towards claw towards open AI codeex from like those elite type programmer people.

[00:16:24]>> Yeah, there's it's funny like and we've always said this because we see it as we use the models to do you know portfolio management tasks uh 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 is becoming kind of a an open secret really is that if you want to write code, if you really want to build um a useful product that's going into production that's going to serve users, I think a lot of programmers are defaulting to codeex if they have a choice. And if you're really just trying to do more product dev, then I think people are defaulting to to claude.

[00:17:05]What's actually interesting in that paradigm is is there's like a higher order question which is well what's the bigger market? Is the bigger market to kind of do the higher order thing and and ideulate on product and imagine things and maybe build simple products or is the bigger market actually building production apps. I I don't know like I think you could make an argument for either one. Certainly right now seems like the bigger market is for Claude but we'll see over time.

[00:17:32]>> So Gene on the model war what are your thoughts?

[00:17:35]We defer to Doug. He's like deep into this. What do you think, Doug?

[00:17:40]>> Yeah, I I'll tell you the current rankings in my mind are GPT 5.5, Opus 4.7, uh Gemini 31 and Gro 43 are in my mind basically tied. And then there's everybody else. Um you know, we we test a lot of these models.

[00:17:59]>> You put GPT at the top.

[00:18:02]>> For me, GPT is the best right now. Yes.

[00:18:05]Um, and before 55 came out, I would have told you that Opus 4.7 was the best.

[00:18:12]Applaud. So, 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 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 Anthropic releases a model, they want to be as sure as they can that everybody's going to sort of feel the same way like, hey, this one is this is the best. I have to I have to navigate to this one again. And and 5.5 is the most recent model and so they're king of the hill right now.

[00:18:50]So what 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 going to be we'll probably hear at the beginning of June about them being able you being 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 legal action on Apple? Like if things were like really cruising for them, wouldn't they just be like we don't even need this? like are the demands through the roof, but uh you mentioned it 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. And 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.

[00:19:58]>> Well, I mean, you look at you look at um the Elon Musk suit in OpenAI. There's >> there's a lot of latigiousness, I would say, amongst all these companies, and you never know what angle they're trying to play.

[00:20:11]>> What I would say is this. I think that I mean Jack asked a question a minute ago about um is it kind of win or take all is it zero sum and I actually think that is related to what you're talking about Jean um there's this perspective in the market that um 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 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 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 if you have a model that is and 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 as you have.

[00:21:48]>> That's my guess.

[00:21:49]>> Does does this play into the whole XAI anthropic deal? Because XAI was one that did have capacity, right? And they they've sold a lot of that capacity to anthropic.

[00:21:57]>> I think that's exactly right. and and you know if if um 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 you know and 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 uh the additional chip of opening up clawed models to be able to use to the to uh XAI now now I think it's SpaceX AAI um uh internally so that they could use cloud code which was previously shut off to them and shut off to some of the other model builders.

[00:22:34]Forgetting about the the revenue part of it though on the model like the model's 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 and they're going to be 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 we've got >> yeah there's four five is Meta now in that AMP.

[00:23:04]>> Yeah, their their new model um 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.

[00:23:14]>> So, we'd have uh remember the name of Meta's model. I I should know this.

[00:23:18]>> Willow or something.

[00:23:20]>> No, it's um Yeah. No, I'm too >> llama was the old one. Um >> yeah, let's pull it up here.

[00:23:29]>> So, we've got >> it was Spark. Muse, Spark, >> Spark. So, we got GBT, Gemini.

[00:23:35]>> Yeah.

[00:23:36]>> Uh, uh, Claude, Spark, Grock, >> Muse. Yeah.

[00:23:42]>> And then you got, uh, then the, and then on the other side of the planet, you've got BU.

[00:23:50]>> 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.

[00:24:09]>> And in five years, are there going to be five still orbiting around the hoop?

[00:24:15]>> I would say in two to three years, there's still going to be the general structure we have. Five is hard. It's so hard to predict because it's moving so fast.

[00:24:22]>> 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 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. Um so they're being sort of trained the same way. The one thing that I think is different right now where it feels like Anthropic is moving faster is that they're using the model to improve itself. So they've got this recursive thing going. I think OpenAI is probably pretty close to getting there too if not already there. They haven't really talked about it as much. Um and I think Google it feels like is and Gemini are probably further behind on that front.

[00:25:13]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 on like the training or the data side.

[00:25:35]>> And uh 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 um you're doing here in terms of this. But before we get into that, could you just talk about what intelligent alpha is and what you're trying to do there?

[00:25:47]>> Yeah, we started the project of intelligent alpha about 3 years ago. So it was mid 2023. It was a little after chatt came out and we had this thesis that we wanted to figure out if language models could be good investors. Could 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 uh two investment funds that we run using our language models using using our AI process to analyze stocks, pick stocks, manage the portfolio end to end. Um and within that at intelligent alpha, a ton of the work we do is actually in assessing these models, right? We want to know which ones are 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 were 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.

[00:27:10]>> 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.

[00:27:18]>> That's exactly right. And so if I scroll down here, so we have kind of our leaderboard. If you if you visit our site, intelligentalfpha.co, we have our leaderboard here um 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 uh stocks across all these models. And I'll give you the drum roll to see any guesses on on who's the top model right now before I re reveal it.

[00:27:53]>> Yeah, Jean didn't even cheat. I know he didn't look at this before on the bottom.

[00:27:57]>> Uh GVT is the top. And so um we test these models just directionally. Did they get if earnings are kind of moving up or down? Um and then we also test magnitude, small, medium, large. we have buckets that have, you know, bands of what percentage that might mean um for the accuracy. Um but yeah, as you can see, and and we've seen this, I'd say across most of our testing. Um there there is a pretty consistent uh run for GPT. 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, which I think is a good thing. It's 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 like financial statement uh type of data that's being fed or is doing like natural language processing on earnings calls and stuff like that too like what is it what are the inputs I guess >> that's right so we have basically built uh what's called a harness and the harness is um essentially a system where the the LLM can access a packet of data that we've prepared so the data includes some of the things that you just talked about Justin um the last transcript of earnings. Um what are some of the current estimates? Basically, what is the street expecting um for revenue and EPS, historical financial statements, um things like that. We package that all up into a 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, you know, where will revenue and earnings both go over the next quarter.

[00:29:54]>> 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 >> it's I think a few things and and I'll give you a few also just observations as we've done this benchmark and use this internally. um 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 before was better than 5.1 which was model before it. Same thing has been true for anthropic with Opus 474645.

[00:30:31]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 in accepting this data and saying okay here's here's the data that's been given to me. We were talking about base rates before we started recording. here are the base rates, right? What are the expectations both for this company and also for the universe of large cap stocks? Um, and here's what seems to be most likely to happen. So, they're getting better, I think, just at that as as kind of a general task.

[00:31:04]>> Do you think that fact that machines aren't emotional? You know, in the asset management business, we're in that business, you strive 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?

[00:31:35]>> Uh, yes is is the short answer. Yeah, there's no sort of endowment effect that these models suffer from. Uh they they don't have any sort of, you know, bias because they did a bunch of work >> on something. Yeah. Thinking something's more valuable just because you own it already. The um as far as earnings though, you know, like there's an adjacent thought to that which is um these models aren't emotional, but there's a funny byproduct to that which can be a negative, right?

[00:32:04]>> Not what promotional uh emotional. They're not emotional.

[00:32:08]Okay.

[00:32:09]>> Yeah. And there there can be a negative byproduct of that which is when you need to make a really high conviction call like if you think a company's 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'll go back to our conversation about base 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, um, whatever Lum is going to have an incredible quarter because the demand for optics is just off the charts right now and so they might beat earnings by, you know, 30%.

[00:32:46]The models are going to be really really hesitant to make a call like that because it just happens so infrequently in the data. And so that's kind of the other the other side of the sword 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 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.

[00:33:27]Yep, that's right. Somewhere between >> big swing.

[00:33:30]>> Somewhere between traditional quant and human. Yeah, is what I kind of how I think about the models.

[00:33:35]>> Quant and human.

[00:33:36]>> Are the uh Doug, are the models that are best at like predicting the earnings revisions, are those the same models that are the best at picking stocks or do you see like different leaders in different areas?

[00:33:44]>> It's actually it's it's really a great uh question because it is a little different. And so we look we can kind of categorize that in two ways. Number one, the 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.

[00:34:07]And that goes back to 2023. A lot of different iterations of the models. Um, and I would say Claude actually ca gained some more ground more recently when their models were more powerful in my opinion than GPT. Um, so yes, there is a little bit of a difference. And then there's some things we do at Intelligent Alpha. We 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 um the 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? Are earnings going to be good or bad? And then what is the relative valuation? I might look at momentum of the stock. You know, do I think some of it's already priced in?

[00:34:54]You know, maybe earnings going to be great, but maybe everybody already knows it. Uh 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.

[00:35:05]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 is actually deepseek so far.

[00:35:19]>> 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 as we kind of think of it.

[00:35:30]They had some of the, you know, big calls really, right?

[00:35:33]>> 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 and 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?

[00:35:52]>> Yeah, I'll get I'll give my quick take and then Jean you you build them in because we have we have I think uh lateral thoughts on it. I think about AI like the moment right now what AI really means just 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.

[00:36:39]uh that might mean small modular reactors. It might mean other things. Um I think alternative uh uh energy as well storing that is is a huge challenge. Um there's a company that we've invested in in our private funds at Deep Water and our venture side called Antora that does solid 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 architectures change, if all these other things might evolve, the demand for energy probably doesn't.

[00:37:17]>> My uh you know a lot of different data points you can pull out on this topic of like how much further do we have to go.

[00:37:24]Couple guide or maybe uh 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 as Doug hinted to 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 uh marker for this it's capex by the hyperscalers capex growth. A year ago at this time, the expectations were that they would grow capex and calendar 26 by 10% over 25. It's probably going to be up 70%.

[00:38:09]As it looks today, next year, the street's looking for about 10% growth in capex next year. And our sense is it's probably going to be closer to 20 to 30.

[00:38:20]It's not going to be 70, but it's still going to be much higher than what people expect in part because there still is cash flow from these hyperscalers to continue to make uh these investments.

[00:38:32]On top of that, outside of the hyperscalers, we're seeing industrial AI being built and sovereign AI. And so we kind of put all this together. The brain, think of the data centers as the brain of AI and like the apps are and inference is the thinking around it. But the brain still is going to expand more than what uh people expect. Quickf finer point on the energy conversation crash course on energy in the US. 1958 was the first nuclear uh 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 mid70s 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 in growth.

[00:39:28]uh from essentially 1985 till 2022 it was essentially flat more people but more efficient HVAC systems and so we basically saw that flatlining over the next 7 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 uh even though it has had a move higher is still underappreciated by Wall Street. Yeah, I was going to I was going to revisit some of the uh Dean's predictions from the beginning of the year. you've already these are you've mentioned something that you got right here which is one that uh capex's growth was going to be very strong which I think we are definitely going to be right on that one um and the NASDAQ being up 10% or more at least so far you're you're in good shape on that one um a third one we talked about though was IPOs um and you had kind of at that point decided I think that that one was not going to be right because you figured these companies might come out but that's that's in the news all over the place right now SpaceX 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 going to IPO this year >> I think they will and and we're recording this today on the day of Cerebris's IPO, which um last time I looked, which was probably an hour ago, I think the stock was up 108%. Uh so they they've had a good day. Uh 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 um not that like a SpaceX or an anthropic or or an open eye 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 comfortable with their prospects at this point after seeing the demand for Cerebra. 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 I would have to imagine there'd be a lot of excitement around them just like there is around Cerebrus.

[00:41:54]>> Do you think these like do these IPOs have an impact on the overall market?

[00:41:57]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 I'm just trying to think about like the supply and like do you think that has any impact on the market when you IPO companies of three companies of that size >> especially Doug like what does it mean for the mega caps? Yeah, we looked >> assuming that source of funds, right?

[00:42:18]>> Could be and I think the indexes I mean it's a huge question for them and 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.

[00:42:33]I think SpaceX will probably uh will probably eclipse that pun intended. Um but you know Ramco I think 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 >> so much more exciting too.

[00:43:03]>> Agreed. Yeah, biased, 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 NASDAQ 100 index, um, there will be an early inclusion 15 days in for SpaceX, I would imagine that OpenAI 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 waitings across the various companies to get these new big guys in there. M >> 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 7 and the hyperscalers 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 their their quarterly earnings that came out Jean you might have even been on CNBC 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 at least the price performances seem you know, rotate back to the the Mag 7.

[00:44:24]Is is that kind of like right? And I guess what are your thoughts? Because and in 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, capex spending 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, too. 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 three or four percent on that comment. Meta bumped up I think it was like from the high into their range from 175 to 185 billion this year. Stock traded down on it. Those two companies don't have cloud businesses.

[00:45:32]Google I believe they raised their bumped up what their expectations were like materially increased what they expect for capex this year and Microsoft did too. Amazon more or less was a wash.

[00:45:44]But both those companies, you know, this the stock if you look in after hours 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 uh 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 uh Wall Street expectations are because they're putting their money where 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 uh they should be rewarded for making these investments. And if the answer is no, then they should be uh penalized for that.

[00:46:47]that I think you could actually even make an argument if you look at uh I'll even narrow our set to just the hyperscalers the cloud providers Google, Amazon, Microsoft one month on their stocks basically back to when they uh reported earnings to today Microsoft is the worst performing than Amazon than Google. Google's the best performing and I think the part of the reason for that is going back to this excitement around Anthropic. Anthropic obviously premier partner early partner with Amazon. Um so if you're using AWS arguably most likely if and you have an AI product you might be on anthropic uh tooling. Uh Google they've signed a deal they've made investments in anthropic. And so my my gut is part of the answer to that question is sort of what I think Gene's alluding 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 have performed better than Microsoft which is really tied to OpenAI still.

[00:47:57]What do you guys think about um the opportunity in some of these second order space stock like the stocks that are currently on the market today that are you know have business lines or will have business lines in space? Is there anything there that um kind of gets you excited? I I personally am excited. I'm very interested in like seeing in 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.

[00:48:41]I think 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. T, you want to talk about that early stage investment we made on on kind of like imagery, the satellite imagery company?

[00:49:01]>> Yeah. Yeah. Well, I'll talk about a few different things. Um, 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.

[00:49:19]Energy provides um the the sort of balance to create so many things in our lives. Um and also productivity, right?

[00:49:28]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. This 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.

[00:49:39]>> Energy is the first one.

[00:49:41]>> 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 uh everybody's favorite topic to either really love or really hate, but orbital data centers. Um you know, Google is rumored to be in talks with SpaceX to potentially create some orbital data centers. Um, I think whether you believe in the physics of it or not is 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. Um, 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. Um, 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 is probably the most exciting reason to go to space right now. The other one that I would give you is uh and this has long been kind of this discussion about if we go to space and we're working in these zeroravity 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 uh the other day uh there's a company called Varta which uh is is a space company. They partnered with United Therapeutics um to potentially develop and create drugs in space. I I mean I think that's I think that's really cool. Again, like who knows if it works, but I think we should hope it works and that it creates something novel and new that we could never have done if we just maintained 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 when we think about space opportunity.

[00:51:44]>> Would you guys bet uh yes or no if we had to bet on like data centers in space like five years from now? I would say there yeah like they'll be prototypes that will be >> the performance will be pathetic but they will be operational which means that eventually we get there it's it'll be the effectively like uh 32 megabytes of uh internet.

[00:52:10]>> I bet there's more than five operational but less than 25. I'll give you a range. I think that's about right.

[00:52:20]>> Jack, you and I should have an internal goal about doing the first podcast from space. What do you think?

[00:52:24]>> Yeah, exactly.

[00:52:26]>> We'll be around up there.

[00:52:28]>> It would be great. All right, guys.

[00:52:29]Thank you very much. We always appreciate you coming on sharing your thoughts with our audience and uh we hope to see you soon.

[00:52:35]>> Can't wait. Thank you for tuning in to 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 excess returns network at excess returnturnspod.com.

[00:52:49]If you have any feedback or questions, you can contact us at excess returns [email protected].

[00:52:54]No information on this podcast should be construed as investment advice.

[00:52:59]Securities discussed in the podcast may be holdings of the firms of the hosts or their clients.