Is AI Still in 1995? Gene Munster and Doug Clinton on the Next Phase of the AI Boom

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[00:00:54] Was 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.

[00:01:12] 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.

[00:01:31] 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 reveal it.

[00:02:03] Doug, Gene, 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, you know, 45 or 60 minutes is we really appreciate it. And our audience does too. Cause 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.

[00:02:32] 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 and Gene of Deepwater 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 intelligentalpha.com.

[00:03:02] So, um, 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, 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 three to five year conviction idea is that AI will culminate in a bubble bigger than the.com bubble.

[00:03:26] It's the nature of major tech innovations to create bubbles. AI isn't close to a peak. We're in 1995. And, and I, I would think, um, you can correct me if I'm wrong. I mean, that's, that's kind of going according to plan. Um, wouldn't you say? And then I guess, you know, what do you, has anything sort of changed your view on this or what's the current state from your perspective?

[00:03:47] 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.

[00:04:01] 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?

[00:04:21] 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 at enterprise.

[00:04:50] What are your thoughts on, and maybe 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.

[00:05:17] That development tying back to some of the semiconductor stocks and sort of other related stocks in the market? 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.

[00:05:40] 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.

[00:05:57] 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.

[00:06:26] 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?

[00:06:55] 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-tenured, five, ten-year employees, PhDs. That's how fast it's getting better.

[00:07:24] Does that mean, Doug, does that mean we're going to be at general intelligence? Well, you know my quirks around the idea of general intelligence. Like, you could make an argument that we're in general intelligence now. So many of these debates about AGI superintelligence are very semantic, because I don't think there's one uniform definition of like, what is AGI? What is superintelligence?

[00:07:52] 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. 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,

[00:08:22] 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 ClogCode and the new model back in November. And, of course, Anthropics revenue going from a $9 billion to a $45 billion run rate over a four-month period. That's like breathtaking. But you said, I think you said mass adoption or widespread adoption.

[00:08:51] 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 ClogCode. Yeah, I think when I say mass adoption, I mean more at the enterprise level. And you just referenced those Anthropic numbers, you know, going from 9 to mid-40s in just a few months.

[00:09:20] 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 that I think are 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.

[00:09:49] 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 ClogCode or Codex 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.

[00:10:10] 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 5 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 collar, their knowledge workers.

[00:10:38] And I guess the question, as we think about these models getting smarter, does it matter that there, 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. How do you think people should view some of what we've seen, some of what we're picking up on, looking at how smart the models are?

[00:11:05] 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, Gina and I have talked a lot about this at Deepwater and Intelligent Alpha.

[00:11:35] 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 going to 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. 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. But ultimately, do they just get religion and then able to kind of keep their job?

[00:12:05] Or do their jobs go away? And does it 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. It's just kind of like conceptually.

[00:12:33] 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 value? Maybe a huge segment of the workforce are detectives. Maybe, yeah. That's, by the way, that's the kind of question that people are asking a lot these days,

[00:13:00] 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? 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 going to be more disruption than what we saw

[00:13:27] and 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 MO 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 five-year gap.

[00:13:52] And if I was going to put some numbers around this, I think we see a step up in knowledge work or unemployment. I use that. I think that is important to look at because I think it's representative of 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 going to see in the near term, we'll call it a five years,

[00:14:21] more acute knowledge work or unemployment than we saw around mobile or the Internet. But ultimately, I think it does fix itself because people realize you got to get on board. I got to become the detective. I got to become the salesperson, the tastemaker, and they will kind of the free hand of the market will push them to develop the skills that are necessary to survive.

[00:14:46] Doug, I want to ask you, when you and Gene entered the debate ring, does he enter with the mean Gene handle? I'm probably usually meaner than Gene. Mean Gene'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 Anthropic. And a question was, we've seen OpenAI really push codecs. 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?

[00:15:15] We got IO coming up next week. You know, it feels like they're 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 codecs and cloud code? You know, 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.

[00:15:43] At Intelligent Alpha, we think codecs is the best tool for actually writing code. So when we're putting something into production, we're using codecs 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 Claude because I actually think it's a little bit better of a thought partner than codecs or GPT-55 at the moment. Although 55 is really good.

[00:16:12] 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 codecs 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. I default to search still all the time.

[00:16:41] 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. 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, they're certainly the slowest, I think, to really embrace the sort of coding revolution and really the agentic revolution.

[00:17:06] It does seem like on codecs, like when you talk to the elite programmers, like they were all cloud code people. And it does seem like you're seeing like movement towards cloud, towards OpenAI codecs from like those elite type programmer people. 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 with the tools we build at Intelligent Alpha. But the different models do have different personalities. Certain models are better at better things.

[00:17:34] 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 codecs 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 cloud.

[00:18:00] What's actually interesting in that paradigm is, is there's like a higher question, which is, well, what's the bigger market? Is the bigger market to kind of do the higher order thing and, 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. Like, I think you could make an argument for either one. Certainly right now, it seems like the bigger market is for Claude, but we'll see over time. So Gene on the model war, what are your thoughts?

[00:18:30] When we defer to Doug, he's like deep into this. What do you think, Doug? Yeah. I'll tell you the current rankings in my mind are GPT 5.5, Opus 4.7, Gemini 3.1 and Grok 4.3 are in my mind, basically tied. And then there's everybody else. You know, we, we test a lot of these models. So you put GPT at the top? For me, GPT is the best right now. Yes.

[00:19:01] And before 5.5 came out, I would have told you that Opus 4.7 was the best. Claude. 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.

[00:19:46] And, and think about the game too. So many of them have made a bit moreWHIP. into different models. And my question is, isn't this a negative read on OpenAI

[00:20:12] 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 the demand's through the roof? But 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.

[00:20:39] 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 the Elon Musk suit in OpenAI. 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,

[00:21:08] Jack asked a question a minute ago about, is it kind of winner take all? Is it 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?

[00:21:37] So 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 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

[00:22:06] 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,

[00:22:34] 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 Anthropic deal? Because XAI was one that did have capacity, right? And they sold a lot of that capacity to Anthropic. 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,

[00:23:04] you know, 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 clawed models to be able to use to XAI, now I think it's SpaceX AI, internally so that they could use clawed code, which was 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,

[00:23:34] like do we expect eventually like one of these companies will jump way ahead or do we think they're all going to just be racing each other and they're going to 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 to a large extent. How many is they? We've got four, five? Is Meta now in that camp? Yeah. Their new model on benchmarks, I haven't really been able to play with it yet.

[00:24:04] We're trying to get API access. On benchmarks, their new model looks really good. Looks pretty capable. So we'd have, remind me of the name of Meta's model. I should know this. Willow or something? No, it's, yeah, no, I'm blanking them too. Llama was the old one. Let me just pull it up here. So we've got. It was Spark, Muse Spark. Spark. So we got GBT, Gemini. Yep. Claude,

[00:24:34] Spark, Grock. Muse, yeah. And then you got, and then on the other side of the planet, you've got Baidu. Yeah, Quinn, that's different. That's 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. Yeah.

[00:25:04] And then five years, are there going to be five still 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 is hard, it's so hard to predict because it's moving so fast. Yeah. And I think like 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 all use transformer architecture.

[00:25:34] So the models 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 OpenAI 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,

[00:26:03] it feels like is, and Gemini 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

[00:26:33] 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, could 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 ChatGPT 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

[00:27:02] and now kind of fast forward three years, we have two investment funds that we run using our language models, using our AI process to analyze stocks, pick stocks, manage the portfolio end-to-end. 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 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

[00:27:32] 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

[00:28:02] 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? That's exactly right. And so if I scroll down here so we have kind of our leaderboard if you visit our site intelligentalpha.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.

[00:28:31] We'll publish some of that data very soon. But we test 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. GBT. Yeah. Gene didn't even cheat. I know he didn't look at this before. I'm not. GBT is the top. And so we test these models just directionally. Did they get if earnings are kind of moving up or down?

[00:29:01] 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 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

[00:29:30] from OpenAI, Anthropik, Google, and XAI, they all seem to stand out above the closed source models. Which I 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 like financial statement type of data that's being, or is it doing like natural language processing on earnings calls and stuff like that too? Like what is it?

[00:29:59] What are 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,

[00:30:29] 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 will 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?

[00:30:58] 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 kind of like we talked about earlier, 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 was modeled before it. Same thing has been true for Anthropic with Opus 4.7, 4.6, 4.5. And so I think part of the reason

[00:31:28] is the models they're just literally getting better. They're just getting 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 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 and here's what seems to be most likely to happen. So they're getting better I think just at that

[00:31:57] as kind of a general task. Do you think that the fact that machines aren't emotional you know in the asset management business we're 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 are the models quicker to cut off of a company cut bait

[00:32:27] 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 you know bias because they did a bunch of work on something yeah thinking something is more valuable just because you own it already the 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

[00:32:56] byproduct to that which can be a negative right and not what promotional emotional they're not emotional 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 is going to crush earnings some of these recent semi stocks 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

[00:33:26] 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 beat 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 sword is on average these models are right very often I think they're probably right more than the average human

[00:33:56] 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 yep that's right somewhere between big swing somewhere

[00:34:25] between traditional quant and human yeah is what I kind of how I think about the models are the 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 it's fun it's actually 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

[00:34:54] 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 gained 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 at Intelligent Alpha we take the earnings prediction as like one signal

[00:35:24] 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 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 you know maybe earnings gonna be great but maybe everybody already knows it we kind of try to create a framework for the

[00:35:54] 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 is 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

[00:36:24] kind of think of it they had some of the you know 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 get my quick take and Gene you sounds good yeah because we have I think lateral thoughts in it

[00:36:54] 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

[00:37:23] 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 a huge challenge there's a company that we've invested in in our private funds at Deepwater and our venture side called Antora that does solid state storage I think that's going to be a huge

[00:37:53] theme and so power in our if the model architecture might evolve the demand for energy probably doesn't my 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 guideposts

[00:38:23] 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 a marker for this it's capex by the hyperscalers capex growth

[00:38:52] a year ago at this time the expectations were that they would grow capex in calendar 26 by 10 percent over 25 it's probably going to be up 70 percent as it looks today next year the streets are looking for about 10 percent growth in capex 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 still is

[00:39:22] cash flow from these hyperscalers to continue to make these investments 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 is 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

[00:39:52] expect quick finder point on the 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 until the mid 70s and during that period the average increase in output of energy in the US grew on average 7% a year I

[00:40:28] 2022 it was essentially flat more people but more efficient HVAC systems and so we basically saw that flat lining 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 3% is a massive investment cycle and so said a different way is that a lot of times the AI

[00:40:58] 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 I was going to revisit some of the Gene's predictions from the beginning of the year you've mentioned something you've right here which is one that CapEx growth was going to be very strong which I think we're definitely

[00:41:23] going to be I think they will and we're recording this today on the day of Cerebrus' IPO which last

[00:41:53] time I looked 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 SpaceX or second tier of companies that might think about going public they have to feel pretty comfortable with their

[00:42:23] prospects at this point after seeing the demand for Cerebrus so you think about a company like Databricks or maybe some of these other coding tool companies who have like there is around Cerebrus do you think these IPOs have an impact on the overall market like

[00:42:53] we've never seen I assume these will be the three biggest IPOs of all time right when they come out like how does that impact I'm just trying to think about the supply and like do you think that has any impact on the market when you IPO companies of three companies of question for them and just as one reference data point Aramco Saudi Aramco I think

[00:43:22] in terms of size and market cap was the biggest IPO ever I think SpaceX will probably eclipse that pun intended but Aramco I think was like a trillion 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

[00:43:52] very early on at a so much more exciting too agreed yeah biased but agreed 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 there will be an early inclusion 15 days in for SpaceX I would imagine that OpenAI

[00:44:22] 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 mega caps because those indexes are going to sell down and adjust their weightings across 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

[00:44:52] Mag 7 and the hyperscalers for their investment into this and questioning what is the payback going to be but then I don't know if it was the earnings their quarterly earnings that came out was such a big earnings day and I feel like now at least the price performance seemed to rotate back to the Mag 7 is that kind of right

[00:45:21] and I guess what are your thoughts in thinking through that I was reward that but it seemed to have flipped the other way so I don't know if 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

[00:45:51] 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 3 or 4% on that comment Meta bumped up up up from the high to their range from 175 to 185

[00:46:21] 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 or 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 the stock if you look after hours trading when those comments were made it took a few minute dip and then came right back so there may be something around

[00:46:51] investors feeling there's a faster return on CapEx if you have a cloud business but that's about the through line just in terms of how it trades around the quarter the bigger picture is 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 all street expectations are because they're putting their money where their mouth is and so I see

[00:47:21] that as you know it's a they should they should be penalized if you look at I'll even narrow our set to just the hyperscalers cloud providers Google Amazon

[00:47:51] Microsoft one month on their stocks basically back to when they reported earnings to today Microsoft is the worst performing than Amazon and 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 so if you're using AWS arguably most likely and you have an AI product you might be on

[00:48:20] Anthropic tooling Google they've signed a deal they've made investments in Anthropic and so 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 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

[00:48:49] Microsoft which is really tied to open AI still what do you guys think about the opportunity in some of these second order space like the stocks that are have business lines in space is there anything there that gets you excited I personally am excited I'm very

[00:49:19] interested in seeing what comes down the road with that and what's the investment opportunity now and in the future there and if you have a minute if there's a company's business model that you know of that is really unique I think it would be a good discussion because I think there are things happening coming down the pipeline that most of investors know nothing about so I'd be very interested in your thoughts on that you want to talk about that early stage investment

[00:49:49] we made on kind of imagery the satellite imagery company yeah yeah well I'll talk about a few different things um I think like space to us is exciting because 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 the sort of ballast to create so many

[00:50:19] things in our lives um 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 this is awesome I was like no what is the purpose of going to space I think those are two of the first one 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

[00:50:48] everybody's favorite topic to either really love or really hate but orbital data centers 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 it going to be SpaceX almost undoubtedly and I think they will try it and we should hope that they're

[00:51:24] 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

[00:51:53] kind of this discussion about if we go to space and potentially develop and create drugs in space I mean I think that's I think that's really cool again like who knows if it works

[00:52:24] but I there'll be prototypes that will be the performance will be pathetic but they will be operational

[00:52:53] which means that eventually we get there it's 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 exactly we'll be great

[00:53:24] 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 you can contact us at xs returns pod at gmail dot com no information on this podcast should be construed as investment advice

[00:53:54] securities discussed in the podcast may be holdings of the firms of the hosts or their clients