What AI really means, just as a first principle, is we're converting electricity into intelligence right now. That's exactly what's happening. And so if that's true, and the demand for intelligence is seemingly infinite, I think the demand for power is seemingly infinite. That's a very basic question that investors should ask themselves: do you believe the management in these companies is competent at seeing the future? And if the answer is yes, then they should be rewarded for making these investments. And if the answer is no, then they should be penalized for that. I do believe we're going to see in the near term, we'll call it five years, more acute knowledge worker 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. 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.
Doug, Gene, 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, we really appreciate it and our audience does too because you guys always have a lot of great things to say when it comes to technology. And I always appreciate your ability to, you know, explain these things in a way that our audience, I think, can get a lot from these. It's not... you guys can go in depth when you need to, but at the same time you can kind of talk high level. So I think a lot of this conversation today will be high level, but then we'll get into some of the details too. Our audience can learn more and follow Doug and Gene of Deepwater Asset Management and also learn how Doug 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. So, a lot to get through today. Thank you very much for joining us. And we wanted to start, I want to start Doug, with, and you wrote this tweet which we talked about I think last time you were on, and we'll put up on the screen here, but you know, you wrote, and this was back at the end of 2023: 'My highest 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.' And I would think, you can correct me if I'm wrong, I mean that's kind of going according to plan, 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?
I would say so far so good in terms of the prediction that, you know, AI will ultimately be a bubble. Maybe it's a weird thing to say when you're sort of predicting a bubble, but the thing I think that has changed for us is if 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 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 we're just really starting to see the beginnings of that being adopted in enterprise.
What are your thoughts on, and maybe you can explain what the Claude model 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 users of AI, we're kind of stuck in the current models that we have. But I mean, I know you guys know and test and look at some of these frontier models. And then is there anything to be said for that development tying back to some of the incredible performance we've seen out of the semiconductor stocks and sort of other related stocks in the market?
I do think there is a tie there. And really, if you try to find like what was the real catalyst to a lot of the run that we're seeing now, especially in the semi side, I think it was probably when Anthropic released Opus 4.6. It was late last year. And there was something about that model where I think it made this idea of using a coding tool, a coding agent, accessible to the mainstream. Like you didn't really have to know that much about programming to really build code at that point. 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. We've had this question dogging AI for two years now since this really began of when will we see the productivity gains, when can it actually do something useful, and we are absolutely in the days of utility now. And I would even argue, you know, you kind of asked about the progress of the models. I think a year ago these models you could compare them to like a high school graduate. I think now the models are probably equivalent to someone who has graduated college, maybe two years in the workforce, and by the end of the year we'll have models that are people who are well-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 at general intelligence?
Gene, you know, my quirks around the idea of general intelligence. Like, you could make an argument that we're in general intelligence now. Like, 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.
And so, I mean, is that general intelligence? That seems like pretty intelligent to me.
Yeah. I think it's a kind of silly conversation, but it's one that kind of orbits around the utility of these models is when we get to general intelligence or some... yes, they hallucinate. Humans make mistakes, too.
I want to pick up on another point you made, Doug. You talked about that kind of explosive growth that happened with Claude Code and the new model back in November. And of course, Anthropic's revenue going from a 9 billion to a 45 billion run rate over a four-month period. That's like breathtaking. But you said the... I think you said mass adoption or widespread adoption. Like the reality 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 Claude Code.
Yeah. 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. I think that is the definition of sort of wider spread adoption at the enterprise because almost all that revenue is incrementally from enterprises that are deploying these models. And I mean a few kind of just anecdotal data points there I think 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.
And now 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 Claude Code 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.
I talked to... I'm not going to name the company. I'm just going to give a range of a tech company that has a market cap somewhere between five and 25 billion. I want to give a nice comfortable range here. But it's a real company and they mentioned that they think that automation could have a massive impact on their white collar, their knowledge workers. And I guess a question as we think about these models getting smarter, does it matter that... 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 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 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, Gene and I have talked a lot about this at Deepwater and Intelligent Alpha. It's 80/20. It's Pareto. 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. 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?
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, just kind of like conceptually, the most useful data in the world is data that the models don't have access to just by definition. And so I think there will be jobs, we call them detectives, but people that go out in the world, can they find this useful unknown data that the models don't have and bring it back into the enterprise, give it to the models, and then create...
So a 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 standing currently. 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, 60% of the jobs didn't exist 40 years ago. So, like this is how humanity works. You know, the detective mode 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. 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. I do believe we're going to see in the near term, we'll call it five years, more acute knowledge worker 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.
Doug, I want to ask you when you and Gene enter 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 Codex, and you can talk about some of the things that you've observed in terms of how good that is relative to Claude Code. What's Google doing on this front? We got I/O coming up next week. You know, 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 Codex and Claude Code?
You know, it's... I think it's been unfortunately slow. And I would give you this perspective, and I think a lot of different enterprises use these tools in different ways. At Intelligent Alpha, we think Codex is the best tool for actually writing code. So, when we're putting something into production, we're using Codex to build that product. When we're doing like product development, when we're doing kind of earlier on stuff, when we're ideating, we actually, at least I do often, I use Claude because I actually think it's a little bit better of a thought partner than Codex or GPT-5.5 at the moment, although 5.5 is really good. So I think you can kind of use these models in tandem. I think that's the best way to optimize them currently. But we've also tested and played around with Gemini and Gemini CLI, which is basically their competitor to Codex or Claude 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 default to search. I default to search still all the time. 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.
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.
It does seem like on Codex, like when you talk to the elite programmers, like they were all Claude Code people, and it does seem like you're seeing like movement towards OpenAI Codex 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 certain things. That's why there's all these benchmarks out there and you see different performance. But I do think that that is becoming kind of an open secret really is that if you want to write code, if you really want to build a useful product that's going into production that's going to serve users, I think a lot of programmers are defaulting to Codex if they have a choice. And if you're really just trying to do more product dev, then I think people are defaulting to Claude. What's actually interesting in that paradigm 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 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 seems like the bigger market is for Claude, but we'll see over time.
So Gene, on the model war, what are your thoughts?
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 test a lot of these models.
For me, GPT is the best right now. Yes. And before 5.5 came out, I would have told you that Opus 4.7 was the best. So it does change. I mean, the leaderboard does change almost every time a new model comes out because each incremental new model does seem to be a little better than the one before. And think about the game, too. I mean, these model builders all know. They're all testing each other's models. They're all paying attention to the same benchmarks. And so when OpenAI releases a model or 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 the best. I have to navigate to this one again. And 5.5 is the most recent model and so they're king of the hill right now.
So one thing caught my attention today was this lawsuit that OpenAI has against Apple, basically saying that they've breached their distribution agreement. Apple, of course, is using more Gemini with Google, and they're 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 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.
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.
What I would say is this. I think that... I mean, Jack asked a question a minute ago about 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, Gene. There's this perspective in the market that the model war is not really zero sum, it's actually that there's going to be so much demand that whoever has capacity will be able to sell their capacity and therefore be a winner. Right. So, let's say you 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 gets their capacity filled and the third best and so on and so forth. And so I've kind of... I think 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, 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 you have.
Does this play into the whole xAI Anthropic deal? Because xAI was one that did have capacity, right? And they've 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, 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 Claude models to be able to use to xAI now, now I think it's SpaceX xAI internally, so that they could use Claude 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 leapfrog 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 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 mix.
Yeah, their new model on benchmarks... I haven't really been able to play with it yet. We're trying to get API access. On benchmarks, their new model looks really good. Looks pretty capable.
So, we'd have... remember the name of Meta's model. I should know this.
No, it's... Yeah. No, I'm too... Llama was the old one.
Yeah, let's pull it up here.
So, we've got... it was Spark. Muse, Spark.
Spark. So, we got GPT, Gemini, Claude, Spark, Grok, Muse. Yeah. And then you got, then the, and then on the other side of the planet, you've got DeepSeek.
Yeah. Qwen, 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.
