Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Dylan Patel
[00:00:00] S0: Are you worried that in the limit, the people that control capital and invest in capital, who are often hiring you for what you do, will just say, well, we have analysts too, who are really smart about this. Like we'll just build this ourselves. If it's getting that easy, at what point does it just all pool into the investment firms that stand to gain the most? Because they have the most leverage on top of the data or the insights that they glean. [00:00:00] S1: First of all, any information services business, obviously I don't generate as much value as my customer does from said information, because if I sell you information for a dollar, you're only buying it for a dollar because you know that information helps you make a decision that lets you make more than $1. And so therefore you have made more money off of me than I did from the information myself. These investment funds all have their own information services – especially like the super, the Jane Streets of the world and the Citadels. They're really detailed on their data. And yet these sort of folks also purchase data from us and continue to do so and continue to grow with us because I think there's just some it factor, right? We move faster. We're more nimble. [00:00:00] S0: You're at the edge, at the frontier.
· Patrick O'Shaughnessy, Dylan Patel
[00:00:00] S0: You're at the edge, at the frontier. [00:00:00] S1: We're a smaller team that's focused on just one specific thing, AI infrastructure, the huge revolution that causes in AI and tokenomics and all these things. And we see where it's headed. And so we're moving faster and building faster. I think investment professionals – yes, they'll try and build some of the stuff we do. And more likely they'll just buy the data from us. And it's cheaper for them to buy the data from us and then build on top of it than it is to build it themselves. [00:00:00] S0: I feel like every conversation I have with you, what I'm always getting at is just supply and demand of tokens. That's the thing that's interesting to me in the right now. What has this experience taught you about the demand? Has it changed your view on the demand side of that equation, just feeling it viscerally yourself?
· Dylan Patel
[00:00:00] S1: If we take a step back and look at the macro lens, right? Anthropic has gone from $9 billion revenue to what they're at $35, $40 billion. Now probably by the time this airs, $40, $45 billion in us, ARR. Their compute has not grown to the same degree. And if you do the calculations and you assume they didn't decrease their research and development compute, they clearly didn't. They're releasing that Mythos. They have Opus 4.7. So they clearly didn't decrease their research compute spend. So ultimately, what they've done, even if you assume all incremental compute they've gotten has gone towards inference, their margins are at a floor of 72%. In reality, some of that incremental compute they've got probably went to research and development and maybe higher than 72% gross margins. To be clear, at the start of the year, there was a leak from their funding round docs. Someone leaked it. 30 something percent gross margins. Where on earth does a business like this grow margins like that? It's in principle, right? Their demand is so high, they're able to cut back on usage limits, rate limits, all these things. What really matters is having an Anthropic rep and having an enterprise contract with them and getting the rate limit increases that you need. Because otherwise, tokens are ultimately super, super in demand. Whoever can pay for them. Anthropic has the same problem. I mean, not problem. It's just the reality of how capitalism works. Yes, people are sending them $40 billion ARR in tokens, but those tokens are generating way more than $40 billion in value. Various businesses will have different value generation per token, but as we get more and more intelligent, what really matters is access to these most intelligent tokens and leveraging them at things. You as a person deciding what is the best way to leverage these tokens to grow business and generate value. Because a lot of folks will want tokens and generate tokens, but the shitty SaaS startup in SF who is using Claude to generate their software product is not necessarily actually creating a ton of value and therefore they're going to get priced out of tokens soon enough.
· Patrick O'Shaughnessy, Dylan Patel
[00:00:00] S0: I had this experience just today, where on the flight here, I got rate limited out on something. I saw 4.7 came out and what I immediately wanted was to be on 4.7 that second. I couldn't think about using 4.6 anymore now that 4.7 is out. I was perfectly happy with 4.6 for the last many weeks. It's amazing. Are you surprised that people are so insistent on going to the most expensive leading edge thing to the degree they are? [00:00:00] S1: Without a doubt. I think one of my funniest memories in the past month and a half is myself and a buddy of mine, Leopold, being on our knees in front of an Anthropic co-founder, begging him for access to Mythos and them pretending it doesn't exist. Because we knew it existed and we're like, please give us access. And he's like, I don't know what you're talking about. [00:00:00] S0: What was your reaction to that rate card or that eval card coming out? [00:00:00] S1: It was rumored in the Bay Area. We knew it was supposed to be really good, but if you just look at the benchmarks, obviously benchmarks change over time. Mythos is potentially the biggest step up in model capabilities in two years. I think that's really, really an important detail that it's so good that they don't want to release it, even though they already announced the price to their people that they did a selective release for cyber for, and it's five or 10x the token cost. They just don't want to release it because they're worried about the impact on the world. And they're releasing a worse version, Opus 4.7 to us. [00:00:00] S0: Us mere mortals.
· Dylan Patel
[00:00:00] S1: And they explicitly said in the model card, hey, we actually preferentially made it worse at cyber. I don't know if you read that. Whoever you are, if you have enough capital, you should get a freaking enterprise Anthropic subscription where you pay per token, not with these subscriptions, because then you won't get rate limited much. And then you need to figure out how to leverage those tokens to the highest value task and make money off of it. Because ultimately what you're doing, maybe a year from now or two years from now, the business is actually just arbitraging tokens. The tokens are amazing, but let's figure out what direction to point them in. And then three or four years from now, the model will know what to do with the tokens and how to make the most value. You can even look at this retroactively. Pick any benchmark. The cost to hit a certain capability tier used to cost X. And now it costs one one-hundredth or one one-thousandth of that. DeepSeek, for example, on GPT-4 was one six-hundredth the cost. And since then, the costs have fallen further for GPT-4 class models. Of course, no one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things. But GPT-4 class models can still be used in stuff. And so people are using them in some tiny use cases. It's just the cost has fallen so fast, it's not really what's driving the demand. What's driving demand is all these new use cases. Current 4.6 Opus or 4.7 Opus tier models a year from now, my spend for the same exact quality of the model would probably be like 70K. I bet you it'll be a hundred times cheaper. Irrelevant because I'm going to be using a way, way, way better model, which can do way, way better things. Anthropic Mythos is more expensive as a model, but it spends a lot less tokens to do the thing. And therefore, it is actually cheaper in most tasks than 4.6 Opus because it's just way more efficient, even though each individual token is smarter. So yeah, there's crazy geniuses creating huge cost efficiency improvements every day. They work at the labs and they're making the models way more efficient. You see it every generation. GPT-5 Nano or whatever was better than GPT-4. Or 5 Mini was better than GPT-4. It was like one, one hundredth the cost. This just happens.
· Dylan Patel, Patrick O'Shaughnessy
And we accept it at its face value, but ultimately you keep making things cheaper and then you keep scaling them up and you keep getting humongous improvements. [00:00:00] S0: When I last saw you, Mythos had just come out, maybe the day before or something, or the card had just come out and you said something like it actually made you feel like a little scared, it was so good. What did you mean by that?