Invest Like the Best with Patrick O'Shaughnessy
· Dylan Patel
[00:00:00] S1: I think I'm in the information business. We sell analysis. We do consulting. We create data sets. I don't see why this wouldn't be completely commoditized on a pretty rapid basis if I'm not constantly improving. My first product that I was selling as a data set, there's more people trying to do it. Now we've made it constantly better and better and better and more detailed. And so therefore it sells a market. But the way we were doing it in 2023 is not terribly different. It's basically what everyone else is doing now. If I don't move up the bar, then I will be commoditized. If I don't move fast enough, I will also lose my edge. So the question is, yes, AI commoditizes things just like it commoditizes software. Those who can move fast and keep control of their customers and keep providing them an awesome service and keep improving the service won't shrink. They'll grow faster. Those who are incumbent and not doing anything, they're going to lose. And so it's a bit of an existential. If I don't adopt AI, someone else will, and they will beat me. Another easy example is the energy space. So we've had a few energy analysts for like a year now. We've been trying to build out this energy model. It's very complex. Energy's data services market is something like $900 million, so obviously a huge market for me to try and break into. And we've been slowly grinding at it. And it's been helpful for our data services business. We really hadn't broken into the energy data services business despite a year of having multiple people on the team. Then Claude Code psychosis hits one of the people who leads the data center energy and industrials business at SemiAnalysis, Jeremy. It hits him. And now all of a sudden in three weeks, he spent a lot. He's spending like $6,000 a day. It was an insane amount. But he scraped every single power plant in the US, every single transmission line above a certain voltage and created this entire mapping of the entire US grid, as well as a lot of demand sources, all from various public sources of data. And they've got like this dashboard where you can view and check. You can see all the micro regions of the US where there's power deficits and surpluses, all of these details built in a handful of weeks. We started showing some of our customers who buy our data center data set, but are energy traders.
· Dylan Patel, Patrick O'Shaughnessy
We showed some of them and they're like, wow, how long did this take you? This is really good. This is better than XYZ company. And then we dig deeper. XYZ company has a hundred people that have been working on this for a decade. Obviously our thing is not fully as robust, but in some ways it is better. I'm going to commoditize these energy services companies, data services company. Who's going to come commoditize me if I don't move faster? And so the question from a business owner's perspective is, yeah, I'm spending a lot, but what does that spend getting these and getting more revenue? [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.
· 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.