Invest Like the Best with Patrick O'Shaughnessy
· Dylan Patel, Patrick O'Shaughnessy
[00:00:00] S1: Last year, we thought we were heavy users of AI. Everyone was using ChatGPT, everyone was using Claude – providing whatever subscriptions anyone wants, on the order of spend of tens of thousands of dollars for our firm. This year, the spend has just skyrocketed. And it really started in late December with Opus. That included Doug O'Laughlin, who's president, and he's very much leading the charge in the sense of non-technical people using AI for coding. And so he's basically pilled the whole firm slowly over time. I think he's been the leader in doing that, obviously, the engineers were using AI anyways. But spend in January just started to inflect and rocket and rocket and rocket and rocket. We signed an enterprise contract with Anthropic, and it's gone to the point where now – I think when I last talked to you, it was 5 million spend, right? It's actually 7 million spend right now. [00:00:00] S0: That was last week, by the way.
· Patrick O'Shaughnessy, Dylan Patel
[00:00:00] S0: That was last week, by the way. [00:00:00] S1: And a lot of that is just the usage. People who have never coded before are using Claude Code and spending thousands of dollars, sometimes a day. And it oscillates. Some people spend thousands of dollars one day or spend a couple hundred dollars a couple days, and then they go back to a thousand dollars. It's very variable across each individual user, but across a firm, we're spending $7 million a year now on Claude Code at the current rate, versus our salary expense being in the neighborhood of $25 million. So we're north of 25% of spend on Claude Code as a percentage of salary. And if this trajectory continues, then we'll spend more than 100% by the end of the year, which is a bit terrifying. Thankfully, I don't have to decide between people and AI because our company's growing so fast. It's more so like, okay, I don't have to hire nearly as fast and I can spend a lot more on AI and it works and we just grow faster. But I think other folks will start to reckon with the fact that – huh, if this person can do the work of 5 to 10 to 15 people using Claude Code, then all of a sudden I should probably cut people. And the use cases are so broad. [00:00:00] S0: Give a couple of examples.
· Dylan Patel
[00:00:00] S1: Okay. So for example, one thing is we have a reverse engineering lab in Oregon that we've been building for a year and a half. We have a bunch of fancy microscopes, scanning electron microscopes. The whole purpose of this is you reverse engineer chips. You get the architecture out of it. You get the materials that they're using to manufacture. And this is some of the data we sell. This is a very slow process of analyzing that data. Instead, one person on the team, they've been able to spend with a couple thousand dollars of Claude tokens, they've been able to create this application that is GPU accelerated, runs on a server that we have at CoreWeave. And anytime we send it an image, it will take the picture of the chip and overlay where every single material is. Oh, this part is copper. Oh, this part of the gate is tantalum. This part of the gate is germanium. This part of the gate is cobalt. And so you can do a finite element analysis of the entire stack up of the chip very, very quickly. Visual with a dashboard, GUI, it's everything. A few thousand dollars of Claude. The person previously worked at Intel and he said that was an entire team's job to build that and maintain that. I'll rack that up across the entire firm. It's insane. Another example that I think is super fun is Malcolm. He was an economist at a major bank before. Their economist department was like 100 or 200 people. What he built was the most incredible thing ever. He piped all of this different data, FRED data, and all these other data sources , employment reports, and all these other things from various APIs. We signed a couple of contracts with folks to get API access to data, pulled it all in, started running regression, started looking at the impact of various economic revolutions on the economy from a deflationary, inflationary perspective. The Bureau of Labor Statistics has this entire set of 2,000 tasks. And so he did that with AI, which ones can be done by AI, which ones cannot, and grading them across a rubric. About 3% are doable now with AI. And so he's created this metric so that you can measure things that can be done by AI, what the cost of being able to do those with AI, and therefore the deflationary aspect of it. Phantom GDP is what he's called it. Output can go up but because cost falls so much, actually, GDP theoretically shrinks.
· Dylan Patel, Patrick O'Shaughnessy
So he created this whole analysis and a brand new benchmark of language models, a set of evals across 2,000 different evals. [00:00:00] S0: He did this all by himself? [00:00:00] S1: This is all by himself, yeah. And he's like, dude, this would have taken the team of 200 economists a year. He's completely cracked out on Claude. He's like, everything has changed. [00:00:00] S0: How do you think about it as a business owner, going from close to zero to 25% accelerating towards whatever percent of total spend? At what point are you like, whoa, I need to put the brakes on this and be careful how much we're spending. Maybe we don't need to spend on the most cutting edge on Opus 4.7, which came out today. Maybe I can throttle it back to something that's a little bit cheaper.
· 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.