Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy
[00:00:00] S0: You said something really important before, which is the returns to being at the frontier are really high. Can you explain that in as much detail as you can? It sounds obvious when you say it, but there's certainly been some camps saying, "I can use the six-month-old model, it's a fraction of the cost, and I'll just use that. That'll be catching up all the time." That just hasn't been the case. The second Opus 4.7 comes out — even me as a consumer — the thing you do is switch it on. Or GPT 5.5 comes out, you switch on the new one right away. You want the best. Talk about the returns to being on the frontier and why it's so high.
· Krishna Rao
[00:00:00] S1: It's a couple of things. Every time we have a new model, there's a set of capabilities that are different. People tend to think about model intelligence as IQ — a single number. "Okay, this model was at 110, and then it goes to 125." We think of it differently. Intelligence for us is multidimensional. It's not just a score. We find that, yes, everyone publishes their model benchmark cards. We find that a lot of those benchmarks are saturated. We publish it too, but our measurement is what the customers tell us. What is the real-world capability of this model? As we've released better and better models, what we've seen is it's not just the outright intelligence. It's also the ability to do long-horizon tasks. It's the ability to use tools, or computer use. It's the ability to do agentic tasks that have specific value even faster. If you have two employees and they're both equally capable, but someone takes a week to do an assignment and someone does it in a day — that second person, if they're continuing to do that, can be seven times better. They might be equally capable at something, but just take a longer time to do it. All of those factor in to how customers experience it. What we've found very consistently is that by releasing new models, the TAM is unlocked in a unique way. More TAM gets unlocked. More use cases are possible. A good illustration of that is this last four months that we've had at the company. We started the year with about $9 billion of run-rate revenue, and we ended the quarter with north of $30 billion of run-rate revenue. That kind of a change is enabled by these model intelligence leaps, and then the products that we build around them. That's what I mean by the returns to frontier intelligence are really high. I think that's unique to enterprise. In consumer, sometimes you don't see that as readily, that consumers really are pushing the limits of what the models can do. Whereas in enterprise, our customers — it started with coding, but it's really expanded beyond that very meaningfully. Each model generation gives you the chance to do more with it, to do it better, to do it more efficiently. Customers see that, and then they invest really heavily in more tokens with the newer models. We've seen that cycle play out again and again.
· Krishna Rao, Patrick O'Shaughnessy
That's a core thesis of our business — that especially in enterprise, the returns to frontier intelligence are not slowing down. [00:00:00] S0: The things that push that frontier — it's like a sci-fi story or something from books I was reading when I was growing up. It seems as though in the major labs, we've reached this point — someone on your team said it recently — of recursive self-improvement, where the models themselves are building and doing a lot of the research to do the next generation of improvement. If I think about the frontier that you're pushing and OpenAI is pushing, and compare that to the open-source models, maybe the gap will widen as a result of you getting there first to this recursive thing. How do you think about that? Tell us how we should think about this idea of recursive self-improvement in the models themselves, because it seems like getting there first is incredibly important — then you just continue to separate yourself versus those that haven't reached it yet.
· Krishna Rao
[00:00:00] S1: We do see progress accelerating. I can't speak for other companies, but for us, the scaling laws are alive and well, and we're seeing that with releases more recently like Mythos. But right now, within the company, 90-plus percent of our code is actually written by Claude Code. A lot of Claude Code's code is written by Claude Code. Why do we allocate compute internally? Why would we forgo revenue for it? It's because the models themselves are helping us build that next generation of models. In addition to this capability leap that you would have from the scaling laws, talent is really important. That talent, with the best models, can really accelerate the development of the capabilities, and we're really seeing that. We don't really think about models as closed or open. We think of them as frontier or not. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. We are investing behind that thesis, and that means both compute, but it also means talent to use that compute and use our own models to really accelerate the development. That's something we've been doing for a long time. That's not a new phenomenon, but we're seeing the fruits of that in everything we've done. The other piece of it is — it's not just the models; it's the products that get built on top of them. We had 30 different product and feature releases in January. The pace of that has accelerated as well, and that's enabled in part by utilizing the models with the talent that we have, to accelerate ways to access this underlying intelligence. That's our theory of the case on the product side.
· Patrick O'Shaughnessy, Krishna Rao
[00:00:00] S0: How do you think about this weird world where you mentioned the talent and the leverage, and they're not writing code themselves, and Claude Code's writing its own code? It seems like the last step of that would be you don't even need the talent to tell the thing what to do. It just figures out what to do on its own, and that's the ultimate thing, that it just runs and is only constrained by compute or something. Am I being too crazy about that, or is that future possible, do you think? [00:00:00] S1: The core of our company is still a research lab. Maybe that's not as well understood, maybe it's getting more understood from the outside. We are doing experiments. We are doing things that push the limits of what our models can do, and that research and that engine is upstream of everything else we've talked about. That is enabled by the models today. It's not entirely done by the models. Over time, we think the models will get better and they'll be more helpful in that process. But having the best talent to set the direction and to help effectively guide that process — not just the priorities, but some of the new areas of discovery — it actually makes that research talent even better. I think of it as accentuating and accelerating the talent that we already have. We talk a lot about how talent density beats talent mass, and that's true here. We want the densest collection of AI research talent and inference engineering talent, and that enabled with the best models is a really winning combination.
· Krishna Rao, Patrick O'Shaughnessy
[00:00:00] S1: The core of our company is still a research lab. Maybe that's not as well understood, maybe it's getting more understood from the outside. We are doing experiments. We are doing things that push the limits of what our models can do, and that research and that engine is upstream of everything else we've talked about. That is enabled by the models today. It's not entirely done by the models. Over time, we think the models will get better and they'll be more helpful in that process. But having the best talent to set the direction and to help effectively guide that process — not just the priorities, but some of the new areas of discovery — it actually makes that research talent even better. I think of it as accentuating and accelerating the talent that we already have. We talk a lot about how talent density beats talent mass, and that's true here. We want the densest collection of AI research talent and inference engineering talent, and that enabled with the best models is a really winning combination. [00:00:00] S0: How are scaling laws talked about internally? The consensus has been you've got different components of them. You've got pre-training, post-training, reasoning, and all of these are moving at different paces, and to hit a true wall, they would all need to fall down. That's how the world is now conceptualizing scaling laws. How are they talked about internally? How do you think about them?