Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy
[00:00:00] S0: Can you bring us into the room for the conversations around the trade-offs between those? I'm so interested by those three buckets of training, research, internal use, broadly speaking, and then serving customer demand. Naively, you might think it's a third, a third, a third allocation or something. How much does that range around? What are the trade-offs? What is that discussion like on an ongoing basis?
· Krishna Rao, Patrick O'Shaughnessy
[00:00:00] S1: In addition to meeting about compute procurement, we meet a lot about compute allocation. What's important is it starts with a place where our culture is one that's incredibly collaborative, and that informs how this conversation happens. There's not fiefdoms. It's done in a collaborative, not zero-sum way. There's a level of compute for model development that we will not go below, even if it means it's harder to serve customers or we have to do unnatural things when it comes to that. We want to continue to make that long-term investment in developing the best models because we think the returns to frontier intelligence are extremely high. And it's especially extremely high in enterprise. That puts a floor on the compute allocated to model development. As we think about the internal use of compute, it really helps us to speed up that model development and to speed up finding those compute efficiency multipliers that get us more from each dollar of compute. So when we're talking about it, each team is representing what they would do with that compute, and then we have a really open and frank discussion about how we think about ROI. Because we can allocate that compute so dynamically, we can make changes and adjustments on a relatively short time horizon. [00:00:00] S0: The efficiency thing is so interesting to me. I'm curious if you have a sense of how much more efficient you are versus your own internal benchmarks from a year ago, or versus others that you have some sense of how efficient they are? How do you measure what efficiency means?
· Krishna Rao
[00:00:00] S1: There are a couple of different ways I would think about it. From a model perspective, the analogy people have when these new models come out is they're like cars. You had a sedan before, and then you might have the higher-end version of that sedan, and you're moving up the chain. That is true in terms of model intelligence. The place that analogy breaks down a little bit is people think, "Okay, I'm going from the sedan to the sports car, and we get much less fuel efficiency. I'm not gonna buy the sports car for the gas mileage." In our case, we actually see huge improvements in capability, but also in model efficiency. If you look at going from Opus 4 to 4.5, 4.6, and now 4.7 — they're not equal, but each one of those leaps to a new model has a multiplier in terms of how much more efficient it is at processing tokens effectively. That doesn't just serve customers, that also helps us internally as well. If we're doing reinforcement learning on the model, it's basically inference within a sandbox with a reward function. So if the model's better at more efficient inference, that RL is more efficient as well. We're able to do this win-win where the customer is getting more capability when we release a new model, and we're able to serve that model — sometimes again, a multiple more efficient than the prior generation. When we're in between generations, we're dynamically deploying efficiency improvements in between these more step-function model changes. It is always getting more efficient over time, and what fuels that is the research team. If you think about it, all these things are very connected. These various tasks and workloads that we have internally all fit together in this way. Doing R&D for model capabilities, for compute efficiency, for serving customers, and then having internal workloads that can be sped up by using the best models — sometimes models that we haven't released.
· 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.