Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Krishna Rao
[00:00:00] S0: I'm always so curious by the metabolism of, in this case Anthropic, for new compute. How fast you could take, if I airdropped on you twice the compute that you have tomorrow, how fast would you consume that? If I airdropped ten times the compute on top of you, how fast would you consume it? Can you calibrate us on that? It feels like demand's unlimited between these three uses — training, internal, customer demand. Everyone's saying the same thing. Shortages everywhere, memory stocks mooning. Is it that extreme that if you 2x or 5x or 10x the amount available to you tomorrow, you would more or less instantly consume it? [00:00:00] S1: This goes back to how we use it and the fungibility of it. The answer is we're constrained across those use cases. A year or two ago, it would have been harder to consume, especially the heterogeneous compute drop in your example really quickly, because these chip platforms are different. Some are harder to operate. Some of them have idiosyncrasies in terms of how we use it. Today, getting a bunch more compute, I think it would be deployed very rapidly across those different use cases. We probably have the same allocation or calibration that we do with compute today, but it's become a lot easier for us to spin up very quickly and deploy almost any type of compute. That's something we think is a real advantage.
· Krishna Rao, Patrick O'Shaughnessy
[00:00:00] S1: This goes back to how we use it and the fungibility of it. The answer is we're constrained across those use cases. A year or two ago, it would have been harder to consume, especially the heterogeneous compute drop in your example really quickly, because these chip platforms are different. Some are harder to operate. Some of them have idiosyncrasies in terms of how we use it. Today, getting a bunch more compute, I think it would be deployed very rapidly across those different use cases. We probably have the same allocation or calibration that we do with compute today, but it's become a lot easier for us to spin up very quickly and deploy almost any type of compute. That's something we think is a real advantage. [00:00:00] S0: One of the interesting tensions and trade-offs that I'm fascinated to hear how you think through is between the platform approach — where I build my business on top of Claude and it powers my thing — versus you doing the thing that I want it to build. This is like the classic Claude Design versus Figma or something like this. How do you think about the right balance of how deep into the application layer you should go versus just being a pure enabling layer of, "We're going to provide the reasoning engine and the intelligence, and world go forth and build whatever you want, pass through the API." That seems like a fascinating internal discussion and tension to some degree.
· Krishna Rao
[00:00:00] S1: The way I would think about it is most of what we're building is platform. We think that there are so many examples of where a platform can accrue a lot of value, but the customers who are building on that platform actually accrue even more value. We think that's what we're setting up for today. It's maybe akin to the early days of AWS. If you think about the Claude platform and all the tools and services that are now built into it, because it's not just the raw model access — it is prompt caching and the ability to use virtual machines and Claude Code being called within there or Dispatch, on and on. The Claude Agents SDK, managed agents — all of these are vectors to access that model intelligence for other companies to build into their own products. That's most of what we're focused on, and really most of where we think the business is going from where we are today. We will also build our own applications on that same platform where a couple of things are true. Number one, if we feel like we have a vision into where the models are going and we can demonstrate that and create customer value in that. That might be something like Claude Code. We're able to say, actually, a lot of what's out there in the market was developer-led. Claude Code is a platform that's Claude-led, and we think the models can't quite do that today when it was launched a little over a year ago. We think they'll get there, and they have. One is building ahead to model capabilities. The second is thinking about ways to demonstrate value for the ecosystem that others might emulate. If you think about Claude for financial services or Claude for life sciences, or even something like Claude Security, these are ways in which we've composed the platform. Again, we're building on the same platform as our customers. That creates a level playing field. We also think that there's so much value that's going to accrue in these areas that our customers can win and we can win as well, which is why as we've launched some of these products, we've done them in a collaborative, partnership-oriented way — whether that be on the security side or Design or financial services. We've partnered with the ecosystem. I think of our strategy as mostly horizontal.
· Krishna Rao, Patrick O'Shaughnessy
We'll build vertical where we think we have some value to add or a perspective that's useful or a way to demonstrate to the market how we think about our platform adding value. A lot of the value is going to accrue to the customers building on top of it. Our goal is to build the best models and then build the products and tools and services that allow that intelligence to proliferate within customers. [00:00:00] S0: How much do you care that it's just a reality that people are scared of you? There is a sense that because you control the most essential piece of these new applications — the underlying intelligence reasoning engine — that may totally be true, and maybe already is true, that more of the value is accruing on top of the Anthropic platform than is being captured by it. But nonetheless, it's still scary to imagine. I guess maybe you could say something similar about cloud and AWS, or something like that. But how much do you think and care about the fact that some of your would-be customers or existing customers are in fact scared of you as a competitor?
· Krishna Rao
[00:00:00] S1: Part of what is hard in this business is it's changing so quickly. The model capabilities sometimes even surprise us. When we release models or products on top of that, there is an element of what's happened in prior waves over the course of five years, ten years, twenty years — it's happening in months now. When we release things, people are also surprised by it in some ways, in the same way that we were surprised by it. Fundamentally, what we are trying to do is be very partner-oriented towards the ecosystem, and that means we have early access programs. We work very closely with customers. We listen to them about what capabilities they want. That doesn't mean that the things we release are sometimes not moments where you're like, "Wow, that's way more powerful than I thought it would be," or "I didn't realize the models would be able to do that this quickly." Part of that is a reality of where we are in this cycle, in this development of intelligence. Part of it is also, we want to make those capabilities really accessible, and that should accrue a lot of value to customers as well — and customers that are front-footed on that and adopt it, and also ones that are building and using the tools that we offer on our platform, we think we can actually accelerate them. Some of it's a reality of frontier model development, but our approach to it is probably a little different and more partner-oriented.
· Patrick O'Shaughnessy
[00:00:00] S0: You said before, going $9 to $30 billion in the first quarter, the pace is so insane. Which makes me wonder about pricing. The dynamic of how to price tokens or use of the system is so fascinating to me, because a lot of people a year ago would say price is going to constantly fall. But actually what's happening is it's going up in many cases. This is true at different levels, whether it be the Mythos pricing that is quite high because it's so powerful. The cost of an H100 — the rental price of an H100 looks like a smile curve. I'm very curious why, if everyone is compute-constrained, why everyone doesn't just raise prices a lot to try to find what the right equilibrium is. I'd love you to riff on pricing, how you think about it, what the trade-offs are. Why not raise prices a lot?