Invest Like the Best with Patrick O'Shaughnessy
· Krishna Rao, Patrick O'Shaughnessy
[00:00:00] S1: We think about the world as scenarios. It's very hard to have a point estimate in this business. And then having a very low bar for updating your current priors, or your current perspective. It could be the case that something a month ago was true that's just not true today, and that breaks your model, and you have to go back and update it. This old, "Well, we'll forecast once a quarter and we'll revisit this in three months at the next board meeting" — that doesn't work for our business. It's so dynamic that we have to always think about, "Oh, our models couldn't do this before and they could do this now. What does that mean for the TAM?" We've seen this in coding first. Starting with around Sonnet 3.5, 3.6, we started to see this really remarkable jump in capability, which was then followed by adoption and usage and revenue. It was a little hard to predict that, but now we can use coding as an analog for a lot of what's happening elsewhere in the economy and elsewhere in our business. We look at pattern recognition in our own business to try to predict what's going to happen in the future. [00:00:00] S0: Literally 15 minutes before you got here, the news came out about your partnership with xAI and the Tennessee facility. It makes me curious about how you are canvassing the world. That is an opportunity you decided to do. I'm sure there's a universe of things that you've explored. What is the strategy for trying to get more in creative ways? Bring us a little more into that.
· Krishna Rao
[00:00:00] S1: We announced a partnership with SpaceX for their Colossus facility in Memphis. Really excited about that. It's going to allow us to continue to expand, especially on the consumer and prosumer side. That's just one example of us looking for near-term compute wherever we can get it. As the compute base grows, that near-term compute becomes a smaller and smaller fraction of what's available and what's out there. We look at it as, can we deploy that compute that's available productively? Sometimes the answer's yes, and sometimes it's no. But if we can, then we look at the economic return on it based on what it's priced, what duration we have it for, where it's located, what type of compute it is, and how efficiently we can run it. We have a process to assess. That same process, we use to assess longer-term deals as well. Last month, we signed a five-gigawatt deal with Google and Broadcom for TPUs starting in 2027. We also signed a deal with Amazon for Trainium, for up to five gigawatts as well. It was an over $100 billion commitment, and a lot of that compute is already landing and will land in the rest of this year into next year. So if you think about it, it's a layer cake of compute that's starting at different times with different capabilities, and we're very dynamically comparing that compute. Its price-performance over time is really important to us — when it lands, and what we think we can do with it internally in the business. There are so many different variables you have to optimize for around what compute it is, at what cost, and over what time horizon. But we have a pretty dynamic way of looking at near-term compute and then medium- to long-term compute. But the things we're assessing are largely the same. What is different is just the time horizon.
· Patrick O'Shaughnessy, Krishna Rao
[00:00:00] S0: What about the trade-off — you said price to performance — between cost per token or something, throughput, and speed? Both are important, of course. From the customer perspective, they care about both. Speed probably unlocks some capability and use cases that are really interesting that we don't know about yet as these things get faster. Can you talk a little bit about that trade-off in compute as you're assessing it? [00:00:00] S1: As we look across three different chip platforms, we also have multiple generations of chips within it. It could be TPU v5e, v6, and v7, Trainium 2, Trainium 3. All of them are at different places on the price-performance curve. We look at how we will utilize it. Price to performance is important because of efficiency. Speed is also important for certain use cases as well. We look at the compute down to a very granular level in terms of what it can deliver for us and when. Our compute team leads that, but we closely collaborate across the business to say, "Where do we need this compute, and for what?" Okay, we might need CPUs for RL. How much of that do we need and where, and what's the nature of it? We might need this more leading-edge compute, and we're going to deploy it for our best and fastest models, or for training them. From our perspective, it's customer demand, but it's also down quite granular in terms of what each chip is best for, and then what we will have when.
· 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.