Krishna Rao - Anthropic's CFO on Compute, Scaling to $30B ARR, and the Returns to Frontier Intelligence - [Invest Like the Best, EP.471]
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My guest today is Krishna Rao, the CFO of Anthropic. The center of our conversation is how he navigates the decision around procuring and allocating compute, which he describes as the canvas on which everything else gets built. We talk about what he calls the cone of uncertainty, the three chip platforms Anthropic uses fungibly across Tranium TPUs and GPUs, and the daily meetings they run to allocate compute between model development, internal use, and serving customer demand. He explains why the returns to Frontier Intelligence keep getting higher, especially in enterprise, and how Anthropic
The compute that we procure is the lifeblood of our business. It is the most important thing in the company. It's like the canvas on which everything else gets built. And so the decisions we make and how much compute to buy are some of the most consequential and hardest decisions to make in the entire company. Think of it this way. If you buy too much compute, you go out of business. If you buy too little compute, you can't serve your customers and you're not at the frontier. Same thing. So we talk a lot about this cone of uncertainty, but the idea of just these purchases that have these real
What does flexibility mean in that example? Means a couple of
different things. Number one, we use three different chip platforms. So we are customers of Amazon's Trainium chip, Google's CPUs and NVIDIA's GPUs. We use these chips fungibly. If you think about the compute we buy, we're using it for model development, we're using it internally to speed up our own product and model development, we're also using it obviously to serve customers. Across those three chip platforms, we're using compute for all of those internal and external uses. And that flexibility took us a long time to be able to do that. We've invested in that over multiple years to be what
Am I thinking about this in the right way that something like CUDA that has been a part of NVIDIA's story for a long time now that allows you to do a lot with the underlying actual hardware that you want to sort of eke your way into being as close to the bare metal as possible. That's part of this flexibility and being able to control as many of the variables as you can. Is that the journey that you've been on?
That's part of the journey for sure. But it's also been actually pretty collaborative. So we work really closely with the Annapurna Labs team at Amazon to help to influence the roadmap of these chips because we believe what we're doing is really stressing the limits of what these chips are capable of. And
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