Invest Like the Best with Patrick O'Shaughnessy
· Krishna Rao
[00:00:00] S1: "There's laws of physics. You're in enterprise, which is great, but the adoption's gonna take so much longer. Look at how long it took with cloud and how many people are still on-prem." The business continued to prove out the thesis that the return to frontier intelligence is really high. What's really happened is model-led growth, enabled by products and our go-to-market team and our distribution. What they also saw was that this thesis of, "Hey, it's really important to build this transformative technology, but to do it in the right way and do it responsibly." That had this really interesting interlink with our business that most people didn't understand or really believe, which was that we invest in research, not just in model development, but also in AI safety research. We've pioneered interpretability. Think of it as an MRI for the model to see inside the neural network, how it works. We pioneered alignment science — you want the model to do what you tell it to do, and how often does it do that, and how often does it stray from that? Those things are important for our mission, and that's why we did them. They had these downstream effects where it turns out if you can look inside the model, you're better at building them. The last linkage is, if you're selling to enterprises — we now sell to nine of the Fortune 10. All of those enterprises are entrusting us with customer information, their data. They're interacting with their employees, sometimes even interacting with their customers as well. Those are the most sensitive workloads. More and more of these businesses are running on Claude and our Claude platform. When you have this investment that we've made and will continue to make in safety, interpretability, alignment — that actually inures to the benefit of the enterprise customers as well, and all of our customers. Because they want that — if they're going to entrust us with all that access and all that data and the ability to work in the most sensitive workflows within their company. They want a company they can trust. That's not why we invested in it, but it did have this downstream effect that we've really seen prove out again and again — to be a company that is both at the frontier, and one that is investing in safety and that you can trust. We've raised $75 billion since I joined the company.
· Krishna Rao, Patrick O'Shaughnessy
We have another $50 billion that'll come in the future from the Amazon and Google deals that we inked last month. That's a tremendous amount of capital, but it's a capital-intensive business, and we need this capital to support that growth. It all goes to the fact that the business is running very efficiently. The reason we raised this capital is more because of that cone of uncertainty than it is to fund losses in the business today. [00:00:00] S0: Your own perception of this prospect for a 10x growth of the business — the first time that happened, did you personally believe that it was possible? Did that seem absurd? Now it's becoming consistent, and maybe it's becoming more commonplace to you or something. But what was your own view, staring at this cone, about the odds of hitting a 10x type of growth so many years in a row?
· Krishna Rao
[00:00:00] S1: When I joined the business, it had about $250 million of run-rate revenue, and the plan was to get to a billion, and I said, "Great. In what year?" That was linear thinking. Consistently, Dario has been a much better predictor of the revenue than I have. I think we're going to close the gap over time as we get better at forecasting and understanding the business. But definitely the first time I saw it — you have all these arguments about the laws of physics and law of large numbers. Where is the revenue coming from, and how can it be added this quickly, and how can customers move this quickly, and is this even possible in enterprise? All of those things start to get broken down over time as you see how the business works internally and you see how the adoption curves and the exponentials that are happening. We have the exponential happening on revenue, but underlying that are these many other exponentials that support that. You start to see and believe in that more. That doesn't mean we're not disciplined and thoughtful about the forecast and how we think about the range of scenarios, but it does mean that my thinking has at least shifted a lot more from linear and incremental towards leaning into this exponential and really believing in its potential, and how this is just different from how other businesses have evolved.
· Patrick O'Shaughnessy, Krishna Rao
[00:00:00] S0: As you've talked to investors at every stage, I'm sure every round that you've raised, there's something that is the most common or hardest thing to explain to investors — where they're struggling the most to understand and get their heads around. What is that today? [00:00:00] S1: I think it is this paradigm of how compute is used. Thinking of it as not just something that is a variable cost over some time period, but really this resource that's so fungibly utilized. We run workloads on one day in the morning on a chip for inference, and in the afternoon or evening, we use it for model development. That paradigm does not exist in a company like a software company or a factory. You can't repurpose — if you have a bunch of people doing R&D and that's your R&D expense, they can't go and become COGS, and vice versa, in most traditional companies. Here, you really have that fungibility . That's possible. That's where the return on compute is so important, and I think people are beginning to understand that. But there's still a tendency towards treating it like, "Oh, I have to separate these two costs," when in actuality they're very self-reinforcing, and that flexibility is what helps to drive revenue short term and long term.
· Krishna Rao, Patrick O'Shaughnessy
[00:00:00] S1: I think it is this paradigm of how compute is used. Thinking of it as not just something that is a variable cost over some time period, but really this resource that's so fungibly utilized. We run workloads on one day in the morning on a chip for inference, and in the afternoon or evening, we use it for model development. That paradigm does not exist in a company like a software company or a factory. You can't repurpose — if you have a bunch of people doing R&D and that's your R&D expense, they can't go and become COGS, and vice versa, in most traditional companies. Here, you really have that fungibility . That's possible. That's where the return on compute is so important, and I think people are beginning to understand that. But there's still a tendency towards treating it like, "Oh, I have to separate these two costs," when in actuality they're very self-reinforcing, and that flexibility is what helps to drive revenue short term and long term. [00:00:00] S0: If I was to force you out of your role and into an investor seat at a great big investing firm, and then I said, "Your job is to go grill these companies and invest in the best ones." What questions would you be asking of the labs or companies building models to really get at the heart of the points of uncertainty, of skepticism, of things that might not make these the best businesses of all time? Curious from that angle how you would approach it.
· Patrick O'Shaughnessy, Krishna Rao
[00:00:00] S0: If I was to force you out of your role and into an investor seat at a great big investing firm, and then I said, "Your job is to go grill these companies and invest in the best ones." What questions would you be asking of the labs or companies building models to really get at the heart of the points of uncertainty, of skepticism, of things that might not make these the best businesses of all time? Curious from that angle how you would approach it. [00:00:00] S1: First, what is the ROI on compute, all up? How are you utilizing it? What return are you seeing today, and how is that coming over time? That's certainly a question I get asked a lot by investors, but I think it's a really important question. These are the massive unprecedented investments that companies like us are making. What's the return that you're generating on that, and when does it come, and what is the shape of it? So I think that's one. A second one is, how do your customers see ROI? I get asked this question a lot as well. Are people just using this for testing? Are they actually deploying this at meaningful scale? For our business, we're seeing that in spades. Our net dollar retention rate is over 500% on an annualized basis. Nine out of the Fortune 10 — these are real customers making significant buying decisions. [00:00:00] S0: These aren't pilots anymore.