Invest Like the Best with Patrick O'Shaughnessy
· 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.
· Patrick O'Shaughnessy, Krishna Rao
[00:00:00] S0: These aren't pilots anymore. [00:00:00] S1: Exactly. On the way here, I was in an Uber, and I signed two double-digit million-dollar commits in the car ride, which was 20 minutes. From that perspective, we're seeing it, and we're now being judged by some of the biggest companies in the world, the most sophisticated buyers. And startups — they have choice in the market, and they're choosing us. One question I get a lot — or I would ask from the skeptical investor seat — is, "How are your customers getting return from this?" Maybe a third one is, how do you think about compute in the future, and where does it come from? Some of the places that we buy compute from, they sell the compute to others. They may use the compute internally. What is the balance of that over time? [00:00:00] S0: So your philosophy there is be involved with great players and have flexibility. [00:00:00] S1: That's right. [00:00:00] S0: There's this crazy stat about AI, just the generic concept being less popular than Congress amongst the general populace. It's kind of funny when you first hear it, but when you really think about it, you're like, this is kind of fucked. We need to solve this problem. It doesn't seem like the general world that isn't in technology, doesn't live in the Bay Area or New York — does not yet feel or understand why this is good for them, just as measured by their opinion of it. What do you think we need to do as an industry about that problem?