Invest Like the Best with Patrick O'Shaughnessy
· Krishna Rao
[00:00:00] S1: It's really hard. The important thing is to think in first principles. Everyone has priors when they come to something new. Thinking in first principles and having intellectual openness. I spent a lot of time with Tom Brown, our chief compute officer. He was actually one of the first people to interview me at the company, and I remember we went on a walk, and he started to tell me about his vision for the future of the company. This is in 2024, early 2024. I'll be honest, it sounded crazy. He walked me all the way home. I remember I came in and I told my wife, "This is gonna be wild. If even 10% of that is true, this is going to bend all paradigms of not just things I've seen, but what most people have seen." It turns out that a lot of what Tom said during that walk has come to fruition. I remember that as an early formative thing — this walk I took with him and coming home and being like, "Holy shit, this is gonna be totally different and new. Really incredible experience, but also a really challenging one." That's what it's been. The other piece of this is just hiring great people. I try to hire people, and I tell people during the interview process, "I'm not really hiring you as a direct report of mine. I'm hiring you as a partner, and I want you to treat it as a partnership, which means there might be things that you and I disagree on. I want to hear that, and I want to whiteboard it. I want to understand." We've hired people from some of the best companies in the world. They come to this from a different perspective. They might come to it from a hyperscaler or a large software company, or from financial services. In another lifetime, I worked at Blackstone in the private equity group. That training is really valuable for thinking about things at a granular level and not losing that. I'm not somebody who is comfortable at 50,000 feet. That's just not me. But you can't be at 500 feet on everything in this business. There's just too much surface area. Having people who can be partners in that is really critical. We've been able to attract really great people, and they've pushed my thinking quite a bit. The last piece is to think about how the business evolves over time and where there might be moments or analogs to things that have happened in the past. I helped lead the financing that Airbnb did in the middle of the pandemic.
· Krishna Rao, Patrick O'Shaughnessy
Very different situation — the business lost 70% of its revenue in seven weeks. I know Brian did a show with you. That was a harrowing time, but it was also a time without precedent, where you had to think about things with a clear perspective when it was rapidly changing, and there was not a good template or pattern to match. On a personal level, it is hard to balance everything. Family and friends, and this job takes a big bite out of all that. What I do try to do, maybe once a week is, in a quiet moment, just think "Wow, this is really cool." It's an incredible opportunity to work with this group of people on this problem at this company at this moment in time. Maybe it's in a car ride, maybe it's late at night or something like that. Having that recognition and that appreciation is really important, so I try to bring that to my life for a couple of minutes once a week. [00:00:00] S0: What did Tom tell you on the walk that sounded most crazy?
· Patrick O'Shaughnessy, Krishna Rao
[00:00:00] S0: What did Tom tell you on the walk that sounded most crazy? [00:00:00] S1: We talked a lot about the scale of the compute infrastructure. What models could do in a short amount of time. He described a world that I would have said is kind of sci-fi. A lot of what we're experiencing here and now has roots in that conversation. There's even more things that he talked about that are probably beyond where we are today. But the commonality of it was that everything is gonna happen much quicker than we think, and that both the implications but also the capabilities of that can change. He also had a really incredible optimism about the future that we talk about internally — holding light and shade. That's one of the things we say. I came from that conversation with just a bunch of questions, but also a sense of positivity about what could happen in the future. [00:00:00] S0: It seems like we've spent most of our time talking about — because it's been the reality — that we exist at the high end of that cone. What can you imagine that would cause that to change to the low end of that cone? If we were to do some sort of pre-mortem on a year from now, where we're like, "Wow, actually, we didn't need nearly as much compute as we thought," or something like that. What can you imagine that would shift us meaningfully in that cone?
· Patrick O'Shaughnessy, Krishna Rao
[00:00:00] S0: It seems like we've spent most of our time talking about — because it's been the reality — that we exist at the high end of that cone. What can you imagine that would cause that to change to the low end of that cone? If we were to do some sort of pre-mortem on a year from now, where we're like, "Wow, actually, we didn't need nearly as much compute as we thought," or something like that. What can you imagine that would shift us meaningfully in that cone? [00:00:00] S1: The capability and the use cases are playing catch-up to where the model is. We are talking about humans in large organizations with a set of tools and practices and things that they've been doing for a really long time. Change is hard. To the extent that that diffusion hits a wall or slows down or something like that, that could affect the rate of change in terms of revenue growth. The scaling laws slowing down or not holding — we don't see that, but we can't say that with 100% certainty. That would be silly. We certainly believe in the trajectory, but the model capabilities leveling off would be another thing. Third is how we think about being at the frontier. Today, we're at the frontier. I think we're defining the frontier of agentic AI. We need to stay there. It's a competitive market, and we're going to continue to invest in the technology and the compute and the go-to-market that's required to be there, but that's not guaranteed either.
· Krishna Rao, Patrick O'Shaughnessy
[00:00:00] S1: The capability and the use cases are playing catch-up to where the model is. We are talking about humans in large organizations with a set of tools and practices and things that they've been doing for a really long time. Change is hard. To the extent that that diffusion hits a wall or slows down or something like that, that could affect the rate of change in terms of revenue growth. The scaling laws slowing down or not holding — we don't see that, but we can't say that with 100% certainty. That would be silly. We certainly believe in the trajectory, but the model capabilities leveling off would be another thing. Third is how we think about being at the frontier. Today, we're at the frontier. I think we're defining the frontier of agentic AI. We need to stay there. It's a competitive market, and we're going to continue to invest in the technology and the compute and the go-to-market that's required to be there, but that's not guaranteed either. [00:00:00] S0: To finish on an optimistic note, if those are the things that could cause us to go to the low end, what are you most excited about? You have a privileged seat. You sort of get to literally see the future, because it's happening inside the business before those outside the business see it. With that perspective and in that seat, what are you most excited about?
· Krishna Rao
[00:00:00] S1: I really think that the biotechnology and healthcare outcomes that can come from this technology are the things that I'm most optimistic about. We may live in a world where you're diagnosed with a disease that is not curable, but in your lifetime, that cure can be found much more rapidly, and you might not die of that disease. A lot of what we're doing today is helping to speed up the drug development process. A lot of the paperwork and clinical study reports and things like that that need to be done — AI, and our solutions in particular, are helping to rapidly accelerate that. I'm most optimistic and excited about when it goes further back into drug development and drug discovery. Because humans are capable at research, but if you think about these molecules and proteins — they're so complex, and such small changes have such big implications for the outcomes. AI is perfect for that. What can happen when the lab's throughput goes up 10x or 100x, and we can run that many more experiments? Probably get better results faster, and that can be something that helps people around the world. It doesn't have to be limited to a small set of diseases or disorders, it can really go much further down the chain. That has the potential to greatly alter the way that we live and the way that we interact, and that's really exciting to me.