Invest Like the Best with Patrick O'Shaughnessy
· 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?
· Krishna Rao
[00:00:00] S1: If we think about the transformation that's happening — there's been other transformative waves before, all the way back to the Industrial Revolution, the internet, cloud, et cetera. One of the things that's different about AI is it's all happening so quickly. You can have years or decades of progress that are being compressed into months. Going back to humans thinking in terms of exponentials versus linear, that can be jarring. We are very optimistic generally about the potential for this technology. We as an industry can continue to do a better job of articulating it. Dario wrote this essay, "Machines of Loving Grace." It's all about the potential for this technology to transform the way that we live, whether that be in drug development and curing diseases that are more mainstream but also rarer diseases, the ability to accelerate that biological progress. Number two, in healthcare and how healthcare is delivered. To raise our standard of living in the developing world and in places where resources are not as plentiful. How do we actually make sure that the economic gains are accruing to not just a small number of people in the world, but across the world? All of those things are part of the promise and the potential of AI. We could probably do a better job of painting that picture, and we want to show more tangible results for that over time. I think that is coming, and that's one of the things I'm most optimistic about. On the other side, though, we do want to articulate the risks. I don't think we should just tell everyone that everything's going to be great. There are likely to be bumps in the road. People generally gravitate towards more honest and balanced assessments. If I feel like somebody's just telling me all the good news and none of the bad news, then I'm like, "Okay, do I really trust this perspective?" That's where there's a need for balance. Look, these are some of the things that happen when change is compressed over a short amount of time. How do we work across commercial and government to actually come up with some of the solution sets? So I think it's about a clear articulation of the opportunities. It's about thinking about what those solutions may be, and that it's not any one company that can come up with it.
· Krishna Rao, Patrick O'Shaughnessy
We don't have this blueprint that's going to solve everything, but to at least have that dialogue about some of the risks and downsides and what we can do to address it. And then I think it's being transparent about that, about both of those things when we talk about it. I do think that over the long term, the opportunity is going to be significantly higher and greater than some of the risks and the downsides that will happen, but that doesn't mean it's going to be perfectly smooth on the curve. [00:00:00] S0: The release of Mythos was such an interesting moment. It was the first time many people — friends of mine that are careful watchers of this stuff — said something like, "This one kind of makes me scared." So it relates back to the safety question. It's also the first example of you coming out and saying, "We want to make sure this isn't used for bad, and it's maybe the first one that we are worried could be used for bad." I'm curious what that discussion was like internally before the world heard about it — the decision-making process around it. And just using that as an example, talk about the things that do scare you as we continue to advance and the scaling laws continue to hold.
· Krishna Rao
[00:00:00] S1: One of the things about Mythos is that people maybe misconstrue it as just a cyber model. It is an incredibly capable model across many, many different dimensions. What we found was that cyber in particular was a place where it spiked. This was the first model that we decided to release in a different way. The way in which we did that is consistent with our mission, our principles. We wanted to do it in that way. We have this phased approach to it because we think that when a model is this capable — and again, cyber is the thing that people focused on, but there are other things as well — we think it can be used in a positive way to patch code bases. You've seen these examples where we had an open-source code base that a prior model found 22 security vulnerabilities in, and Mythos then found 250. That is kind of scary, but that informed the way in which we released it. We didn't say we're never going to release it. We said, "Let's do it in a phased way. Let's do it to a group that will expand over time, where we can focus on this one cyber capability and how it can actually be used positively in a defensive way, as opposed to in an offensive way." We think that's a template that could be used for the future, but because of this one particular area, we wanted to be cognizant of that in how we released it.
· Patrick O'Shaughnessy
[00:00:00] S0: You're so big now that you run into everything and everyone. One example of this is the government, just a couple of days ago, said maybe there'd be this new system where you have to pre-approve the release of a new model with the government before it was released to the public. Obviously, you had the crazy experience with the Department of War, which I'm really curious what that was like as you went through it. Now everyone cares about this company and this technology and the couple of other companies that are building it. How do you navigate that stuff? Some of it's just beyond your control, but I'm sure you're trying to work with people as best you can. Maybe talk about those two examples of the government now as a very relevant partner, player, overseer, et cetera.
· Krishna Rao, Patrick O'Shaughnessy
[00:00:00] S1: We prioritize having a strong relationship on this because we do think that regulation has a role to play. We've been pretty vocal about that. We are very America-first in our approach. We want the technology to support the US as well as democratic countries around the world, and that's one of the reasons why we've been working closely with the administration. I do think that there's a balance. You want to be able to have innovation happen really quickly and have that not be slowed down, but you also want to have this responsibility framework for how these things are deployed. Because we've long said that this technology has implications, and we should have an honest conversation about them, and that includes with the government. I think the Mythos process is a good example of that. [00:00:00] S0: Can you teach us a bit more about how you would describe the cultural tenets to your parents or something like this? What feels like it really drives most of the culture? I'm especially curious about the writing. You hear often that Dario publishes these long essays — every so often — externally. My understanding is he does that way more frequently and there's a lot of writing culture internally. I'm trying to get a sense of what the culture is like to be in and what makes it the most distinctive from other companies — maybe that you've worked at, or from other companies that are trying to do the same thing. What's your sense of the differences and the distinctiveness?