Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Jesse Zhang
[00:00:00] S0: What is your perception of the very biggest companies? The entire market has been focused on, rightly so, the 7, 8, 9, 10 biggest technology companies. Which of them feel most important to you? And I don't mean OpenAI and Anthropic, I mean Microsoft and Amazon and Apple and these sorts of companies. What is your relation and thinking about them today? They've been the driver of equity markets by a huge margin. They're important companies. How do you relate to them? [00:00:00] S1: I mean work-wise it's not super relevant but of course we have our own opinions. I'm very bullish on Google actually. [00:00:00] S0: Why? [00:00:00] S1: I just think that with AI use cases having individual consumers is so important because that's where all the data comes from anyways and Google is much stronger there. I mean you could say that Meta Facebook also has that element and so yeah, maybe their new superintelligence lab will be able to make it work. But something with Anthropic for example where they haven't done as well on the consumer side compared to a ChatGPT, I think long-term you do need that consumer buy-in because that's where all the new data is going to come from. Yeah, Google and I mean Google just has an amazing team and they've had a couple of rocky starts but hopefully they'll make it work out. Of course all the large Mag 7 basically are obviously super strong so we don't really have strong views on them.
· Jesse Zhang, Patrick O'Shaughnessy
[00:00:00] S1: I just think that with AI use cases having individual consumers is so important because that's where all the data comes from anyways and Google is much stronger there. I mean you could say that Meta Facebook also has that element and so yeah, maybe their new superintelligence lab will be able to make it work. But something with Anthropic for example where they haven't done as well on the consumer side compared to a ChatGPT, I think long-term you do need that consumer buy-in because that's where all the new data is going to come from. Yeah, Google and I mean Google just has an amazing team and they've had a couple of rocky starts but hopefully they'll make it work out. Of course all the large Mag 7 basically are obviously super strong so we don't really have strong views on them. [00:00:00] S0: If I let you build a portfolio tomorrow where you got five slots, 20% each and five private companies building in and around AI, what portfolio would you build? Decagon excluded. [00:00:00] S1: Yeah, Decagon excluded. Let's see, just kind of gravitate towards where most of the talent is forming. I mean I mentioned Cognition earlier, Cognition would be in there for sure. Cursor also I'm actually kind of interested in where those might run into each other in the future. Let's see. [00:00:00] S0: Three more slots.
· Patrick O'Shaughnessy, Jesse Zhang
[00:00:00] S0: Three more slots. [00:00:00] S1: For me, definitely would want to take a bet on the hardware layer even though it's a much higher variance. So last night we were talking about Etched, companies that, I think you probably put one of those in there still on the earlier side, but obviously very high potential. [00:00:00] S0: Two more, I like the portfolio so far. [00:00:00] S1: Another friend of mine is building a company called Pika, building video models. So I just think very highly of that team as well. So we probably put them in there and the last one probably would want some sort of bet on the underlying model side even though all the large language models are out there. But another friend of mine I think very highly of, they're building models but not the types of language models, but still foundation models for healthcare, for example. So a friend of mine, Josh, is building Chai, they're building a foundational model. So stuff like that, I think it's quite interesting, or even I would put Lachy's company Physical Intelligence, and I think those are very exciting. It's just obviously too early to see how those will turn out. But if I'm building a portfolio, definitely want one of those in there. [00:00:00] S0: What do you think are—I'm interested in both ends of the spectrum, the people that aren't in your position who are both technical and commercially at the center of this wave. What do they overestimate and underestimate about the capabilities of AI today? Where is it further along than the world thinks? Where is it further behind?
· Jesse Zhang
[00:00:00] S1: It's a little bit more behind in being able to unlock a lot of enterprise use cases, I would say, because of the non-determinism. So two things that need to happen. One, you need to reframe the way that people think about agents. There's the Waymo effect that often happens where it will be objectively just way better than human drivers, and human drivers make a lot of mistakes. But because we're investing in new technology, the bar is a lot higher. So it has to be near perfect. So there's that dynamic that needs to adjust in some folks' minds where instead of evaluating AI in a way where you're just trying to find mistakes, you're evaluating holistically, looking at the success rate. And then if you can frame it that way, well, the success rate is going to be way higher than humans because, again, humans are not perfect. So I think that needs to happen, and I don't think that's fully happened yet in the enterprise. So that trend needs to happen. And then on the AI use case side, I think the models still need to get better in a lot of areas. We were just talking about voice-to-voice earlier. Hallucinations are too high there. As those models get better, I think more enterprise use cases will be unlocked, but I do think the general public will just see a really cool demo and be, "Okay, oh wow, voice is solved now." And as a result, enterprise should be adopting it left and right. And C-suites will see that too. But then when they actually get into it, it's just harder to go live. That's, I would say, the piece where it's not quite fully there yet. And so it's a little bit slower than people think. I think the thing that's on the flip side of that, where it's underestimated, is just things are growing exponentially, improving exponentially, and no one is good at conceptualizing what exponential means and things this. And so that could be from a performance-cost perspective right now. I would argue that if you're building an application, your margin shouldn't really matter that much. People will often critique the coding agents—they're hemorrhaging money. Yeah, but again, things are improving exponentially, and the cost will go down exponentially as well. So zero margin doesn't really matter. What really matters is you need to get market share and you need to get mind share of users. Perfectly fine not to have good margins right now.
· Jesse Zhang, Patrick O'Shaughnessy
And it's just everything just improves way faster than people think. It's slightly different at the enterprise. I mean, the same principle applies, but generally don't want to be hemorrhaging cash with an enterprise deal because those are just much longer-term, and even though the cost will go down, their expectations might also change and so on. So you generally want to be fairly healthy there. I think that's what's underestimated right now. [00:00:00] S0: How do you know that costs will get way lower? I'm very curious about this margin question because if you knew for sure, then actually you want to run super negative gross margins. If you knew that it was going to get 98% cheaper or something, cost of goods to serve coding agent or something that. I think the argument would be get the install base. Just have the best product and get the users and build the affinity with that user base and don't care at all. But that hinges a lot on the confidence that you have that those costs will fall. So how do you know? How do you think about that equation of win install base versus demonstrate good unit economics now?
· Jesse Zhang, Patrick O'Shaughnessy
[00:00:00] S1: Well, I just think it's quite unlikely that where we're currently at is the best that things will be. There's so much effort getting put into it, and one of the main metrics is efficiency. The other piece is that even if things don't get better, there's a lot of ways you can re-architect your system so that it is more cost-efficient. It's just that it's not worth putting time into that right now where you can put that same amount of time into getting new customers because you know that things will change in the future. So I think that's just one thing where people are, "Oh, it's kind of a scheme where you take VC dollars and then the VC dollars go to the chip companies." These companies are losing money. If they wanted to, probably they could just spend a month or even less and just massively improve their margins, but it's just not worth doing that work right now. What you're really optimizing for right now is just quality and growth. So if you can do that, then the optimization can always come later on. [00:00:00] S0: How do you think about your margins? Just putting it on Decagon—do you care at all? Do you set them? What's your guardrails or parameters for what's acceptable or what you're targeting?