Invest Like the Best with Patrick O'Shaughnessy
· David George, Patrick O'Shaughnessy
And, oh, by the way, we used to have this debate at GA, I think that's way less risky than something where you're buying a 12% grower in PE for 15 times EBITDA. In a weird way, it's less risky, because growth just takes care of so much for you and de-risks so much for you. I think above 30% growth, the market still doesn't fully value the growth rate. [00:00:00] S0: Why is that the case?
· David George
[00:00:00] S1: I think it's just hard to model. My conclusion, I've studied all these companies that are, I call them the model busters, but I've studied all these companies, and it is just so hard for any investor to build a 5 or 10 year model where high growth persists. It's just not natural. No one built a financial model for Google or Visa that had them growing 20 years into existence at 15% or 20%. It would just be totally unnatural to do so. If you look at the moment of the iPhone, and this goes back to the point about product cycles and how much you can get surprised, in 2009, if you looked at consensus estimates for Apple, and then compared for 2013, so 2009 consensus estimate for the year 2013, and compared it to actual performance in 2013, consensus estimates were off by 3X. That's a massive number, and that's the most covered company in the world. So I think you can be surprised on growth on these things. I get a big kick out of that, I try to learn a lot about it, but I think it's not natural to model anything that way. It's so natural to just say, "Hey, this company's growing 80%, then they're going to grow 65, then 50, then 40, then 30, then a terminal growth rate, and a terminal margin, and here's what our numbers are." It's very different than a company if it grows 80, and then the growth rate persists, 75, 65. It's like a 3X difference in your valuation. So you can just get it massively different. So that's why I love high growth. It's obvious, that's the math behind why I love it. But again, it's actually just hard to appreciate it because it's not natural to build a model that way.
· Patrick O'Shaughnessy
[00:00:00] S0: You and I have talked before about this idea of push versus pull companies. Can you describe that difference, and how that's an idea that you care about when evaluating them?
· David George
[00:00:00] S1: It's magic when you find a pull business. So I have a Post-it note on my computer in the office that says, "Is the market demanding more of your product?" It's the most special thing when it happens. By the way, a lot of these AI companies, what's so magical about the way ChatGPT has grown, it's a billion users, it's organic, it's all brand. And the shocking thing about that one, by the way, is it doesn't have a network effect. That was one of the more surprising things for us. So is the market demanding more of your product? Is probably the most important question that we can answer, because when it happens, especially in consumer, it tends to create the most special companies in the world. So we've seen it in companies like Roblox when it really works, and that one has two network effects, and so it's super special. We also see it in companies that aren't network effect or consumer. Like in the case of Anduril, turns out the market really, really, really is demanding more of their product, and there's many reasons for that. We've reached all at the same time this confluence of AI capabilities, autonomy, know how, and how to navigate governments, mostly from alums of companies like Palantir and SpaceX. At the same time, that we have a desperate geopolitical need. The market is demanding more of their product, and that's really special. One of the things that I say about push businesses, which is you got to go sell it. Sometimes those are really successful, and there's industries where this is the case, like cybersecurity and things like that. They don't tend to get easier over time. They tend to get harder. If you have to go sell or market your product, the bigger you get, often it gets harder. So that's not always the case. Sometimes, you get increasing returns to scale from brand and things like that. But especially on the consumer side, it almost always gets harder if you're a push business. TikTok maybe is the exception of the rule.
· Patrick O'Shaughnessy
[00:00:00] S0: Because they pushed it early?
· David George
[00:00:00] S1: They pushed it early and so aggressively. And obviously, if you're Facebook, you probably sit around and think about that decision forever. Maybe it's not even a decision. I wasn't on the inside, obviously, but the growth of TikTok was fueled by advertising on Facebook in large part, which is crazy to think about. But especially if you're a Google or Facebook-driven ad business, it almost never gets easier. It always gets harder, and Google and Facebook are the ones who accumulate better economics over time at the expense of the people who advertise on them. So the push first pull thing. So right now we talk about this in the age of AI. I think there's how do we assess AI businesses right now? Is an interesting thing. One is ease of customer acquisition, and we see this with the really, really special ones like Cursor, which has been largely viral growth. So ease of customer acquisition, it happens even with things that need to be sold like Abridge. You got to go sell to hospital systems, it turns out hospital systems are dying for this because the doctors love it, and it's really good. It saves them a lot of time, and it's really valuable. So ease of customer acquisition is something that is sort of a must for us in this AI wave. The second is customer behavior, customer retention, customer engagement. There are some head fakes that we've seen of things that grow really fast and then they fall off, and they're experimental. So the things that have durable behavior, things like Cursor, where the users really, really use it and ideally or increasingly use it over time. Harvey is an example of a company where, as the models have gotten better, customer engagement and usage has actually really grown. It actually took a step change, which we've seen, which is interesting to see, because it happened at the same time as the reasoning breakthroughs. We were like, "Oh, that makes sense, actually. Lawyers need to reason." And turns out models got really good at reasoning and people use the products a lot more. So ease of customer acquisition, the behavior that we observe on customer retention and engagement, and then there's gross margins. And we give a little bit of a pass on gross margins right now.