The Exchanges

Q that in their early T row days that you were studying media and you studied 20 or 30 years worth of media history and condensed it down to a very small three or four page report. Can you bring us back to that study and what you learned about me? I'm obviously interested in media. I'm curious what you learned then about media and how that has evolved ever since.

A Yeah. I mean, we, we tend to want to, I tend to want to do this. I always believe that if you really understand something, you can make it super concise and you know, where we are with robotics and less so with AI. Our internal memos are probably too long, because frankly, there's too much unknown, and so we can't be concise. I was very lucky in media, because, and it's something we try to do at Durable with people, because I was an outsider to the media industry, I think I brought fresh perspective to it. And so when I, when I was assigned to basically be a media analyst, This is so hard to believe, but the companies that were viewed as the darlings of balancing durability in terms of competitive modes and having strong growth were companies like Comcast, Time Warner, Disney, Viacom. And anyway, I, I, you know, did a bunch of Work on the companies individually, and then I started to really think hard about it, and I started to realize that the best businesses inside of all of them Had been the cable networks, right? And so if you go back to, if you read about media back then, the entrepreneurs that, you know, became the most famous were the ones that launched cable networks, right? John Hendricks, Ted Turner, Bob Johnson. By the way, John Malone basically backed almost all these people, put the most invested. So John Malone was at the center of all this. And, you know, basical…

AI assessment note: “I started to realize that the best businesses inside of all of them Had been”

Q potential with an unknown timeline that we might get like a second wave of, you know, the physical labor economy is way bigger than the, The digital labor economy, that we may get a second application of Kaizen, um, over the next 40 years, like the first one we saw from Mitch, Mitch and Company. How do you think about, how do you think about that opportunity and potential change?

A It's something we have really thought hard about, and here, here's what I, what, what we have tried to do, right? So first of all, we have tried to get smart by, by meeting with the entrepreneurs And talking to the companies that we're involved in that we know that are leading this area just to try to learn. And we only recently did this with robotics. So if we started writing down our conclusions and then being humble that it could change every, you know, six months in AI, in what you would say is more data businesses or digital businesses. We only started doing this literally in the last month where we documented it for the first time. And so I'm going to, I'm going to do something that I don't love to do, which I know our views here are very early and probably deeply wrong, but I'll give you our initial conclusions, right? Which is in certain use cases, it's pretty clear that already the cost is lower than the, um, equivalent analog process or basically physical labor, physical labor process. And yet, as we all know, this is the earliest and worst robotics going to be. And because machines are iterating with machines and it's being powered by general purpose models, not by specific purpose models, this is riding, you know, a curve that is definitely geometric. And so back to this mental model of Amazon that drives so much of what we think about durability at durable is what …

AI assessment note: “in certain use cases, it's pretty clear that already the cost is lower”

Q Is that like the, uh, Steph Curry wizards at their peak approach to, to contrast against the Jordan approach or something?

A Yeah. I mean, that's exactly how I think about the warriors, right? Like Steve Kerr, I think is amazing. John Wooden. Amazing. I think John Wooden is the greatest coach of all time. Think about what John Wooden wanted from his players. He wanted them to be great people. He didn't necessarily believe they all had the same modes. I mean, Kareem Abdul-Jabbar and Bill Walton, you know, maybe the two greatest college basketball players of all time in their eras, but definitely in the top five, totally different people. And he accepted that, but he wanted them to be great, not only as basketball players, but as people. And he was measuring UCLA against that. And frankly, of course, the output of that is, you know, the success they had. And to me, that was the, that was the Lakers with Magic Johnson, right? Like you watch those guys play basketball. And they just were having fun and they were elevating the game. I remember going and seeing the warriors, you know, when Steph and Draymond and clay were just kind of coming up and The energy of those people was amazing, and they transformed the game, right? They changed the three-point shot, and then of course, when you see greatness like that, you gotta go learn from it, and of course, I've gone and understand the way Steve Kerr is, and how he cares about competitiveness, but he cares about mindfulness, he cares about fun. If you're gonn…

AI assessment note: “Yeah. I mean, that's exactly how I think about the warriors, right?”

Q one on your hands? And I know you do late stage private investing as well. So you're, you're looking at these companies and when they're, you know, at or near their IPO, um, we'll talk about going public later on and why that's valuable. But what have you, what have you refined to be the most important signposts of a company that might be one of these one percent valedictorians?

A If you've been one before, you have a higher probability of being one again. Right, which just sounds so simple, but is actually really interesting. We look at who, who has actually done it, and if we don't Really know the company and we haven't studied before. We'll go double click and go essentially study it. See if there's an opportunity, but if not go do a case study on it. Right. So we want to go learn it. One of my partners, Anouk Day, basically, um, you know, teaches a class at Columbia business school, the value analysis program. And partially this is our way of kind of going back to school as an organization and partially it's our way of giving back. To our community, but, you know, the class is based on this, and the students literally do about six case studies a year, but over time you kind of build out a library of them. You just start by basically studying the ones who've done it, and then obviously trying to study the patterns of those who've done it, right? So that, that, that's, that's number one on how we do it. Second of all, they're diversified across the economy. So, you know, look, we're in a period of time where What's going on is an AI is so impactful. My view on the impact of this is probably no different than a lot of the other speakers you've had. I would say what I think about when I think about the power of AI and really studying it, I don't only thi…

AI assessment note: “If you've been one before, you have a higher probability of being one again.”

Q We've talked before about some Crazy percentage of just marginal volume that happens inside of the platforms, the, you know, Citadels, Millenniums, Ballyasne, these .72 of the world. How are you, how do you feel that? And I'm curious just for your thoughts on changes to market structure in general, since you've been doing this, uh, how it contributes to that volatility, what opportunities it creates, what dangers it creates.

A It's something I thought a lot about. I started thinking a lot about it two years ago, and in hindsight, I probably started, I should have started thinking hard about it. You know, you know, three years ago, but you know, you know, you don't get everything right. There was a period in my career where the quant funds really started to do great. The two, you know, the two sigmas of the world. And, you know, one of the things I've, I re we really stress at durable is humility, right? So we never look at a problem and assume we're right and the other person's wrong. And we never assume we're good and the other person's bad. We actually look at things and assume the other person's really smart and what we can go learn from them. And so this, this relates back to for, you know, I found a durable, but I went and I studied the quants. And what I concluded was, the short-term alpha game is probably gonna be won by the machines paired with the humans. And at the time, it was when, for the first time, computers paired with machines could be the best human chess player. And this is obviously, um, when I went to spend time, you know, with the principal at Two Sigma, I learned he was doing exceptionally well. But anyway, um, I got to know him and we talked a lot and I realized actually there were very, there were real limitations to what the quants could do. And so I started realizing if it'…

AI assessment note: “the short-term alpha game is probably gonna be won by the machines paired with the humans”

Q When you were doing your tour to learn about the franchises that were better at the end of the founders run, um, and those that didn't make it, what, what, what did you learn?

A I won't mention the names of those who didn't make it, because, you know, the thing about, um, not making it is a little bit like our investment memos when we invest in great people who are trying to build companies. A lot of them do great things, and they just don't make it, right? I mean, as you know, there's a lot of, in success, there's a lot of good fortune. So, I think what I learned was if you don't architect the system on day one, For success, then you end up with a lot of conflicts that sometimes undermine what you could have accomplished. We try really, really hard and durable not to make compromises. If we go hire someone on the investment team, We want to go hire someone who one day could be a senior partner or one day go, you know, basically manage, you know, the capital base, right? Or if we were to ever launch a new product, go launch that product. We're looking for people who can be as good as I am or Anouk is or Corey is or Catherine. We want great people. And so for sure, you usually start in our parlance as an associate, right? So you're You're going to start supporting someone and you're truly going to be an apprentice in their way. And then even when you become an analyst the first three years, you're probably doing real analytical work, but we're probably, um, you're early in your journey. We don't want to hire you. We don't want to promote you unless we t…

AI assessment note: “what I learned was if you don't architect the system on day one”