The Exchanges

Q Well, that's so cool. And am I right that, that y'all actually went, uh, to the Santa Fe Institute and, and took courses there? Like, like, how deep did you go in this?

A We did, um, you know, like, Like a lot of rabbit holes, we go down very deep. Uh, we, we quickly became members of the Santa Fe Institute. They call it the action group. It's, uh, this group of non-scientists that are allowed to sit in on a lot of the, the science. And so then we took this complexity course over a weekend, Brad and I did at Stanford. Um, and, and that was just a ton of fun. Actually, John and Joe, uh, the two other investors on our teams, they took a longer course. They actually had to do real work. Brad and I didn't have to do homework. Um, But, but we just learned so much. And, and I remember sitting outside of this cafe at Palo Alto with Brad, and, and we just sort of been at this course with Deborah Moore, this lady that teaches at Stanford who studies ants. And I thought, man, this concept of resilience is really fascinating. You know, it's really more about resilience than it is about predicting the future. And it's about adaptability. Like biology doesn't really care that much about the future. They care about, they care about adapting to this wide range of futures, right? Like my bees don't really care if it's going to snow tomorrow. They can adapt to snow. They've learned how to do that over millions of years. And what if we looked at companies like that? And so then, you know, of course we kept reading, we kept writing. This was probably. 20 11, 20 12…

AI assessment note: “We did... we go down very deep. Uh, we, we quickly became members”

Q Well, Brent and John, where can folks find you on the internet?

A You can go to nzscapital.com. We do try to write a lot. Um, we put it all on the internet immediately. Um, it is everything that we use internally. Nothing's held back because we know when we put it back, when we put it out there, we're going to get more value back. So this is our way of trying to create more value than we take. Um, our partner, uh, Brad also writes a newsletter every week. And so if you want to speak, see how he spends his time, um, you can sign up for the newsletter on nzscapital.com. It's called sit all week. It's just Brad's process. Um, Of sitting all week, and what he thinks about, and Brad is, um, well, he's like a microprocessor. He's, he's literally a small, smartest person I've ever met, and the way his brain works is incredible, and so if you'd like to sign up for that, you can do that there as well.

AI assessment note: “You can go to nzscapital.com.”

Q On the note of ants, uh, this is like probably the first and, uh, best example of an extreme version of resilience in an organization. Can you share the insight you had there?

A Yeah, so we attended this class by Deborah Gordon, and she has been studying this group of ants for 30 years in New Mexico. So like, they obsess over this group of ants, right? And they know what every ant is doing at all times. And what they found was really fascinating. They found that about half the ants in the colony weren't doing anything. They were just sort of sitting around, and then they had half the ants doing these defined jobs. And that's very counterintuitive. We think of ants as sort of the ultimate productivity machines. Um, But it turns out ants aren't optimized around productivity. They're optimized around longevity. They're optimized around resilience, around living as long as possible. Let's say it that way. Um, so, so that was really insightful for us. We thought, man, all these companies are optimized around productivity and Wall Street only makes it worse because we're obsessed over quarterly earnings. And so what if companies were really optimized around this long-term thinking? Of course, we see that with lots of companies. Most of them tend to be run by founders. Because founders have a lot of skin in the game, and they think long term. Um, but there are CEOs that think that way also. We know that the average tenure of a CEO in S&P 500 is less than five years. So this is not, they're not optimized like ants are. They're, they're trying to get a lot of r…

AI assessment note: “ants aren't optimized around productivity. They're optimized around longevity.”

Q So what's an example? And those are the companies that like you bump up to seven, eight percent of the portfolio, right? What are some examples of those?

A Well, I mean, sort of, there's a couple, right? The classic example would be Amazon and, In, in 97, when they went public, you know, I think around a billion dollar valuation, nobody could have foreseen AWS, right? That wasn't anybody's DCF. Oh yeah. They're going to also create infrastructure that everybody in the world is going to use, create businesses. They sound so silly, right? But, uh, another one that we had in the portfolio years back was, was eBay. Um, I don't know if you guys remember the marketplace business was struggling. Uh, they brought in a new CEO, John Dono. Um, they had PayPal, uh, and really you weren't paying for any of PayPal. If, If the marketplace business would recover, that was, that would more than cover the cost of, of entry. So, um, of course, you know, marketplaces did recover. PayPal ended up being great. John Donahoe is an amazing leader. Um, and, and that was a classic Rootmo stock.

AI assessment note: “The classic example would be Amazon and, In, in 97”

Q the Zooms, you know, and the like, I'm thinking Zoom, uh, you know, Zoom went from, I don't know what, 70, 80 dollars a share to, To 600 dollars a share and then back down to, you know, that I think it's at like two 80 right now. Uh, so you've kind of seen this happen, right? Like the optionality played out. That was correct. But then returns pulled back.

A We're always looking at like what's happening to the range of outcomes. Is it widening? Is it getting broader? Is the prediction becoming safer or is it getting there or is it remaining narrow? And so with a company like Zoom, which we never owned because, you know, I don't know, this bad call. Um, uh, it, it, it looks a lot to us like a feature, and, and then, so now the question is, can it become a product and eventually maybe a platform? Can it develop an ecosystem around it? We don't know. Um, but let's say, to take your example, let's say in the middle of the pandemic, it was a sort of a cool feature, it was better than everything else on the market, still is, and, uh, and then this big ecosystem came around it, and it became a full-blown platform. Well, then the range of predictions, actually, Would know that the range of outcomes would narrow and the prediction would get safer, right? And so then that would warrant that becoming a bigger portion of the portfolio. Valuation is a key piece. And this is the piece that we get every day as public investors and valuations, expensive valuations force predictions. I have to believe a lot more at 10 times sales than I do at 10 times earnings, right? So we're seeing, okay, what is the prediction of the company and what is prediction the market is forcing us into? And are we comfortable with that?

AI assessment note: “with a company like Zoom, which we never owned because... it looks a lot to us like a feature”

Q like, well, of course I want to know, like I could glean so much more information and like subtle signals from like talking to somebody in person. On the other hand, I kind of think like, well, I really care about what you do, not what you say. Uh, and I can just see what you do in your filings. Um, yeah. How do, how do y'all think about that?

A This has changed a lot over the past decade, because of course, seeing a management team talk is easier than it's ever been, right? It's, it's publicly available. Um, there are some times when it's helpful. There are some times when it's harmful, you know, it probably nets out to be net helpful. Uh, but I'm just thinking of one interaction, um, that we had with Rich Templeton, the CEO of Texas Instruments in early February, of 2009, right? It's a terrible time. Everybody's unhappy. Uh, it's, it's really rough. And Rich walks in the room, big smile on his face. How's it going, boys? You know, a recession is a terrible thing to waste. And you're like, what's going on?

AI assessment note: “There are some times when it's helpful. There are some times when it's harmful”

Q Santa Fe Institute, which I want to get into. I think, uh, Bill Gurley talks about this, uh, uh, fairly frequently and, uh, Michael Malvison, and, uh, that's how I kind of originally got turned onto it. But, um, but, but tell us a little bit more about like, what, what is it? And because, you know, it's, it's not, it's not at all about investing. It's about the world.

A That's right. Um, yeah, in fact, I think it was Bill Gurley that recommended complexity to Brad. Um, so, you know, complex adaptive systems are all around us, right? That's what governs the world. That's how the world works. We don't know how the future is going to unfold because the system is interacting together, and it creates what's called emergent behavior, and emergent behavior makes predicting useless in most cases, and we can have guidelines and heuristics, and those are all helpful, but As far as exact, you know, sort of outcomes and what's going to happen in the future, those are, those are a lot more difficult. Um, Santa Fe Institute started with a group of scientists from the Los Alamos National Institute. Labs, and they came together, and they, they were mostly physicists, and they started talking to economists. It was sort of hard sciences and soft sciences, and the physicists were like, hey, economists guys, you guys seem really smart, but you know, your theories, like, they don't work. Like, all your math doesn't work. So what's, so what's up with that? Like, you know, with, with our math, like, it's, it's extremely precise. In fact, you know, when the math is off just a little bit, Einstein's like, oh, your math is off. Pluto should really be here, and he comes up with a theory of relativity, right?

AI assessment note: “Santa Fe Institute started with a group of scientists from the Los Alamos National Institute.”

Q And that's like not him trying to say that purely because it's good for the world to care about all your constituencies, you know, not just your shareholders, but also your customers and partners. Like he's literally making an economic argument for shareholders that that's the long-term value maximizing thing to do, right?

A I think that's right. And, and, you know, he was a consultant for Intel, um, back when, uh, ARM processors were starting to come out and actually dominate the mobile space. And they came out with this sort of Dumped down processor. Uh, right. Yeah, exactly. Um, but, but even before that, I think they came out with another one and John, I'm blanking on the name of it. Um, but, but, uh, it was, uh, it's in the paper. It's in the footnotes. I'm sorry. I forgot it, but they came down, they came out with a cheaper version of it, but in reality, that's not what they should have done. They should have, you know, actually embraced a totally different business model like arm dead. We're just, they were just selling IP and enabling a whole ecosystem. Instead of trying to take all the profits for themselves.

AI assessment note: “I think that's right. And, and, you know, he was a consultant for Intel”

Q So let's, uh, uh, maybe even before getting into some of the nerdier semiconductor topics, let's stick with an investment one. What semiconductor companies do you own right now in the name of resilience and which in the name of optionality?

A Let's start with the two different versions of semiconductors, right? So there's a lot of semiconductor makers that are on the digital space on the leading edge, right? They're, they're making You know, three nanometers and, and, and on, and these are the high compute functions. And there's other semiconductor makers that aren't really dependent on that leading edge. They're more dependent on having the breadth of a catalog. That would be like Texas Instruments that has a 100,000 parts or a microchip. And so in our top positions, we're more heavily weighted towards the catalog names, these names that The lifetime of a part is 30 or 40 years. Um, you know, the, the, the margins are high, the, the growth is pretty good. There's clear NCS in the business. They're definitely creating more value than they take. And they're very hard to replicate, not because what they're doing so technically hard, it's hard, but it's because the breadth of what they have would take you decades to recreate.

AI assessment note: “in our top positions, we're more heavily weighted towards the catalog names”