The Exchanges

Q Any of your portfolio companies have gone public?

A Um, we've had, uh, a few, um, in the public markets. We had a company called Prodigy that sold to Upstart. Upstart went public in the United States. It's more around machine learning and AI for lending. Um, they made a company called Momentus on the SPAC side that went public that didn't do as well. Um, and then we've had a numerous amount of protocols that have gone from, uh, what we call, uh, seed private, um, funding to, um, protocol listing on the exchanges like, uh, Binance, Kraken, et cetera, that are out there, uh, within the United States and outside the United States. So all in all, I think we probably had like, you know, tens of twenties of, uh, folks that have gone, uh, liquid. And I call, I consider it liquid because when a company goes public, That's not the end of their journey, and we do have a tendency to hold in our companies and make sure that we're there for the long term or long duration if we believe they're going to continue to compound.

AI assessment note: “we've had, uh, a few, um, in the public markets.”

Q Right. Among the companies that are returning the most amount of capital today, or have returned the most of, what is, what is your pattern recognition say about the background of these founders?

A I mean, if, if you take a look at any of these, um, founders and or management team members that are running these companies, it's so different. So I'll talk about Carta. So Henry started the company. He's got no financial background. He didn't know anything about capital markets, illiquid markets, private markets, but he learned and iterated very quickly. So his insights were that cap tables are gonna digitize. And so he formed a version of those, uh, that product. It didn't work first. And then he created another product, which is called, which is a regulatory product in order to take a look at fair market value of people's stocks and options. And then he fused those two products together to create his product market fit. Um, he didn't know anything about any of these regulated products, right? He's just learning and iterating very quickly, which is what is the problem in the market? What am I trying to solve? What's the software I'm building in order to solve it? And how do I grow compound, retain, and gain new customers through that? So he created a, he created a product that was a marketing wedge product. That eventually, uh, people then realize why cap table was really strong, created a network effect between employees, fund managers, and limited partners, because there's lots of people that are stakeholders and shareholders, and then his atomic value was shares, and then…

AI assessment note: “it's so different. So I'll talk about Carta. So Henry started the company.”

Q You have mentioned quite a lot about Peter Thiel and that you are inspired by his investing style, right? Would love to know more about it.

A So the, if you take a look at the, uh, the best investors in the world and you take a look at some of the best operators in the world, I actually think there's not too much of difference, right? Like when you think about what Jeff Bezos did with Amazon, Amazon became a capital allocation strategy of human capital and where they put their capital towards Return on invested capital for their initiatives. If you take a look at what Warren Buffett does with his capital and how many companies he's in, it's pretty concentrated and he's very long there. And then he's got what we call them a cash money tree of a specific company that gives you inherently more and more cash to continue to reinvest for insurance companies. Um, and if you take a look at what Peter Thiel has done, if you see where they've made the most investments, um, and the most concentration of their investments, It's into great companies that become monopolistic and compound over time. So it's a very concentrated, I would call power law of companies. Typically what you see for VCs, I think they're, they have this problem where at the early stage they invest in, I don't know, 50 companies or a hundred companies. And then they think for series A, they have to do the same thing. For series B, they have to do the same thing. And if you look at historical returns and averages of any company, public markets or private marke…

AI assessment note: “It's into great companies that become monopolistic and compound over time.”

Q Understood. And what are your learnings from Chamath, uh, during that phase?

A Yeah, Chamath, I think what was special with Chamath's perspective in the world was that he believed that data trumps all. And I think it was, we always, we have an interpretation internally that, um, data isn't your strategy. It's just something to interpret. And your strategy and your mission has to be, you know, like it has to be based on like what you really care about. And so we, we believed that we had a framework to understand and amplify product market fit. And with that, we can find what we call N of one companies. And what we mean by that is that companies have a, are monopolistic in Tennessee. I don't mean from a regulatory perspective. It just means that you can monopolize someone's time by just having a better product and a better experience and how you do that has, you have to be able to measure how you impact someone's life or their workflow. And so, um, because, uh, not just Chamath, but my, You know, my co-founder Jonathan, um, my co-founder Jake, who was at Bridgewater, and then another Co-founder Brendan, who used to be at Facebook, they were all cut from the same cloth. They had the same DNA. So they thought about how do you build these systems? And so when I came on board, a lot of what I talked about and thought about was how do I productize these pieces, not just for an investment team, but for a founder, for a limited partner, um, for a public company, h…

AI assessment note: “what was special with Chamath's perspective in the world was that he believed that data trumps all”

Q Yeah. So, so, uh, uh, what, what makes you put your 20% of the capital just in one of the emerging markets?

A Um, so the, so our background at Tribe, uh, is obviously much further beyond Tribe, is that over the last 20 years of our career, we built some of the fastest growing and largest companies in the world by being in charge of their growth and data science teams. So if you take a look at Square or Uber or Facebook or Airbnb, there's a very specific way in which they built their companies around leveraging data to make better product decisions. And those product decisions were really about how do you like slim down the amount of time it takes in terms of a fast feedback loop. So we took that infrastructure. We built out our investment prowess and structure. We spun out a company that's also separately working with a lot of companies worldwide, funds worldwide, um, sovereign funds worldwide, et cetera. That company's called Termina. Um, and I think the way we look at the world is that again, a bottoms up perspective, the more data we get, um, we can build systems and the more systems we have, the more decisions we can make around alpha. And then the more decisions we can make around even like beta in some cases. Our, our whole focus is around, uh, how do we identify a product market fit and how do we help accelerate that in any sort of market? The reason why we focused on India is that you've actually had a ton of capital over the last 20 years, which I'd call arc one, arc two, and …

AI assessment note: “The reason why we focused on India is that you've actually had a ton”

Q And, uh, right now, almost you will put, like, in your new fund, two fifty million dollars in India. Let's say, what would, what data points would help you make a two 52.5 billion dollar return on that capital?

A You mean a 10 X from the two 50? So, take a step back in venture. So, there's a power law. There's only a few set of companies that can get you, you know, 10, 2030, 40, 50 X. Um, and when you, When you are a seed fund, if you, um, out of 10 companies that you invest in, you're probably gonna have one or two. If you're very good, that'll work out to return the fund plus more. Um, and then if you're a very good investor at the early stage, you're gonna have, again, if you're a very good fund, four or five. If you take a look at most investors in the ecosystem, they have, um, even the early stage series A and series B firms, they have one or two out of their 10 again, if they're lucky that are going to hopefully return the fund. That's not happening. And it's because that people have barely one company in each fund or vintage that's going to return their fund. So when I think about like, how do you return a net five X, right? Return profile. That's kind of always your target is you have to reduce your loss ratio. And so if you focus on, you know, one or two companies return the fund, your chances of returning the fund actually are pretty low, which is why You know, fund sizes that are small at the early stage They're small so that they can return a higher amount. Once you get into the two 5500 or a billion dollar funds, you have to start thinking about your loss ratio in a very di…

AI assessment note: “you have to reduce your loss ratio”