The Exchanges

Q All right, Arjun, before I let you go, I want to ask you a couple of closing questions. What is your favorite hobby or activity outside of work and family?

A So I grew up playing video games, so I play that with my son. That's probably like the number one de facto thing I go to. For me, it's a little bit of meditation. Starcraft II is something that I play, or I try to, at a higher frequency with my son. I play Roblox and Fortnite, and I'm not very good at it. So I guess I'm getting to the old stage. I shoot guns. It's a passion of mine, not because I I'm some sort of a freedom person that, like, guns are, like, necessary for defense, but I'm really impressed with the way they're built. I'd spent a lot of my time thinking about, like, mechanical engineering when I was younger, tried to do it, and so just the process of honing in on that craft, for better or for worse. And I'd say lastly, and I've only recently gotten into it, and that might be a California thing, is just my veggie patch. I feel like it's my family.

AI assessment note: “I grew up playing video games... I shoot guns... my veggie patch.”

Q So when it came time to launch your own firm, how did you take all of your entrepreneurial experiences and your investing experiences and bring them together into what you felt was the approach you wanted to take?

A The piece that I think most people do is they go out and pitch a fund, and they said, I was working at this other firm, or I might have had this other experience, or it could be India-focused, or Africa-focused, or diversity and inclusion-focused, and they say, I'm gonna do everything the same way, but my focus for this fund is gonna be in this sector. And then you go from fund to fund to fund if you are successful, and you're trying to figure out how to build a firm. My whole aspiration, day one, was I wanted to build a Technology company. And so a technology company that deploys capital. And how do you do that? What are the first principles approach that you need to take? Like, what do you need to learn as quickly as possible and the folks that you need to have around the table? So I took the same lessons from my companies. What are ways in which we had to reinvent the wheel? What are things that we needed to augment? And we internally tend to not view ourselves as an investment firm, but a technology company. That's the, that's the lingo that we speak and that we deploy capital across multiple opportunities across early, mid and late stage equities and crypto. It's a different mindset. And the reason I say that is that part of our Mondays and Wednesdays and our standups every day, just like a company is that we're building product, we're building infrastructure, we're automa…

AI assessment note: “My whole aspiration, day one, was I wanted to build a Technology company.”

Q What was it that Jonathan said that resonated with how you wanted to run Teams?

A So today we call it a quantitative approach to product market fit, but at the time you wrote gap accounting for growth for startups, which is what are all the things you need to measure to see at a high level, your business and your software metrics on like, are you on the right path? Do people use your product and taking a very consumer style attitude to any business. And you would look at that and say, you could munch together. The frameworks we had and the finances that we're thinking through together and have a very, again, elegant way to get to your goal, which is the North star of a company. What is it that you're trying to build? What is that product trying to do? And that was it. And it's simple in concept. It's just very hard to actually execute on it. We did growth in data science consulting for Snapchat, for Uber, for Airbnb, all these companies. And when I say that, I don't mean it to be facetious. Like we actually sent and built teams for them. We would do multiple offsites with them. We would actually build products on the side for them so that it could help them scale out like their SMS system when Uber first started it. Myself and my team, we're a part of all this, and every single time we did it, we started from scratch instead of having a framework to use.

AI assessment note: “today we call it a quantitative approach to product market fit”

Q So I'm kind of curious to dive into, you've mentioned this quantitative approach to product market fit and these 50 page data driven reports. What is it that you're trying to see in all this data?

A Growth. It's simple. How do you measure growth, growth at high velocity? What are the aspects of what growth could be built off of? What's the foundation? So when we say a quantitative approach to product market fit, we really just say, does the product work? Do customers engage? Are they using it? Is it a certain amount of integrations for open source all the way to a B to B company, to an API driven company, to a B to C B to B to C. It doesn't matter. Like all of these frameworks, if you really think about it, you're engaging with the customer and you're looking at. The holistic view of how it interacts with each other. So some people say I focus on marketplaces. Okay, great. Well, technically anything can be a marketplace depending on how you measure it. But what you're really looking for is as a business from your software metrics down to your financial metrics and you put it together. If you have financial metrics yet, what is the product market fit look like? And in order to identify a product market fit, a lot of people make up qualitative judgments. Our whole goal has been if you have Thousands of companies' data sets in raw form, which is what we do, that we have a better view of the world, we have a better point of view, and we can actually benchmark. Like, you can't benchmark one company at a time in your brain. You can only do it if you can put that into a system an…

AI assessment note: “Growth. It's simple. How do you measure growth, growth at high velocity?”

Q When it goes into making the decisions on your portfolio, are you then primarily focused on those quantitative metrics?

A Earlier, less so later, more so. And the reason I say that is if you look at a traditional approach to investing, where do you have valuations that are the highest or inflated? It's really, really early at the seed stage. You have a, a, maybe a product, a team or an idea, maybe you have a demo, but you're mainly going off of a narrative that people get excited about. And so people bid that. And that's the market price that you pay. So it becomes very high. At the really, really late stage where you have more data, it becomes much more obvious. Then you have inflated valuations again, right? Because there's enough people that do the same thing, the same measurements. It's usually lagging financial metrics. And then you bid it up and you pay the market price. So we live everywhere in between. I don't want to call it the gray. But all the areas where there are aspects of identifying product market fit and growth at high velocity that other people might not be seeing. I talk a lot about this internally. We don't do too many podcasts anymore, but, you know, as we build out these frameworks, it's more about how do we build these quantitative frameworks to think about intrinsic value versus option value, and then within the option value, what's the extrinsic value, and how to think about what that premium may or may not look like at any given time, and how should we think about it ove…

AI assessment note: “Earlier, less so later, more so.”

Q Once you're invested, how do you go about helping the companies?

A It's a good question. I think we're getting better at this. Our whole approach has been, if we have better context into the company, We can be better stewards and conciliaries around the table. So that could be strategic. It could be product. It could be helping them to build out their growth and data science team if they need that or their marketing team, whatever it might be, because we have those frameworks. The most privileged position that you can be in is to be a pure conciliary and strategic, and I think that's the goal of where we try to take our companies. The earlier we invest, the more hands-on work. The later we invest, the less. There's more governance. But the much more later the company becomes in their life cycle, let's call it five, 10, fifteen billion these days that you see for a lot of our companies, they actually need a lot more help. So what's funny is our earliest companies need a lot of help, and our latest companies need a lot of help. And the reason why that's the case is that they're looking at their narrative from there. What are the data points that I need to see? I need to think through that. I need to build around to speak to the capital markets that are these late stage investors, or I'm going public. And we do the exact same work. We build out the framework. We show them what that looks like. And then we work with them. I don't think we do this.…

AI assessment note: “It could be helping them to build out their growth and data science team”

Q So what was your path after the sale of a little apps to angel investing first?

A During little apps, we would literally sit around that. This is a dog patch area into the mission in San Francisco and basically say, if you help me, I will help you. So give me advisory shares. And then advisory shares turned into angel investments. And angel investments at that time really meant five K here, 10 K here. It's all we really had. I think the most amount of money I had in my bank account, even though I was running a huge operation was like 16,000 dollars and taking secondary at that time as well was like a huge no, no. Like if you did it, You've committed some sort of sin, especially during the 2008 timeframe. And I think it was really a early and special time because the communities were being formed and people were bonding because we didn't have anything else. You had nothing left to lose. So let's just try as hard as possible and let's support each other. So I did everything anyone else did. If I like some people, I like the idea, I just shot the check and you get lucky every once in a while. But I decided to take something else away from it over time is that because I was buying my opportunity to see how these companies were being built, I saw the products that they were building. I saw the culture that they were creating. I looked at that and I said, they're doing it better than me at my own company.

AI assessment note: “give me advisory shares. And then advisory shares turned into angel investments.”

Q So once you've dove into that and you feel like you have a pretty good sense that you're interested in working with a company, how do you find the deal dynamics around that relative to a very competitive world for capital?

A So the reason I said that we operate within these zones, right? So between a plethora of data and the absence of data where the evaluations are extreme, you'd be surprised that there's not a lot of folks that come to the table at the same pace and come to conviction at the same pace that we do. In that middle ground. And the reason why that's the case is that it's a traditional approach, as I had mentioned, right? You have a tribunal, those folks are getting their data room ready. The speed at which we move is pretty, I'm quite proud that we were able to do it. There were some days that I was skeptical that we would actually get there, but if I met a company today, like you pitched to me today, and I liked the overall directionality of what you had mentioned about your company, I'd say, look, I really like this, and I want to do our work, and so here's our process. You give us a raw data dump, we'll adjust that, we'll build out a report, and we'll send it to you, and that'll be the beginning of our discussion. Now, what happens is that the moment they give us that raw data dump, which is It's easier than building out a data room, right? Like I'm not looking at a data room. I don't look at their deck. I don't care about any of that. The moment I get it on average, if we prioritize it, let's call it a P zero at the firm, it's 20 minutes to get a 50 to a hundred page report. That'…

AI assessment note: “there's not a lot of folks that come to the table at the same pace”

Q I'm curious when you see as much as you do underneath all these companies, where do you see other investors consistently making mistakes?

A I go back to the basics that we had mentioned before. You tend to make mistakes because you're, you're using your feelings of what you think the future could look like with absence of data. So you either actually have absence of data in the opportunity or you're not looking at it, right? So when someone pitches to you and you say like, I just want to partner with these people, I really like them. Or where you have an excess of data, as I mentioned before, where you are trying to price to perfection and you're, and you're looking at the macro and capital markets and trying to be a crossover investor. And I think there's some people that do that well. There's some people that don't. I think historically what you can see is that the loss ratios were pretty high for the really early stage folks. Rightfully so.

AI assessment note: “You tend to make mistakes because you're, you're using your feelings”

Q Which two people have had the biggest impact on your professional life?

A So I'll start off with my father. Again, I watched him succeed. I watched him fail. I watched him succeed. I watched him fail. And then I watched him continue to try again and again and again in areas that he wasn't an expert in. So he was an Network gigabit switch stack. He went to security from the startup ecosystem. He went to the medical industry and started building out ultrasound devices, and he started selling it in India and China. Like, he just tried things that he felt were interesting, and he started going in deep. It didn't mean that it always worked. It just means that he continued to try. And then he would use those collections of experiences, then work on the next thing. Now he's like a real estate developer in his seventies. Like, this is passion. It's one house after the next, and then he wants to do bigger projects, commercial properties, et cetera. Like, he just likes it. So you watch that, and you say, As crazy as that sounds, he enjoys it. He's doing well. That's kind of admirable. I wouldn't say I have like a second person that I would say embodies like my biggest impact. I would say it's like a multitude of people, but you can say it's a persona. So it's a persona of other investors that are in the ecosystem that I like to learn from. And I would say they have philosophies, right? So when you look at Peter Thiel and how he thinks about using his framework…

AI assessment note: “So I'll start off with my father... I wouldn't say I have like a second person”

Q So what was it that led you eventually to set off on your own with Tribe?

A So my biggest problem is that I always feel like I can do it better. I think I walked into the organization Thinking, what are the pieces that I need to learn so that I could build this myself one day? So there's a little bit of hubris in that, right? Cause it's an entrepreneur attitude. But at the same time, my thought process was that it's going to take me 10 years before I started thinking about building something on my own. And I know how hard it is and how long it takes to cultivate the relationships, the LPs, make sure you have a track record. So my goal was that I want to be the biggest bad-ass investor that's out there. And to be able to do that, you need to make good investments, and you need to make sure that you have some sort of edge. So I spent all my time in two places at the firm, actually, in the first couple weeks that I got there. I spent all my time with the finance and back office team. Because no one spends any time with them. Just understanding what they do, what they build, how they communicate with the LPs, just as a pure learning experience. And the second was the data science team that was there. As I learned more and more about what they were building and the thoughts that they had, how could we productize this? What are ways in which we could automate it? Like it was in my mind, but it didn't mean we could execute on it. And then I started voicing th…

AI assessment note: “my biggest problem is that I always feel like I can do it better.”

Q You mentioned first look, and I know this, this question for co-investors, how do you assess through a lot of potential opportunities where you don't have expertise? Sort of seems like it's a consistent application of that. I'd love you to talk through what that is.

A So the whole firm, again, is predicated on These quantitative models and approaches. So you build that. And so you ask yourself, okay, great. I can build value and derive value from it. Now imagine I can get that report, assuming our founders want it. And that's what the first look program is built on. Imagine taking that to a strategic partner or a firm or a company or advisors that are individuals that have that same context. The question you will ask is, can they help because they have that context now more because they can see what's happening at the company and they can think through. The business a lot more clearly. So a good example, actually, I'm probably making this more public than it needs to be. There's just two companies that are in the regulated space. So in crypto, we have a company called Terra is you are building an ecosystem and an economy. So these reports really matter. And the co-investors around the table could be application developers, other folks that are thinking about moving from web two to web three, what they call it today and build on top of the Terra blockchain. Another one is we have a company called Invenia. Super stealth. Almost make about a billion a year in revenue. It's 60 people. You've never heard of them, but they're completely stealth. Probably hear about them by next month. And they're in a very regulated space. They work with independe…

AI assessment note: “that's what the first look program is built on. Imagine taking that to a strategic partner”

Q I'm curious how you size positions. You mentioned 12 names, billion dollars, pretty concentrated portfolio, and you might see the data today. But of course, in a trajectory of any of these companies, the data can change going forward. So how do you go through that thought process of deploying capital?

A So it depends on the stage of the company. You're right. Data can change. But one thing that never changes, and we've seen that across every company, we've been a part of Uber, Facebook, Airbnb, Lyft, et cetera. The list goes on where we built a lot of these growth and data science frameworks is that their product market fit and how they interact with their customer never changes. That doesn't mean pricing doesn't change. It doesn't mean The way in which you can monetize on top of that product market fit may or may not change. It just means the interactions of how you work with the customer never changes. It's almost exactly the same. And in fact, it never gets better. It gets worse in the rare cases. It gets better. You should put all your money to that company. We rarely see it that way. So when we think about pacing or portfolio construction, look, a lot of the reasons why people focus on ownership, it's a hard thing to say is that they're just bad pickers. So if you say I can focus on 20, I want 10, 15, 20% ownership in this company. It's because they believe that if they make failure somewhere else that we need this company at least return the fund and get to a higher multiple. If you don't have that issue, if you are doing a reasonable job about thinking about what your loss ratio will look like, then you can focus on your mid-tier to high-tier companies and say, okay, gr…

AI assessment note: “if you are doing a reasonable job about thinking about what your loss ratio will look like”

Q And so this idea of identifying firms early, I know you went about this in a very data-driven way. How did you approach it?

A So we had a network. So we're privileged to know folks in the industry. And we were privileged that they already knew our capacity for helping companies grow. We were consulting, we were advising. So we basically said, okay, great. Why don't we go to those same companies that we've known for awhile or ones that we had identified in the first place, go and do the same data work, present it back to them. So the value exchange. And again, today, when we do the data work, when we meet a company, just to kind of give you the scale, the sheer size of how we do it, we see like a, 2000 companies that are at the top of the funnel per year, roughly, give or take. We do this analysis between, I would say at the low end, 500 times a year, at the high end, up to 800. You have 800 artifacts. These reports are like 50 to a hundred pages at a minimum for a company at the early stage, right? Like a series A. And then all the way up to series B, C, D, or E. It gets even deeper and deeper. And these are modules and quantitative frameworks on how we view the whole business and all of your products and how they interact with each other. You would spend millions and millions of dollars if you're running a company. To build out this infrastructure with a team specifically for your company. And so for us to come and say, Hey, even if we don't invest, we'll give you a value exchange was our wedge in to…

AI assessment note: “We do this analysis between, I would say at the low end, 500 times a year”

Q What are those key growth challenges you were able to help companies think through?

A So it seems basic, but the first thing you needed to do was essentially really figure out what was your North star. And so if it's users, if it's usage, if it's integration, it's like number of share count on Facebook, it was something called an object that changed it over time. It was really, how do you build out and substantiate the systems and the metrics from the North star? That was the beginning. And as common as it sounds like this is what you need to think through, there's lots of blog posts, people write about this. When you're a founder, sometimes you're a deer in the headlights. You can see it. You can see what people are saying, but just going through the process, seeing the artifact, seeing how that can help you orient your business or your product and how you want to change it. It's a journey in itself. You know, part of why I think you see accelerators and people talk about this is that you have to train yourself to do it because you're going from an irrational passion of building something because you believe it needs to exist. To a place of, as it's starting to exist, how do you evolve it and form it into something else that might not be your original intention or vision?

AI assessment note: “the first thing you needed to do was essentially really figure out what was your North star.”

Q So how'd you decide to go from Yahoo where you were sitting into investing full-time?

A I've only learned a couple of ways of investing in and watching folks. And the traditional path is you're known for being an operator or a founder. And then a typical VC comes to you and says, He or she, Hey, why don't you join VC? I think you could be good at it. Then you say, Oh wow, me, I could be good at this. And I never thought that I was hold you guys in high esteem. There's still this like weird aura that any VC brings to you and they say, Hey, you can join our club. And so in the beginning that got me excited and I would visit a lot of these funds, you know, some of these top tier firms that we all know and love, and you would sit in the room and the path of making an investment decision was extremely archaic to me. And so what's the process? Someone meets with the founder, they write some notes or a quick memo. Whatever it might be. It's all done in email or like a document format. They send it to the rest of the partnership, or they look for a sponsor, and then they explain it to everyone, their version of it. And then if it seems interesting enough, some of it is qualitative. Some of it is quantitative. They, of course, people, if they had enough time, they do the work. They look at the data room. If there is one, depending on what stage of the company it is. And then they say to the founder, Why don't you come in and pitch to us like a court jester? Just show us th…

AI assessment note: “the traditional path is you're known for being an operator or a founder”