Guest: Jonathan Hsu is the co-founder and General Partner at Tribe Capital, a venture capital firm focused on using product and data science to engineer N-of-1 companies and investments. Previously, he was a Partner at Social Capital.
Date Recorded: 1/20/2021
Sponsor: Masterworks – Use Promo Code “MEB” to skip their 15,000 person wait list
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Summary: In episode 287, we welcome our guest, Jonathan Hsu, the co-founder and General Partner at Tribe Capital, a venture capital firm focused on using product and data science to engineer N-of-1 companies and investments.
In today’s episode we’re talking about using a quantitative approach to venture capital investing in a way that hasn’t been done before. Jonathan us through his background, with stops at Facebook and Social Capital. Then we hear how Tribe Capital leverages their data science capabilities to assess the product-market fit of companies to invest from the seed stage to late stage. Jonathan explains how this process led him to invest in both Slack and Carta.
As we wind down, we learn about the firm’s co-invest vehicles, which allow others to access their deal flow.
All this and more in episode 287 with Tribe Capital’s Jonathan Hsu.
Links from the Episode:
- 0:40 – Sponsor: Masterworks: Use Promo Code “MEB” to skip their 15,000 person wait list
- 1:46 – Intro
- 2:37 – Welcome to our guest, Jonathan Hsu
- 3:17 – From physicist to venture capitalist
- 4:25 – Tribe Capital
- 5:38 – Accountants as the first data scientists
- 6:53 – Using data in investing
- 8:32 – Data-driven approach in venture capital
- 11:15 – A Quantitative Approach to Product-Market Fit
- 13:40 – Leveraging data science to analyze product-market fit
- 15:57 – Carta – a case study of Tribe’s analytical process
- 18:34 – The competitive advantage of CartaX
- 20:22 – Separating luck and skill – A Quantitative Approach to Seed Investors
- 23:42 – Being a great partner for portfolio companies
- 25:17 – Why Jonathan prefers working in the early stages
- 26:59 – High-level characteristics of potential investments for Tribe
- 28:38 – The appeal of the space sector
- 30:11 – Prodigy in the post-COVID era
- 32:13 – Firstlook, a new model for venture co-invest vehicles
- 35:11 – How you can be a part of Firstlook
- 36:00 – Consistency in the world of seed companies
- 37:44 – The future of Tribe
- 39:21 – Tribe’s involvement with portfolio companies
- 40:36 – Business-investor fit versus business-customer fit
- 43:52 – Focusing on building business value
- 44:44 – Planning your approach to selling
- 46:01 – How to use data to influence your decisions
- 48:17 – Applying regret minimization to portfolio allocation
- 51:32 – Jonathan’s most memorable investment
- 53:15 – Outlook for 2021
- 54:22 – Global investment opportunities
- 55:34 – The indirect public expression of Tribe’s data and insights
- 56:12 – Learn more and reach out to Tribe – email@example.com
Transcript of Episode 287:
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Meb: Howdy, friends? Fun show today. Our guest is the co-founder and general partner at Tribe Capital, a venture capital firm focused on using product and data science to engineer in of one companies and investments. In today’s episode, we’re talking about using a quantitative approach to venture capital in a way that really hasn’t been done before. Our guest walks us through his background with stops at Facebook and Social Capital. Then we hear how Tribe Capital leverages their data science capabilities to access the product-market fit of companies to invest all the way from the seed stage to later stages. Our guest explains a few case studies and how this process led him to invest in both Slack and Carta. As we wind down, we learn about their co-investment vehicles, which allow others to access their deal flow. All this and more with Tribe Capital’s, Jonathan Hsu.
Jonathan, welcome to the show.
Jonathan: Glad to be here.
Meb: Where’s here? Give our listeners insight where in the world find you. It’s Inauguration Day, context for listeners. So, if the world has ended at any point in between now and publication, things look bright. The future looks sunny as always here in Los Angeles. Where do we find you?
Jonathan: I’m located in Burlingame just outside of San Francisco in Bay Area.
Meb: Beautiful. Love it up there. Listeners, I’m going to apologize because we have an engineer and a physicist on the show today. I promise it’s not going to be boring. It’s not going to be all science and nerdery. Give us your short one-minute overview of your origin story and lead up to Tribe, because it’s a pretty interesting one.
Jonathan: I actually started life as a physicist. I did my undergrad at Berkeley and then my PhD at Stanford in theoretical physics where I studied string theory and black holes. Towards the end of my PhD, it was clear I didn’t want to be an academic, so I ended up joining Microsoft for a little bit. When the Facebook platform opened up, me and a couple of friends built one of those early social networking applications that grew super quickly and we ended up selling it to a company called Slide, another social gaming company which was run by Max Levchin at the time. This was his social gaming foray in between PayPal and the firm. So, I went to run data for Max for a couple of years, and then joined Facebook in ’09. I was one of the early data scientists. I was there for several years really working on forming and leading the data science and analytics organization for Facebook. I was there ’till 2014. By that time, I’d gotten tired of being part of this massive company. I was interested in venture and joined Social Capital in 2014. I was there for four years, really sort of exploring, became a partner, heading up the data science activities, really exploring all the ways that data science is relevant for venture. There are many different aspects of that and we explored pretty much all of them. Did that till mid-2018 and then we spun out to form Tribe Capital, which is about two and a half years in now.
Meb: Tell us quickly, what is Tribe?
Jonathan: Tribe Capital, we’re primarily an early stage oriented venture firm. The focus of our activities is really all around recognizing and amplifying early-stage product-market fit using a bunch of these data science and analytical techniques we’ve been developing for many years now. The investing activity at Tribe tends to be anywhere from late seed all the way up to later stage, checks anywhere from $25,000 to tens of millions of dollars, but with a particular focus on leveraging our data science capabilities really to understand and measure and quantify product-market fit and use that understanding to help companies as well as to help work with our co-investors, with our LPs, really the entire ecosystem.
Meb: You guys are structured a little bit differently, and we’ll get into the various ways. But I figured we’d start with when I wrote my first paper a million years ago, it was a quantitative approach to tactical asset allocation. And I was loving as I was going through your site, you have all these articles that are a quantitative approach to blank. And there are all sorts of different things. So, from one data nerd to another, it warmed my heart. Talk to me a little bit of how you guys use data. It’s something you talk a lot. It’s a consistent theme, string throughout all your presentations, all your commentary and podcasts. How do you guys approach it? How do you think about it in the framework of VC investing?
Jonathan: The framing we use, really, it stems from an analogy. It’s well illustrated by an analogy that I like to make. I like to say that accountants were the first data scientists. That sounds kind of crazy. So, what do I mean? What does an accountant do? An accountant takes a pile of raw data, right, like the ledger, every entry in the ledger, and they turn them into something useful, like an income statement. That’s really all a data scientist does. They take a pile of raw data. It’s important that it’s raw. That’s kind of where data scientists work. And they turn it into something useful. Maybe they do a bunch of fancy wizzy math, fancy statistics, but in the end of the day, none of that matters if you’re a decision-maker. If you’re a decision-maker, what really matters is, “Do I trust this? Is this something that’s useful to me?” And when we use the term data science data, that’s kind of what we think of as sort of the philosophical basis. If you look at sort of the history of accounting, it goes even deeper because accounting is about 500, 600 years old at this point. For the vast bulk of history of accounting, it was really not used for the purposes of investing, but rather for the purposes of running your business. It’s like you had if you were a merchant, you had to keep track of what was going on and use this thing called accounting, really, just to give you visibility.
Even in early stock market days in the late 1800s or early 1900s, investing in public equities was they weren’t looking at data, they weren’t looking at financials, it was all like rumor and speculation. We call it like FOMO today. In some sense, that’s kind of like what Benjamin Graham did. You could encapsulate what Benjamin Graham did as being the first person to be like, “Okay. I’m going to use data to help me invest. And specifically, the data I’m going to look at is the data that expresses itself through income statements, through financial statements. I’m going to use this technique that people use primarily to run their business, but I’m going to use it as a way to recognize value.” And sort of where we are today, we’re about 15 years out now from this big data, big bank. About 15 years ago in the early mid-2000s all of a sudden, all these companies developed, it became really cheap to just store and compute on a lot of data. And what’s the first thing that companies did with it? Well, they used it to execute. They used it to help their businesses just like the early accounting, right? That’s what we did at Facebook when we were building out data science. All these companies were figuring out, “How do we use data to just execute better to grow faster?” But similarly, you know, at least in the venture side, that style of data analysis wasn’t really highly utilized and really utilized at all. That’s really sort of where our focus tends to take those approaches and use them as a way to help us recognize value as it’s being created, not too dissimilar from how the first people who were really using accounting to recognize value in companies.
Meb: It sounds like, you know, where we are today in 2021, it almost sounds like table stakes. It would be weird to have a conversation with someone who says, “You know what? We don’t use data. We’re totally a subjective process.” So, tell me a little bit, like, practically speaking, how that informs what it is you guys do, because venture in so many ways you talked to so many people, and particularly early stages, it’s tough. In many cases, the ideas are not well-formed, you talk about people pivoting, and you’re betting on founders and all the million inputs. How do you guys think about it? What’s the framework? How does it, practically speaking, all come together?
Jonathan: I would say it’s not table stakes in venture. And it’s not table stakes. It’s actually I don’t really think table stakes in public equities either, in the sense that the specific data that we’re talking about is operating date. Most quantitative hedge funds that are sort of trading quantitatively, the vast bulk of what they’re studying are market movements, not fundamental, whether or not the company is going to generating a moat is sort of secondary to what the other traders are doing. In similar way, I would say that table stakes in venture is really primarily around sourcing and network, your network, your ability to get access to a deal. That is table stakes. How you choose to underwrite a deal. That’s up to you. And in so much as you can convince an LP that that’s a sensible way to go about your business. So, for us, it’s data-driven underwriting to understand fundamental value. It’s really the core of what we do. Our standard process on a typical year will meet well over 1,000 leads. And basically, if the company is at a reasonable stage, reasonable sector for us, we will ask to do the data work, which usually involves them sending us big piles of anonymized data and talking like, these are millions upon millions of rows, giant datasets, and we use those to build a bottoms-up view of product-market fit. We’re doing this 300, 400 times a year.
And the most common thing happens, we pass. Obviously, we see we’re like, okay, we don’t see the pattern that we really get excited about and we pass. But even when we pass we get the work back, say, “We’re passing. Here is why.” This number here is kind of median. This number is kind of bottom quintile. This number here is good, top quintile, just keep doing that. And we’re able to give the founder back that valuable sort of detailed feedback. And founders pretty much to a tee they say, “Oh, I’m bummed you’re passing, but thanks so much for this. This is more work than any investor who’s passed them who’s ever done by far. Can I send my friends? Can I come back in six months?” And it’s really important because we’re using it in that way to build our brand and drive more deal flow. So, that’s the bulk case. Now, when the data looks good, it doesn’t mean that we write a check. It means that we sort of have the green light to do all that traditional venture work. You still, obviously, have to underwrite the team, have to understand the market deeply, you still have to do all that work. But we really exercise that after we’ve sort of developed a viewpoint on how the company is performing in a bottoms-up fashion. We use the data as well as all the traditional techniques, really, to get to that next level.
Meb: What percentage of the companies are actually even generating this amount of data? Because when I think in general, I imagine a bunch of listeners are thinking seed, investing series A. Many of these companies, in my mind, maybe just start to find that product-market fit. So, maybe talk about where your sweet spot is for companies that, I guess, have launched a product or service or whatever it may be. And then also, we’d love to hear you kind of go deeper on the actual product-market fit discussion, because you guys have written a paper on it. I think it’s a lot of fun. We’ll add it to the show note links, but you have some very specific points on what you’re looking for. Everyone loves talking about product-market fit, but what do you guys mean, what do you look for and what’s kind of the sweet spot for those companies?
Jonathan: We treat product-market fit kind of like accounting. We treat it like the term profitable. People like to think the term profitable is well defined, but as we know, it’s not. It’s actually like a whole family of ideas. Gross margin profitable, contribution margin profitable, EBITDA profitable. It’s a family of measurable concepts. And to us, product-market fit is like that. It’s a family of measurable concepts. When it comes to stage, for us, the typical companies that are in the late seed, series A stage these days, they pretty much all have users using the product somewhere. That is sort of what it is. But typically, it’s not at the form yet where it’s so scaled up that you can invest only on that. And so I think that’s really where the difference comes in. We’re going in to use these techniques to understand that early-stage product-market fit, articulate it, articulate it in a way that makes sense to us, that makes sense to a founder, makes sense to our other co-investors and LPs. Really, it’s about sharing it and adding that visibility and letting all of our experiences and operators come to bear and express itself through those analytical techniques. But it really starts to show itself, really, as early as when the company has a product that’s been in the hands of customers for at least three months. But frankly, it keeps going. A lot of our work in product-market fit really was built up in the days of the early social web where we had phenomena where when something was growing, it was already very big and things like Facebook when I joined Facebook, it was just over 100 million users, and we already knew there were these patterns of growth that were going on in the way the product is being used. And looking back, historically, the thing to learn is that those patterns that you see at the early stage, they keep going. They don’t stop. And they will keep going to a scale that people don’t usually appreciate at that time. And it’s that same insight that we used to understand Slack early on, that we used to understand Carta, that we’ve used to understand all these great companies, this notion that really product-market fit, that core interaction, the way that product and customer interact, that’s what leads to all the durable value in the very long run.
Meb: Other than simply revenues accelerating and growing every month, you guys talk about a few things, growth, accounting, cohorts, distribution of fit, even magic, eight balls, and end of one. I just gave you a mouthful of great things that you guys have touched upon. Maybe walk us through a little bit deeper on the product-market fit other than simply, in my mind I imagine most listeners are thinking, “Okay. It’s just increasing sales.” But what else is it that you guys are really teasing out or is there anything in particular that you find is a signal that says, “Hey, maybe there’s something here.”?
Jonathan: It’s much more general than that. Maybe think of it this way. Abstractly, a product interacts with a customer and they exchange some value. Now, that value can be in the form of the customer paying. That’s kind of the most obvious thing. Now, remember, all this stuff was developed in the days of the social web. In the days of the social web, what it was that the customer used the product, but they were just giving it some time. There was no money-changing hands. I uploaded a photo to Facebook. I spent some time commenting on something. And that little unit of value, that little unit of engagement, you can imagine there’s a giant log of that somewhere. And you can take that log and you can create standardized analytical approaches to it to help you understand it and articulate it. Not dissimilar to how, like, an accountant would take a ledger and turn it into an income statement. All of our approaches are geared towards, “Okay. Take a log that is of some form of value-changing hands, whether it be photo uploading, money-changing hands, whatever, and what are the standard analytical techniques that you can do on top of it?” In particular, standard analytical techniques that are feasible in the current world.
So, when you talk about something like cohort, cohort is a great example where, like, yeah, we do it, everybody does it. I mean, I think part of the power is that nowadays you can do it in a very systematic standardized way. You can do it extremely quickly. You can benchmark it to hell. You can understand it in many context beyond revenue. And in some sense, that’s some of the insight there. Just like accounting sort of gives you a standardized set of definitions to understand the past and a bunch of well-defined calibrated very variables, in a similar way where we are today, our frameworks allow us to sort of talk about product-market interactions, whether it be money or not, and measure it in all these standard ways, benchmarking all these standard ways so that it can help us make our decisions as investors, but also, frankly, be useful to the founders as they are, as they continue to develop their own business.
Meb: Maybe it’d be helpful to walk through… You guys have done maybe about 50 investments, $500 million and deployed? Is that ballpark correct?
Jonathan: Ballpark, yeah.
Meb: One of the ones that you’ve published on quite a bit recently which also may be interesting to the listeners, I think, is certainly Carta, which has been a rocketship. But feel free to choose another one, if you like. We’d love to hear kind of walk through how that product-market fit revealed itself, how it was something that you knew as an investor that you saw the hidden gem or not even that hidden, what was it there that was attractive, and tell us a case study almost of how that played out.
Jonathan: If you look at early stage Carta through the lens of an early stage regular investor, it may look like this. It may look like simply a vertical SaaS application to attack a relatively niche market of cap table management, which prior to Carta was basically lawyers messing around with spreadsheets, which is kind of dangerous. As we know lawyers should not be allowed anywhere near Excel. So, that’s kind of what cap tables were. And then the idea was, okay, let’s build some SaaS for that. And if you sort of do the back of the envelope math on the size of that market, how much money is there to be made from putting that in software, it turns out to be an interesting number, but it’s not super big. That’s sort of is part of the way it would look early on. Now, what the founder, what Henry posited to us and what we also were interested in was the concept that this product could actually grow via a dynamic that was unusual, namely, that there could be some form of a network effect whereby there’s an investor who has some companies that use Carta that would induce the investor to tell their other companies to use Carta. So, there could be a growth dynamic that is outside of the normal SaaS growth dynamic of throw salespeople at it. There could be an unusual growth dynamic, so, that’s one. Part two is if you could use that growth dynamic to completely dominate this first business, it would give you the ability to build other business lines adjacent to it. Right now they’re working on all of these adjacent services and financial services, things like CartaX, the secondary exchange, a bunch of things around developing services for the investors themselves.
So, when we invested in Carta initially several years ago, none of that existed. That was a story. What they had at the beginning was like a bit of traction on the early cap table management thing, end of story. And for us where the measurement comes in where the product-market fit comes in is really getting a good sense of, “Okay. This story makes sense, but the feasibility of it is going to be largely down to your ability to do step one. Can you get big enough to get enough scale in that first business to give you the privilege of being able to make the attempt on the secondary businesses that are attached to it?” And that’s really where the delta is. See, when you look at early-stage companies, a lot of them come around and say, “I have a network effect. I’m going to exercise my network effect. It’s going to allow me to get huge, and then I can do all these other crazy things.” And if you look at the stories of how those companies fail, they don’t fail because they’re unable to do the other things. They fail because they fail at step one. They’re unable to dominate their first market. And their inability to do that is because they don’t have our product-market that’s strong enough for them to be able to get at it with a reasonable amount of capital invested in a reasonable timeframe. That’s kind of how it fits together. Our quantitative approach product-market fit really helps us understand your ability to do this first piece, and then the rest of the story will depend on the specific company, but in the case of Carta, that’s kind of how it fits together.
Meb: Before we leave Carta, I’d be curious to hear your just general thoughts on the concept of the CartaX for listeners. This concept of private secondary marketplace, particularly, as you have the private markets develop as they have in the last 20 years seems like such an obvious opportunity. And there have been a handful of players out there, but for someone who’s speaking personally who’s tried to transact and a lot of these companies and offerings, it’s so bulky. It’s just like a high-time effort process that just seems like it’s an opportunity for somebody to go around and fix it. Is that broadly what they’re trying to do with CartaX?
Jonathan: You’re right that a lot of these ideas are kind of obvious. What makes it difficult is how do you achieve enough scale to enforce some level of standardization as the process? Kind of what you’re saying is how do you build a single marketplace? Clearly, I can just build a marketplace, but the problem with building it is no one will come. So, the delta with something like Carta is that, well, you know, by nature of their original business model that gives them a sufficiently large base of existing business, existing customers, existing relationships that they have a much better shot at sort of being able to do this than anybody has in the past.
Meb: It would be fun to watch that develop because it’s been so many emails exchanged just on companies, “Here’s where it’s trading at. Do you want this offering?” But hopefully, it can become a little more standardized. You guys wrote a little bit about seed investors in general. And one of your trademarks is finding a lot of data, compiling it, becoming a bit of a resource in-house. As you guys looked at seed investors in general, what were some of the main takeaways as you built out looking at historical returns and patterns, and how did it inform you with what you guys have started to implement as successful investors as well?
Jonathan: Some of our work is oriented around source. The question that kicked off our work in seed investing was really this question around, “Okay. How do I systematically know which seed investors I should be spending time with and which ones shouldn’t I be spending time with? Is there a way for me to use data to help me understand that question?” And so the approach is roughly, like, given all the seed investors out there, all the data we can collect on them, it’s just out there in public, can we ascertain which ones would be a good return on our time in terms of spending time with them? It’s a bit of a different question than what an LP might ask. From an LP’s point of view, an investor in these funds, you might say, “Well, you have 1000 losses, but then you have one that’s amazing, then that’s great for me as an investor.” From a co-investor’s point of view, from a business development point of view, that may or may not be the right use of time. If you spend all this time creating all this noise, but none of it is interesting to us, but there’s some other seed investors that just cherry-picks the one, that might be a better use of our time. This leads one down the alley, this path of can one statistically look at a track record and basically separate out luck and skill? And that’s what the question turns into. Michael Mauboussin has written a whole bunch about this stuff. We’re big fans of his work. He’s done a whole bunch of work on how do you analytically separate luck and skill using sports as a really interesting model? How do you make a statement that basketball is less luck-oriented than football? That’s actually an analytically tractable statement. He’s demonstrated a bunch of models that help you make those kinds of statements.
And so we’ve taken some of those models and applied them to the world of seed investing to help us basically combine short track records with long track records in a way that’s sort of statistically sensible and help us identify folks we should be talking with. That was kind of that approach. We wrote a little bit about it. We generated some PR around it. An interesting aspect of it is that in the world of data-driven sourcing and venture, the conventional thing that a lot of VCs do is they may hire some PhDs and say, “Do some AI for me. Do my sourcing for me.” And then what they do is the PhDs give them a list of companies, then the traditional venture investor goes out to the company and says, “Hey, my machine says I should talk to you.” But the reality is that a great company usually has plenty of investors around the table, lots of people who want to talk to them. Why would they want to talk to you? It’s a little bit of unusual bias in that strategy. And you contrast that with the case of this data-driven seed investor thing. If we go to a seed investor and say, “Hey, my data says you’re really good at this.” Seed investors are usually like, “Oh, awesome. That’s great to be recognized for this. I would love to have you mark up my deals.” The dynamic is different, the bias is different. And so generally, it’s just a different approach of doing this that fits well with our broad approach of using data in somewhat unconventional ways maybe.
Meb: You have a lot of different personalities and approaches, like you mentioned, in some cases, and I talked to so many investors where, essentially, their goal is to have a company hit the next funding round. And that’s kind of all what they think about is, “I want a high batting average of seed companies to go to series A.” Then you mentioned other guys and gals that are like, “Look, I don’t care. I’m just going to invest in 20 and I’m looking for the one totally crazy idea that goes 100X, 1000X.” It’s like players on a baseball team. Cleanup hitter versus the guy that’s out there that singles, I’m going to steal a lot of bases, and the other guy that gets a ton of walks. And it’s interesting because I think a lot of people think of the asset class in terms of stages. “Hey, this is seed. This is series A.” But in reality, you have a lot of dispersion of styles of investors within that sort of umbrella or category. Did you guys kind of find that in the data as well?
Jonathan: You’re sort of referring to the dispersion in the style of investors, but it also… The underlying thing, of course, is that there was a dispersion in the approaches to building a company. Remember, that’s the underlying economic activity we’re talking about. How do I build a company from nothing? And to think that they all just follow this pattern of seed, then series A, and then series B and it’s just sort of this line of valuations that go up where everybody is just buying equity, inserting equity into the company. The idea that that’s the only way to build a company, well, that’s rather narrow. That’s why the viewpoint really has to start with, like, “How do you build a great company?” Recognizing, acknowledging there are many ways to do that. And then given all these ways of building a company, what are the appropriate ways to finance that? That’s how I think about what we should be doing as venture capitalists, not making great seed investments. We need to be like great partners for our companies and help them navigate the land of capital, which is complicated.
Meb: The timeframe is such a tough problem too that you mentioned. We joke a lot about this on public markets where we say that so many people get so focused on time horizons where in the public markets, they look at like one or two years, three years maybe, even a decade. And what often works in the public markets because of different regimes is the opposite of what works in the next 5 or 10 years. My favorite was every time there’s like mutual fund managers of the decade, we go to show that in the ensuing decade, they end up being some of the worst. But the private side is even harder because you end up having, often cases, no signal for 5, 7, 10 years. You have no idea of investments made in 2021, some of these may play out in year two, but many of them it takes a really long time. From an LP perspective, it’s a tough thing to think about.
Jonathan: It’s interesting. I think the way that I’ve thought about this when I got into investing, my background, as I mentioned, was in data science, I was a technologist. So, the day to day of a technologist at some level is typing. You’re building, writing code, and you’re adding value one step at a time. And at some level, that’s what happens in the economy, right, like, in particular, in this part of the economy and start-ups and such is that people are building something that’s deterministic. If I build this piece of software, I know what will happen. This lever will go here. And if a user touches it, this is what will happen. Completely deterministic. And that adds some value to the world because you brought value to that customer. But then if you go up the ladder, as we know, how does that translate to the equity becomes valuable as a tradable commodity at some level? There’s a sort of line of causality. Things are very causal at the level of the way technologists build things, but then things are very random, as we know, sort of in markets. And so in some sense, philosophically, the way that I deal with that is sort of by working in the early stage where, really, we’re just focused on building great things and not really on trying to outguess the market. We’re going to fail at that. It’s part of why when people think about valuation in their early stage, it’s just a completely different set of conversations than happen with regards to valuation for late-stage or public companies, because it’s really about building something. It’s not about outguessing the other investors.
Meb: We haven’t actually talked specifically towards what companies and opportunities you guys are looking for. I’d love to hear, broadly speaking, what you guys are looking for, sectors or approaches as I flipped through y’alls’ holdings. I think I’ve co-invested in about six or eight. So, I’m not going to tell you which ones unless you mention them, and you don’t have any foreknowledge. But some of them have been previous podcast guests. What are you guys looking for? And then feel free to use any of the current portfolio as case studies as how it played out. We’d love to hear a name or two as well.
Jonathan: The things that we’re looking for are high-level abstractly at some level. We’re interested in things that grow in unusual ways, peculiar growth, I think is something that we’re interested in, or product-market fit, peculiar product-market fit, something that maybe one would not have guessed from the outside. That’s sort of at the level of tactics. It’s at the level of, can you tactically show me that this thing you tried to build is going to defy my expectations of how something ought to grow in this space? If I look at a B2B SaaS company, the naive expectation is, “I hire salespeople. They sell the product.” Now, if you show me something that doesn’t do that, that’s usually interesting. That’s the tactical level. In terms of the sector level, for definitely sector generalists, although most of our portfolio is B2B, there’s a lot of Fintech in there. We’re fascinated by B2B payments companies, B2B Fintech, as well as consumer Fintech, several insurance companies, things that are Fintech adjacent. Carta is, at some level, Fintech adjacent. Now, it’s considered more core Fintech. But we also do a bunch of other things. We’ve invested in several things that might be called frontier deals, companies that are building rocket ships. We invested in a company called Relativity that built literally 3D-printed rockets, and a company called Saildrone that builds autonomous seafaring drones. These are literally like drones that sit on the ocean. And we definitely have several consumer companies in there as well. It’s a very broad range, I’d say for us.
Meb: Could you tell us a little more about one or two about what you’re particularly excited about? You mentioned a topic that’s particularly increasing investor interest in now, which is the space sector. We’ve had a couple of space guests on here. I still haven’t seen my first launch. I was supposed to go see a launch at the end of the year, but they cancelled it up at Vandenberg, so it’s still on my to-do list for 2021. Any of those you want to detail or any other particular names in there we’d love to hear a little more about?
Jonathan: We’re getting to this time where a combination of factors. There’s been a lot of money pushed into this sector for a long time, but a lot of it is starting to show that fruition, the idea that we can have transport infrastructure in space, if such a thing could exist and that such a thing could lower the costs of getting things into orbit by orders of magnitude. And we’re starting to see that now. And what happens when capabilities become cheaper by orders of magnitude, it will oftentimes generate opportunities for new businesses to exist that one hadn’t thought of before. And so that’s kind of where the thinking is, when you’re starting to see companies that can turn it into software almost or relatively low-cogs operations and be able to move these things around in space. It’s generating interesting demand in ways that we hadn’t thought of before. Yes, there could be an industry of science experiments that are being done in space. There does exist government-level capital to finance that activity. So, what are the picks and shovels that need to exist to make that a reality? There’s sort of this macro interest from the public sector. There’s a bunch of investment that’s been playing out over the last decade. And we think that we’re sort of in this era right now where there’ll be a large explosion of innovation. That’s part of the interest in that area.
Meb: One more minute on actual portfolio companies, then I want to talk about a couple of other things. Are there any in particular that had been recently funded in the last year that you think are particularly oddball or interesting where it’s an idea that you’re just like, hah, that’s like a totally novel approach to an industry or a product that you’re particularly excited about or optimistic about?
Jonathan: One that just kind of popped into my mind. When we invested in a company called Prodigy, almost two years ago now, which sells software to car dealerships. They do digital retailing solutions for auto dealers. Auto dealers are a really interesting bunch. They’re not really tech-forward. But for a bunch of reasons, Prodigy has been able to build a platform that’s gotten a lot of traction there. And I think one of the reasons why it just came to mind is because when I think about things that have particularly played out differently in the COVID landscape that I would have thought otherwise has been cars. It has been in the news a bit about how, like, car sales are way up, yet we know that you can’t browse around in a car dealership. What is the tension? What happens to all these car dealerships, these individual proprietors who are running their businesses, but all of a sudden have to run their businesses very differently? And so it’s causing a big shift in the car landscape. The electric car makers are in the news a lot. But there’s other stuff going on there more in the economics of how cars are sold, how this asset gets moved around in the real world. And what is software’s capability to address that and enable that? And in particular, what is the role of dealerships in the world going forward? That’s an area that has surprised me in terms of the amount of tailwinds that they’ve received in the post-COVID era. That one surprised me even though it’s weird because it may not be a surprising. It’s like car dealerships at some level, but it has responded differently than I would have thought largely just because I think that macro conditions have played out in a way that have been surprising to us.
Meb: We talk a lot on this podcast about this entire category of frustration arbitrage, which is all over the place in various levels of Fintech and particularly real estate. But my God. The car buying, selling, leasing, all of it, experience, it has to be one of the lowest NPS scores on the planet. One of the reasons, obviously, that Vroom and Carvana and others have exploded in popularity is simply because it’s such a sucky experience before. So, hopefully, software and tech can upend that pretty quickly. I got a couple of other spots in particular that are equally as bad, but that’s got to be up there. You guys are structured a little bit differently. You have a program that you call Firstlook. Do you want to tell us a little bit about what that is, what that entails?
Jonathan: Firstlook is our program for co-investments. So, what Firstlook does is you can think of it as having three different audiences. The first audience is co-investors. It gives us the ability to offer co-investors direct access to individual companies. These companies tend to be not just early, early stage, but more like mid to late-stage sometimes. And then we have opportunities in there that have been everywhere from series A all the way up to late-stage and gives our co-investors the ability to invest directly in those opportunities. That’s the co-investor side. Now, for the company side, what Firstlook does is Firstlook gives us the ability to offer the platform as a service to a portfolio CEO. One way to think about it is giving portfolio founders access to a pool of capital that is non-traditional. There’s sort of this notion that raising capital as a start-up means just walking up and down in Sand Hill and pitching to, like, whatever, the 30 firms or so that are located there. But the reality of the last 5, 10 years is that the pool of capital that has interest in doing this type of stuff is much bigger now. There’s a lot of capital out there that has interest, not just in putting capital in there, but being involved. Frankly, it’s inspiring to hang out and work with founders who are building these businesses from nothing. And so far our CEOs, for our founders, Firstlook is a way for them to interact with that world in sort of an organized way. We help them navigate that world, which, obviously, leads to capital, but maybe even more interesting leads to all sorts of interesting business development, strategic value, strategic relationships that they would have otherwise not been able to meet.
Meb: If you’re an investor listening to this, when you say co-investor, just give a little clarity on what that means. Does this mean family office, endowments, large funds? Are there minimums? And then how does the actual experience play out?
Jonathan: Co-investors for us really means everything down from, like, just regular accredited investors all the way up to large institutions. We actually have a big network, well over 1000 co-investors that we’ve worked with through this program that span the size scale up and down. That’s kind of the co-investor side of it. Part of the thing that’s been interesting to us is that while it’s clearly a way to move capital into the companies, help them fill out syndicates, help them fill out rounds, there’s just so much value in the professional capabilities of these people who are investing either full-time or otherwise that are out there. And I think that’s really proven to be really interesting. The third audience is really ourselves and our fund LPs. And I think something that we do that is very different from everybody else is the economics of our SPVs don’t flow to the management company. They actually flow to the currently active fund, which is a little bit different maybe from the way other folks do it. So, what that means is like in theory, if we have some SPV that generates a little bit of carry, maybe a few million dollars of carry, that few million dollars of carry does not go to the GPs, it goes to the fund. And from the fund’s point of view, it appears like the fund made a small investment basically the GP commit on the investment and received a very high return back on that investment. It’s really a form of enhancing returns for the fund.
Meb: So, let’s say I’m listening to the “Meb Faber Show,” I want to put 1 million bucks in the Carta. Maybe I’m crazy, I want to do 10 million. How do they interact with you guys? Do they apply online? Is it a software portal? Do they just start calling you after hearing this show? What’s the approach? How does it work?
Jonathan: They can just shoot an email to us really. That’s it now. Part of our thing is that we don’t believe that people really necessarily want to be interacting with a portal. You want to have someone you can chat with, and so that’s what we do. We sit there and we chat with you. You shoot us an email if you have interest, then we’re more than happy to get you on-boarded.
Meb: We’ve had people that have been emailing me every month that are interested in investing in some of these private companies. So, you guys don’t email me. Email Tribe. They can handle it. They have a bigger staff than we do. How many people do you guys got over there?
Jonathan: It must be around 20 now.
Meb: Talk to me a little bit about VC in general today, or you can call it the seed investing landscape. It was a bit of an oddball year last year. What’s the status today of the way the world looks? Anything you guys worried about, excited about, particularly concerned? Any requests from the listeners as you look to the horizon of a new decade?
Jonathan: Clearly, we’re in a world right now of free cheap capital. It’s been interesting to see how that trickles down into early stage. Cheap capital for public companies means they can raise cheap debt and means that their valuation of their equity is super high in the public market. But it doesn’t quite work that way for a seed company because a seed company raises money, yes, but they still have to use that money to hire a software engineer to write code. And that layer of the work doesn’t really change at some level. When you pair that with the fact that in pandemic world, you don’t see the software engineer right there with you anymore. They’re remotes. They don’t need to be in the Bay Area anymore. You don’t need to pay Bay Area rents anymore to hire that individual. That part of the equation has moved in a different way, I would say, than the pure capital market side of it. There’s definitely a bunch of big rounds that happen pretty early these days. We definitely see some sort of valuation creep upwards from sort of the seed and the early-stage world, but the reality is that companies still have to take that capital and turn it into something valuable. They have to turn it into traction, they have to turn it into product-market fit, right, or revenue, or some combination thereof. And the dynamics of the ability to turn invested capital into actual more business, that doesn’t really change. And that’s really where we focus our time. When I think about things that have changed. Oh, yes, capital market is totally different now. But what hasn’t changed? Well, what hasn’t changed is, you still need to build your team up from four people to eight. You still need to hire those four people, and then you need to find a way for them to add value. And that’s never easy. That’s really where the rubber hits the road. And that piece of this stays pretty constant regardless of the capital markets moving up and down.
Meb: What does the future look like for you guys? Is it kind of just, at this point, you’re a pretty young firm, three years in? Is it just traditional blocking and tackling for the next few years? Are you planning on rolling out various funds? What’s Tribe going to look like in 2024, 2025, 2030?
Jonathan: Our core capability is really our experience, our expertise at building companies, in particular, scaling companies in a measured, analytical, rational way. And that’s really what all that background and growth, data science, product development that spans our firm. That’s what all that expertise is. And so really, the goal as Tribe is to bring that skill to bear to the ecosystem in a way that makes sense. Now, different players in the ecosystem want different things. If you’re a founder, you want capital. If you’re an investor, you want returns. But if you’re an investing, you also want somebody to help guide you through this world. You want someone who can be a partner to help you think. You want a partner who can show you, like, how you think. You want the partner who can articulate the world in a clear way. And that’s really where all our data work comes in. We view our data work not really just data, it’s really about giving a clear articulation of the world. And then the question is, how is such a clear articulation valuable? How does that deliver value? Well, there are many ways. It helps the founder understand if they’re taking the right direction and helps them think about what they’re doing and the avenues of growth. But it also helps investors think about these various opportunities as more than just like, “Okay. What is the valuation multiple?” That’s kind of a simple way of thinking about it. The real question is, like, what is the thing you are buying? How do you articulate the value? Can you do so in a way that’s reasonably defensible?
Meb: How do you guys actually interact with these portfolio companies at post-investment? Is it, you say, “Look, we have this data expertise. It’s not just for the investing side, but we can help you plug into some of our capabilities or maybe find people that can assist you in certain areas.”? How involved are you guys, typically, with the portfolio companies post-investment operationally?
Jonathan: We’re very involved. It’s pretty normal in the sense, you know, we take board seats and we’re active investors and we try to be helpful in all the ways that we can. That part is pretty normal. We make introductions where we can. That part is pretty normal. I think where it becomes not normal is when it comes to our specific areas of expertise. Our specific areas of expertise are around being able to tell a story, utilizing your own data and helping you articulate that story to other members of the ecosystem. That’s a really big piece of it. And then, two, having such a network co-investor of sitting right alongside us that can help you…that you can lock into and all are plugged into in all these different ways for business development purposes, sales purposes, strategic purposes, and investing. The sort of medium by which we keep all that together, really, is this data work because it’s our ability to just speak the truth. It’s a really big difference. If you look at our memos compared to other memos, you’ve probably looked at other venture investor memos or private opportunity memos, they read, like, sales pitches, and ours don’t. Ours read like an auditor’s report. That’s on purpose. We’re really focused on just telling the truth, not on selling you something. And it’s that core thing that helps us to work with portfolio companies. That’s why they want us around. And it’s the same thing that we use to attract home investors towards opportunities.
Meb: That actually hits on a topic that I’ve been thinking more about recently, and this applies to both public and private. I’ll give a hat tip to Calacanis on this one, but he was talking about certain companies that instead of having product-market fit, they have product-VC fit, meaning the companies are particularly attractive to VCs. But also, this sort of concept applies everywhere, in your case, speaking to ideas of a GP-LP fit. And I see this a lot having seen, I think, over 2000 syndicate deals on AngelList over the past five years, there’s some that read almost like a novel, there’s some that read like a used car sales pitch, some read like an audit report. But we see it in my world too of public investments. And listeners, as you think about this, there are a lot of funds out there that I’ll scratch my head and say, “Why in the world? Who would ever invest in this fund?” It seems crazy. And then as you think about it, it doesn’t have the kind of the product and investor fit, it has the product advisor fit of the financial intermediary. A classic, of course, is all the conflicts of interest with high fee, things like annuities where people get paid to be the middleman. It’s fun to think from that standpoint. I don’t know if there’s a way to describe that actual principal agents on a concept, but I think it’s accurate. And I think it’s some that colors a lot of our world.
Jonathan: Business-investor fit is an entirely separate problem at some level from business-customer face. And if you think about sort of the role that accounting and data plays in the two sides, it’s pretty different. Does accounting play a role in a business articulating its value to investors? Well, obviously, because we all live in the post-Graham, post-Buffett world, obviously, it does. And so then the question is, now that there is all this other data, but is there a way for that to systematically affect that world of business investor fit? And we think yes, and we’re out there writing it. Now, on the flip side of it, we can talk about the business to its customer that’s kind of a similar thing. How does a business work with customers? How do they know that they’re doing a good job there? And then can we help on one side and then help them tell the story to the other side? That’s really sort of where all our focus is, being able to sort of create that value, and then help them articulate it and show it to the world.
Meb: I want you to put on your portfolio manager hat for a minute. And this is a topic that is well understood in private markets. It actually plays out in public too. As you mentioned, Mauboussin earlier actually put out a paper on it last year that we’ll link to it. But the concept of all markets, public and private, particularly in businesses exhibit power laws. And you have certain securities that end up having these monster, 10 times, 100 times, 1000 times returns that ended up contributing, most if not all, the performance of, in the public markets, the case would be something like the S&P 500, in the private markets, it may be 50 investments, one or two end up being the Ubers of the world. And this can be personal or public Tribe messaging. But how does Jonathan/Tribe think about selling? And this is an area that I think most investors spend 99% of their time thinking about, “Do I buy this investment?” And then once they have it, they kind of just wing it. You hit the lottery. You got a Carta. You got a Chipper, whatever. One of these companies that’s on a rocket ship and, let’s say, it goes up 10 on its way to 100. How do you think about selling? Are there any thoughtful guideposts you have, any suggestions to listeners? Because most people get in this binary thinking mode, I’ve seen a lot of investors where it just drives them nuts.
Jonathan: This is one of the great things about investing in these early-stage private companies is that this doesn’t come up too often. It’s like you invest… Most of the time it either just simply goes to zero and you never have a chance to sell, or it works somehow and then you maybe get the opportunity to sell. I think one of the things that we found from history is that selling prematurely in these companies is like you don’t get any kudos for that at some level. The things that are able to win, they keep winning, and in many ways you just kind of hang on. One of the things about us as managers of these vehicles is kind of somewhat built-in. When a company goes public, we’re not paid to figure out the sell. So, we distribute the stock and then our LPs can figure it out. I think they are probably smarter about selling than we are. They’ve done it more than we have. Our job is not about buying and selling. Our job is about building business value, and then helping to translate that into financial value to even give you the opportunity to have something to sell.
Meb: It’s a hard problem and I need to write an article on it because so many people once you own something you have a psychological attachment to it that’s different than before you had it. So, if you don’t believe me, listeners, go look in your garage and you can see why there’s just mounds of just junk in there that no way you would buy again tomorrow given the choice. And so you get attached to these stocks too.
And it’s hard on both sides because it’s hard to let something compound. If something doubles, you’re like, “Oh, my God, I just doubled my money.” But the doubles, for many cases, it’s on its way to 10X 100 bagger and many of these can take a decade, two decades or more, play out. And so a good exercise is to write down on a piece of paper when you enter a position and say, “Here’s what I’m going to do if this goes down 50%. Here’s what I’m going to do if it goes down to 100%. Here’s what I’m going to do if it doubles in the next year or doubles in the next…” Whatever it may be. “Here’s what’s happened if I get a divorce.” At least walk through it. You don’t have to stick to it, but at least walk through it with the added suggestion that you also don’t have to think of all in or out. A simple way to do it is go halfsies. Sell half, sell a quarter on and on, but at least think about it. That’ll put you ahead of 99% of people.
Jonathan: I think that it also goes back to this question about, like, portfolio allocation. There’s a piece of me, definitely, that wishes I didn’t own so many things even on personal portfolio level. And the preference would be to own, like, two or three things only. And how do you get there? Well, you can get there by buying your way into them or you can get there by buying something small and having one of those things suddenly become huge. And so maybe another way to frame it is, like, how much of your portfolio, whether it be a professional or personal, would you be willing to sit in one asset? And if you’d look at the way Buffett wrote about this stuff early on in his career, it’s the same thing. The answer is fairly high. Maybe not 100%. You’re probably okay with 20% to 30%. And so if you’re in a position that went up 10X, okay, cool. Is it 10%-20% of your portfolio? If not, then the way you think about concentration can be different. Interestingly enough, this relates to another topic I like to talk about. So, I like to say that for high-end decisions, you should only use data and technology. And for low-end decisions, you should not only use data and technology, but you shouldn’t not use the data.
A high-end decision is like this. Let’s say I’m Facebook and I need to figure out what ad to show you. Well, I’m going to do that like a million times a second. That’s a very high-end. I should use data and technology to do that, and only data technology. Cool. Now, what’s a low-end decision? A low-end decision is like, where do I go to college? How many times do you make that decision? Maybe twice in your lifetime. Does that mean you use data? Well, not no. You do look at how much it costs. You look at what the graduation rate is. You look at how they rank in the U.S. News. You still look at data. But how does that data influence your decision? Well, it’s not obvious. It integrates in some way. It’s part intuition, it’s part data. When you look at venture investing what we do, we only make like fewer than 10 investments a year. It is kind of in-between, but it kind of steers a little bit more towards low-end. I think the problem is that if you treat it like low-end, it doesn’t mean you just use your gut because your gut has to be informed by data. So, that’s kind of how I think about that. And when it comes to your question about selling, it’s kind of the same thing. It’s like, “Well, if I only own five things, and these are low-end decisions, in which case, knowing when to sell well, there is no point in trying to make something completely systematic just like there’d be no point in trying to make a systematic machine to decide what college I go to.”
Meb: That was really thoughtful. It speaks personally, I remember, to visiting my university and yes, trying to use a bunch of quantitative inputs and then showed up on an absolutely beautiful spring day, everyone out throwing frisbee and football and girls in sundresses and people just wandering. I was like, “This is heaven. I’ve never seen anything like this.” So, there you go. You spoke exactly to it.
Jonathan: It should be the same way in your portfolio at some level. You should hope that the companies are one that make you feel good on many levels.
Meb: Echoing a couple of your comments, I like this combination of almost what we call this coffee can portfolio which goes back to, I think, is 50 years ago Kirby writing about this. It’s just putting an investment in, leaving it, forgetting about it. And that takes away so much of the psychological problems of people overtrading, which is absolutely taking over the world again very similar to the ’90s when I cut my teeth. And almost any research shows that overtrading is a negative. But on the flip side, say, look, adopting this sort of Bezos-like concept of regret minimization, if you have a stock or a crypto or a house or whatever that becomes 10%, 20%, 30%, 40%, 50%, whatever percent of your portfolio, take a step back and think, “Well, what if that goes to zero?” And there’s gazillion examples in history of Polaroid, Enron, GE, on and on and on. I mean, even Amazon, what, declined 95% and 50% Berkshire multiple times and it’s hard. So, thinking in terms of not all in or all out, which is so many investors just want to gamble. They wanted something to cheer for or not, and telling them to sell a quarter or half is not exciting. It’s not sexy for anyone, but is often a prudent idea.
Jonathan: And I think it does go back to that notion that, like, I don’t think that there is a systematically fully defensible answer to how it is, one, allocates one’s portfolio. Invariably, that answer is going to be attached to whatever sort of exogenous requirements and constraints it faces. “Oh, I need to buy a house. Oh, I had a kid.” And even if you’re a bigger and if you’re an endowment, it’s still like, “Oh, the hospital I’m attached to all of a sudden have a different cash flow situation.” Portfolios serve some function outside of getting big, always. They are owned by… They’re associated with some other entity, with something else that imposes a constraint. And that’s really where it comes from at some level.
Meb: The reality is life is messy. There’s a great book came out last year called something along the lines of Josh Brown and crew put it out called “How I Invest My Money.” And I’ve been disclosing how I invest my money for years now. But the funny thing is, you read these chapters of 20 financial professionals, and I think exactly zero of them said, “I do a mean-variance optimization. I optimize quantitatively my portfolio.” Each one is totally different and totally, in most, if not all cases, thoughtful and well put together, but subjective and messy in many ways. Like me, for example, I talk a lot about investing in farmland. If my heritage or my family didn’t come from farmers, would I be investing in farmland? Probably zero chance.
Jonathan: It’s also where the inspiration always comes from founders. If you’ve worked inside of a company and when they’re sort of in that middle stage, how does the company decide to put 30 employees over here and 20 over here? Are they doing some mean-variance optimization? No, they’re just trying to take advantage of an opportunity, investing some of their resources towards it, and then owning the outcome. And I think it’s the same thing. And it is not really any different. But if you put some percentage over here is not too different from throwing some employees over there to work on something if you’re a company.
Meb: Well, that’s what makes it fun for every day, and interesting and the problems and challenges involved in all of this as well. Jon, this has been a blast. We get to start to wind down. What has been the most memorable investment for you? And this could be at Tribe or prior. It could be good, it could be bad, it could be public, private, anything in between. Anything come to mind?
Jonathan: The one that certainly the several of us at Tribe sort of point to is one that was important for us was our involvement in Slack and Social Capital. We led the first round of Slack at Slack. It didn’t start out as Slack. People remember this. Slack was originally a gaming company, and then it pivoted into Slack, and then Social Capital subsequently lead that first round of Slack and then doubled, tripled down over subsequent rounds. And a bunch of that was important for us to understand how this history in social gaming, how the history in the social web, understand what those patterns look like, but also have an understanding of B2B and enterprise and how all that comes together. And when you see companies or phenomena that doesn’t fit neatly into existing categories, that’s oftentimes a signal that something interesting is happening. Before Uber existed, ride-sharing wasn’t a category. You know, if you saw Uber at the beginning, I’m sure you weren’t like, “Oh, this is a ride-sharing company. I invest in ride-sharing companies.” No, that’s not what you did. You saw this company that didn’t look like other things. And then if you did the data work, presumably one would have seen something unusual to verify that the thing that looks unusual, actually, is unusual under the covers. And I think that’s been a guiding light for us to think that the categories by which one thinks of opportunities are usually themselves an artifact of some thinking, to say, “Oh, I invest in B2B SaaS only.” Well, by traditional B2B SaaS, something like early Slack wouldn’t have made a lot of sense because it exhibited a bunch of these social web things. That’s been an important component for us, how to categorize the world and separate, in some sense, the category of the business from the underlying behavior between the product and the customer, which is sort of that’s where the actual value is created regardless of what you call it.
Meb: As Jonathan starts 2021, anything particularly on your brain that you’re super excited about, super confused about? Anything come to mind?
Jonathan: We’re constantly thinking about more and more ways to leverage data to help our companies and to help our co-investors. That’s obviously the biggest piece of it. That activity looks like investment activity, but it’s also like entrepreneurial activity. We’re just building our own capabilities and trying to bring them to bear and add value to all of our various customers. But then, as an investor, a lot of worth thinking about is what are the parallels for what we’re doing that are going on in other places. It’s really a form of Fintech. We’ve invested in many other Fintech companies and there’s been this ongoing movement of taking financial services, financial products, and presenting them in a way that looks different, that is possible because of technology, things like Plaid that didn’t exist before, Marketo, all these peculiar companies that exist that make financial products different now and will likely be different over the next decade than they were in the last decade. I think that’s the trend that we keep watching. In particular, we don’t believe it’ll be a world where financial professionals disappear, rather, it’s a world where their role is different. And so part of that is figuring that out as we do it ourselves for ourselves as a firm, but also watch it play out through the rest of the ecosystem.
Meb: What’s the opportunity look like beyond our shores? I imagine you guys are mostly U.S.-focused, but you do a bit elsewhere as well?
Jonathan: We’re actually, like, a properly international firm. We have investments in India and Europe. We have several in Canada and Mexico, in Latin America. We invest all over the place. There are plenty of opportunities all over the place. I think that this is actually an area where our data works really well because our data sees through the international stuff. It’s just looking at the customer product and interaction, which is sort of indifferent with regards to the market, and then it helps us to identify situations where entrepreneurs are able to build things quickly on low amounts of capital and generate product-market fit in ways that one wouldn’t have thought of before. It helps us to recognize them. The opportunities are different in different parts of the world. I think, obviously, we spend a lot of time in Fintech. So, observing Fintech in the rest of the world has continued to be really exciting. We’ve done some logistics investments in terms of shipping logistics globally. And I think these are a couple of areas where we’ll continue to dig into.
Meb: I imagine the answer to this is no, but you can correct me. The data set that you guys are accumulating, it sounds like an internal Google Trends almost, but with a lot more data involved on specific companies. Is that something that’s ever not public-facing, but something that is a resource outside of the individual investing sphere that you guys internally? Is there ever any sort of concept of that being a product in and of itself?
Jonathan: Definitely, no, in the sense of showing it directly. No. That said, we definitely have all that data and it expresses itself through the things that we do show publicly. It’s usually a bit more indirect. I like in what we do to what in quantitative hedge funds, a lot of them have this structure where there’s like a data acquisition team that goes out and buys data, they shoehorn it into the system, and then they build this abstraction layer on top, a software abstraction layer so that PhD economists can sit on top of it and be like, “Get me the, whatever, Zimbabwe current account deficit for the last 20 years.” And they can speak that level and not have to deal with, like, this spreadsheet, this column. And that’s how they put themselves together. In many ways, that’s what we’ve done. Our data acquisition activity is just a venture. Day-to-day venture generates a lot of data especially data that really is unique to us. But then we’ve had to spend a bunch of time to build a bunch of software to package this up in a way that we can do research on top of it in a reasonable manner. And that research yields usually macroscopic insights. Really, we’re looking for insights into how companies get built. How is it the product-market fit unfolds from early product-market fit to scaled revenue? How does that journey happen of the early-stage company? From all the data that we’ve gathered and from how we think about the world, can we articulate that story in a special way to all of our co-investors and to other investors? And so that’s the way that the data sort of shows itself. I started that platform that we’ve built, we call it All Spark, this whole computation platform. There will not be Transformers there.
Meb: Funny you mentioned that. My son just got his first transformer this past weekend, Blurr. By the way, I made the mistake. I bought him, like, the really complicated 18-move Transformers, so I learned from my mistake. I need the one that just takes like one move. Jon, this has been a blast. Where do people go if they want to invest in your funds, if they want to buy some SPVs of these killer companies, if they want to send you their pitch? Where’s the best place to find you guys?
Jonathan: They can just email me at firstname.lastname@example.org or hit our website, email@example.com, I think.
Meb: Awesome. Thanks so much for joining us today.
Jonathan: Yeah. Thank you. It’s been great.
Meb: Podcast listeners, we’ll post show notes to today’s conversation at mebfaber.com/podcast. If you love the show, if you hate it, shoot us a message at firstname.lastname@example.org. We’d love to read the reviews. Please review us on iTunes and subscribe to the show anywhere good podcasts are found. Thanks for listening, friends, and good investing.