How I Invest with David Weisburd - E5: Jonathan Hsu | Co-Founder of $1.6B AUM Tribe Capital on What Data Shows is Product Market Fit

Episode Date: August 14, 2023

David Weisburd sits down with Jonathan Hsu, Co-Founder and General Partner of Tribe Capital and one of the top data scientists in the space. Hsu is a physicist-turned tech entrepreneur-turned VC. In this episode, they discuss the importance of growth patterns and product market fit (PMF) in venture capital. Tribe conducts intensive data work on approximately 400 companies annually, a unique dataset that cannot be purchased or obtained from other sources. If you’re ready to level-up your startup or fund with AngelList, visit www.angellist.com/tlp to get started.

That's kind of the underlying thesis of the firm. What really matters in the long run is that pattern of growth and that pattern of product market fit. It sounds kind of obvious, but it's actually like a bit contrarian, right? Because the vast bulk of investors are like, the founder matters, the team matters. We think the founder matters.

We think the team matters. But in the long run, remember my story about Facebook, in the long run, what matters is that pattern of growth, not the growth. Welcome to the Limited Partner Podcast, where we talk about venture capital through the lens of limited partners. I'm your host, David Weisberg, co-founder and head of venture capital at 10X Capital. The world of LPs can be notoriously private and discreet, but on this podcast, we speak candidly with limited partners about their true feelings on venture, the ecosystem, and 2023 and beyond. Jonathan Hsu, pleasure to be introduced to you. Welcome to the Limited Partner Podcast. Special shout out to Abe Othman, Head of data science at AngelList, who previously interviewed,

who was kind enough to make the introduction. First, Jonathan, we're going to start with your origin story. Every great superhero has an origin story. So what is your origin story? Yeah, yeah. Happy to walk through it. Thanks so much for having me here. My own background, I actually started life as a physicist. I did my undergrad and PhD in physics. My PhD was actually studying quantum gravity at Stanford, truly sort of the height of uselessness. And towards the end of it was just clear I didn't want to be an academic, so I ended up going into technology.

This was like 2006 era. Spent a little time at Microsoft. Ended up co-founding one of these early social gaming companies. This was sort of in the era of crappy games on Facebook. Co-founded one of them back in 2006. It was called Superpoke, if you happen to remember that. And we ended up selling that to a company called Slide, which is Max Lepchin's company. In between PayPal and Affirm, he had founded a social gaming company and he acquired us. And so I went to run data run data for max election for a couple of years before being recruited over to facebook in 2009 joined facebook at that time i was about the fifth data scientist roughly at the company really ended

up building out the whole data science and analytics org for facebook by the time i left five years later in 2014 you know was like 15,000 people. I think the data science was 200 people at that point. Half of it reporting to me at that point had been spending a lot of time in big company, so really wanted to try investing, was interested in finance and knew the folks over at Social Capital, which is a firm run by some ex-Facebook folks and joined Social Capital in 2014, which is where I was for four years before we spun out to create Tribe.

So going to Berkeley, so tell me about that experience. What did you study and how was that experience? I went to Berkeley initially wanting to be pre-med, wanting to be a medical doctor, but very early on sort of fell in love with math and physics and sort of just wanted to be a physicist. I wanted to be Richard Feynman. What about physics intrigued you? There's not many venture capitalists that are in the physics space.

Why physics and why VC? Back when I was, you know, coming up sort of working with Max Lepshin, data science wasn't a term yet, right? What happened sort of in the late 2000s, there was sort of this big data, big bang that occurred in the mid 2000s, you know, and in about 2005 to 2008, all of a sudden it became cheap to store and compute on very large amounts of data, right? You know, if you go in the 90s,

like people didn't have a ton of data because it was expensive and impractical to store it, but then starting in the mid 2000s, all of a sudden people had tons of it. And so if you have a bunch of data, what do you do? Well, you hire some PhDs to throw at it. Let the PhDs figure it out. And I was one of those PhDs in that era,

that early era of data science. And really what we were trying to do is we were just trying to figure out, we have these big data sets, how do we leverage them for business purposes? Whether it be for building product, generating strategic intelligence, helping us with product strategy, whatever it is, how do we use the data to get to the next level?

And there was a small group of us who were right of that era. Several of us happened to be PhDs in physics who were all exploring these things. And invariably, when you have a bunch of physicists around, they try to make models out of it. Well, let's create, let's use this model, that model. And, you know, we developed a bunch of these different analytical techniques to leverage that data. This really sort of came to its really full expression at Facebook. This was an early stage, move fast and break things.

Mark Zuckerberg famously called that era of Facebook. How was the early days in Facebook? And what did you learn from that experience of working with Shamath, working on the growth team and working at Facebook as an organization? Yeah, I would call it, it was kind of middle days. It wasn't super early, right? This was sort of the era of a few thousand people

at the company. So it wasn't like tens of people, but it was a few thousand. And it was nonetheless a very interesting, it was sort of in that interesting zone between, because it had gotten to that scale of employees very quickly right um and so still had a lot of these behaviors internally that were much more small company oriented you know which was included the ability to move super fast which was really good and uh you know there were sort of it was an important episode there right in 2009 when i joined facebook was just the era when it was just barely clear that it hit. Well,

it was more or less clear that it had defeated MySpace and that it was going to win, right? At that point, those other social networks were still bigger, but the speed was there, right, for Facebook. What allowed Facebook to really leave the pack? As you mentioned, it's not just MySpace. MySpace, people forget, had a good outcome with a half billion dollar exit. There was other social networks. What really differentiated the team at Facebook? Yeah, I think this is sort of the fundamental question and the fundamental driving question

for all of my work since Facebook, right? And I think there's a bit of intellectual humility of noting that there's no one thing and it's kind of foolish to think it's just one thing. Right, and I think that's, there's sort of an aspect of it that's the team, right team was special the era in history was just right right like they were able to sort of catch there was this moment right they sort of did a really good job through the desktop era and then when the phone happened when the mobile phone happened facebook did a really good

job of taking advantage of it facebook was able to take advantage of a lot of that right um and whatsapp so on and so forth and you know what we believe here at the tribe is that the underlying bit is really about the sheer momentum of product market fit, right? Once you get the right pattern of product market fit, once it starts really moving, it'll just keep going regardless of anything, right? And it just had the right pattern. And when I say that, you know, I guess the thing, the things that I'm thinking of when I say product market fit and, you know, something that just keeps growing, when the things that i'm thinking of when i say product market fit and you know something that just keeps growing when i say that i'm i'm in i'm referring to things like religions right like if you look at early christianity it behaved the same way sort of grew from you know when when jesus christ died it was just a few hundred people right and then

sort of you know 300 years later you know by the time of constantine it's like 30 million people it just happens it just it's able to maintain its momentum through a lot of, through a long era. And, you know, obviously Facebook's probably not able to do it over a 400 year era, but over that era was able to just generate this momentum of adoption and product market fit. It was just right. And it was able to drive its own growth. And it was able to do that, you know, all the way out. So we'll move on to social capital in a bit,

but first let's cover SuperPoke. So behind some of the best venture capitalists, there's always founder experience. Tell me about the SuperPoke experience. Me and a couple of friends founded it while I was at Microsoft, while we were at Microsoft. We kind of did it on the side, as a little side project. It ended up growing really quickly, sort of,

this was in 2007, and we never raised any outside capital. We started building it. We were all fairly new graduates. None of us had really any experience in startups. It wasn't like today where everybody knows everything about cap tables and investing. Back then, we didn't really know. We just built something because we thought it was amusing,

and it grew really quickly. And then it was actually Keith Raboy that reached out with an interest to acquire us. I think we were only two months old or so we were like oh this this looks cool let's go work at a startup in san francisco we didn't really think through oh did we build something super valuable here blah blah blah you know it was more like oh you know these these guys max legend he's a big guy they want to acquire us so we were kind of star struck by that and we took an offer and we we went and got acquired quickly. Let's go from the transition from Facebook, which is obviously a once-in-a-generation

company to Social Capital. How did you get involved? How did you get recruited in Social Capital? And what was the original vision for Social Capital? Yeah, so Social Capital was founded in 2011 after Chamath had left Facebook. It was him and his two co-founders, Ted Maidenberg and Mamoun Hamid. Ted is now one of our co-founders at Tribe and Mamoun is over at Kleiner. And I joined them in 2014. And really the idea when I joined them, there were a couple of folks there from Facebook. And the idea of me joining was really to sort of continue building out this data-driven growth mentality and applying it to the venture capital world at large, right?

The vision back then was, this was 2014, right? So Andreessen Horowitz at that point was a couple of years into building out their market development team, right? Prior to Andreessen's market development team, venture firms were all kind of the same. They were all just kind of like small boutique investment firms. Andreessen Horowitz had this idea of, oh, we're going to build a capability. We're going to invest in this capability, market development. We're gonna help our portfolio companies with enterprise sales, right? And it became a thing, right?

And so, and that social capital part of the idea was, oh, let's take the growth team thing that we did at Facebook. Let's make that like our special thing that we give to portfolio companies. And so that was kind of the initial bit. What does that mean that give the growth team to startups? Can you give an example? Yeah, so we would to portfolio companies. And so that was kind of the initial bit. What does that mean, give the growth team to startups? Can you give an example?

Yeah, so we would sit with companies, portfolio companies, and dig through their data and help them, you know, sort of build out that analytical motion of developing that internal tension, right, between their desire to build a beautiful product and sort of balance it with something that's sort of analytically backed. We tried to help them create that tension, help them build that mindset. And, you know, without overriding, obviously, their preexisting product mindset.

And so we ended up working with a bunch of portfolio companies in that regard, sort of helping them build out their data capabilities, build out their analytical capabilities. It very quickly naturally became, oh, well, if we're gonna do this data work on portfolio companies, we can actually do some of this stuff

before even investing in them, right? And so it turned into this broader question of, well, how do we use data as a firm? You know, we can use it, we can go into portfolio companies, help them with their data, but we can also use it in the investment process. And it wasn't just us, you know,

you guys chatted with Abe, you know, before, there are several firms that sort of in the mid 2010s, were playing around with using these data techniques to do various, to help with various aspects of venture investing. We happen to start from the lens of portfolio company, you know, sort of value add, but it's very clear that you can do many other things, right? Sourcing and evaluating and such. You know, also with my partners here at Tribe, as we spun out, is that when you work with people who have been venture investors for a really long time,

like five, 10, 20 years, all of their regrets, their regrets that say, oh, I saw this thing, but I didn't do the right, I didn't make the right move. Right? Like it's not, they don't, they don't usually say, I wish I had seen that thing. Right? Because people who've been in venture for a long time, they see enough good stuff, but they make the wrong call. Right? And so from our point of view, using data, the question was, okay, well, we could use data to generate more sourcing, but that's actually not the structure of your regret. The structure of your regret is more about the decision-making on the things you saw. So speaking about those regrets, why do VCs pass over good opportunities? You know,

what are the most common biases and what has the data showed you are biases that could be corrected? They're sort of infinitude of biases, right? People pass for all sorts of reasons. That's the nature of VC, right? Is that you pass on like 98% of the things you see. And so, you know, maybe the question isn't so much like, what are the reasons you pass? Just because your default is passing.

You know, one of the things that we really came to see is that, you know, in particular in the cases that we were looking at, the passes were usually situations where they didn't take the time to really look at the data to make sure, to see what the actual pattern of growth was, right? Because in almost all the cases of regret that we were dealing with in social capital, there were cases where when they saw the company was already working, it was like the series A, the series B, something was happening. And we had the shot. And for whatever reason, they didn't take it. They were like it they were like oh they didn't you know they thought the person who sent it over to them

was an idiot the the referring vc they're like that guy's a i don't want to look at his deal right like dumb right and so like we're like uh found this founder i did he did it i saw him at his last company that company was crap there's no way he's going to build a good company so they don't even look and so the question is when do you do the work right and that's usually the structure of the regret the regret is that i didn't even do the work it's not like i did the work and i passed and i regret passing it's like i didn't even do the work right and so the question was like how do we do the work and make sure we do that really well and do it at a high volume you find a bias that's not actually at the beginning and not the sourcing and not in the diligence phase, but somewhat in a first look phase,

first or second look phase, where the mistakes being made, the false negatives are on which companies to actually double down on. Well, to spend time on. Because remember, like, the thing is an investor can only really look, like really look at like 50 companies a year.

You just can't, like you can't dig deep. You know, you can't call the customers, do all the work. You can't do that like hundreds of times a year. You can only do it a few dozen times a year, realistically, right? And so the question is how do you open that aperture? Right, and that's really what we ended up doing, right? Like what we ended up sort of prototyping at Social Capital

and really have built to its full fruition here at Tribe is basically the ability to dig deep into hundreds of companies a year, not dozens. So that we can get, you know, what we do at Tribe now, you know, we're able to get like extremely deep on hundreds of companies a year to make sure we're not missing anything, right? To basically tell us, okay, should we pay attention or not? Because the reality is we still limit, you know, you can't call the customers and do the, you know, founder reference checks. You can only do that a few dozen times a year. I think this is an important lesson for everybody to understand whether they're a startup raising capital or a venture fundraising from LPs. If somebody's in their data room, if somebody's doing diligence, if somebody's checking references,

they're interested. It might sound obvious, but when you look at it from their perspective, there's a finite amount of companies and funds they could be looking at. So let's move on to Tribe. So you met Arjun and Ted at Social Capital. What is it about Arjun and Ted that made you want to partner with them? You know, at Social Capital, we had sort of developed the early versions of this process of using data to like quickly understand and diagnose the pattern of growth and be able to give that back to founders, give them a really good experience. And we had sort of a glimmer of what it would be to build a firm around it. Hey, we'll continue our interview in a moment after a word from our sponsors. The Limited Partner Podcast is proudly sponsored by

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That's AngelList slash TLP to get started. Back to the show. It was clear to us that if we went to sort of, if we just parted ways and went and joined other firms, you wouldn't be able to build this kind of thing in an existing venture firm, right? Existing venture firms, they all have sort of an existing process

and existing motion. They're not really gonna change it, right? They just don't. And so the only way to really make it live would be to build a firm around it. And so, you know, the three of us were all sort of aligned that's what we wanted to do. And we spent many years doing it together already. And so we partnered up to go for it in 2018. So you started a new type of firm in 2018 focused on first principles

investing. Tell me about what is that first principles and how do you look at companies at Tribe? The focus at Tribe is really all around being experts at understanding, measuring, and amplifying that pattern of growth and product market fit. That's really what the focus is. We meet thousands of companies a year, but we end up doing really intensive data work on about 400 of them a year, where they will literally send us their data, their first party data, that analytics data that I kind of referred to at the beginning. They will send us that data and we will generate you know 50 to 100 pages of really hardcore benchmark analytics for them right and we do this 400 times a year as you can imagine we're passing on the vast bulk of these but even

when we pass you know the whole point is when we pass we say hey we're passing but here's why you know this number here is median this number here is kind of bottom quintile give them really detailed feedback now the reality is the founders don't know that stuff they may have a sense that their burn is high but they don't realize it's bottom decile high right they might think their growth is good but they in their mind they might think it's like top decile good but no it's like just above median right so to be able to give them this really detailed information and so founders you know what they say is that i'm bummed you're passing but thanks so much for this you did all all this work for me, even though you're not investing. Can I come back for my next round?

Can I share it with my investors, with my board and so on and so forth? And the point is really that we're giving them something valuable. We're helping them even though we're not investing. We generate about 20,000 pages of analysis per year, but like 80% of that is just straight pass. Obviously collecting data the whole time. 20,000 pages of analysis per year, but like 80% of that is just straight pass, obviously collecting data the whole time. But then in the top quintile, when the data looks pretty good, that's kind of the signal that we should go dig in and do the work. We're calling the references, calling the customers and so on and so forth. But it's really all about like generating that value upfront so

that we make sure we're looking at the right companies. And then if we do the diligence, if we pass and we mess up, at least like we saw, we knew what we were messing up versus like, versus not even doing any work at all. You provide a lot of data, another form of feedback to the entrepreneurs. Do you find a correlation between the entrepreneurs that take the data to heart and those that are successful ultimately?

Not very much, but not for the reason you think. It's just because there are many founders who are very good founders, who do a good job of taking the feedback. But the reality is that if their company doesn't have a good pattern of product market fit, it's not going to be successful. And that's kind of the underlying thesis of the firm. The underlying thesis of the firm is that what really matters in the long run is that pattern of growth and that pattern of product market fit. It sounds kind of obvious, but it's actually a bit contrarian. Because the vast bulk of investors are like,

the founder matters, the team matters. We think matters the team matters we think the founder matters we think the team matters but in the long run remember my story about facebook in the long run what matters is that pattern of growth not the founder yeah i think you have two different camps in silicon valley i think the reason the narrative around the founder uh is more pervasive is because it's a form of marketing for the VC firms themselves. Yes. Back to my Christianity example is the question of Christ himself versus the machinery of the church, the early church and their evangelical behavior. So going back to Tribe and the way that you're able to assess the 400 startups per year. So tell me a little bit about,. Is your data mode essentially recursive

in that you're using all the data from the startups to create these benchmarks and to create these medians? Is that the right way to look at it? Yeah, that's right. I mean, at this point, we have like 1500 companies in our database. And when I say companies, I mean like utterly raw first party data. You cannot buy this data. You can't go. It's not like the kind of thing like another firm can't just be like, oh, I got set up. I buy this data you can't go it's not like the kind of thing like another firm can't just be like ah i got set up i have this you can't buy this stuff right like the companies have to give it to you trust you with it one company at a time the only reason why they trust us with it is because we give them back something so valuable that it's worth their time to come back that's the fundamental piece the goal for us right the

underlying driver is for we want to understand the nature of growth and product market fit it's bizarre that some things grow and some things don't it's like a weird thing and that's fundamentally what we're studying and obviously we're trying to invest in companies that have the most exceptional patterns it's not solvable right like why do some things grow and some things don't you know if you study this in macroeconomics you study this from the microeconomics point of view you study phenomena like religions it It's not really clear. Like there's many reasons why something's growing, something's don't. And like, that's fundamentally what we're interested in.

And in terms of this data set, one argument might be that the data becomes, as a pretty quick half-life given, the disruption of industries, the disruption of platforms. But the reality is this, like if you're top quintile in this stuff you're basically going to be top quintile maybe things move around on the edge a little maybe

you know maybe the thing that was median is now like 58th percentile whatever but we're not looking for the 58th percentile we want to make sure you're loosely top quintile so in which case we start paying attention right so over time things shift around a little bit for sure but we're not trying to be ultra precise, right? We're trying to, because there's no point in being ultra precise, right? We're trying to pick a company who's gonna live over 10, 20 years.

There's no way you're gonna know. You're not underwriting to academic research or 95% confidence interval. You're underwriting to return the fund. Well, yeah, that's right. I think the way we think of it is the data work is there to help us really ensure, okay, is this something that's worth spending our time on? Remember going back to the other thing that we discussed earlier, the real scarce resource is, you know, your ability

to do all of that traditional venture diligence work. That's pretty scarce. You just can't spend, you only have so much finite time to do that. And so the data work is there to help us figure out where should we direct that resource. And so the whole point of doing the data work is there to help us figure out where should we direct that resource. And so the whole point of doing the data work is so that we can do as much as possible through the data, make sure that we can direct the human diligence piece to the places where we think it's going to be best used. And then most of the data tells us where not to invest and then tell us where to pay attention. And then when we do invest, it's a combination of the data and all the traditional diligence work folding together. So another way to look at that is that the VC is essentially making investment at the time of diligence, not at the time of capital deployment.

That is one way of looking at it, right? Because that is like, you know, it's a key scarce resource internally. To be clear, like our 50 page analytics, it would be actually basically impossible for like whatever a traditional VC principle to do something equivalent. Like they just don't have access to the data. They don't have the analytical skill. It would take them a year or more to generate the level of analytics that we generate. And we generate that level of analytics in like a few hours. So a lot of our listeners, we like to brag, we have over a trillion dollars in assets listening to us. And a lot of our listeners, we like to brag, we have over a trillion dollars in assets listening to us. And a lot of them come from other assets and other asset classes that have much more efficiency than I would argue venture capital has. How efficient is the venture capital market, specifically looking at the data that you have versus the top quartile investments and investors that follow on in that and what you perceive to be the

best companies how efficient is that well it's an interesting question i actually you know i would say that i don't think venture capital as an asset class is particularly efficient and the reality is you just don't know what's going to work at the beginning right like you kind of have a loose sense but i mean the whole history of venture is stuff that completely surprises us i promise i wouldn't delve too much into your secret sauce but our our listeners have been listening now for a bit. So can you open up the Komodo a little bit and tell us a little bit about what the juicy data says about what's important for startups?

Yeah, in fact, it's all on our website. It's not secret at all, right? If you go Google Tribe Capital or you look at our website in the essays section, there's an entire article about how we think about product market fit, quantitative approach to product market fit. And there's a second article that's entirely our view on unit economics, right? And between those two articles, it basically outlines the entire thing. The whole point is to create a company, right? Where you can

find some pattern of growth, some pattern of product market fit that is economically reasonable, which is to say has unit economics that makes sense and such that that pattern will continue to hold as you scale the thing up by orders of magnitude right you look for a company that expresses some unit economics that expresses some growth pattern then the question becomes okay if i plug 10 20 million dollars into this thing and they go spend that money on more engineers, on more sales and marketing people, is the pattern that I see, is it going to stay the same? Is it going to get worse? Is it going to get better? And at some level, we obviously want to see ones that are going to stay the same or get better over time. Realistically, they almost all get worse. They get worse because

your early customers are early adopters. So your acquisition costs are lower they're going to be more fervent users of your app of your product so their lifetime values are going to be higher so your unit economics are only going to get worse from the beginning so when you see a company that already has like weak economic structure at the beginning you're unlikely going to be in a situation where it's going to magically improve all the data that we do is all oriented around an implementation of this thesis around growth and product market fit. It's all out there. So let's talk about a topic no one is talking about today, artificial intelligence and AI.

How does that affect your strategy? And do you see Tribe ever going 90, 95% AI driven? Yes. And to be clear, we use it, right? I mean, like a lot of our technology, you know, I told you that, you know, we have this giant technology stack that we've built. We're generating tens of thousands of pages of analysis per year. More and more of that right now, we're actually utilizing AI to actually help us write those

memos where you're basically there's no way to generate that volume of analysis with a small team. Right. If you don't use a lot of machines to help you and now the machines include, you know, some of these ai components you know we definitely believe it's an important aspect of it do i think that ai is going to do the full investing well i mean the thing is remember the decision making is only one small piece of the investing right most of investing is like you know meeting founders giving them a good

experience meeting yet more founders and then like yes there's a moment of decision making but then there's all this stuff that happens after right you? You have to sit on the board. You have to like be a good partner to the founder. There's all this stuff that occurs that is a bit unrelated to that decision-making piece. You know, where AI could be really good at that piece, on the other side, it's less clear how it's going to help. Much less talking about the LP side, right? Like remember that a big part of what VCs do, right?

Is fundamentally we're there to help implement, you know, views for our LPs, right? Make returns for our LPs via whatever mandate it is that they're giving us, right? And so in so much as asset management itself could be taken over by AI, perhaps we could. But since I don't see that happening anytime soon, I doubt that it's going to fully take over our working. So we're in the middle of a correction right now in 2023 uh where do you see the future of vc are there going to be more firms like tribe that come come across or is there's going to be amalgamation of aum alongside the the top top the sequoias and then dreesons of the world it's been a you know fairly good past half decade or past decade to start vc firms there are a lot of firms

that came up over this last decade that are sort of in the early part of their life. It's kind of incumbent on us to go find capital and to go develop our mandate and make good investments, right? Like I said, I think the underlying driver is really the growing appetite of venture capital as an asset class amongst allocators. And while it's true that they all may want to be in benchmark or whatever, it's just not going to make sense. the largest funds that you know for the very big mega funds that that believe that they can provide you know this this product to that whole variety of lps i think they can but the thing is that from those lps point of view they will find over time that they want some more specific mandate to be executed and it's unlikely the case that the mega fund will be

able to execute their specific mandate because they're going to have preferences. And that's really what it is. It's about venture being able to provide venture as an overall asset class to provide LPs with various tools, various mechanisms. Yeah. And I think the nature of what is a return has changed in 2021. It was TVPI in 2023. It's DPI.

I think that's interesting. I think another factor to add is of course diversification stage hold period all these things well Jonathan it's been a pleasure you came highly recommended from Abe I could see why venture capital ultimately when it's seen as an asset class over the last 10 20 years I think venture capital will ultimately be seen as driven as a science, non-art. So it's a pleasure to meet with one of the top

data scientists in the space. Thank you and hope to chat soon. Yeah, absolutely. Thank you. Thanks for listening to Limited Partner Podcast. If you like this conversation, please like, subscribe and review on YouTube, Spotify or Apple.

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