Siddhartha Ahluwalia: 00:00 – 0:07
You are interested in India right now. You have invested in three companies to know, Shiprocket, BlackBuck and Khatabook. What makes you excited about India?

Arjun Sethi: 0:08 – 0:26
You can take China out for a second. You just basically have Indonesia and India. And then within those two countries and pockets, what you’re seeing is a lot of infrastructure build out.

You have a large educated mass of men and women. You have a English as a sort of standardized language that is being used. You have a lot of factors of what I would call old economy growth and new economy growth.

Siddhartha Ahluwalia: 0:26 – 0:27
What are you learning from Chamath?

Arjun Sethi: 0:27 – 0:58
I think what was special with Chamath’s perspective in the world was that he believed that data trumps all. US is a fragmented market, but it’s huge. Consumers inherently trust companies because of the laws that are there.

And so they’re willing to try products. In India, consumers are not willing to trust companies. And so you have to build a brand to a certain size and scale, and then they’ll start enabling trust to be able to try multiple products.

It’s why you see so many companies like Alazam Auto, not in just one business, but three businesses. So one of the reasons why you’ve seen me not invest in quick commerce, A, they didn’t have you in economics. They still don’t today.

Siddhartha Ahluwalia: 0:58 – 1:01
There’s a saying right now in enterprise SaaS that it’s crowded. What’s your view on it?

Arjun Sethi: 1:02 – 1:04
Look, I think there’s a bubble in every market, no matter what it is.

Siddhartha Ahluwalia: 1:04 – 1:34
How did you choose FTX? Why did it go bust?

Siddharth Ahluwalia:
Hi, this is Siddharth Ahluwalia.

Welcome to The Neon Show. Today I have with me Arjun Sethi, founder of Tribe Capital. Tribe Capital is a US-based fund investing in Series A, Series B in private markets.

And they have $1.6 billion raised till now, $2.4 billion of assets under management. And Arjun is quite bullish on India. So Arjun, first of all, welcome to The Neon Show.

So excited to have you today.

Arjun Sethi: 1:34 – 1:35
Yeah, thanks for having me.

Siddhartha Ahluwalia: 1:35 – 1:49
And today we are going to discuss a lot of Indian macro, compare Indian public market, private markets versus the US market, and some of the data-driven stories of investing from you. So first of all, what’s the background you have, right? And what makes it so unique to be in venture?

Arjun Sethi: 1:50 – 2:48
So my background is pretty simple. Born and raised in the States. I worked in the tech ecosystem really early.

And then from high school to military to college, was mostly math and history-oriented. I’d say a sort of a combination of art plus science. And then towards the latter part, I want to say of the 2000s, I spent the majority of my time working at startup companies in the States.

And so I consulted with companies like Myspace and Orkut, which is under Google. So a very, I want to say, a large set of mobile, early mobile, which is like J2ME, Brew, et cetera. And then went into social.

We call it like web 1.0 to 2.0, data 1.0 to 2.0. And then I spent a large portion of my career there. And then eventually started a large set of companies and started investing in sort of the mid-2000s up.

Siddhartha Ahluwalia: 2:49 – 2:56
And it’s very interesting. You started your journey as an investor in 2005 or 2007, when you were just 24 years old?

Arjun Sethi: 2:57 – 2:58
22 to 24, depending on which year.

Siddhartha Ahluwalia: 2:59 – 3:00
How do you get started?

Arjun Sethi: 3:00 – 4:14
Yeah. So the reason I started investing wasn’t to be an investor, but I had started multiple companies and those companies didn’t do well. So as soon as I started getting into company number six, seven, eight, et cetera, I thought the best way to sort of learn was how much capital could I deploy into a cohort of my friends that were also starting companies?

And then what could I learn from them so that I could execute at a faster pace? So you could think of it as my way of doing an accelerated MBA with a cohort of people in the ecosystem. And at that time, Silicon Valley was pretty small.

So we had kind of two, not two recessions, but two recessionary factors during that timeframe. And so my whole goal was that if I could learn from the best of the cohorts of individuals that were starting companies, I could apply the best of what I thought was necessary to my own companies. And that’s essentially what I did.

So the first checks that I wrote were in either areas I wanted to learn all about in order to understand those frameworks to deploy into my own companies, or people and how they artistically articulated their company internally and the culture that they built out so that I could apply, again, the best learnings there for my own company.

Siddhartha Ahluwalia: 4:14 – 4:21
And what are some of the breakout companies from that era, like between 2005 to 2012, when you were cutting individual checks?

Arjun Sethi: 4:21 – 5:37
I mean, I had invested in primary and secondary. So there was this ad company. It’s so long ago, I can’t remember.

I’ve invested in like 100-plus companies. But there was a company called iSocket that eventually sold into, I think, Rubicon. There was another company that sold into Facebook and then put more money into Facebook, which that’s what started the process of just understanding that ecosystem.

There was a bunch of mobile games that sold into Zynga and a couple of others, but I was also in the same ecosystem. So it was just one after another. And mostly they were, again, mobile 1.0, 2.0, sort of that trajectory of companies that were starting to move. There was a large set of companies that were starting… I had seen Instagram, but I passed because I was in the same… There was an area in San Francisco called Dogpatch.

And then there was an accelerator called Dogpatch Labs. There’s another one called Startup2Startup, which eventually became 500 Startups. And a lot of the early cohort companies were there.

And so I had invested in basically any of my friends. And then some of my friends that were starting companies, I didn’t invest into. So I invested in Square when they first started with the black, small square of credit card processing.

But I didn’t invest in Instagram. I didn’t invest in a bunch of other companies. So just one lesson after another.

Siddhartha Ahluwalia: 5:38 – 5:44
Got it. And what was your source of capital initially? Was it selling your own company or was it working with other companies where you got off secondary?

Arjun Sethi: 5:45 – 6:12
I basically invested every dollar of capital that I had in my bank account. So if I ended up having $15,000, I would say like, okay, either I put $5,000 per company, so three companies, or I would wait till the 15 became 25. And I would just do everything possible to keep my burn as low as possible in terms of how much money I spent per month.

And then if I had any excess cash, then I would just put it into startups.

Siddhartha Ahluwalia: 6:12 – 6:15
And what was your first large exit, if you can remember?

Arjun Sethi: 6:17 – 6:39
I think there was a company. Wow, I’m starting to forget. There was a company called Orbitera, which was a pretty large exit.

I think Facebook was like the second. Actually, Facebook was first. Orbitera was second.

There’s been a whole host of secondaries that have sort of come over time. But I tend to stay long. So I guess probably the best way to put it is I haven’t really sold too much.

Siddhartha Ahluwalia: 6:40 – 6:44
There’s a point in time. As part of that capital, still invested in these companies?

Arjun Sethi: 6:44 – 6:52
Yeah. So if a company goes public or they’ve been acquired, and the company that acquired them is a public company, I tend to just stick around for a long time.

Siddhartha Ahluwalia: 6:52 – 7:02
Wow. That’s amazing. And I think what today is 2024 mid we are sitting in, you would have almost had your longest company for 19 years?

Arjun Sethi: 7:04 – 7:21
Yeah. I mean, there was one company that got acquired by Alibaba. I’m still in it today.

I’m not sure if it’s a great idea or not. But I think when they went public, their share price today is the same thing. I think it’s more that I just don’t really sell my private, my PA.

I don’t really sell that much assets.

Siddhartha Ahluwalia: 7:22 – 7:32
Got it. And you are interested in India right now. You have invested in three companies to now, Shiprocket, Black Buck, and Khatabook.

Arjun Sethi: 7:32 – 7:42
Well, that’s the largest concentration of what we’ve invested in. We’ve had a whole host of seed stage and early stage. But the majority of the capital is in Black Buck, Khatabook, and Shiprocket.

Siddhartha Ahluwalia 7:42 – 7:44
And what are the other seed stage companies, if you can recall?

Arjun Sethi: 7:44 – 7:56
I think we’re in Atomic Jar. There’s another one called Transact. So there’s a whole host of, I’m going to call it at the low end, 50 to 250K, and then maybe upwards of a million, but not that many.

Siddhartha Ahluwalia: 7:56 – 8:00
Got it. What makes you excited about India right now?

Arjun Sethi: 8:00 – 8:58
There’s, it’s a, it’s a, it’s multiplicative. There’s a lot of factors. So if you, if you take a look at worldwide growth, where it’s happening, it’s US, you have parts of Eastern Europe, then within Latin America, you have Mexico and Brazil.

And then here in Southeast Asia, I’m just take, you know, take China out for a second. You just basically have Indonesia and India. And then within those two countries and pockets, what you’re seeing is a lot of infrastructure build out.

You have a large educated mass of men and women. You have English as a sort of standardized language that is being used, which helps them sort of work, not just in their local economy, but external economies. So let’s call it United States.

So you have a, you have a lot of factors of what I would call old economy growth and new economy growth, which is what you see here in India. So it’s, I wouldn’t say that it’s just India. There’s a block of emerging economies that we focus on, and it’s where you’ve seen us, start companies, incubate, invest, et cetera.

Siddhartha Ahluwalia: 8:59 – 9:04
But in other economies, you don’t have a dedicated fund. You have a dedicated fund for India.

Arjun Sethi: 9:04 – 10:37
We don’t have a dedicated fund in India yet. We have a dedicated team in India, which is a separate management company. And the mandate in India is actually much more expansive.

So if you take a look at TRIBE, take a step back at TRIBE of what we do today. So TRIBE has its venture funds, it has its crypto funds, and we have our incubation arms where we’ve started companies in crypto that are multi-billion dollar protocols. We started companies on the venture side that are also multi-billion dollar companies.

And so the way in which we think about India is a little bit different in that when we think about the India team and its entities, it’s that we want to be able to replicate the playbook of work really well for us, for our global flagship, which is investing at the early to mid stage. Then we want to be able to have more of a investment banking style mandate, which is in the U.S. 20 years ago and up to today, you only had investment bankers that were taking companies from private to public. And then you had investment bankers that were very good at like the low end of M&A.

And there were two or three firms, one’s called Catalyst, which is very famous in the U.S., where every mandate that they take in the U.S., they’re basically putting their stamp of approval, their brand and reputation behind the companies. In some cases, they might put their own capital and then they’re very focused on like one company at a time. And so their mandates might be between six and 12 a year, which isn’t very many for a typical investment banking house.

I think for India, we see the same thing, which is there is a set of companies that we know very well, where we’ll invest slash take on the investment banking mandate, which is what will help them to raise the capital itself. But we’re 100 percent aligned with the success of that company all the way towards the public markets.

Siddhartha Ahluwalia: 10:38 – 10:46
So, for example, if you mention about investment banking, that means any investment banking fees that you raise, you will put back that in the company?

Arjun Sethi: 10:46 – 10:54
It depends on the mandate of the company that we’re looking at. But I think that’s a pretty safe bet, which is we’re 100 percent aligned with the company that we’re working with.

Siddhartha Ahluwalia: 10:54 – 11:07
And you mentioned in an offline conversation that you have almost $350 million invested by you and your LPs in India out of the $1.6 billion that you have raised, which is almost more than 20 percent.

Arjun Sethi: 11:08 – 11:10
That’s right. Yeah. 1.6 is a little old, but yeah, that’s roughly correct.

Siddhartha Ahluwalia: 11:11 – 11:17
Yeah. So what makes you put your 20 percent of the capital just in one of the emerging markets?

Arjun Sethi: 11:20 – 13:30
So our background at Tribe is obviously much further beyond Tribe, is that over the last 20 years of our career, we built some of the fastest growing and largest companies in the world by being in charge of their growth and data science teams. So if you take a look at Square or Uber or Facebook or Airbnb, there’s a very specific way in which they built their companies around leveraging data to make better product decisions. And those product decisions were really about how do you slim down the amount of time it takes in terms of a fast feedback loop.

So we took that infrastructure. We built out our investment prowess and structure. We spun out a company that’s also separately working with a lot of companies worldwide, funds worldwide, sovereign funds etc.

That company is called Termina. And I think the way we look at the world is that, again, a bottom-up perspective. The more data we get, we can build systems.

And the more systems we have, the more decisions we can make around alpha. And then the more decisions we can make around even beta in some cases. Our whole focus is around how do we identify a product market fit and how do we help accelerate that in any sort of market.

The reason why we focused on India is that you actually had a ton of capital over the last 20 years, which I’d call Arc 1, Arc 2, and Arc 3 of India. Arc 1 was where everyone talked about how India is going to be the next big economy, which is around 99 to 03 timeframe. It didn’t happen.

And then between 03 to 2010, Arc 2, you had the first what I’d call Web 1.0 style companies. But it was just a veneer of the type of company in the front. But the back end was still like a typical company.

So brick-and-mortar style infrastructure. And then Arc 3 is, I think, where India is right now, it’s a deflection point for these new economy companies, where they’re actually starting to build software for their workflow. They have ways in which they can monetize that workflow.

And then there are different methodologies as GDP per capita is continuing to grow. And I think Arc 4 will probably happen in the next, I’m going to call it three, five years, where you start seeing these new companies that are leveraging actual software that’s been around for 20 years, machine learning, AI, and then AGI on top of it, which should help compound productivity.

Siddhartha Ahluwalia: 13:31 – 13:40
Got it. And now I would like to go back in your journey. What made you join Social Capital way back in 2012?

How did you both meet each other?

Arjun Sethi: 13:40 – 17:05
So in the Silicon Valley ecosystem, I’d mentioned that there was a large expertise behind growth and data science. So when people use those terms, growth, you have to figure out how to grow a company. But in order to do that, you have to be able to measure how your product is being built.

So for software, what’s really great is you can quantify and measure everything that happens in software. And I don’t think a lot of people realize that it’s all accessible, but you have to be able to have a framework to understand how to measure that and then make decisions with it. So I would say that between 2005 to 2010 timeframe, there was maybe 20 people in the world that knew how to take this data and then make decisions with it.

It’s because in order to store that data and then be able to interpret it was pretty expensive. Then between 2010 and 2015, you had what I call the data 2.0 revolution, which is that data became cheaper, better, faster, and the analytics on top of it to interpret became cheaper, better, faster. And so then you had these new cohorts of companies grow up and essentially technology was inherently deflationary.

So what was really interesting was that then that group of people that had that expertise taught the next set of folks. And then you had maybe 50 to 100 people that knew how to do it. I was one of them.

Not because I was smart about it. I just lucked into being a part of that same cohort of people that built social games, mobile games, social networking, how to quantify network effects, et cetera, et cetera. Now people use those terms at a very high level, but it’s really important to understand is when you want to quantify network effect, you’re actually looking at how does something become viral?

How do you retain your customers? How often do they use it? Those levers and quantifying each and every aspect of like step-by-step of how people use the software is actually still a skill set a lot of people don’t use today.

And so taking that expertise and morphing it to the next stage is what ended up bringing a lot of people that had that expertise at every institution. So a lot of folks that were on the growth team for Facebook went to Uber. A lot of folks that were on the growth team for Uber went to the next company, Slack, et cetera.

And so my cohort of individuals that were in social gaming, we went into mobile messaging. Then we went into helping companies like Square and Uber out. And so we took all of that knowledge and said, okay, how do we apply that towards helping companies deploying capital at the same time while helping them?

And so social capital was one of the first firms that was thinking about the early versions of what does this look like in order to measure and quantify not just one company, but 10 or dozens of companies. And that was a framework that was built up by my co-founder, Jonathan. And then the next stage of that was like, okay, how do you build software around it?

How do you get data and perpetually have an edge to be able to underwrite the next set of not just a couple of hundred companies, but a couple of thousand companies. And if you look at the number of private companies that are venture-backed per year, it’s actually pretty small. And the number of venture-backed companies in the ecosystem today is between 40 and 50,000.

So if you get data on 4,500 companies, or if you get data on 10,000 companies, you’re actually starting to get into the five to 10% of the sample size of datasets, which is what we have today. And you have a very good directionality of how you should spend your time with your companies, how you should spend your time with yourself in terms of, do you want to incubate? Do you want to build?

Do you want to buy? Do you want to invest? That’s essentially what TRIBE is a manifestation of those points of view.

Siddhartha Ahluwalia: 17:06 – 17:10
And at what stage you started TRIBE after Social Capital?

Arjun Sethi: 17:11 – 17:17
In terms of like what year? We started TRIBE officially in June of 2018.

Siddhartha Ahluwalia: 17:18 – 17:21
Okay. And by the time you were already a partner at Social?

Arjun Sethi: 17:21 – 18:13
Yeah. So when I joined… So I first joined Social Capital in 2012, where I was incubating a company and I was a partner looking at essentially social consumer products.

But then my company messaged me, took off very quickly. And then I sold that to Yahoo. And then I ran their growth and data science team, as well as their mobile and emerging products.

At that time, mobile was new. And then I was on the executive team that sat on the board of Alibaba as a board observer. But that whole timeline sort of taught me around how I wanted to spend my framework of where I wanted to spend my time.

And then when coming back to Social Capital as a partner for their venture and growth, we did the exact same thing. It was just without the software. We were just trying to formulate if I have the data, what else can I do with it?

And then my teammates and I started, I would say it was the early versions of what TRIBE looks like today. We were ideating there.

Siddhartha Ahluwalia: 18:13 – 18:17
Understood. And what are you learning from Chamath during that phase?

Arjun Sethi: 18:19 – 20:02
I think what was special with Chamath’s perspective in the world was that he believed that data trumps all. And I think it was, we have an interpretation internally that data isn’t your strategy. It’s just something to interpret.

And your strategy and your mission has to be based on what you really care about. And so we believed that we had a framework to understand and amplify product market fit. And with that, we can find what we call N of 1 companies.

And what we mean by that is that companies are monopolistic in tendency. I don’t mean from a regulatory perspective. It just means that you can monopolize someone’s time by just having a better product and a better experience.

And how you do that, you have to be able to measure how you impact someone’s life or their workflow. And so because not just Chamath, but my co-founder Jonathan, my co-founder Jake, who was at Bridgewater, and then another co-founder Brendan, who used to be at Facebook, they were all cut from the same cloth. They had the same DNA.

So they thought about how do you build these systems? And so when I came on board, a lot of what I talked about and thought about was how do I productize these pieces, not just for an investment team, but for a founder, for a limited partner, for a public company? How do you build these products into software from a framework that was created?

That’s what I basically was maniacal about trying to figure out how we can make sure that we could serve the customer and then who are the set of customers that we can serve. And so that was the beginning. And so I think a lot of what we learned was like, how do we become more ambitious with our framework rather than staying status quo with what you saw in traditional invention?

Frankly, what you see in venture today, it’s pretty archaic and pretty old.

Siddhartha Ahluwalia: 20:03 – 20:22
And the data about private companies is still not disclosed. Available platforms like Pitchbook, Crunchbase, it’s still really old. How do you get the data in real time?

Because you can’t interact with 10,000 companies at the same point in time. And why would a founder disclose if you’re not an investor in them?

Arjun Sethi: 20:22 – 20:30
Yeah. So think about it this way. If you’re a founder, what do you want to do?

I’m asking you a question. What’s the number one goal that a founder has when they start their product?

Siddhartha Ahluwalia: 20:30 – 20:33
My goal is to reach customers and improve product market fit.

Arjun Sethi: 20:33 – 23:38
Yeah. So if you think about where a founder is in any of their journey, is that the primordial stage, they’re trying to come up with a product. Once they’ve figured out the product, they’re trying to figure out, okay, well, who’s using my product?

And what’s the efficacy of how they use my product? And so are more and more people coming because of the product getting better? How do I refer more customers in?

Am I retaining more of them or am I losing more of them? So there are all of these things that you’re measuring. And if you get down to the basics of making sure that you can build aha moments and a better customer experience, you need to be able to measure and quantify that.

That’s our full expertise of where my team’s background comes from. And if you are able to get data from your founders, who are your primary customers in the management team or the company, and you give them a 50, 100 or 300 page report around how to think about their product, even if they’re at the series C or A, they can’t do that themselves. And that actually costs tens of millions of dollars worth of infrastructure and just understanding the world and how do you benchmark them against a comp of let’s call it another 4,000 companies.

That’s the value. It’s kind of thinking about how do I private markets comp myself and benchmark myself to anyone else there? The reason why that becomes important is then as a founder, you know where you rank.

Are you top 25 here, bottom 25 here? Are you more efficient here or not? And then you have an idea of how to think about the roadmap of what you want to build, the type of people you want to hire, milestones that you actually want to hit.

The goal of any company is to grow efficiently. If you’re thinking about growing shareholder value, no matter what market you’re in, you’re trying to figure out how to grow effectively. And you have to understand what is the high level metric or the atomic unit is what we call it that you’re fighting for.

And so the reason why founders or management teams or companies, public or private, work with us for that side of that house is because they don’t have access to that. They’ve never seen anything like that. And so for them, that’s really valuable.

And so then you turn that value of what I call the value exchange. You give me your data, I give you a value exchange of like insights around how to think about your company. Then you can decide or interpret how to make decisions from there.

And that could be either with us, it could be with partners of ours, it could be with someone else. But the whole point is that it alleviates a lot of the question marks that you have around the table. And I think more importantly, we’ve proven this over and over again.

So a tribe we’ve invested in, I think it’s like 15 plus unicorns since we started. And these are companies that are 50, 100, 250, a billion revenue already. We’ve started our own companies where we’ve used our own framework to say, hey, in crypto, here’s two multi-billion dollar protocols that have revenue that are intrinsic.

Let us show you the pathway of how we’ve done it. In venture, here are two companies that we’ve started from scratch that are essentially 50 or 100 million plus in revenue. And they’re also multi-billion dollar companies.

We’ve done this all in the course of five years. And so here’s a pathway of how we did it. Here’s the framework that we use.

Here’s the data sets that we use. Here’s the benchmarks that we use to make those decisions. It’s very hard to argue with that when we keep substantiating this again and again and again.

Siddhartha Ahluwalia: 23:39 – 23:55
And at any point of time, let’s say the founders are giving you your data for insights in return, even if you’re not investing. At any point of time, how many companies would you be in a year churning out this data on? Because you also have limited capacity.

So it’s not like agents, AI agents running this machine for you.

Arjun Sethi 23:56 – 26:38
Actually, we do. So if you take a look at machine learning, AI, and AGI, what does it allow you to do? So with machine learning, what you’re able to do is take in a load of data.

And if it’s normalized, if you actually take a look at the way in which most companies are structured today, the most elegant form of any language is coding. So if you look at how people architect with Python versus Xcode, et cetera, it’s all pretty structured. So I consider that structured data.

And you can ingest that. And it’s one of the reasons why you see a lot of companies build products against that. If you take in all the data around, I would make it akin to gap accounting and frameworks for financials.

So if you have a framework to be able to quantify product market fit, then you can take data from 1,000 companies, and you can figure out what’s working and what’s not based on your framework. We call that gap accounting and growth accounting for startups. So it’s fairly well known.

We’ve kind of open sourced that. And then if you build software to kind of understand where that company ranks, then you can move on to the next steps. What happens, which is really great with, you know, OpenAI or Cloud, Surge, GPT, Cloud, et cetera, is that now you’re able to take unnormalized data from what a conversation like you and I, you could take it from a deck someone is like putting together.

You can take it from emails that you’re putting together, and then you can spit out now a new structured data and put two different versions of structured data together to come up with how do you want to sort of interpret this company? The more data I have, the better I can get at that. And so we have the world’s largest repository of private data of private companies, more than anyone else in the world, because we’ve been doing it for 10 years.

And then we’re able to sort of take that and say, okay, now how do we spit out something that’s like automated in terms of the way in which we very structurally look at a company? So today, I think we look at maybe across all of our organization, but I think we’re running right about 4,500, about 5,000 companies per year. That’s a lot.

And the team that runs that for a company called Termina is eight people. And so when you think about if I had to do that before the advent of this whole infrastructure, let’s call it five years ago, I probably needed like, I don’t know, 500 people. I don’t need that today.

And so if I’m able to deliver the same insights, again, cheaper, better, and even faster than I have been before to companies, not just in the United States, not just in Silicon Valley, but in Mexico, in Indonesia, in India, where you have a lot of these emerging companies across all stages. Just think about how much more impact we can have with these companies, even if we don’t invest. And then what does that impact look like if we have invested in these companies?

Siddhartha Ahluwalia: 26:38 – 26:56
Got it. Oh, that’s a lot to process. Thanks.

And you mentioned that you have raised $1.6 billion. That is both for the fund and LPs invested directly in your portfolio companies. How much would you have returned?

Arjun Sethi: 26:57 – 27:26
I’m not sure how much capital or LPs are deployed on top of us. It’s probably double that. That’s just the amount of capital that we’ve raised and deployed into companies.

In terms of returns, we’re a registered investment advisor, so we won’t disclose it. If you see any numbers, you’ll just see it in our form, ADV. But prior to that, the way to think about how we look at each of our primary funds, our global flagship, is that we have a goal of hitting a net 5x return profile for all of our funds over a 10-year period.

And we’ve always been on track for that. That’s kind of our historical record.

Siddhartha Ahluwalia: 27:27 – 27:30
So your first fund was $100 million, right?

Arjun Sethi: 27:30 – 27:42
That’s correct. $100 million first fund, $330 million second fund, and then just shy of $400 million for our third fund. And then the rest of our AUM is our co-investments or single managed accounts for our LPs.

Siddhartha Ahluwalia: 27:42 – 27:46
Got it. And you have already started returning principal on the first one?

Arjun Sethi 27:46 – 28:47
I think we’ve started returning principal across all of our funds. We also have Cryptofund 1 and Cryptofund 2. They’re a little bit different in terms of duration in our timeline for Horizon.

But yes, DPI is a form of a metric that matters for LPs as well. But I think what’s really important more so is what is the intrinsic value of the portfolio that you have? How are they compounding over time?

And so depending on your entry point, and when you look at any of these companies over a 3, 5, or 10-year horizon, we actually have more of like a 14 or 20-year horizon for our vehicles and our investments. Are these companies compounding? So for example, here in India, when we invested in Shiprocket, they were less than $5 million revenue.

Today, they’re just shy of $200 million. For BlackBuck, when we invested, they had barely any revenue. And today, they’re close to hitting over $100 million revenue.

For Khatabook, when we invested, they had no revenue. They had just large distribution. And I think by the end of this year, they’re going to hit between $30 and $40 in revenue.

And all of these companies are cashflow breakeven and positive.

Siddhartha Ahluwalia: 28:48 – 28:51
You’re particularly interested in the logistics sector in India. Any reason why?

Arjun Sethi: 28:52 – 32:00
I wouldn’t say. So again, everything we do is bottoms up, no matter where we are in the world. And so I think every market, you have to look at it different. So take a look at the US. The US is a fragmented market, but it’s huge. Large amounts of liquidity, large amounts of distribution, uniformity of law.

And you have a huge influx of people coming in for all stages of immigration that helps towards essentially all aspects of the economy. So for a new economy, you can see where we’re putting our effort. So you’re going to put your effort into insurance, real estate.

You’re going to put it into payments, etc. So the list goes on. If you take a look at emerging markets, so China, in order for them to be able to grow over the last 20 years, every company had to start building all of the ways in which you enable commerce vertically integrated.

So Alibaba had to build Alipay. They had to build out their own data stack infrastructure. So it’s why they look more like Amazon, Google, Facebook all at the same time.

Tencent had to do the same thing with gaming. So if I go step by step in every company, you can just take a look at their story of how they scale. In India, they tried to copy the United States and it didn’t really work because you didn’t Really have a fragmentation ecosystem to support each other and then you didn’t and then you had a lot of companies that were more competitive with each other rather than Actually, I’d say more zero-sum versus trying to figure out how they can all grow together That’s changed and I’ll get back to that If you take a look at places like Indonesia Most of the companies that have been working have been insurance logistics commerce.

But enable in order to enable that you have to do payments and banking, right?

So that’s why you see those economies growing in that specific way If you take a look at Brazil and Mexico same thing the difference between These companies is that you have much more of a conglomerate style strategy where they have to enable trust So again going back to the United States consumers inherently trust Companies because of the laws that are there and so they’re willing to try products in emerging markets Consumers are not willing to trust companies. And so you have to build a brand to a certain Size and scale and then they’ll start enabling trust to be able to try multiple products It’s why you see so many companies like Alazamato not in just one business But three businesses right and that there probably be at six businesses twelve businesses at some point It’s why you see reliance is so large with multiple business. It’s not because like that’s what they wanted to do It’s the only way in which they can roll out new innovation because there’s trust Ecosystem from the consumer and that’s starting to change where you have consumers here in India that are willing to trust new brands because they’re getting acclimated to it and they’re being educated to it.

So when you take a look at India When I say bottoms-up approach, it’s like what can I measure that’s working and what can I measure that’s not working? so one of the reasons why you’ve seen me not invest in, you know, quick commerce or Any of the delivery companies that were there they a they didn’t have you in an economics. They still don’t today They’re they’re trying to do growth at all costs I think that can make sense if you have some sort of unit economics with free cash flow They still don’t have that today, which is why I’m skeptical of those models And so you have to look at what I consider compounding growth models where you have new customers that retain and Because they retain every new customer that comes in you continue to grow that base

Siddhartha Ahluwalia: 32:00 – 32:10
and right now, you know If you have to compare between India and US ecosystems between public and private markets, how would you compare them?

Arjun Sethi: 32:10 – 32:12
In what way you have to be more specific?

Siddhartha Ahluwalia: 32:12 – 32:20
in terms of liquidity in terms of ability to go public in terms of returning money In X amount of time to investors.

Arjun Sethi: 32:20 – 34:44
Yeah, I think I think it’s an unfair It’s comparing apples and oranges. And so let’s talk about the US. So the US Is a very robust liquid market in private markets So they have secondaries that are pretty liquid You have a large amount of brokers that are in the ecosystem that when a company gets to a certain stage They can provide liquidity for employees, stockholders, you know preferred or common. So that’s pretty it’s it’s not only set It’s continuing to grow and compound.

So that’s a that’s a market in itself M&A is pretty robust in the United States regardless of like what people see in the headlines M&A still is robust between 50 100 Billion dollar deals. So there’s a pretty high frequency You see some highs and lows, but it’s the slope is still going up into the right And then in the public markets in the US. It’s the most robust most liquid most safe market in the world I don’t think that’s actually going to change and there’s a thesis behind that too and that’s actually gotten better and better not worse over And so that’s a that’s a very hard market to compete against so if you take a look at you know The Australian markets or the London markets, they’re they’re nothing compared to that Then if you take a look at other Public liquid markets in the ecosystem India’s just started to rise And then you have like Hong Kong and Singapore and obviously the Japanese markets now Some of these markets are becoming more robust Some of these markets are more liquid and then some of these markets have more inflow of capital from foreign direct investments as well India’s just starting that pathway. So I’d say that you know on the 20-year curve of what you want to see in terms of liquidity in India. I think they’re just starting

Let’s call it they’re right at the product market fit state. Okay, I have to think about go to market and scale Hong Kong and Singapore and Japan. Let’s just talk about Japan specifically again extremely liquid Safe amounts of capital a lot of foreign direct invest capital and then Uniformity of law and rule of law and like the structure is pretty well set.

So again India’s still pretty early in that It’s not to say that it isn’t there but every year, you know, SEBI has a new ruling that freaks out Foreign investors RBI might have, you know currency control problems, whatever it might be that freaks out foreign direct investment So I think there’s a set of infrastructure laws and uniformity that are still forming But that’s actually also the best time to invest which is if you’re bullish on India over the next 20 years You know those things are being set and getting to a place that’s becoming more commonplace and stable

[Siddhartha Ahluwalia] (34:44 – 34:51)
And you are also backer in some of the emerging fund managers or fund managers VCS globally in India. Who are those?

[Arjun Sethi] (34:51 – 35:57)
There’s a pretty large set. I think the way we think about Investing in funds is similar to how we think by investing in seed is that we’re making a bet on people. We’re not using any science behind it and what area of the stack do we think We’re gonna be able to help them most enable it so the best way to kind of think about it is we’d like to invest in managers that are right at the inflection point between Let’s call it seed to series a I’d have the ability to also leverage our infrastructure and data terminize our product We have we have a few here in India They can make better data-driven investment decisions and we can help enable them so my whole goal is can I make someone like you as effective as You know entries in Horowitz with a fraction of the cost or tribe at a fraction of the cost depending on the stage They’re at their scale And so that’s that’s what terminus is able to do and then If we build a relationship with them Then I can start thinking about what my alpha generation looks like for deploying a check that might be 15 25 50 or 100 into these companies wants to get to a certain size and scale

[Siddhartha Ahluwalia] (35:57 – 35:59)
And which are the managers in India that are in the company?

[Arjun Sethi] (35:59 – 36:15)
So we don’t really disclose

Personally, I don’t disclose too much anymore, but the ones that we do disclose are all on our form on our website So there’s a section where we talk about all the companies invest in funds and companies And then there’s a section for funds that we invest in outside of our global flagship

Siddhartha Ahluwalia: 36:15 – 36:25
And There’s a saying right now in enterprise SaaS that it’s crowded and you mentioned one third of your portfolios in enterprise I thought it’s uh, what’s what’s your view on it?

Arjun Sethi: 36:25 – 39:01
Look, I think the There’s a bubble in every market no matter what it is So take a step back and think about how to build a business either selling direct to a consumer So you and me directly through some channel you’re selling it to another business that will sell to a consumer Or you’re a business that sells to another business like the the business models of the world are actually quite simple Yeah, so when you say a marketplace trade B to B to B to C.

Okay. Awesome That’s that’s a very it’s a very structured way in which you can think about it And so there’s software at every juncture that is being built today That’s measurable in a marketplace company in a financial services company in an insurance company Yeah in an enterprise infrastructure or healthcare for me. They’re all very similar business models So we’re really looking at is like how do they monetize off of the engagement that they have and how do you measure that efficacy in terms of how you want to think about the investment and each market thinks about monetizing their base or their Or their partners in a very specific way. So I think just making a blanket statement is Is is pretty uninformed because when you say fintech is dead Well, then you are watching fintech companies come back very quickly when you say AI is dead Obviously, I mean it people said AI I was dead in 2015 and 2016 Like you can go back and look at people’s quotes Every product has its bubble period and we call a trough of sorrow and he has a product market fit and then it can grow in compound And so the way to sort of think about it at the early stage seed it’s about 80 to 90 percent attrition at the series a it might be 60 to 70 series B sort of comes down and We just look at Ways in which we can measure bottoms-up what company has product market fit is it efficient and how can it grow in compound?

So if they’re using machine learning and AI to grow cheaper better faster It’ll show in its numbers as a business model that they’re leveraging only a specific way in which they think about software infrastructure It’ll show in its numbers. So really just depends in my opinion We’re what market are you focused on and is it continuing to grow in compound or not? And is it is it right for innovation or revolution and that’s that’s really all it is you can have any company in any sector in my opinion grow and there’s different versions of how you can do that and so our whole our whole point here is that if Software and technology infrastructure is inherently deflationary.

You will find companies in every sector That’s good and there’s gonna be winners in every category and as long as you can benchmark that that company is a good business and I can Measure that there’s a top 1 to 25 percent depending at the stage of the risk profile Then it doesn’t matter what they do.

Siddhartha Ahluwalia: 39:01 – 39:07
You have mentioned quite a lot about Peter Thiel and that you are in Inspired by his investing style. I would love to know more about it.

Arjun Sethi: 39:10 – 41:10
So the if you take a look at the Best investors in the world and you take a look at some of the best operators in the world I actually think there’s not too much of difference like when you think about what Jeff Bezos did with Amazon Amazon became a capital allocation strategy of human capital and where they put their capital towards Return on invested capital for their initiatives If you take a look at what Warren Buffett does with his capital and how many companies he’s in it’s pretty Concentrated and he’s very long there and then he’s got what we call them a cash money tree of a specific Company that gives you inherently more and more cash to continue to reinvestment insurance companies And if you take a look at what Peter Thiel is done if you see where they’ve made the most investments And the most concentration of their investments, it’s into great companies that become monopolistic and compound over time So it’s a very concentrated. I would call power a lot of companies Typically what you see for VC’s I think they’re they have this problem where at the early stage they invest in I don’t know 50 companies or 100 companies and then they think for series a they have to do the same thing for series B They have to do the same thing And if you look at historical returns and averages of any company public markets or private markets There’s a power law So if you look at you know The S&P 500 we have like 12 companies driving that if you look at the Indian companies You actually have five companies driving the majority of the public markets today. It doesn’t mean that doesn’t change different companies it’s more about what are the Concentrated best you want to take how do you concentrate your time towards the things that really matter?

And so for the capital that we’ve deployed, you know, you’re asking these percentages, but they’re in a 20 to 25 companies And if you ask me how many companies are we going to be in in the next 10 years? It might be another 10 in terms of the amount of capital that we deploy So we have a fairly concentrated strategy of our own companies that we incubate We have a fairly concentrated strategy of a company we might buy We have a fairly concentrated strategy in the terms of the companies we deploy into so we just keep thinking about work I mean, you know double triple quadruple down into our companies.

Siddhartha Ahluwalia: 41:11 – 41:16
And what would be in the IRR that you have delivered?

Arjun Sethi: 41:16 – 41:28
Yeah, so like I said the net the net return focus for us is 5x So you would basically look at you know, 25% net IRR return for all your vehicles That’s the goal of what we look for.

I think we’ve always kind of been on track for that historically

Siddhartha Ahluwalia: 41:28 – 41:37
But let’s say if LPs are comparing today We see funds versus public markets public markets in India are able to deliver 15-20 percent returns

Arjun Sethi: 41:37 – 41:55

If you’re very smart at what you do You could do it But most people don’t so if you look at the law of averages of where people deploy their capital Retail versus institutional versus beta. That’s not that’s not what they get if they did I’m happy to give them my capital as well But it’s usually between 6 and 12 percent if you’re good and it’s usually between 4 and 8 percent on average.

Siddhartha Ahluwalia: 41:55 – 42:05
Got it.
So our LP is asking you a question like say globally that hey when public markets are buoyant like today in India Why should we give money to private?

Arjun Sethi: 42:05 – 44:04
I have always told people if you think you can make 15 to 25 percent you should do it So again, I always go back to basics. So On average what has been the historical return profile in India on the public markets? It hasn’t been very good until maybe the recent five to seven years So you have to take a look at his economy continuing to expand then there’s obviously you can just put you know capital into a Bank account yet like 8% in terms of investment interest It’s more about like where do you want to grow and compound your capital over a three five or ten year horizon?

It’s really easy to say like hey in the public markets last year I got 12 percent then the next question you ask them is like what did you get the year before and you’ll find out it was 4% and you ask them what did you get for the year before that and it might have been maybe less than that more of That so yeah if you’re consistently be able are able to make that type of IRR then that’s where you want to make sure you deploy capital into And you want to make sure that some of the investments you make are anti cyclical.

They can continue to grow and compound and so There’s this you know term intrinsic value versus options value you you want to be in the best intrinsic valued companies So when they do get valued option options value And you sort of want to retreat and or sell your assets just depends on where you are on that cycle And so you can you can make an argument today for India You’re probably right in the middle between intrinsic and options value because of the India story So helpings don’t really question You know should it where should I put my capital because they’re not Concentrating a hundred percent of their capital in public markets They’re trying to figure out how much capital allocation goes into real estate show a liquid versus liquid How much goes into bonds or Treasury yields a liquid versus liquid what goes into private versus public markets? And then within private buyouts versus venture And so if you if you look at the Yale endowment perspective, you know between 15 and 20 percent goes into a venture in private And so I’m not saying that’s good or bad but everyone’s got their target between 10 and 20 percent and If you can generate alpha from that 10 or 20 percent and that outperforms typically you know your your your beta and public markets

Siddhartha Ahluwalia: 44:05 – 44:11
And which are your top three portfolios globally, and how do you spot them?

Arjun Sethi: 44:11 – 45:48
So I I Look at every one of our portfolio and my team looks at every one of our portfolio like their children So every one of our companies is our top companies and what I mean by that is that We want to spend more and more time with every company that we believe is in a compound over Let’s call it three five ten year period then there’s the other metric which is what are companies that have Grown quicker and continue to compound from there because growth doesn’t always accelerate forever So we had a company so obviously in India all three of them are growing and compounding So I would say like they’re all top three whenever we come to India There are primary targets in terms of roadshow and marketing in the US We’ve had crypto companies that have gone from a couple You know tens of millions of revenue to a couple billion revenue like Kraken We had a company called docker when we invested there were just shy of five million They’re roughly just shy of 200 million today, and you know, it’s basically between 18 and 24 months. We had a company called Carta, which is More of a network effect company around cap tables in the United States and in Europe When we invest again, there’s sweet spots between zero and ten million revenue, but they were around six today.

They’re just shy of 400 So this kind of goes on with the companies that we’ve been a part of we incubated a company called where I’m a co-founder called Capital it’s a Mexican FinTech Bank when we incubated the company barely had any revenue and then we went from zero to 20 million and lending and software and they went from 20 million to Just you know over the last couple days just shy of 120 And that and we’re also now a regulated bank in Mexico, so it just depends on Where we are in our life cycle of these companies that were focused on.

Siddhartha Ahluwalia: 45:48 – 45:51
And any of your portfolio companies have gone public?

Arjun Sethi: 45:51 – 46:39
We’ve had a few In the public markets. We had a company called prodigy that sold upstart upstart went public in the United States It’s more around machine learning and AI for London They made a company called momentous in this backside that went public that didn’t do as well And then we’ve had a numerous amount of protocols that have gone from what we call seed private funding to protocol listing on the exchanges like Binance Kraken etc that are out there within the United States and outside the United States So all in all I think we probably had like, you know tens of 20s of folks that have gone Liquid and I call I consider it liquid because when a company goes public That’s not the end of their journey and we do have a tendency to hold in our companies and make sure that we’re there for the long term or long duration if we believe they’re going to continue to compound

Siddhartha Ahluwalia: 46:39 – 46:55
and Right now, you know, you’re using a lot of data to make your all decisions So, how do you decide that Where’s the current macro economic market globally headed because some folks are saying that we are at the lowest point in the last many years

Arjun Sethi: 46:56 – 50:02
depends on the market so I had mentioned two things one is So we leverage data to be able to make decisions and bottoms up of a company So if you come in early enough for a company series A and B And your entry price is good. It’s pretty anti cyclical and that company will do well over the long term Which which I think is a fairly important metric to think about intrinsically Now what happens is that same company could be valued at 30 X 50 X 400 X depending on their size scale efficiency and growth And so in India, that’s those multiples are starting to grow The reason why the multiple grows is that the supply and demand of capital and private and public markets is growing Yeah, and so if you have less supply of good companies and more amount of capital valuations go up Just like any other asset class. In the US We were kind of at the lowest point. We’ve been since 2001 time frame well the last 25 years on that’s right, and it’s starting to come back slowly But again, that’s the eyes and scale is still pretty large, right?

So we’re kind of equalizing for that In Latin America is very similar to India and Mexico and Brazil and starting to have the same path So I think a lot of people focus on headlines, which is like oh this one company raises money in valuation at this High valuation so the markets back. That’s an end of one. That’s not happening at high frequency So if you take a look at sort of series B and series C funding in India, it’s still relatively frozen And what you’re seeing is now an arbitrage between companies Okay, if I can’t write raising the private markets and I’ve got 25 to 50 million of revenue what’s my path towards IPO because the Public markets in India or are a little bit more liquid Yeah, and are willing to value these companies not too dissimilar to where they were in the private markets So it’s a better option to go to so if you’re in India you go public at 50 million plus in revenue Which is great in the US You got to be between 200 to 300 million revenue as a starting point We have to be growing 50 to 60 percent sort of year over year and pretty efficient with your capital So it really depends on where you are. I don’t think You know if you take a look at the last six years about a trillion and a half dollars have gone into venture such venture growth And I think people are in my opinion and what we’re geared up for is that about 80% of the that capital is gonna get Into consolidated or zero so it’s actually pretty stark when you think about it Which is how many of these companies are going to go to zero? and I don’t think India is any different which is there’s a ton of what I’d call a Oregon in the United States We say that Oregon trails when you went from east to the west and you had a bunch of folks that died along the way Yeah, and the startup ecosystem.

We think there’s gonna be an Oregon trail of dead bodies here in India as well I mean, I think that’s gonna be the real inflection point, which is where the winners versus the losers I think what’s cool and really interesting about India is that because companies can go public they can think about the M&A strategies They want with that currency for the companies that are doing relatively Okay, which is how do they use their go-to-market their distribution engagement to accelerate some of these companies that are stagnant in the private markets? I haven’t really seen it yet But I think if companies are smart here in India that they could use that pathway similar to what you see in the US Market.

Siddhartha Ahluwalia: 50:02 – 50:16
While you talk about you know The success is the data behind it, right? I want to talk about a couple of the things that didn’t work. For example FTX How did you choose FTX? What were the data points that supported and what why did it went bust?

Arjun Sethi: 50:16 – 52:32
Yeah, so I always bifurcate every investment decision into I mean a couple a couple spots. So when you take a look at any company At the early stage or the mid stage or late stage at the early stage They don’t work because their product doesn’t work at the mid stage You might have the product but their go-to-market doesn’t work and at the late stage That’s because the management team decides to do something extreme and then the company fails though the one Copy out to that is that the government can come to come with some sort of regulatory regime and sort of kill companies as well And obviously there’s you know outside forces like competitors, etc but I kind of looped them all into the late stage issue and probably when you When I take a look at the companies that have failed for us, it’s usually in those buckets And given that we invest in a company at its want to call early ask to mid-stage series a B and C, so it’s like I Basically think of it as 50 million enterprise 30 to 50 million enterprise value all the way to 250 to 300 million enterprise value That’s our sweet spot on average is That’s where you can think about the failure buckets when I take a look at companies like FTX or others that we’ve had in our portfolio is You had very clear product market fit. You’re growing a pretty high velocity It’s probably one of the fastest growing companies that ever seen in my life Where at the time that we had measured and quantified it there were about a billion in revenue about 600 million in EBIT Yeah, but what happened after that is that they had a couple so forget like the Illegalities of the company if you took take a look at first principles What are the mistakes that they made is that they had an asset and liabilities mismanagement issue just like any bank So in the US you had first Republican SBB banker an issue You had a couple of those issues happen in other countries is that the decisions that they made Led them to that mismanagement of their assets and liabilities where the liabilities became larger than the assets that could Support it. So that that’s what you saw with FTX now.

There’s obviously no other things that are more, you know fraud related I don’t want to comment on those things specifically, but what I think really matters is that you had a Management and execution sort of, you know debacle got it

Siddhartha Ahluwalia: 52:33 – 52:39
But while making a decision does that come into picture or is mostly the revenue in the data details on customer?

Arjun Sethi: 52:39 – 54:26
Yeah, I mean, there’s a combination of art and science now what happens is that a lot of people will focus on what I call Loss aversion, which is a you invested in this one company. See I told you it doesn’t work But that’s one out of ten or one out of twenty or even in some cases now over our history one out of fifty so if I take a look at some the times that has happened now, I think for us now in our are our Hume amount of time from our history over the last ten years But two of those types of things out of fifty companies that we’ve seen where we’ve invested around that capital and so I’ve always kind of told people if If my track record is eight to nine times out of ten and successful Yeah I’m gonna do it all day long sure and so if you have data and you can build systems and you can make investment Decisions with it and it’s you’re relying on your data and systems more so than anything else Which is what I mean the science the art part here is we’re in venture and so things Will change and you are relying on a Team and it’s the founders in this management team to try to get to the next stage But if you if you look at raw product market fit and many of the best companies in the world You actually have pretty average to mediocre teams But they have very strong products with very strong product market fit pulls and if you’re an early stage or mid-stage investor into those companies your Investment horizon and your returns for that company is going to be massive regardless of what the team executes on so if I went through and what I won’t do it publicly, but if I went through all of the companies that we have In our portfolio, I would say that 50 60 percent plus are what I call average teams But they have amazing products and product market fit now.

What happens is Ten years later people will say that was an amazing company with an amazing team. That’s usually not the case

[Siddhartha Ahluwalia] (54:26 – 54:50)
Got it. One of my friends, you know Shripati Acharya from Prime Ventures he mentioned that you know on M&A in India the tech companies are acquiring other Indian tech companies in India still in very infancy and What we expect is like US-based strategic acquires like Stripe picking up more companies like Reco in India We just they acquired in last year, right? What is Stripe view on M&A globally as well as on India?

[Arjun Sethi] (54:50 – 56:08)
It’s so this is more of an artistic question So depending on where we are in the cycle of M&A, you’re gonna take a look at how companies decide to purchase one another I Think if you’re relying on the US companies buying Indian companies, that’s a pretty bad strategy that to me That’s akin to just hope as a strategy, which is pretty poor. So the way I think about it is what are the companies that are going public in India?

They have a currency to be able to grow organically and inorganically and how do they use that capital and again in a Capital allocation strategy where so I go back to what are the best investors and operators do they do have things very similar and so what does Amazon do they’ve built and adjacent products in adjacent categories that have a unique point of view that’s centered around the Amazon way of thinking and they’ve acquired Companies to be able to get there. So AWS acquires companies right that are open source as well as closed source. So when you think about companies in India, I think my hope is and that’s obviously not a strategy but when you see some of the moves that some of the companies are making is that they’re using the inorganic ways in which to grow as a strategy once they’re public because they have access to capital access to Capital markets debt plus equity which gives them the ability to go acquire companies with the currency

[Siddhartha Ahluwalia] (56:09 – 56:20)
And Right now almost you will put like in your new fund 250 million dollars in India Mm-hmm Let’s say what would what data points would help you make a two fifty 2.5 billion dollar return on that capital

[Arjun Sethi] (56:20 – 58:56)
you mean a 10x from the 250 so Take a step back in venture. So there’s a power law There’s only a few set of companies that can get you, you know, 10 20 30 40 50 X and when you when you are a seed fund if you Out of 10 companies you invest in you’re probably gonna have one or two if you’re very good That’ll work out to return the fund plus more And then if you’re a very good investor at the early stage You’re gonna have again if you’re a very good fund four or five If you take a look at most investors in the ecosystem, they have Even the early stage series a and series B firms have one or two out of their 10 again If they’re lucky that are going to hopefully return the fund That’s not happening and it’s because that people have barely one company in each fund or vintage that’s gonna return their fund So when I think about like, how do you return a net 5x right return profile? It’s kind of always your target is you have to reduce your loss ratio And so if you focus on, you know One or two companies return the fund your chances of returning the fund actually are pretty low Which is why you know fund sizes that are small at the early stage They’re small so that they can return a higher amount Once you get into the 250 500 or billion dollar funds You have to start thinking about your loss ratio in a very different way.

You just start thinking about where do I Where do I make riskier bets at the series A and B? And then where do I double down with the other 50% of the capital? So let’s just say you had a let’s make the math easy a billion dollar fund your first maybe 300 to 500 is gonna be into what’s called series A through C And then the rest of your 500 is gonna be the double down into your best company so you can have a Large return profile and if you’re lucky again, you’re gonna have like a 2x We don’t operate that way the way we operate is that we think about okay? We’ve inherently over time been pretty concentrated. So we’re gonna deploy a ton of money into one company that makes it risky The other aspect is that we deploy Because we have a lower loss ratio of companies that were only invested in companies have product market fit That we should have instead of one to two drivers per fund out of ten We should have like more between four and six We were on the cusp basically between six and eight historically and so that puts you in a pretty good position to basically Do singles doubles triples and quadruples and then you can sort of hit that return profile A 10x return profile is very hard for 20. It’s not impossible It’s just that you have to sort of think about concentration of your capital which is going to give you your power parallel return.

Siddhartha Ahluwalia: 58:56 – 59:01
And how you’ve seen funds in the US Done it occasionally? Take a 10x return.

Arjun Sethi: 59:01 – 59:50
Yeah, absolutely I mean, I think you’ve seen companies So firms get to know between 10 and 15 X, but it depends on vintage depends on your capital a deploy Like I have one year goal. That’s a 17 X. Okay, but that’s just but that’s like a you know, it’s like one vehicle That’s like I don’t know 50 to 75 million that we put in these are SPVs so again, the reason why you have such high return profiles with a limited amount of capital is because it’s usually driven by a very Specific company or a power law of companies that are helping to drive that So if you’re in my opinion a good asset allocator You’re reducing your loss ratio and you’re concentrating your capital and the products that are working. So you’re not bridging companies You’re not reserving companies just because they got a series B or a series C and you’re putting in Reserves into it.

You’re allocating the capital of the companies that are working regardless of who is putting in the capital It’s like in subsequent rounds of that.

Siddhartha Ahluwalia: 59:50 – 59:55
And what’s your view on first time founders versus second time proven founders? Where would you back your money more?

Arjun Sethi: 59:55 – 1:01:44
Yeah, I mean, one of the reasons why we started Tribe and one of the reasons why we incubate our own companies and then we started Termina was for us to be able to recognize companies and founders. And no matter who they were, where they came from and where they went to school. So if you take a look at for the companies in India, Sahil, Gotham, Akshay and Vishesh, they’re all first time founders. If you take a look at KathaBook, he’s a second time founder. You take a look at BlackBuck, he’s a first time founder. So I don’t think it matters where you come from.

I think what really matters is what was your historical background? What did you learn at a rapid rate? Do you have the ability to have strong beliefs but loosely held, which is when you see something wrong, you change it very quickly.

And I think that, in my opinion, comes out in the the science behind how they’re performing. So if you see that over the last five years or even the last two years, a company has been performing and hitting milestones and compounding. That’s all measurable and quantifiable.

That’s actually a very good indication of how the management team interacts artistically. And what I mean by that is like culturally, how do they execute? How do they learn and what do they do with that as they get that information?

It actually shows in the metrics. And so one of the reasons why we like our approach more than anything else, that system that we have there is that I can find a entrepreneur from anywhere in the world that’s building something that’s really special. And we can figure out how to wrap ourselves around them, around what their strengths and weaknesses are and their deficiencies, similar to what I had.

And that we should be able to not have a checklist of what it means to invest with, sorry, to work with a founder. But it means that we are chameleons around the table. We’re conciliaries and advisors around the table.

And we’ll do everything possible to make them win. That’s kind of how we think about it.

Siddhartha Ahluwalia: 1:01:44 – 1:01:53
Cool. So let’s say, but in the drive in the US, you have maybe invested in across all the three funds, 30 to 40 companies?

Arjun Sethi: 1:01:53 – 1:01:55
No, 20 to 25. It’s concentrated, yeah.

Siddhartha Ahluwalia: 1:01:56 – 1:01:57
Across all the funds.

Arjun Sethi: 1:01:57 – 1:01:57
That’s correct.

Siddhartha Ahluwalia: 1:01:58 – 1:02:10
Right. Among the companies that are returning the most amount of capital today, or have returned the most of, what does your pattern recognition say about the background of these founders?

Arjun Sethi: 1:02:11 – 1:06:55
I mean, if you take a look at any of these founders and, or management team members that are running these companies, it’s so different. So I’ll talk about Carter. So Henry started the company.

He’s got no financial background. He didn’t know anything about capital markets, liquid markets, private markets, but he learned and iterated very quickly. So his insights were that cap tables are going to digitize.

And so he formed a version of those, that product. It didn’t work first. And then he created another product, which is called 409A, which is a regulatory product in order to take a look at fair market value of people’s stocks and options.

And then he fused those two products together to create his product market fit. He didn’t know anything about any of these regulated products. He’s just learning and iterating very quickly, which is, what is the problem in the market?

What am I trying to solve? What’s the software I’m building in order to solve it? And how do I grow, compound, retain, and gain new customers through that?

So he created a product that was a marketing wedge product that eventually people then realized why cap table was really strong. Created a network effect between employees, fund managers, and limited partners because there’s lots of people that are stakeholders and shareholders. And then his atomic value is shares.

And then he started building fund administration, which is a separate company. And then he started to think about other products that you can build on that system of record. That means he’s got thousands and thousands of customers.

And you have to learn and figure out how to iterate while he’s solving one part of the market, he’s also creating a new market. You take a look at a company like Docker. It’s about 80% of the world’s container images and container desktop environment systems.

And you take a look at Scott. Scott’s not the founder of the company. He was the VP of product that became the CEO.

And he has a founder-like mentality, again, where he iterated on the product very quickly, built out infrastructure to understand how the open source community was using the product. And he started building a closed source product to be able to service and enable the developers that are there. So he’s got developers all over the world, right?

Like millions and millions of people that use this product. That’s awesome. What’s his background?

It’s actually more traditional, but he’s a non-founder, but acting like a founder and running this company. Take a look at the Shiprocket founders. Again, no background in any products that they had built here in the United States.

They were building products for like insurance companies. They had worked at… They were consultants themselves.

So they were entrepreneurial in thinking, but then built a product that was separate. And what’s really interesting about the Shiprocket story is that they had a product called Cartrocket, and that didn’t work. And the Shiprocket was a one specific feature that was working, that they sort of built their company around, that grew and compounded.

So I think when you take a look at pattern recognition, I think it’s more… If you go back to what I had said earlier, I really like businesses and people that are focused on large distribution, what I call unbounded markets, that have a large amount of engagement off of that software. And you can figure out these really unique ways in which to monetize that engagement that you have across the board.

And through that, that can be someone who’s gotten insight from anything. If you take a look at the companies that I’ve incubated, I don’t know what my pattern matches, but I have two crypto protocols. One crypto protocol is in the reinsurance market, and that’s going to be a multi-billion dollar protocol.

I have another one that’s around providing liquidity in the Cosmos ecosystem for protocols. And we’re building derivatives and perpetuals and futures. So it’s like a exchange on that market.

And then we’re building like a stable coin ecosystem to be able to move money between Bitcoin, Ethereum in that market. Okay, I never knew anything about the capital markets over the last 10 years. But you have to learn and iterate with your team that’s around the table.

Then we have another company that we incubated called Capitol. It’s a regulated bank in Mexico. So a regulated bank in Mexico, where we’re creating a product that’s in Colombia, Mexico, and Peru.

And in those countries, you have to build the bank. You have to build account, treasury, accounting, payroll, finance, like corporate expense management. So you’re building actually not one product, you’re building like 10 products.

So you have to learn all of that too. So my whole point here is that if you take a look at the pattern recognition of the folks that I’ve partnered with, or the companies that we’ve invested in, their backgrounds come from all walks of life. But the pattern is that the speed at which they’ve built their company, the speed at which they’ve built the team around the table to build those products, you can make an argument that they have patterns of growth that are similar to each other.

But again, that’s when you go to the art and science. So when I go back to your earlier question, my job is to see what’s working very clearly today, what I think is going to continue to grow and compound over the next 10 years. And what is everything that I can do to help them succeed and grow like 10, 20, 30, 40, 50X from there?

Siddhartha Ahluwalia: 1:06:56 – 1:07:08
So your approach would be more Jeff Bezos who is thinking that I’ll invest in what will make what change in the next 10 years or like Mark Zuckerberg who is saying that I need to find what’s the next big idea and back it.

Arjun Sethi: 1:07:08 – 1:08:06
Yeah, I think a lot of people use different terms for the same thing. Which is, how do I look to find a product that’s going to grow and compound for the next 10 years? That’s the same thing as saying like, how do I find a product that people are going to use for the next 10 years and don’t change too much of their workflow?

Or you can say that, I only buy businesses that are like Constellation Software, that are workflow software products that I believe are going to be entrenched because it’s hard, the switching costs are too high. Everyone has a version of that anyways, and they’re just looking for things that just don’t churn immediately. And obviously, software products that are highly ingrained into your workflow are something that you’re going to continue to use.

And so, if you take a look at the apps that you’ve used, as you grow older too, you become more and more habitual in your nature in terms of what are all the units of time that you have throughout the day, and what are all the products that you’re willing to give time to. And that usually doesn’t change depending on your age.

Siddhartha Ahluwalia: 1:08:08 – 1:08:20
Thank you so much, Arjun. It’s been a very dense information-packed podcast. I learned a lot.

Hopefully, my listeners learn a lot from this conversation. And I’m looking forward to hosting you, maybe after a few months, on more database insights.

Arjun Sethi: 1:08:21 – 1:08:22
Yeah, thanks for having me.

Siddhartha Ahluwalia: 1:08:22 – 1:08:23
Great to have you. Thank you so much.

Arjun Sethi: 1:08:23 – 1:08:24
Pleasure