B (2:52)

It wasn't so, I mean it turned out to be unbelievable picking of people that when you look, I mean there.

A (2:55)

Was Will Gabric, Chris Paik. Chris Paik, Jared Weinstein.

B (2:59)

Yep.

A (2:59)

Lots of amazing people. Yep. But all those people did something that was unobvious at the time. And so kind of like when I think about the evolution, a big part of it is the people that have been around the journey for a long time are people that self selected into this environment that was different.

B (3:12)

Do you think that actually selected for. Do you think that selection itself was part of why the talent hit rate seemed so interesting?

A (3:19)

Certainly like today we think about it now that we do have a brand. Right. People want to work at Thrive. We think about recruiting as a lot of people we want to come join the team are not necessarily the people that come inbound to us. We go seek them out. Always be recruiting. Yeah.

B (3:31)

Like how do you get a contrarian minded person now?

A (3:34)

Exactly. It's hard because we are more consensus now. So a lot of what we think about and look for are people that aren't looking necessarily to work at Thrive. But at the same time we're trying to set up kind of the most important thing about our team is how do we be the place that the most talented young people want to work. And if you don't maintain that then obviously you can't attract those kinds of people.

B (3:51)

It's an interesting thing about becoming consensus sort of de facto as you scale like this, when I know basically the whole team there and I think everybody doesn't prefer to be that way, but obviously are all pragmatists and acknowledge that there's sort of this self reinforcing reality when you become this big. So what is the sort of like internal struggle or thinking around this, like, you know, rooted in contrarian, doing our own thing, building something out of nothing. But like now you're kind of one of the top groups.

A (4:22)

I think I do. Let's go back to evolution because I think it is. And it's kind of been in the journey. Like people don't know this, but 2012, we invested in Instagram. Okay. It was important for obviously it got acquired by Facebook, so it's a big deal. But more importantly, Josh had spent I think a couple years beforehand just trying to break in and get into this hot company by getting to know Kevin and Mike and the whole team. We got in, we got a $20 million allocation. We had a $40 million fund at the time.

B (4:46)

Yeah.

A (4:47)

So in terms of like big bold.

B (4:48)

Bets, did you put 20 in?

A (4:50)

Yeah, we put almost. We put almost $20 million into the company. We had to raise a little bit on top because we couldn't put a 50% position in the fund. But like big bold bets were a part of the firm since the beginning. Wow. And obviously it was also important for us because no one knew Thrive Capital, no one really knew Josh. But then it's like Instagram, this hot company, benchmarks Sequoia and Thrive Capital, and everyone's like, well, who's Thrive Capital?

B (5:12)

Yeah.

A (5:12)

And so it kind of got us into the game. You fast forward a few years and we invested in GitHub. Okay. Well, GitHub obviously was this kind of high flying developer company. All of a sudden we invested. Literally within months, the CEO stepped down. It kind of went from being consensus to consensus, like in trouble as a company. Nabil, who one of our partners, went in and was interim CFO of GitHub for a period of time. We got to know the business really well, which is part of the whole pitch of us is we deeply partner with you. We started to see the data, meet the people, built more conviction, such that we actually made GitHub became one of the largest investments we ever made in the firm's history at that point in time. And we were able to buy all the shares because it was so non consensus in the Valley. But part of being in New York, part of being an outsider was like we weren't basing our conviction off of what was happening, kind of the echo chamber of the valley. We were definitely outsiders to the valley. And so take that even further. Like Stripe more recently, if you think about the evolution, obviously the quantums have gone up, but Stripe to me is kind of the evolution of this exact same confidence in making these big contrarian bets. Where Covid happened, all these companies were high flying. Obviously then it all went down and numbers decelerated. Everyone thought, oh shit, like is this over? And if you were really grounded in the numbers, you were scared or it looked like an unobvious deal. But if you kind of take a step back and say, okay, well Stripe, what does it lever to? It's levered to the growth of payments online and E commerce. And I said, jack, what do you think E commerce penetration is going to be in 10 years from now?

B (6:42)

A lot.

A (6:43)

A lot, a lot. Like no one doubts that Karim is this line. It's a lot easier to predict the long term than it is the short term. But everyone in that moment, what are they trying to do? They're trying to predict what will Stripe be in two years from now. And so we went in, we spent all of our time with Patrick and John, we've known him for a long time, we spent time with product, all the things that you would think about, not just the numbers. And we decided to lean in. And then what was unique about that and I think reinforcing to how we've evolved is we put in almost $2 billion for the company, but they needed to raise 5 or 6 so we had to then go help them raise the money. So a bunch of us went out and pitched to other investors why we had so much confidence to put that much money into Stripe. And this is now in early 23. And so the market environment was not go go. It was coming off of this post Covid hangover. And everyone to us was basically saying it looks way less profitable than Idean. It's not as good of a business anymore. Patrick and John, they're great, but are they really kind of the founders for the next era of the business?

B (7:36)

It's interesting because I think you get categorized for that investment as like consensus, compounding investment type of mindset comp to us.

A (7:43)

It's so visceral how not consensus it seemed. It was a really good formative moment for us in what it feels like to continue to double down at scale with independent conviction.

B (7:56)

You sort of described also an investing mindset that I would traditionally ascribe to early stage investing. But applied at Growth plus stage, you're talking about the founders, you're talking about the product, you're talking about the long term and not being too close to the numbers. Do you think that when you think about Thrive's great late stage investments, it was more of a growth mindset or an early stage mindset that led to those decisions?

A (8:23)

I think the growth mindset, if you want to kind of call it that, which basically a shorthand way of saying it's very quantitative, numbers driven is important. But our whole philosophy is we start with the qualitative, we develop hypothesis, and then the hypothesis has to be confirmed by the quantitative and it has to be in that order. Because if you start with the numbers and you get so excited about the numbers and then the numbers go down, you lose all your confidence. But if you start with the people and what they're doing and the customers and the product and you build confidence and then they miss a quarter or they miss two quarters, you don't react of like, oh shit, my returns are now going away. You try to unpack why and what happened is our assumption on the product wrong and that's what should change our confidence almost. And so I think like, what do I think my superpower is? It's, I spent a lot of my career on the financial side, but now at Thrive, I spent a lot of it empathizing with how do you build a company, what does it mean to hire an exec, how do you build a team? If you kind of intersect those two things, I think that's where our sweet spot is. Databricks, we did a year ago. Why was that a great time to invest in the company? Well, I'd be like, oh, it's growing really fast, it's got the tailwinds of AI. But I think the most important point, it became clear that it was going from this single product company to a multi product platform. And, and the value of the platform is much bigger than the value of the single product. And so inherently there's a mispricing because it's a lot harder to build a multi product platform than it is a single product.

B (9:39)

You talked about how like, you know, the Instagram investment, whatever number it was, was a huge percentage of the fund. You've obviously made a lot of those like very bold bets, early days. But recently too you've made bets that in, you know, maybe not betting the firm, but are much more aggressive than most venture firms. I think one of the reasons people often talk about founder led companies having an advantage is because People are willing to risk it all. Many times do you feel like you all are still doing that because it's a founder led firm and you have those dynamics? Is that the sentiment or are you now in a place where you're like, we can't risk the firm anymore?

A (10:17)

Well, I think this is the mentality of why doing what we do is really easy to say, but hard to do. Like in reality, to put that much capital into a single company, it's scary.

B (10:27)

I mean you've done it with Stripe Databricks, OpenAI and obviously you guys have a lot of SpaceX, you guys have a lot of capital, but as a percentage basis you're very tied to these companies.

A (10:36)

And we think about an ideal growth fund for us is 10 companies. And ideally it's 10 companies. That's it. It's highly concentrated. And why? Because if you believe the power law is true, there's in some sense it's easier to kind of catch a company that's really established going to 100 billion or 200 billion than it is to try to pick the breakout company from a pack of a few thousand. And so what we're really trying to do with these big concentrated bets is find what are those great generational technology companies and then concentrate all of our capital into them.

B (11:04)

Would you rather try to pick out of $10 billion companies going to 100 rather than pick $1 billion companies going to 10?

A (11:10)

I think so. I did this. We had a thing with our LPs more recently and so I cut some of the data because it's something that people talk about a lot of. And it was basically what's the number of trillion dollar companies, $100 billion companies, $10 billion companies over the last decade. And what you'll see is a decade ago there were no trillion dollar companies. Zero. The biggest tech companies were 3 to $500 billion large. Fast forward, today there's 10, I think that are around over a trillion. The top are 3 to 4 trillion. Okay, in 10 years from now, what will the biggest be? Probably bigger than that. I mean all of the stuff that's benefiting AI technology means these companies will get bigger. But the kind of thing that's missed in this analysis is there's been 75 companies in the last decade that have reached 100 billion or more in value. That's a lot of companies. There was only a few hundred in the $10 billion bucket. So if you think about it, picking the 75 out of a few hundred is much better odds than trying to pick the thousands of unicorns that will get to 10 billion.

B (12:01)

Yeah, I mean this actually confirms kind of an intuition, I think a lot of people have been thinking and saying, which is that the winners are getting bigger than ever. Which basically what you're saying is the number of $100 billion companies has grown much faster than the number of $10 billion companies.

A (12:14)

Yeah, or like think about a different lens, more product centric lens. You know, Stripe obviously dominate in online payments, but now we're talking about stablecoins or AI in payments. Who's going to be the company that wins or takes a lot of market share there? Is it going to be Stripe or is it going to be startup? I hope a lot of startups have a shot at gold because that's the nature of what we believe in. But right now, guess who's really excited about it, investing a lot of money at it, It's Stripe. And they have amazing founders, a great technology stack, great talent. And so I think what people underappreciate is these really well positioned companies that are getting to scale. They benefit from the scale, just like Google and Microsoft and Amazon and Facebook all benefit from that same scale.

B (12:53)

Why is that happening now, different than it was happening 10 years ago? Like why did these dynamics not exist in 2015 in the same magnitude?

A (13:04)

I think just people don't appreciate the power of compounding the Internet. The iPhone came out 15 years ago about a little bit more than that. And so mobile Internet is the thing, it's not that old. Everyone wants things to happen in a short period of time. But the reality is most of These companies are 10 or 15 years old. If you looked at, obviously I studied a lot of these companies at scale, the vast majority of dollars of enterprise value that get created are in the second or third decade of a company. And so if you think about all of the great companies we talk about now, Stripe, Airbnb, Doordash, Uber, these companies were all built out of the mobile era. We're now getting into the second decade of their lifetime. And that's where we know historically, if you look at Shopify or Salesforce or Tesla, the second and third decades were far more lucrative in value creation than the first decade. Well now all these companies are coming of age the second decade and I think you will realize that scale is really powerful and it just took time for them to blossom.

B (13:55)

Do you think that same thing ought to happen with new startups 15, 20 years out, in other words, is what you're saying, does it describe just that all of tech is going to do really well? Or is there some particular reason why right now investing in the big companies is better than investing in these small companies, some subset of which will compound forever too?

A (14:15)

I don't think this is some rising tide lifts all boats. I think it's more of an expression of the nature of technology, is there are benefits to scale. So like if you get to scale, you have more distribution, you can reach more customers, you can ship new products, you can kind of compound on yourself, by the way, in the talent flywheel. You know, obviously a lot of these companies started with great talent and they developed it. But now if you're a young person, where do you want to go work? You want to work at OpenAI or.

B (14:40)

SpaceX, which by the way wasn't true 10 years ago. Like it didn't used to be that the OpenAI sized companies 10 years ago were appealing in the same way that these are now.

A (14:48)

Maybe, but the OpenAI size companies 10 years ago was Google.

B (14:51)

Yeah, I know, but recruiting against Google as a startup was doable. Doing OpenAI right now is very hard.

A (14:58)

I think in general, I think the market just got a lot more competitive in technology. So what I would say is I don't think this is a rising talent for all votes. I think there are a select number of these companies that will benefit from the scale and when they're very founder driven, they will benefit as they get bigger. And we know with the power Law that there are kind of accumulated advantage to those companies. And so all we're trying to do is map our fund strategy to the power law, which is to be concentrated in these really phenomenal tech platforms. I think on the, like, you know, the whole asset class in general has matured. So like 15 years ago, how many companies raised $100 million in capital? Not that many now, I think. I think the numbers are something like four or five hundred companies a year raise at least $100 million capital.

B (15:35)

It's crazy.

A (15:36)

That's a lot. Like how many companies do you know that you would put $100 million into?

B (15:39)

I don't know, but 500 a year is a lot.

A (15:41)

And so I just think about it is there's a lot more saturation in the game on the field for picking out the billion dollar company like that same, you know, because we were pulling for the same LP event, you know, the average growth check, so say $100 million plus round is $150 million round at a billion to $2 billion valuation at 20 to 100 times revenue. Yep. Okay, well, that Doesn't. To me, that's not growth investing. That's kind of like large check venture investing. And you hope it works, but the likelihood is the vast majority will not work.

B (16:11)

And so in your view, the growth, the real growth investing starts at more like the five to $10 billion valuation rounds, and things have nine figures of.

A (16:19)

Revenue, or said differently, I think about less quantitatively and more. The real growth investing happens when you can actually wrap your head around something that's solidified and then you can look at the data to substantiate it. And if you can ground yourself in the product and what's solidified and then substantiate it. Okay, now we have a baseline that we can work from. But if you're investing in a company that's been around for three years and has 50 million in revenue, I mean, it's amazing momentum. But as you know, building a company, a lot of things can break from there.

B (16:46)

Yeah. Given this backdrop, what do you feel like are the winning strategies in venture? Like now, obviously there's this. I don't know how you'd categorize what Thrive does, but like large check concentrated investing and like breakout winners, are there other strategies other than Thrive that you're very bullish on now?

A (17:06)

I mean, I'm obviously most bullish on the strategy we're executing on. I think there will always be a place for very focused early stage investing, because at the end of the day, where else can you go put in 10, 20 million dollars to a company and get a billion out? And the reality is, if you're good at finding people early and you can help them build companies and you can get a large chunk of their company, you'll be able to drive great returns. I personally think it's gotten a lot harder because of how competitive it is, but there will always be a market for that.

B (17:34)

So what are you skeptical of now? Or like, what's the hardest part in venturing?

A (17:37)

I'm skeptical. I mean, I think we have bar. We really barbell our strategy. We're either early with companies and we work alongside them, roll up our sleeves, we own decent chunk of the company, or we invest when it's kind of becoming a clear platform company. We do do stuff in the middle, like we call it breakout companies. Like we invested in Cursor. It's obviously a phenomenal team. There's so much momentum. It's kind of got the zeitgeist in this AI moment of coding.

B (18:02)

But like, A's and B's are rare.

A (18:03)

No, but the investing in cursor in some sense should be the exception because that part of the market is so competitive.

B (18:10)

Yeah.

A (18:10)

There's so much capital chasing these 500 to $2 billion companies that really a lot of them don't even have product market fit. But we're capitalizing them like they are a company with product market fit and growth. And so I think that to me is where I'm least optimistic. Not because I don't think there's any winners. There will be winners, but risk of capital loss matters there. If you're investing $10 million checks or $5 million checks, you're investing, you can afford to have zeros. If you're investing $100 million checks, it's hard to have zeros. And if you look at the, if you look at the composition of a lot of these growth funds that are 2, 3, 4 billion dollars big, they have 30, 40, 50 investments in a growth fund, which means the average check in that fund is 70 to $100 million. But why is it, why do we get to that point in time? Well, because as these funds got bigger in scale, they hired more partners. But it's like the law of people. If you have more partners, you do more deals. If you do more deals, you're less concentrated. It's that simple of an equation. And if you're not very concentrated and you have a bunch of hundred million dollar checks into mid stage companies, I think it's just hard.

B (19:06)

Yeah. How many people work at Thrive now?

A (19:09)

The whole firm? Something like 75 people. We're about eight investors.

B (19:12)

Eight investors. Yeah.

A (19:13)

So this is a good example. Like RLP's take our assets and they divide it by investors and they say, okay, put you on a benchmark. Where do you rank?

B (19:21)

Yeah.

A (19:21)

And they look and say, wow, you guys have a lot of assets per investor.

B (19:23)

Yeah.

A (19:24)

I'm like, that's wrong way to look at us. You should look at. Because we don't think about it as, oh, each dollar requires more work. It's basically the investment decision. Each company is a commitment. So if you just flip the numerator of that equation from dollars to companies. We make 12 early stage investments a year and we make a handful of growth bets a year. That's 18ish round numbers on eight people. So we're roughly two, maybe three investments a year a person.

B (19:48)

You just put a lot of zeros when you send the check.

A (19:50)

Yeah, exactly.

B (19:50)

That's a good way to do it.

A (19:52)

But seriously. But that if you, okay, then just on the same benchmark, like who else does two investments a partner a year.

B (19:57)

I mean, there are firms that do that, but.

A (19:58)

Yeah, but it's very different, I think, in the philosophy relative to a lot of the other growth plans that we compete with.

B (20:03)

So actually when I was asking the number of people that work there, I was thinking of it this way because I was thinking of, let's say, 10 concentrated positions, eight investors, whatever. I guess the net of that is most of the time you're making new investments. Rarely.

A (20:18)

Yeah.

B (20:19)

So like, what does that look like for you? Like, what's your mentality given the, I guess really the infrequency of decisions?

A (20:26)

I mean, we turn over a lot of rocks. I spend a lot of time looking, evaluating, trying to get to know people. Like, you know, there's a different way of thinking about concentration, which is when you write a billion dollars into a company, you have to have conviction. You can't be like on the fence about is this gonna work or not. You have to have almost dogmatic conviction. It's gonna work. So how do you build up that level of conviction? The wind up period to doing these investments is super long, like years. It could be years. I mean, stripe, we made our first investment almost 10 years before we made this big $2 billion investment. Or I invested in a company called Isomorphic at the beginning of this year. We spent 18 months getting to know them. But how did that journey work? We were partnering deeply with OpenAI. We were thinking about what are the other domains where it might not be kind of on the path to AGI for the core labs, like Life Science was one of them. I got in touch with Isomorphic almost just for learning, just to meet people in the industry. And that kicked off 18 months, almost two years of getting to know the company, which then kind of cracked the door open to investment. And so if you looked at my calendar, my calendar looks like meeting a bunch of early stage companies and then a lot of irons in the fire on new initiatives. And then obviously I spent a lot of time with our portfolio and internal and so forth.

B (21:35)

Is it likely that the next large investment you, you'll make, you already know that company? Well, like, are you basically working out of a relatively confined list and you're definitely gonna invest off that list?

A (21:46)

Yes. Yeah. I think what people underappreciate about the motion to do this is how much work it takes to go create the opportunity. Like, you know, we started in New York, scrappy, having to go get the opportunity. I think a lot of the way venture's been built is you go to Sand Hill Road, you receive the pitch. We need to raise money. Oh, I have money. Let me do diligence for a month. And that's how it works. That model to me is not competitive anymore. You know, we are going, if you look at Stripe or databricks, we are going to the company. We are saying, this is all we know about you. It's a lot outside, in, inside out. We are willing to make an investment, but we want to kind of engage with you on these handful of things and then we go do it. Yeah, that's a very different motion. And by the way, there's no process. Like we don't wait for a process. We, we kind of have to wait for our point in time and sometimes things aren't clear and so we don't do anything. But in that moment, what am I doing? I'm spending time learning about it, learning about what happens with product, customers, people you get to know, people that join the team. You get to know more of the team. So a lot of times investors only spend time with the CEO. CEO is one person in the company. Do they spend time with the head of engineering, the head of product, with the sales leaders? With people lower than that, you really learn how clear the flow of information is in a company by meeting a lot of people on the team. Are people singing from the same song sheet? Do they understand the decision making framework? You know the best companies, it's super simple and it just ripples down and everybody knows how to act.

B (23:11)

One of the challenges for a lot of groups though is if they can't, if they're not capable of writing a billion dollar check, these late stage companies aren't going to give them the right to go spend that kind of time because there's a small list that can, you know, the rounds you're talking about, you know, databricks, round, stripe round, an OpenAI round. Like you just can't price those rounds. So you don't even get the right to spend that kind of time otherwise. So I guess part of your argument basically here is like the scale drives a lot of the competitive advantage.

A (23:39)

Yeah, I want to play in a game on the field that's less competitive. And I don't know. How many firms do you know that can write a billion dollar check into a company? 3, 4, I don't know how many. So I'm competing with a handful, two handfuls of people versus if you're trying to write $100 million checks in the growth stage or $10 million checks, you're trying to, you're competing with at least an order of magnitude or two orders of magnitude more firms. Maybe that won't last forever and people will get capable of doing what we're doing. But today it's a more advantaged game in the field because there's just not that many people that can compete.

B (24:08)

What's interesting is I think there are a lot of firms in sort of like the size rung below, call it one to $3 billion firms who I've spoken with, who I think see what you're doing, what Founders Fund's doing, maybe Green Oaks. I don't know what the number of firms would be and want to, but I think it's actually very hard still. Even if you're close to it, do you know what the thing is that you think you know? Like what's the dominant thing it takes if you're a leader of a $1 or $2 billion firm to get to this? Like, what is that? What is the thing preventing more people from doing this?

A (24:45)

It's a question we think about. I do think while some of this seems easy to kind of articulate as a strategy, as we talked through our journey, part of it has been a long buildup to get to the level of confidence to do that because it's unnatural for a single partner to want to make that big of a bet because it feels like career risk every time you do it. And so just in general, building a culture that rewards this, I think is not something you can do overnight, which kind of goes to the like. Why is it hard for firms to do? There's politics. Like we have a founder led firm and you can debate there are pros to that and cons to that. But I think a lot of the pros are what enable us to do this, which is at the end of the day, we have a super small team, we have clear direction and we can go make these decisive, big, bold bets. If you're at a firm that's governed by a handful of people and one partner wants to do it, or even one partner in the handle doesn't want to do it, what happens, especially if it's viewed as firm reputational risk, oh, you write a billion dollars from this company, what happens if it doesn't work? Is it going to tank the firm? Well, now all of a sudden you have bureaucracy that creeps into decision making. And I think it's hard to compete partially just because most firms have that kind of setup. They don't have kind of a singular small team that makes Decisions.

B (25:58)

This is a good segue. I want to talk about Carvana, which was, I thought one of the most interesting and in the long know, in the full arc, it was extremely impressive to me where basically you made an investment in Carvana. I think it was public when you initially did it, but you obviously knew the company for a long time. But you make this investment, it goes down like a lot and you buy more, you hold and it ends up doing great. You distribute shares, you know, and you crushed it. But like that seemed like a nail biter to me because you're, you know, it was a lot of capital. It was a public company and you know, you're Thrive. You're not a public, for the most part, obviously you do it, but I was just like. And you were partially proven, but also earlier in your career when you did it and it worked. So just talk me through that whole thing.

A (26:46)

I mean, I think in a lot of ways this embodies who we are as a firm. The journey of Carvana wasn't just random. I think some people say, oh, you guys saw the public markets go down and you went and moved on a company. It's like, just like it's not how we're not just looking to go to do random things. I first got to know Carbonics when I was at Tiger. One of my closest friends was the guy who was looking at it, spending a lot of time with it. So I got to know it. Then when I joined Thrive, it was on a list of things, but it kind of traded to a price in public markets where it didn't maybe make sense. And then the post Covid kind of tech cycle happened and it was one of the names that went down 50% in six months. And so that kind of ears went up. What are the opportunities? Well, the public markets were moving faster than the private markets. And so one of the things that's nice about our model is, okay, I can write a seed stage investment or these big late stage or I can go to public markets. Well, the public markets move faster than private markets in terms of pricing. And so we went and spent time on it. But like going back to the stripe analogy, a lot of people looking at the company were kind of grounded in, okay, well the numbers are turning. There's a cycle. If you just looked at the product, you and the team who I've gotten to know for a while, it was very clear that companies invested billions of dollars in infrastructure to build out this logistics network that makes it a great company. So it's not just an online listing for cars. It's a logistics company, which is its core advantage. The team is kind of an amazing story. If you look at the LinkedIn profiles of the people of Carvana, they're people that have been there their entire career and they've tried to hire Amazon execs and have them come in and it hasn't really worked. And they've kind of found this way to self develop talent that's super unique. And so a lot of things we got excited about were that and as it gets to scale, as a business gets better, as it gets bigger, more inventory, you have more people that convert, more people that convert. The better economics are the better economics, the more you invest in that whole flywheel and it spins and spins and spins. And so we made an investment thinking like this is one of these could be generational giant companies. I think we first bought shares, it was like a 10 or $12 billion company. And then the used car cycle turned. They actually did an acquisition financed entirely by debt, which was we don't deal with leverage in the private markets. And so there was definitely learnings from that. And the combination of that and a cycle and burning money per car sold meant that the business kind of completely unwound in a short period of time. I mean the stock went down like all the way.

B (29:04)

90%, right?

A (29:05)

90 plus percent. So obviously that's not a fun journey to ride down particularly because when you watch it get marked every day when you do a contrarian investment in the private markets, you, your friends might give you some crap about it or grief about it or people you talk to or LPs. But when you're investing a public stock, everyone knows every day, every day you wake up down 5%, down 4%, down 5%. And you have to answer the question why? Which if that happened in the private markets, no way.

B (29:31)

No one could do this job.

A (29:32)

No, yeah, everyone would sell their shares. But I think we really had an advantage. Mindset is this was the same period of time that we were working with private companies and what were we doing in private companies? We were going through the get fit era of private companies. Everyone hired way too much in Covid and now needed to restructure their companies or let go of people. And right size the P and L. And so that's what Irony and Carvana did. They basically said we can't focus on growth anymore. We have to do a whole 180 shift to focus on profitability. So the whole lens you have to look through, the company has changed. It's Just like if you were operating a company which you were, or lattice through that period of time and you said growth has gone down, yes, you want the company to keep growing, but during that moment in time, the only thing that matters is getting kind of the trains on track and making sure you're right, sizing the company for what it is. So the lens we started evaluating Carvana through was that lens they were making a tremendous amount of progress. And so by the end of that year, stock was down a lot. But through the dimensions we were evaluating on, did they have control on the levers of operating the company? The answer was kind of unequivocally yes. And they were making progress. Now, it wasn't fully where they thought they could get it, but. But it was making this. The trend line was very good. And so we ended up doubling the number of shares. We bought the company at a very low price for a fraction of the price. And so there's also this combination. I think it's less, which, by the.

B (30:47)

Way, that's a hard move to do. Like, you know, the knife has fallen all the way. Psychologically, that was the moment that I was like, this is crazy.

A (30:53)

Yeah. Well, I think what you learn, even if something goes down 50%, it can still go down 90%.

B (30:57)

Yeah. And after it's gone down 95 more is another halving. Yeah, it's tough.

A (31:01)

But I think what you realize is in private markets, people don't think about the concept of risk reward that much. But in the public markets, everyone talks about it. And the Sharpe ratio is a thing and you can measure it. And so what we were able to do is double our position size in shares for a fraction of the capital. And so the risk adjusted bet we were making on the data points we had were actually very good. And so we were kind of isolating what we were betting on. And in the fullness of time, obviously it's really worked out. But I think it took having the ability, one, to operate in a culture where people are used to making big, bold bets. And if you're not used to that, I would have got fired. Or we definitely would have sold the shares for sure. Then you have to have an environment where people aren't just like nitpicking the numbers, because if all people are doing is nitpicking the numbers, we were going to sell. But we have an environment that's very much grounded in the product and people believe in the product. I don't know if you ever bought a car, used car, but used car dealers are literally the canonical example of what you don't want to be in sales. And so having a pure transparent online experience is the epitome of kind of what a quality product experience is in this industry. And so people believed in that at Thrive, the number of people that are like, oh, I bought a car on Carvana and we're so excited about it and sent it to our Slack channel, there was a lot. And so I think people believed in the product and it enabled us to do this. And then in the fullness of time, obviously.

B (32:15)

And it's a good market. I remember at the bottom I was texting every day. There's cars everywhere I go. It's a big town, it's a big market, it's a big market, there's cars everywhere. I just think it's amazing that you were the. The buy at the bottom, I think is what impressed me the most and the ability somewhat is you. But mostly I'm going to give credit to the firm to allow you to do that. I think that is hard to have.

A (32:34)

I think it's a firm thing. I mean, obviously going through it personally is hard, but I do think the whole firm went through a lot. Our LPs asked about a lot. Everyone paid a tax for us doing this. And I think in the fullness of time we've benefited from it, but in the moment it's really hard.

B (32:49)

How important is like managing conflicts like for thrive, basically, like, you know, you've got to invest in such. You're doing so few with such a large fund that you've got to be investing in like big winners with most of your capital. If you pick something earlier, you conflict it out. Like, how do you manage?

A (33:05)

Yeah, we take this super seriously because.

B (33:07)

Like, if you do a series A in a company, you can't go do a series D or E or F in something else.

A (33:12)

It's part of the calculus. But if you look at the market today, a lot of investors, it's just by the index across the companies that are working. Yeah. And so they don't care about conflicts. And for us, because we're not doing a lot, we implicitly are making kind of a commitment to the companies that we are all in on. Your company feels like a founder's all in on the company. We're going to be all in for investment.

B (33:28)

No, I'm just thinking it's one thing if you're. If it's open air SpaceX, it's one thing. And you know, you can't go invest in Blue Origin and That's. I feel sad for you for that. Even though it's obviously a good company, but you're in space X. But when you go and invest in the series A of you know, an ERP or a CRM or whatever, is the category done for you at that point?

A (33:47)

It's not done, but this is the. People talk about the advantages, I think about the advantages of full stack investing. This is maybe in the disadvantages of it. Whereas if you're just an early stage investor, you look at the seed or the A, maybe the B. If you can't get it there, you never think about it again. Oh, it's in your anti portfolio kind of thing. If you're in our shoes, it's like, I can invest at the seed, the A, the B, the C, the D, when it's public, who knows when. And so we have.

B (34:15)

You can take it private.

A (34:16)

Exactly. We have. Seriously, like no, I know, but this happened. I mean we're joking but like this happened in this Thrive holding strategy that we announced. What is it? We ended up raising a billion dollars in a company. It's not a fund, it's a company. And why did we do that? People are like, oh well, you're expanding strategies and you're doing stuff. It was very organic. We started looking at accounting AI companies and we were, you know, one of our partners is very good friends with this guy who's running this accounting roll up strategy. And so we started talking to them about AI tools. Why? Because we're just looking at early stage AI companies. You know, we started partnering more with them. We thought their thing was interesting. We wrote a growth investment into that company, the technology started working, our conviction went up and we all of a sudden said, okay, well wait a second, what's the best way to play accounting in AI? Is it to invest in a software.

B (35:06)

Company early or be the accounting firm?

A (35:08)

Or be the accounting firm. Because we're taking this kind of scrutinizing lens from all sides of the equation. We decided that's the right way to do it. And so also the right way to do it is not in our fund structure because these assets need a lot of time and you have to do different things than you do in a typical growth fund. And so we raised dedicated pool of capital towards that strategy. But the genesis of it is very much looking full stack at a company, which I think is very different. By the way, there's $800 million of venture investing going into the software tools for accounting.

B (35:36)

Really?

A (35:37)

And so yeah, so we're riding the R and D dollars to the entire industry as the service provider and accounting's sticky. Are you going to change your accounting provider because they have 10% lower price? No, probably not. Now the risk is going back to the competitive stuff. If these startups are so successful that they radically change the cost curve that you can take your business and go to ChatGPT and do your accounting for 10% of the cost, maybe you'll change. So one risk in our discussion of this is if the change is so radical, should we be doing it now or should we waiting? And I think our assessment of accounting is it was not going to be as radical as it could be. And therefore you actually want to be the service provider because you'll capture the value.

B (36:11)

Yeah. In general, across sort of functions and verticals. Do you more fall into the camp at the moment right now, September 2025, are you more in the camp that most jobs are going to be completely overhauled? People are going to have to find new stuff. AI is going to do things end to end in legal, finance, healthcare, et cetera, et cetera? Or are you like, this is just like super sick software and everything's getting more efficient? Do you think about this question at all?

A (36:39)

We do. I'm probably more in the latter camp. I just think we're also humans and in a lot of places we want to deal with humans, not with software. There are some places where I actually think we'll prefer to deal with software. Like customer support is a good example. Of course, if you're United Airlines. Yeah.

B (36:57)

You don't ever want to talk to.

A (36:58)

Somebody, but I can call a number and get an instant response from an.

B (37:01)

AI and I don't have to wait ever.

A (37:03)

That's amazing.

B (37:03)

Yeah, of course.

A (37:04)

And so in some functions like that, I think it'll probably shift towards software. Now, by the way, there's going to be abstractions of that where rather than doing the actual job, you're going to be managing the system. And so those people will get kind of upgraded in what they're doing. But in certain things, like I don't. I think the value a lot of times is with the humans doing it. And so I think making them 10x more efficient is better. Like take creative. Are we gonna automate away all the creative people? Probably not. Are we gonna make them 10 times as productive? So there's 10 times more creative content?

B (37:31)

I mean, I would guess there's a certain type of creative content that will get automated and then there's a certain level of it that won't I think.

A (37:37)

It more is like you lower the floor of entry so now someone who wasn't talented enough could probably do it. So they can take their culture and lens and now do it. And then you also increase the output of the people that are really good at it. Yeah, I don't think you really. It maybe eliminates people on the margin that you don't need, but I just, I think the vast majority is make people a lot more efficient with their time.

B (37:55)

So you basically, so far you've listed, you know, you obviously we all believe in like not search but like, you know, chat. We all believe in support. I think Cogen, although we could talk about that a little bit more. Any other areas that you're like quite bullish on that you're like this is, this is working now and it's going to, you know, go very far.

A (38:11)

I think the, the thing I'm most bullish on that seems early and broadly the market hasn't acknowledged it yet is life science, like drug development should radically change.

B (38:21)

On what dimension do you think it will?

A (38:23)

Well, the reason we invest. So this company, Isomorphic started inside of Google. Dennis Hospice, who runs Gemini, this is his kind of second project or company and he won his Nobel Prize for the work he did on protein structure prediction. And so that has now morphed into this company. And the entire objective? Well, the mission of the company is to cure all disease. Just pretty big mission.

B (38:44)

It's a good mission.

A (38:45)

It's one of the only companies in our portfolio that I think about as having the potential size as OpenAI. I mean I think it is truly that if you, if you have.

B (38:52)

Yeah, of course, if you could do that, it's huge.

A (38:54)

It's trillions of dollars.

B (38:55)

Yeah.

A (38:56)

And then when you go spend time with them and what they're working on, you know, the objective is we're going to take an entire wet lab of experimentation and simulate it computationally. Well, if you do that, you flip the entire model of how drug development works on its head rather than having this kind of waterfall like you just.

B (39:12)

Do it all on a computer.

A (39:13)

Yeah. But now the speed of iteration is different. The scale which you can run computer, that's different. And so what needs to be true.

B (39:18)

For that to happen, like what needs to be different in the future for that to work?

A (39:23)

A lot of things, which is why this is such an interesting problem to work on as a company. Obviously there's the AI part of it today which is how do you take all of this process and embody it in A model with the right data and results and stuff. So you can, at the end of a, at the end of a computational run, get a good drug. That's a very hard problem. But even once you do that, then you have to run it through trials because today it's regulated.

B (39:43)

Yeah.

A (39:43)

So you can't just come and put drugs into people's bodies in the US.

B (39:46)

Which is going to take the same amount of time.

A (39:48)

Yeah. And so what's, what's another problem? We're working on regulation. How do you work alongside regulators to change the way the FDA thinks about it? Because the top of funnel might go up a lot. That's another thing. How do you, how do you find people with disease?

B (40:03)

Yeah.

A (40:03)

How do you look for the right biomarkers? We can use AI for that though. And by the way, there's a huge kind of upswing of startups that are working Daniel X company like, let's go scan your body, take your blood, do it in a high class experience. That stuff will become more important as you get better drug development because you're going to need better biomarkers to target the right people with the right diseases. So this time from identification to cure is extremely compressed. That to me is an area that, it feels far away in sci fi when we talk about it this way. But when you go spend time with these companies, the rate of progress feels very early, OpenAI oriented.

B (40:41)

How about CodeGen? And actually maybe one of the things I'm curious about is can you talk about the stack from a cursor subscription down to the middle and how the 20 bucks moves?

A (40:56)

I mean, codegen is so fascinating because it's one of these areas where there's been extremely high return on marginal intelligence, to use a fancy word.

B (41:04)

I like that.

A (41:06)

And I'm paraphrasing that from one of our brilliant young people that works on the team, Mohit. But because that's the case, you want the frontier stuff. And so all of it goes through the frontier models or most of it. And so what you have is you have this dynamic where the coding companies, which are doing a great job, I think are doing amazing and they have amazing people. But their subscription, they pay a big chunk of it to a model provider and then that model provider pays a chunk, pays a chunk to someone running the compute. In Anthropic's case, they'll run on Amazon and Google and then that provider pays money to build the data centers. And ultimately the biggest toll taker there is Nvidia. And so if you trace this ecosystem and you look at the dollars of profit today, I think Stan DruckerMiller said this, 120% of the profit in AI is from Nvidia, which implicitly means that, you know, they are.

B (41:55)

That's good.

A (41:56)

You know, most companies are losing money and they are the ones making money.

B (41:58)

Yeah.

A (41:58)

And so, you know, Cogen's in this fascinating state where these companies are growing really fast. There's lots of promise and potential, but the economic equation is very much unknown.

B (42:08)

Yeah.

A (42:09)

And I think they will figure it out. I think there will be big, big winners that come out of it. But today what you have is a lot of dollars that are getting handed around and there's less discretion on who should get valued appropriately. And really everyone is getting valued at a high multiple on those same dollars.

B (42:25)

To connect back to an earlier part of the discussion when we talked about Stripe and how you were obviously understanding the financials, but that came later and more you were understanding the product where the market might shape out those kinds of things. When you think about, let's just take that stack and you're obviously an investor in both cursor and OpenAI. And we talked about a bunch of others. Is it very important for you to think hard about where the money's gonna ultimately land, or are you more able to just think about dynamics that have more to do with the product, the adoption, the customer, the founders, et cetera?

A (43:01)

I think the answer is both. I think if you're too qualitative, you miss a lot of details that are important. And if you're too quantitative, you miss a lot of the details that are qualitative. So you have to. This is, we call this east coast meets west coast venture. You know, where East Coast, I'm surrounded by public investors and people are very quantitative. West Coast, I think, is highly product and people oriented. And so we try to sit at that intersection. The reason it's important is, you know, okay, if you're investing something in a billion dollars, it's a big price. But if it works, we need to believe you can get paid for the risk. So if you have an unknown economic equation and we're investing at those prices, yeah, we need to believe it's not a 3 or 4X. We need to believe it's like a 10 or maybe 20X. If you're investing in something at 50 billion, okay, it's hard to put 20X's on paper or 10Xs on paper. But that means that the confidence you have to have or the kind of band of outcomes you have to have a tighter standard deviation on the variance of the outcome in those kind of more certain equations versus if you're investing earlier in the curve. You just need to get paid for the upside. The challenge with that philosophy is if a lot of people believe a lot of things can get big a of lot, a lot gets funded. And so you live in these periods of time where the economic equation can be distorted because there's a lot of funding in the environment. Bill Gurley's talked at lengths at this, but you can do all you want on paper. But when you're living the strategy in the boardroom and all of your competitors are raising billions of dollars of capital, the economic equation goes out the window because everyone's competing for a theoretically big prize. Totally. And so I think right now what we have is a bunch of companies that are operating that environment. And so we have to be grounded in where do we think conceptually the value will accrue. And if you look at cursor, we think distribution matters and they have amazing distribution with lots of developers, lots of love and people that use it every day. That's valuable. An amazing team with OpenAI. They've done a lot of hard work on the model. But it's not just that they have to scale all the infrastructure to do that. They have to run the inference really efficiently. There's a lot of IP in that. There's only anthropic and OpenAI are basically the two independent scaled model providers. And then they're competing against Google and Meta and Tesla. They're competing against big, big companies. That's hard. And therein lies the value of what they're doing. And so even if you can't know the end state of the financial equation, I think you have to try to telegraph it through what the kind of quality of the product and business model is.

B (45:18)

Yeah, Just to move through a couple of the other areas of AI, how do you feel about, for lack of a better term, vertical specific, like AI workspaces? So like, you know, we're both investors in Rogo, there's obviously in legal, there's like Legora and Harvey, you know, there's, there's a bunch for different verticals and it's sort of like not the agent doing all the work, but like maybe like a bit more copilot type work. Do you feel bullish on those type of companies? Is it vertical by vertical? Do you look at a lot of that?

A (45:49)

We do spend a decent time looking at it. It's kind of, I would say in the fullness of time. It's a little ironic that if we're one or two innings into a massive technology wave that we're starting with vertical.

B (46:00)

Market software, you think that should come last?

A (46:02)

Well, if you looked at the last generation of software as a proxy, you start with the big horizontal categories because they're massive. And then you work your way up the kind of product pyramid because it's much easier to build the big CRM than it is to build vivo, which is specific for life sciences. Okay. You go to AI and who are some of the early adopters of the technology? Lawyers.

B (46:22)

It is surprising.

A (46:23)

And doctors. Yeah, they're just. There are categories that you would not put in the early adopters of technology developers. Makes sense. Yes, but lawyers and doctors.

B (46:31)

Surprising.

A (46:31)

It's surprising, yeah. So I think from that perspective there's something interesting to study, which is like, why are these the folks that are adopting early?

B (46:39)

I think it's because they feel like they were a little slow last time around and so they're a little more front foot this time.

A (46:42)

I do think that plays into it. Like most companies in general don't want to lose out in the Internet. Like you said with crypto. How many companies got on the crypto bandwagon because they just lost in the Internet and they don't want to lose in crypto. So even if it doesn't make sense, they're doing it.

B (46:54)

I mean, the other thing is a lot of these law firms as just like to take one example, like they spend all day with startups, like they understand the tech, you know, they know it. And so I think.

A (47:03)

And law as a modality is highly text based.

B (47:06)

Yeah, it works very well for LLMs.

A (47:07)

And the models are very good at that. And so it makes sense. Or doctors, you know, a lot of it is kind of evidence driven or publication driven or diagnosis driven, which you can ingest that in modalities that are good with LLM. So you could argue a difference out of this, which is the reason those are early adopters is product.

B (47:21)

Yeah.

A (47:21)

But I just think it's ironic in the fullness of time that investors are so focused on it so early because it doesn't seem like the biggest markets to go after. But therein lies the kind of opportunity which is we are excited about some of them. We invested in Rogo in financial services. You know, I think the amount of competition in these markets early doesn't. It breaks the heuristics of success historically. Like the old equation of success in a vertical market was, you Get a lot of market share. Why? Because as people professionalize on you, you take a lot of market share. Like, how many competitors are there to Adobe Photoshop or AutoCAD? Yeah, there aren't. Right. And so how many competitors are there going to be in legal chatbots today? There's a lot.

B (47:59)

Many dozens.

A (47:59)

And so now there's a couple of companies that have broken out of the pack and exist, but over time, you would hope that it consolidates to a small few.

B (48:05)

And so, I mean, in general, we're in a moment in time where there's probably more competitors than at least I've ever seen in a lot of these categories. And I think it's because in that quadrant of looks good and is good, a lot of things look good and might also be good. And so you have a lot of people flocking there. There's capital markets to back on.

A (48:24)

Yeah. So anyways, to answer your question, I think we're selectively excited. Like, why do we think Rogo was really interested? Starts with a person. I mean, you know, Gabe, I think he's 12 out of 10. And so we have to get excited about that. The category has properties I think are interesting. Like, I personally believe that exposing data businesses via the chatbot.

B (48:43)

Yeah.

A (48:43)

Is actually more interesting of a thing to look for than to look for, like, big labor markets. And so I've been spending time. How do you find data assets that you can now expose that way? Because the models are amazingly good at structuring and looking at and searching across big data.

B (48:57)

What else would that include?

A (48:58)

You know, I would love to find one in real estate.

B (49:00)

Yeah.

A (49:00)

Costar is this amazing, you know, almost monopoly, like, data asset, you know, is there someone who can go after that? You know, I think there are other categories too, you know, that we could talk about. But in finance with Rogo, what's so fascinating is there's a couple million seats that people can go sell to.

B (49:16)

Yeah.

A (49:17)

And so if you're competing against ChatGPT, is ChatGPT going to prioritize integrating?

B (49:22)

No, it's like, perfect.

A (49:23)

There's not that many public market research.

B (49:24)

Well, I guess the flip is they're willing to pay a lot and they have a lot of money. Like in Bloomberg got really big.

A (49:28)

Of course. But the thing is, then if you're ChatGPT, you can't sell them ChatGPT off the shelf. You have to build a custom ChatGPT.

B (49:33)

Better just go get a billion users. Yeah.

A (49:35)

And open any scale. And so to me, that's the advantage that they can do. You know, if they have to compete with ChatGPT head on at their own game, it's gonna be hard. But if we can go compete on workflows and doing spreadsheets really well and public market research and exposing all of that data with great sources, that'll be an opportunity for us.

B (49:52)

Yeah. Are there any other areas in general in like the application layers or like the startup side that you feel like particularly interested in with AI Robotics is.

A (50:01)

The one that it's kind of, we didn't talk about is probably next to life science is the biggest opportunity, like I think the biggest market. It could be the biggest, just the biggest market today. You know, today cars are one of the biggest industries globally. Yeah, they might be the biggest industry globally, which means robots for consumers should be the biggest market.

B (50:20)

That's what you think.

A (50:21)

The utility of a robot in your house doing things is way higher than a car.

B (50:24)

I mean if everybody in the world had one robot, 10,000 each doesn't even, you know, it makes a lot of sense.

A (50:28)

Yeah, exactly. And so to me that is the biggest market. The question I think right now is, you know, are we in 2015 self driving or are we on the precipice of cracking full stack robots? Yeah. And I think that's what's the hard question to answer right now. Yeah, and I've heard arguments on both sides. I've heard arguments that we are not in self driving. Actually self driving is a harder problem because if you make the wrong decision when you're going 60 miles an hour, you kill somebody. But if a robot doesn't put the apple in the right spot or breaks a dish, everyone's going to be okay. And so the problem, while complex because there's more degrees of freedom, there's more complexity in the environment, is actually in some sense easier because the risk of failure is, is lower. Right. I've heard other arguments though that because the components are brittle and there's a lot of hardware that has to get built and also software that has to get built that was going to take a long time. So I don't know. But that's an area that we are super excited about. We're investing in physical intelligence. I know, you know, Locky and Carol and those guys. I think it's an awesome company. Yeah. But it's early and it's an area that we're definitely interested in looking for more.

B (51:31)

How do you decide putting an incremental 50 or $100 million into one of these companies versus just, you know, what OpenAI is definitely working. We've got a great relationship. Let's just put more there. You could, like, knit to crypto or something where, like, you know, the right answer looking back might have just been like, buy bitcoin along the way. Like, how have you thought about, you know, that sort of mashup?

A (51:53)

I do think there's some parallels in the buy bitcoin. You know, the best thing to do is just go long bitcoin, because at the end of the day, it is kind of the derivative, you know, value indication of everything. You know, meaning all these AI companies have to use models. Opening is a leading model. They're also leading consumer product.

B (52:07)

Yeah. You could also just say, like, if you believe in crypto, you're sure bitcoin's going to. If it doesn't work, bitcoin's not going to work. You know, if it works bitcoin.

A (52:13)

And this is this kind of thing, being so close to OpenAI. I think this is one of the things that helps us in a lot of ways and sometimes can hurt us because we. We kind of know, maybe too much. But, you know, they are well positioned to do a lot of things. The investor community has basically now narrowed OpenAI and said, oh, ChatGPT is the next consumer chatbot globally, and that's how you should underwrite the company, and that's what it's going to do. I mean, obviously, Sam super well, I think what he promotes is this kind of do a lot of small teams betting on new things, and it's a very entrepreneurial culture. And so I would be shocked if there are not lots of new products in OpenAI that are massively successful.

B (52:53)

Well, I mean, as we're talking about a lot of these other categories, it's like, if they are, in fact really big, I mean, this was one of the things historically with, like, Google, Fang was like, it's either not that big of a market and they won't compete, or it's a really big market and they'll compete. I think what turned out to be the case in a lot of things in the cloud cycle was they tried to compete and they just didn't do a very good job at it and they lost the market. You know, it seems like the companies today are much more probably including faang have woken up. I just feel like the incumbents are much stronger than they used to be.

A (53:22)

I think the incumbents are super strong and why we've never had global distribution. There wasn't every user connected on the Internet prior. Again, if you Think in decades, not years. A lot of the capabilities right now are a couple decades old. Everyone didn't have access to the Internet two decades ago, not even one decade ago. It's now. You take today. And so over the next two decades those companies are going to be able to flex of this distribution and technology they have in a way that will be hard to compete with. And so I think the thing that we absolutely can't have happen is that new companies can't break into that era. Like I think one of the things that is hard is right now everyone's basically ganging up on my mental model for you on how the competitive landscape looks like for OpenAI is OpenAI's in a corner and every big tech company has a bazooka pointing at them to try to take them down.

B (54:15)

Yeah.

A (54:16)

Because none of those big tech companies want a new big tech company. Right. Right. And so we should all want it to be a competitive fair fight for a new company to break into Mag 7.

B (54:26)

Yeah.

A (54:26)

Because that's what our whole ecosystem lives and breathes off of. Yeah. The flip side of this is, you know, as these big going back to this number of hundred billion dollar companies as the big get bigger. Yeah. They're already self reinforcing properties like Uber. They have a driver network, they have millions of consumers in their app in a geography are they able and best off to do food delivery? It turns out yes. If you would have said five years in Uber's journey, was that their focus? Everyone said no. So now that market has largely kind of coalesced around doordash and Uber. So one company did do it without having the ride share but the rest of the market consolidated around basically an incumbent in a different area. I think you'll see the same thing in AI. And so this is where we have a hard time which is, I think a lot of our bias is this isn't a risk adjusted better return than open AI.

B (55:14)

Yeah.

A (55:14)

And therefore if we're given a decision we should do it at the same time you can't take, you can't not take risk because of that. And so we are investing in other companies for sure.

B (55:21)

I mean but it's also like, I think one of the things when I think about like Founders Fund and why they're so impressive to me is that they made that calculus for you know, a decade into like SpaceX and others and to continually have the discipline to say it would be really fun to go make a new investment. But if I know these two things and I can just put More into something that I already own a lot of. I'm like, I find that very impressive, partially because the patience is brutal.

A (55:49)

I think there's a firm that has kind of the most similar strategy. It's probably Founders Fund. Yeah, I think they have very similar DNA of making big, bold bets on.

B (55:58)

Companies, thinking about what matters most for Thrive in the next leg of the journey. And obviously, if I know one thing about Thrive, you're not going to stand still. I've never known Thrive to do the same thing it did last year. What matters most either organizationally, brand wise, capital wise, investment wise, what needs to be true for you guys to get to a place where you look back and in 2025, Thrive feels somehow small.

A (56:25)

I mean, we talk about this a lot. What worked for the last decade is not going to work for the next decade. And so you have to evolve, which I don't think is a given for most firms. I think the most important thing for us is we continue to be a place that attracts the most talented and ambitious young people. To the end of the day, a lot of what we do is new, disruptive, and you need the right combination of experience and naivete. And as people get older and older in their careers, I hope I continue to learn. But you learn through young people. And if we're not able to attract that kind of person to us, will we attract the right companies and founders? Will we attract the right investors? We have people now. Thrive is a product. We are building Thrive as a company with our own products and technology. And so we attract those people that way too. And it's going to be how we attract new strategies to grow the firm. And so I do think the single most important thing is we maintain that. And then we also have a healthy dose of, you know, we gotta change. You evolve or die in this industry.

B (57:27)

Love it. Vince, this was super fun. Thanks for doing it.

A (57:29)

Thank you so much, Jack.