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- [00:00]
A
Well, we can bring in our next guest, Thomas Lafont from CO2. He is here live with us in the TVPN ultradome. Thomas, great to see you. How are you doing? Thank you so much for taking this time.
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B
It's been too long.
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A
It has been too long. I want to. I want to actually begin at the very beginning. Can you tell us where you grew up?
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B
Yeah, I was born in Paris in 1976. I'll be turning 50 in.
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A
Congratulations.
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B
Actually about a month and a half success. Thank you. And really split my growing up between the US And France. My father was an executive who kind of moved around. So between Paris and New York, did a back and forth twice. Was kind of used to moving around. Settled in New York in 1988. One of the first questions I get is why I don't have an accent when my brother does. And I think it's really related to our kind of where we grew up and our difference in ages when we moved to the US but obviously incredibly grateful to have moved to the US What I do remember from France is the feeling of a country that kind of felt stuck in neutral.
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A
Sure.
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B
And so coming to especially a city like New York with the dynamic economy and just the feeling of life just in the buildings construction was incredibly motivating. I went to a really international school in New York that had a lot of different types of people. It was a French language school. So you had your kind of classic expats. You had a lot of diplomats from all over the world, since French is a diplomatic language in a lot of countries. So I was really grateful to that exposure. And then I went to Yale. I studied computer science my junior year. I realized what a good programmer was and that I wasn't one of them, because what would take a good programmer an hour would take me six days. So that was kind of sobering. And I thought, okay, I got to think about something else. And I love movies. Always watched a ton of movies. It was kind of the peak for, I think, kind of the movie business. We were about to roll into the DVD era, which, if you think about it now as an analyst, was essentially the studios monetizing a library again at virtually no cost. So everybody's building DVDs, media is at the peak. You had Mike Ovitz on the COVID of Newsweek. So I learned as much about that industry. Kind of reading the New York Times on Mondays, which was kind of the digital edition, reading Vanity Fair and Premier magazine and anything I could get my hands on, I said, okay, well, Hollywood sounds Like a lot of fun. And CAA has this training program. I don't know anything, I don't know anybody. I've never really been there. So getting trained sounded pretty good. I went to my alumni house and we had these binders and you could look up industries and there was one that said entertainment. And I saw there was one agent who had gone to Yale who was at ca, so we're now in my senior year. I called her every day for six months at the same time. And I got to actually know her assistant pretty well because agents have to pick up the phone, right? Because that's how their business is built.
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A
Wait, what time of day? Like early morning, late?
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B
No.
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A
How did you settle on one particular time?
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B
Because 3pm by default.
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A
I would like mix it up and try to.
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B
I knew first thing in the morning.
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C
No, it's kind of an interesting thing. Like if you just get a random call here or there, you're not really paying attention to it. But if every single day at 3 o' clock you get the same call, by the third or fourth day you're
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B
like, I knew she was taking my call first thing in the morning.
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A
Too busy.
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B
Yeah, exactly.
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A
Yeah.
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B
So I'm like right about, right after lunch, right? It's like the mid morning, the mid afternoon nap, maybe I'll hit them. So I got to know the assistant and we would kind of joke around, but finally one day I got a call back from, from her. Her name was Sally Wilcox, she was a book agent. And she said, well, what's it going to take for you to stop calling me? I said, well, I want an interview. And she actually gave me, I thought, an amazing answer to that. She said, well, if you agree to move out and you call me after you've moved out to la, I'll get you the interview.
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C
She wanted you to move, move before
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A
you even get the interview.
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B
That is what I realized. What she was doing was she was kind of testing my conviction. And she probably got a lot of calls from people who said, oh, I want to do this, I want to do that. And she's like, well, if you have the conviction in yourself to move out with no job, that shows you really kind of want it. It was probably a filter on her part. So I did and it was my second time, I think in la. And I called her and I only had one egg in the basket. There was no other egg in the basket. This was the only egg in the basket. So I called her, I said, well, you remember you told me if I moved you would give me the interview. And she did. I interviewed, I think on some. I moved out May 22, I think after I graduated. I got here first week of June. And by July 7th was my first day of 1997. In the CA mailroom.
- [05:28]
A
In the mailroom.
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B
And I think you guys were just maybe there. Right. There was just this iconic place. And a lot of people I kind of looked up to had started in the mailroom. Ron Meyer started in the mailroom. David Geffen, Barry Diller. So I'd read about all these legends of the business kind of starting.
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A
There was a dress code back then too.
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B
What's that?
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A
There was a dress code back then?
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B
Oh, absolutely.
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A
Yeah. There's still a dress code today. And I think that's the thing that sticks out the most about the mailroom is just the number of talented young people that you have in a pretty small space. And they're all dressed perfectly.
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B
Yeah. Which is every experience you have in that mail room is kind of unique. So I'll tell you guys this story. I don't think I've ever shared this one publicly, but we did a lot of work for Ralph Lauren. And this is back kind of in the Friends era. And if you remember, Jennifer Aniston's character on Friends worked at Ralph Lauren.
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A
Yeah.
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B
So he's coming in and he's doing a taping. And so I'm asked to go pick him up at the Beverly Hills Hotel and just escort him for the day while he's shooting the scene. And if you remember the scene, it's in an elevator and Jennifer's character Rachel bumps into Ralph. And it's like a 12 second scene. You can look it up on YouTube. So it took like three minutes to shoot. So we get on set at nine. He, by the way, I think he's in denim, on denim, just classic Ralph. And right before he goes on, he's like, call her up or call her down. I'm like, call her up, let's go. We're rolling with this. And so it takes like three seconds. And so we arrived at 9 and like 9:08 and we're kind of done. And Ralph says, well, I didn't think this would be that quick. And my next meeting is not until 4 o'. Clock, so why don't we go and spent some time together And I'm really kind of interested to go where you shop. Oh, interesting. Oh my gosh.
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C
Ready? He wanted to jump right into marketing research.
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B
Yeah. So we spent the whole day going together, shopping around. One memorable one was Going to Fred Siegel. Back when Fred Siegel and Melrose was every new brand was kind of getting broken into there. And I just watched him walking around, and the way he engaged with every single salesperson. Never talked down to him. To your point. Did market research. Why are people buying this style? Not that one. It was just amazing to watch and how. Just curious he was and how much he wanted to learn. So we finish, and I'll get to the end of the story. But at the end of the day, he's like, look, I gotta ask you something that's really bothered me the whole day. I'm like, yeah. He's like, why aren't you wearing Ralph Lauren? And I said, look, honestly, the cut's not great, and the store on Beverly Drive's a little old, and then there's a lot of wood. It doesn't really feel kind of new. And he's like, man, you're so right. But he's like, you really should be wearing Ralph. So go there and just tell him I sent you, and you can get anything you want. Wow. So we kind of parted ways, and he was really lovely, but I debated whether I should go or not. And the next week, I go and I go and I introduce myself to the very pretty lady at the counter, and I said, look, I'm sure you get this all the time. You're going to think I'm a crank. But Ralph sent me, and he said I could get whatever I want. And she paused for a minute, and she looked me up and down. She said, oh, we've been waiting for you.
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A
No way.
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B
And I kind of spent the, you know, an hour kind of going through the store. But, you know, the exposure that you got to people through that job was really amazing. Watching, you know, actors, watching directors, watching entrepreneurs like Ralph. So I was really grateful for that opportunity.
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C
Talk about. You had a. We were catching up yesterday off air, and you had a story about. You can just talk generally about
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A
your.
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C
What, what, what? Maybe entrepreneurs could learn from actors and actresses that are breaking in through Hollywood and what they have to go through from a competitive standpoint to actually break out. I'm sure you got to see a bunch of different stars over the years, but the, you know, everything from, you know, the rejection to the constantly having to, you know, constant hustle. Because, you know, as soon as a project ends, it's like, okay, what's the next thing?
- [09:48]
B
Yeah. I think people underestimate, first of all, how hard it is to be an actor and how competitive it is. First of all, especially in the movie industry, right, you have to kind of find a new job every, you know, six or seven weeks. So you're constantly unemployed. You're constantly having to search the new job. Not only that, when you go and you actually interview for a job, you might run into 10 other people that look exactly like you, right? All of your competition. Like, imagine if I was pitching an entrepreneur and I'm like, oh, there's Sequoia and Andreessen and Benchmark, and we're all kind of sitting lined up next to each other, waiting. That's kind of what actors go through when they audition. And then, by the way, when we give you feedback, it's not even feedback on your business, it's on you. We don't like how you talk, how you look.
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C
Yeah, it's your ears, Your ears.
- [10:35]
B
I represented Jordana Brewster, who's a friend now and is married to a friend of mine. But one day on one of the casting sheets, it said, hey, a Jordana Brewster, like, actress for this role. So I called and I said, well, what about Jordana Brewster? This is literally who you said. They're like, no, no, we just want someone like her. I said, well, what about her? Right? But that, that is the kind of stuff. And obviously the competition is intense, right? And it actually reminds me a little bit of what it was like to be an entrepreneur in China. Right? Very similar. Like you would have if you had a ride sharing company. There were 150 other ride sharing companies that you had to kind of get through just to then win your city, to then go and compete with all the other cities, to then at the end compete against Uber in China as an example. And Didi kind of won that. So it's an extremely competitive kind of industry and I relate to that. As someone who worked with actors, you would sometimes talk to an actor and say, how'd the audition go? They would tell you, I nailed it. It was just so good. And then you'd call the casting director or the director and they would say, that was the most unprepared bad audition. And so you have to figure out how to communicate that feedback in a way that's constructive to the client. Right? Because just telling them, oh, you did great, but you didn't get it isn't necessarily useful. But in a way that doesn't also, you know, is detrimental. Right. To their mental health and things like that. So it's very much a people, a reputation kind of business. And I really enjoyed it for the seven years I did it.
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A
Yeah. What was the process of getting out of the mailroom. I imagine that you have, you know, a few really iconic stories from the mailroom, but there were plenty of days that were just photocopying. Is that roughly correct?
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B
Yeah. I mean, we did shopping for groceries. We copied scripts, we delivered scripts. So there was a whole set of kind of stories on that. And honestly, we could fill a lot of podcasts, just the different things that we kind of did. But look, it rewarded hard work. It rewarded attention to detail, ultimately. And you got invited to the retreats, right? And CA did these retreats every year. And after one of them, I had some thoughts. I wrote them down, and I hand delivered a letter to the managing partner saying, I was at the retreat and here are my thoughts. I wish I still had it. I don't know what I said. And then I didn't hear anything for a long time. But when I came off what was called the runs, where you delivered scripts, you then waited to be picked for a desk. And only the top five got to interview for desks to try and not keep people jammed there too long. But the desk of Brian Lord opened up, and he was the co chairman and is now the CEO, and he asked to interview me because he had read my memo and he said, I want to interview the kid from the memo, even though he's not in the top five, I don't care. So I went up, and I'd never really met him or spent any time with him before, but we sat down and somehow we got talking about John Steinbeck, which was my favorite writer. And I was reading a biography of him at the time, and we spent 30 minutes talking about John Steinbeck. Nothing about business or how do you answer the phone or what he's looking for in an assistant. So I came back down and all the guys were like, how'd he go? I'm like, well, I don't think it went well because he didn't ask me a single question other than, you know, talking about John Steinbeck. So I'm like, there's no way I got this job. And then he ultimately kind of gave me the job.
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A
Yeah, that's amazing.
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B
And I worked with him for almost three years. Unbelievable experience coming off of Mike Kovitz, having gone to Disney, then having. Having flamed out and starting his own company. So much kind of turbulence in the industry. And he was an amazing guy, and it was a great formative experience for me.
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C
Since we are in Hollywood running a show that covers venture, was there anyone in that era of Hollywood that was from the talent side that was leaning in and investing in startups of any kind of.
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B
No, I mean, what I remember from that era is no one really thought about the investing side of it per se. People thought that, wow, the business is going to be digitized, so let's think of creating content for these new digital channels. And so it was more on the monetization side of, here's a new distribution channel called the Internet. And how can we adapt our businesses to that new channel? I had always loved kind of investing, so I was kind of trading public market stocks on the side, and my brother and I kind of started kind of doing that together. And I just bought companies that I liked and was kind of doing it as a hobby. Eventually, when I was promoted to an agent, I kind of realized two things. One is I preferred being an assistant to an agent. I preferred being an assistant rather than an agent. So that was one. And then two, I kind of really like this stock thing on the side, right? And someone said, well, always invest in someone's hobbies because that's what they choose to do in their spare time. And so Philippe called me and this was a couple years into CO2 and said, well, we're kind of trading this account together anyways, why don't you come over and do this? And, you know, that's how I got started.
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A
And. Yeah, what year did you get started at CO2?
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B
What was it then?
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A
Walk us through post.com crash how are you feeling? What's the strategy?
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B
2003. So we're just coming off of 2000. 2001. 2002. Down 80% on the market.03 kind of had a snapback. So the market was up 50%. We were well positioned for the downturn, not as well for the snapback, but now it's about, okay, what did the next kind of decade kind of look like? Right. The easy money's been made on the quick rebound. Right. Nasdaq, I think, was up 50% year to date or something like that. And we were really looking for a semiconductor analyst and couldn't find one. And this is something I'll forever be grateful to my brother for. But eventually he said, look, since we can't find one, he dropped a copy of the Universe. And this Universe was a printed memo of essentially all the stocks and key metrics about each name, pe, volume sector, the semiconductor universe. And he said, you know, why don't you go ahead and do it? Can't find anybody anyways. And, you know, I had no real training as an analyst. And you know, I would sit in our bullpen and there were analysts from Morgan Stanley and Goldman Sachs. And I thought, man, my training was in a mail room. You know, I don't know anything. But what I started to realize is the downside is I didn't know anything. The upside is I didn't have any bad habits either in terms of how. So I really learned from my brother directly and I started to define how we want to invest versus how maybe you learned it at Goldman or a mutual fund or something like that. And so I kind of relearned from first principles, Philippe, and I kind of working hand in hand and I started learning semis. And pretty soon, if you started in semis, at the time, all the roads led to one company and that was in Cupertino and it was Apple. Why? Well, because the ipod was starting to really gain traction. And an ipod was a semiconductor product. It had nand, Flash, it had a processor. So that was the gateway into Apple, which eventually led to the iPhone. And it was kind of an iconic investment for us. And we got to know that management team really well.
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A
Yeah, I wanted to ask you about that. How much of your investing philosophy at the time was quantitative Pulling metrics, building models versus doing expert calls, talking to management teams, listening to earnings calls.
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B
The model was a really sacred place for me. And because I hadn't trained as an analyst, I felt I needed to build every model myself because I didn't trust myself with someone else's work because I wasn't good enough. So I'm like, well, I'm going to build every single cell myself. That's going to mean I'm going to understand it if I built it myself versus if I take someone else's complex model, I'm not going to understand. I'm going to rip a DC syndrome number 10 for the day. So let's go. I knew when you had coffee, by the way, after dinner last night, I
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A
was like, okay, get ready for the caffeine.
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B
Yeah. So I felt like I had to rebuild every model myself because I couldn't understand someone else's model.
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A
Sure.
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B
And what happened in the process of building that model is as I would go through a sell side model and there were lines that I thought were irrelevant. I said, well, why should I add that to my model? Like, this is driving no value. But it was kind of a verboten thing at the time. It was like, well, hold on, the company reports it this way or this is a revenue line. And for the sake of accuracy, you kind of need it in there. But I didn't know enough to basically say, well, to me, it doesn't drive any value to my investment thesis. So I'm just going to lump it into this other bucket called other, and I'm going to rename things the way I understand them, not the way the company chooses to report it for. Yeah, basically just what made sense to me and what my thesis was about. So then when I pitched my thesis, my model actually reflected what my thesis was not let me pitch you my thesis. But now I have a thousand line model, and I have to go from row two to row seven to the other tab, then back to line 250 to kind of explain it to you. That made no sense to me. The narrative was, no, let me show you from the top line all the way to the bottom how it flows and what the key functions are.
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A
Yeah, what's. What's that?
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B
Started developing, by the way.
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C
Yeah. Doesn't that kind of still define, like a partner meeting? Like, aren't you guys, like, let's say you meet an entrepreneur, you're excited about them, you do the work, and then from what I've heard, you guys will spend like, hours and hours and hours still just in the model, like, ignoring, ignoring.
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B
And we will. But a lot of times you might be getting a model from the sell side.
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A
Sure.
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B
Right. Because it's faster and it's more convenient. But it might not be exactly how your thesis is being laid out. So one of the things I try and talk to all of our analysts and say, well, let's have a model that really reflects our simple view of what the thesis is and what the drivers are. So I think a model to me is kind of a sacred place. And in fact, our Apple model, which I then passed on to Jamin Rangwaller, who's now our cio, was kind of this sacred. I didn't let anyone edit a cell on that model. I knew every single cell. I knew the color. I wanted a very specific kind of color for the background of certain cells. And eventually that kind of got passed on. But to me, at the end of the day, I learned this actually back in Hollywood, we had all the trainees one day got brought into a meeting with Steven Spielberg. And Stephen said, every great story can be pitched in three sentences, no matter what the story was. And he said, so pitch me a story or a movie and I'll pitch it to you. And no matter how complex the movie was, he understood the essence of it. And in three sentences, you got the whole movie. And what I realized is it takes a true understanding of story to be able to crystallize it in three sentences. Right. If you don't understand something, you'll say, okay, well, TVPN is a podcast and it's about these two guys and they do this well, do you really understand what it's about? Because you just gave me 10 minutes of rambling stuff. And all the great investors that I've met, like Stan Druckenmiller or my brother Philippe or Dan Loeb or some of these kind of legends of the hedge fund world, they have an ability to take any kind of story and just drill it down into its essence to what the key pivot points are that are going to make or break that stock at that particular moment. And so we really try and say our thesis should be simple, we should be able to explain them very in few sentences. Right. And you should have a model that reflects that thesis. So on the public side in particular, then we dive into, okay, let's go into your model and let's say, okay, Can Apple sell 50 million phones? Can the ARPU be in the out year? Is it going to increase? Is it going to decrease? When I look back at our old Apple model, I actually think we did a pretty good job on units. Where we were way off is we had the price of the phone declining 5 to 10% a year because that's what every consumer electronics product did.
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A
TVs.
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B
And in fact, the ARPU doubled, right?
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A
Yeah.
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B
The first iPhone was like 500, 600, right. Unsubsidized. And 1200 easel.
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A
Right? Yeah.
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B
So never would have kind of forecast that.
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A
Interesting.
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B
So, yeah, so models are quite important to us.
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A
What was the, what was the mood in the hedge fund industry broadly during that time around? Different strategic expansion opportunities? There's obviously a high frequency trading boom that's happening. There's more quantitative strategies, there's debt strategies. There's so many. Hedge fund can mean so many things. How are you thinking about defining what you would do best and where you would expand to or decline to expand to?
- [24:03]
B
Look, I think for tech it was an amazing environment to be in because almost every company was getting swallowed up. So if you were in tmt, you really felt like you were the center of the universe.
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A
So there wasn't much pull for distractions.
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B
Correct, that makes sense. And then also we had companies going public pretty early on, so you could do a lot of differentiated work in companies that the market cap was a couple billion. Yeah, right. I think what really changed for us in the early 2000 and tens was METTA, then Facebook and Alibaba staying private for longer. Yeah, we just never seen companies of that scale who were that important to our research in our market not go public.
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A
It was Metta or Facebook went out at like 60 billion, I believe something.
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B
Yeah, I think around there. Right. And right around 2012.
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A
So if you're used to buying a company, potentially 2 or 3 or 6 billion, that's a big miss.
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B
It's like a 10x difference. But not only that, we actually were an investor in Google from pretty shortly after the ipo. And Google had this stretch post financial crisis where it kind of traded sideways for a long time. And the reason was here comes Meta or Facebook and they're going to replicate a private Internet that Google won't be able to search. And so Google's going to be under pressure and we weren't able to talk to Meta, so we didn't know what they were thinking. As soon as the company went public, ironically, Google stocks started working because they didn't come out and tell you we want to kill Google or replicating the Internet. We're doing kind of something different. Both stocks ended up working. So that was kind of a big eye opener to us. It felt both offensively minded and defensively minded.
- [25:48]
A
How could you tell at that moment? Obviously it was correct that companies would stay private longer. But. But I'm sure you were debating the question, is Meta the outlier? Are they the exception that will eventually prove the rule versus there's a structural shift in private equity venture capital that will propel many more companies to stay private well into the tens of billions of dollars in market cap.
- [26:11]
B
We would not have foreseen what ended up happening. We had an instinct that it might happen. And remember that Spotify is kind of getting built at the same time. Right. And it's redefining music. And we really, you know, as I mentioned, Apple was a core thesis for us. And now we'd here come subscription music, which goes directly against Apple's model is selling you an album. And so what's going on? They just did around at 3 billion. Felt like a lot. And then Uber. Yeah, right. So it was. It was just kind of. You felt an Airbnb. Right. So I would say like those two companies, the mobile Internet coming out, these companies getting big in. In private markets, it felt like undeniable momentum. Right. And so. And China, by the way. Right, the same. So we felt like we had to participate.
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A
So what was the first private investment you made?
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B
Evernote. Evernote I think was one of the first. And we said, look, we're going to do later stage deals, 100 million plus in revenue. So we did deals like Evernote and Box. And ironically, our most successful deal is one that broke all of the rules that I just laid out, which was Snapchat.
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A
Okay.
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B
Where I think we led the Series C in that at a valuation of about a billion and a half.
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C
What was the reaction from other more traditional venture investors when you guys started leading around?
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B
I think that, look, venture felt very different back then. There was fewer firms, there was more atrophied thinking. Right. Andreessen is just kind of starting. We actually shared a building with them, so we were kind of starting at the same time about as they were. And it felt like, wow, we're going to bring a bit of a different competitive energy to this market. It felt very clubby. You hadn't seen like these new firms, like founders, that obviously, you know, and a bunch of others kind of really make their mark.
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A
Yeah.
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B
So it was sharp elbowed for sure and still is in many respects, which is probably what I like the least about that market because I love talking about ideas. I love trying to be a positive sum thinker, which the public.
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C
Easier to do in the public market.
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B
Correct.
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C
Yeah.
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A
Did you bring the models to private markets? Were you building financial models or the team?
- [28:36]
B
We did. I think we brought that. We brought analytical thinking, brought kind of deep research. Like I remember reverse pitching Aaron Levy at Box, a big deck that we had done. And we had just done what we thought was kind of public market like research. But we brought that to a private entrepreneur and he hadn't seen that kind of work before. So that was a differentiator for a minute until other firms realized, wait, we can do that or even better, we can outsource it to Bain. Yeah, right. And so we had to kind of quickly kind of adapt to that. But in the beginning it was novel and that was an industry that was really done in word and we were Excel thinkers.
- [29:16]
A
Yep, yep.
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B
So that was kind of a very different kind of mindset that we were bringing to the table.
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A
Was there any shift required in the messaging to LPs, the fund structuring, anything that you had to work through in order to actually set up the fund for success in the private markets.
- [29:37]
B
I think our LPs, first of all, LPs are not talked a lot about in venture, which is kind of interesting. Like we talk a lot about the founder, we talk a lot about the companies. But I would say for us we are pretty clear that our customer at the end of the day is our LPs. And so the trust that they give us means a lot. I virtually no outside investments. Right. I do some as favors and things like that, but almost everything I have and own is in our funds. So we kind of act as entrepreneurs and as owners ourselves and we ask for trust from our LPs. And I think at the end of the day they were willing to give us a chance in our first fund. I don't think they held us specifically to exactly what we said we were going to do. But they're like, these guys are pretty disciplined and they're pretty smart and they're entrepreneurial and aggressive and let's see what they can do. Right. And so I think over the years what's helped us most in our business and I think why we're still in business 25 almost years or 27 plus years later, when a lot of our peers have disappeared over time, is we never lose sight of our investors. And hopefully we've made some good decisions, but we've also made some bad ones. But I think our investors learn more about us on how we deal with our bad decisions. Right. And so I think we've earned hopefully some trust from them over the years. So I remember distinctly an investor when I called about the Snap deal and saying, look, I know this is a bit off brand, this was one of the largest investors in the fund, but I just have a lot of conviction in this deal. And he said, then do it. That's what ultimately why we're investing in you. And so I think the trust that you build, the relationships that you build with your own investors over those periods of time are really important.
- [31:34]
A
So relationships with entrepreneurs, building models for Apple, arpu, projecting units, that feels all very micro. How have you processed macro statistics and factors like interest rates? Everyone talks about when interest rates rise, all the DCFS change. There's a pullback in the private markets. We, we lived through this with like the end of zirp. But how much are you tracking? The labor market, the GDP numbers, the interest rates? And how much of a factor is that on the strategy? Day to day, month to month, year to year, or even like broader terms.
- [32:13]
B
Yeah. So data science has really become a much larger part of our business than it was back then.
- [32:18]
A
Sure.
- [32:19]
B
So we now have, I don't know, maybe 20 or 20ish, 20 to 30 people, something like that in data science that are just processing different types of data and alternative data. So think it's not Just macro data, it's app store data, it's clickstream data, it's credit card data. So we use a lot of that for our investment research and some of that we even make accessible to our portfolio companies. Right. So that made us smart about a trend. We were early customers of databricks and Snowflake as an example that let us invest in those companies. So that is way more of a presence in today's world than it was, you know, 20 years ago.
- [33:00]
A
Yeah.
- [33:01]
B
So we're constantly looking at Data as an example. You know, I think OpenAI is probably the most important company in the world today in the sense that it's the driver of AI, both consumption and spending. So I look almost every day at the chart of ChatGPT users, download, share, how it's weathering the storm versus other competitors. That's really something that wasn't available 10, 15 years ago with this amazing data set called Onavo, which actually gave you engagement data from users on phone. And then Zuck bought it and turned it off. And I remember thinking like, damn, that guy. He. That's one great move. It was the only data set that really gave you engagement data. So we're always looking for kind of new data sets. Right. And then obviously. So that felt like a major shift. Right. Going from data science enabled research and now obviously we're getting to AI and agentic research.
- [34:05]
A
How are you thinking about AI as a category from an investor perspective versus the data bricks and snowflakes, which to me feel it's easier for me to maybe understand the financials, the model that I would build, how I think about value accrual and competition in databricks and Snowflake fantastic businesses. But feels like easier to pattern match against previous eras of software and tech innovation versus AI where you have infrastructure and capex and training costs and inference budgets and all sorts of different. Your entire product's getting copied by open source every three months and it just feels like a different puzzle to solve when you're thinking about underwriting those businesses. Like how have you grappled with that? Do you see it as an extension of the tech investing or is it an entirely new motion?
- [35:00]
B
Well, for me it was almost coming back home to what I knew because the infrastructure layer was really semiconductor driven.
- [35:06]
A
Sure.
- [35:06]
B
Right. So I think our knowledge of semis and our team's knowledge of semis was a great head start because a lot of people just hadn't done semis. So we're kind of new to semis and I had a lot of relationships in the industry. And that led us to leave the series being Cerebras. Then I think we'll be kind of a generational kind of company.
- [35:28]
A
Yeah, he's been on multiple times.
- [35:30]
B
Yeah, great. So that felt very natural to us and pretty quickly when we saw what Jensen was building and the momentum that Nvidia was building in the data center. So that was kind of our first telltale that, wow, something big is going on here. So we had seen semis before in the mobile era, so we felt very equipped when AI first came around to look at it from a semiconductor GPU's memory. TSMC. You know, I've personally been to Taiwan many times, visited tsmc. Great anecdote. I'm driving back to Taipei City with my host from TSMC and we're on the highway and there's a golf course and it's nighttime, so there's lights and people playing. And I just very innocently turned to him and said, wow, you guys play golf at night here? And he very innocently looked back at me and said, well, when do you play? And that's when I realized like that's we're in a different level of work here. Yeah. So we saw it at the infrastructure layer first. Right. Where it just became obvious you didn't have to necessarily worry about who was going to win. Like the whole infrastructure layer will win.
- [36:39]
A
Yeah.
- [36:40]
B
So I think that was kind of layer one, I think layer two. Then came kind of the models and obviously we're investors in a number of them. I'd say the most complex element of AI today is you can almost talk yourself into a bull case and a bear case for almost any name in tech. And I think software is kind of seeing that right now. Right. So is software going to win because of AI or get displaced? Right. So you've got that in infrastructure, you've got the, well, when is the peak and what multiple peak should things kind of trade at? Right. So it's both an exhilarating, dynamic, but also very complicated environment in the sense that like for example, when, when databricks came around, no one thought, well, gee, databricks is going to put Salesforce out of business. It just felt like a new architecture. There's something about AI that feels a lot more disruptive. And what if your model not today, but in two years can just build you a workday right off the bat? What does that mean for workday? Does that mean their data is more valuable on the left hand side or no, does that mean they get fully wiped out yeah. So I think that battle and it's being played out kind of in the public market today. Right. You kind of see it in these names is how can a public company
- [38:06]
A
CEO actually communicate a vision for that case, the bull and bear case around AI, what effect AI will have on their company?
- [38:18]
B
I mean, you're seeing it. I read something that we're kind of moving into a selection market. Right. So now some companies are going to do well, some companies are not. I mean, look at square, right. Jack came out and just said, no, I'm pivoting the entire infrastructure of this company for an AI era. We think you're going to need to be remote. Right. Because you're going to move faster if you're remote. And so he's kind of laid out a whole vision about how he wants
- [38:50]
C
to run because you're sort of generating the necessary context because you're not getting the in person interaction. So you'll.
- [38:56]
B
And small teams.
- [38:57]
C
Yeah, small teams. You'll more. More easily be able to be AI native if you're basically explaining process.
- [39:03]
B
Yeah, very small teams moving really quickly without a central organization or you know, kind of like the Borg, right? No central organization, organizing force. The model drives everything. And then you have other companies that are saying no product. Well, the labs themselves. Right. Are all in person and they believe. Right. That you product development needs to be done kind of in person. So you're kind of seeing a lot of these different ways of. You have different models. Some people are going to charge for tokens, some people are going to charge for data access or ingress and egress. So I think what we're seeing right now play out is a true Darwin, like survival of the fittest where software companies are like the mail room. Exactly. Right. You saw Aneel come back to workday. Right. He probably thought that. I think Carl is an amazing executive and is a good friend of mine, but maybe he thought in order to make the changes, I need to have that kind of founder mindset. Right. Founder mode, as Brian calls it. Right. So we're seeing now a lot of these different approaches kind of compete with each other. I think it's too early to tell who's going to win, but eventually I think we should see kind of separation between winners and losers. Right. Which should be good for our business. But I think right now it's still so early that it's not clear who will win.
- [40:28]
A
Is it enough to look at reaccelerating top lines? I imagine we've talked to a lot of founder CEOs maybe their unicorn status, decade in, completely reinvigorated by AI. They come on, they see that the growth has returned. It feels like a new startup. Even though they're maybe coming back from a sabbatical, maybe they're coming back in after hiring an outside CEO. Maybe they're just coming back in with a new vigor. But what are you seeing more at the earlier stage or mid market stage around companies that are starting to show signs of being winners in the AI age?
- [41:06]
B
I think. Look, the good news is no one is head in the sand about this. So I do think in prior cycles you had more of a head in the sand mentality. Right. So for example, if I remember when cloud got started there was. Do you remember virtual cloud? Right. That was going to be the big thing. Right. I can't just have my stuff in Amazon, I'm going to have this virtual cloud and obviously that hybrid cloud is another one. No, it's going to be half big data. All those things basically just got torched by the wayside. So there was a lot of head in the sand. Similarly with the iPhone, you need a keyboard, it doesn't have 3G, it doesn't
- [41:45]
C
support Adobe, doesn't have copy and paste.
- [41:48]
B
Exactly. The battery life. Right. Ben Gate, you may remember that one. That was a whole weekend, whole weekend wasted on Ben Gate. So there was a lot more to me, head in the sand in prior investments cycles, in prior tech themes where people were just pushing back against the idea that this was going to work.
- [42:10]
A
Yeah.
- [42:11]
B
I think AI is one where the consensus view is it is going to work. It's not, it's an extension, extension level event. And so the sense of urgency is high. So that does feel a little bit different to me than maybe prior cycles where I think that took time for. Right. The carers are like, well, I'm not going to allow Apple to have an app store and I don't want to be a dump pipe and. Right. So they all these things that they fought.
- [42:36]
A
Yeah.
- [42:37]
B
And over time tech won.
- [42:39]
A
Yeah.
- [42:40]
B
The difference to me with this specific cycle is everybody agrees it's going to happen and it's happening quicker and the stakes are higher than any other.
- [42:51]
A
Yeah.
- [42:52]
B
So I think every board is ultra motivated, every founder is focused on this now. They're bringing different approaches and you know, we'll see which ones win out. But I would say they're tracking token consumption. Right. So how much of my revenue is token based? Right. How much of my cogs is token based? How much of my gna is token based. How much of my spend per developer on Cursor, OpenAI and Anthropic is happening? Right. So that in, you know, I do sit on a bunch of boards as an observer most. Most of the time, so I kind of see a lot of that kind of happening. So the awareness is absolutely there. People are doing different approaches. There's some that are, no, I'm going to white box a totally new product where I think I'm uniquely positioned to build it. And then there's others where like, no, my data set is so valuable. I'm not going to allow my customers to build apps directly using my data. So you're seeing a lot of kind of different approaches, but everyone's awareness is at a 12 out of 10. So there's no convincing needed. Right. Everybody's aligned, Every board member is aligned, every investor. And now it's about, okay, what does that sense of urgency mean for this company? What are the things that we need to track and the things that we really need to go and execute on?
- [44:12]
A
What does it take to make it as a new hire at CO2?
- [44:16]
B
So we do. And I'm assuming you mean on the investment staff. Right. We do case studies. Very important part of the process for me. We usually pick a public name. Right. Because we want to test your thinking. My favorite types of names are names where there's a good bull case and a rare case, and whichever one the prospective analyst argues, I will vehemently argue the opposite. Just to see how they think. Exactly. Understand their thinking. So that's really, really important.
- [44:50]
A
Are you trying to pick obscure names or household names? Everything?
- [44:54]
B
No, I mean, for a long time we used Netflix. Okay, yeah, Right. As an example. That was a really controversial stock, both in the DVD era and when they moved into streaming, then when they moved into proprietary content, and it was a heavily shorted stock over that period of time. So there was a lot of interesting ways to look at that name. Right. And you could ask interesting questions like, well, if they increase, you know, price by a dollar, what happens to eps? And what you realize is it was almost all profit, so EPS went up a lot. So we're just really trying to test thinking.
- [45:29]
A
Have you ever had to revisit a candidate who made a really great bull case or bear case that the firm maybe didn't agree with, and then they came back with an I told you so five years later?
- [45:41]
B
You know, I've never had someone kind of email me that, which is surprising because we've done a lot of case
- [45:47]
A
studies, you've done a lot of case studies. I can imagine there's a couple I told you so where it was like, I called dominoes or whatever.
- [45:54]
B
And. And honestly, to me, it's not about whether they say the right. Yeah. That sometimes people. It has to be. If I say bull, a bear, it's the. Yeah, doesn't really matter. It's like, how did you articulate your thinking? Did you lay out a clear model? A lot of kind of where. Where we were starting. Right. And so that's my favorite part of this job, is thinking through a name and the opportunity and what could happen. And it's kind of the intellectual backbone of what we do. You know, I would say that I think the key to our platform is number one, like, seeking big themes and big ideas. That's a big one. And then the kind of the risk management piece. Right. That's kind of kept us in business for a long period of time.
- [46:37]
A
How is AI changing the role of early analysts or career or analysts who are earlier in their career on the investment inside?
- [46:46]
B
Too early to tell. Too early, obviously. Look, we use AI every single day.
- [46:51]
A
Yeah,
- [46:53]
B
I use it a lot to test my thinking, to clarify my thinking. I've always had a weird dichotomy, personally, where I love reading, but I'm a terrible writer. And one of the things I like about AI and ChatGPT specifically is it's helped me actually write in a way that I can be proud of. Not just sometimes I write, you know, I'll write something, an email to somebody. I'm like, this is just so badly written, you know, and it's just, I don't know how to make it better. I know it's not good. I don't know how to improve it. And it's so frustrating because I know what great writing is from my reading, but I just can't do it. And at least, yeah, I've had a
- [47:35]
C
family member send me some. Something that was very obviously obvious to me, AI generated. And I think people have an aversion to AI generated text.
- [47:46]
B
I totally don't get that.
- [47:47]
C
But. But the thing is, like, I was reading through it and I was like, this is very cool because I know this person would not have been able to articulate their thoughts in this way, but they went line by line, and I know they. They mean it. Right? And so they were able to communicate something that they never would have been able to communicate with text. Maybe if we sat down and spent, you know, a couple hours talking through it, I would have been able to get the gist.
- [48:11]
B
Exactly. But some people take out the EM dash because they. Right. Oh, I don't want it. I'm like, what do I care? Like I don't leave it in. Yes. A lot of emails that I write are helped by chat.
- [48:22]
C
Like the Arnold Schwarzenegger line, you know, he's, he's. I smoke my stogies.
- [48:27]
A
Oh yeah.
- [48:28]
C
Everywhere.
- [48:29]
B
Why judge me on what I said?
- [48:32]
A
Yeah.
- [48:32]
B
My idea and whether it's well written.
- [48:35]
A
Yeah.
- [48:36]
B
Who cares whether it's was written by AI or not that. I totally don't get that.
- [48:41]
A
Yeah. Or polished.
- [48:42]
B
In fact, I hope that more people are able to communicate things that maybe they couldn't before. Yeah, right. Because they didn't know how or they only knew granular things, you know. And now more people can write, more people can communicate, more people can express themselves. To me, that's an incredibly empowering. Right. Vision.
- [49:05]
C
Talk about how you guys have approached investing in multiple companies in the same category or the same general category. How has that evolved over time? Were you mocked early for doing that from maybe some of the more traditional funds? And then how have you managed to make it work in practice and maintain the trust of entrepreneurs?
- [49:31]
B
I think conflicts, which is kind of what you're has definitely changed a lot in the Valley as companies have stayed private longer. And I think we have to be kind of precise by what we mean by conflict. So as an example, funding two series A companies at the same time that are pursuing the same opportunity is an obvious conflict that I think no firm, including us would ever do. So let's just kind of be very clear on that. I think it's quite different when now you're talking about these very late stage companies that are kind of competing with each other. But look, every company is kind of competing, cooperating. Apple and Google are great examples. They compete, but they're partners. So I think the distinction and the conflicts distinction has to be kind of changes as companies and markets kind of mature. Right. So I think you're seeing that become much less of an issue in mid to later stages, even by firms that would typically viewed conflict as core to what they do. Like traditional venture firms. Right. Have not kind of moved in that direction. And I think it's just the nature of the market. So that's kind of would be my first point. I think the second point is the execution of it I think really matters. If I view a perceived conflict between companies, even if they're later stage. I will always let the founders know directly. I'm not asking for Their permission. I think you also have to be clear with the founder because what if they say no and you still want to do it? Then you're in trouble. Now, you've just broken your word. And I won't do that. But I will inform them. I'll be very direct. I won't let them hear about it from somebody else or something like that. And I'll explain kind of the rationale, right? So I think communicating directly, both good news and bad news, that is something that I learned as an agent. I'm not afraid to have difficult conversations because I think we can grow from them. And I ask the same of founders or employees that I work with to both come to me and say, if you have an issue, let's just kind of talk about it. And look, I've hired a lot of people, I've fired a lot of people over the years. I've asked a lot of people to go and look for different career paths. So I'm comfortable having those conversations. So, you know, that's not something that, you know. I think your reputation and trust then is kind of the second point on the execution, right. Of we take information security incredibly strongly. That's when we're SEC registered. And even before coming here, I got a four page memo from my lawyer about you can talk about this, but not that and SEC this. And so of course I have to read it.
- [52:22]
A
You can tell the Ralph Lauren story.
- [52:24]
B
Yes, by the way, the way. Well, so side note, I believe that meetings should be recorded as an example. Now my compliance will say, shit, we can't have meetings be recorded because it creates a paper trail.
- [52:41]
A
Insane discovery that can take.
- [52:43]
B
And let's just not even talk Code two. Specifically, let's just talk. An enterprise.
- [52:46]
A
Yeah, yeah.
- [52:47]
B
But then I say, okay, let me pause it. Enterprise. CIO says, no, I can't have my meetings recorded. I'm too afraid. I'm like, okay, let me posit two scenarios to you. Okay? Scenario one is in each, you have a bad actor that's doing bad things, right? Whatever that is, you know, like belligerent, talking down to people, whatever. So scenario one, nothing gets recorded, you know, nothing. And 10 years later, someone comes out of the woodwork and says, by the way, X, Y&Z. 10 year pattern of deception, nothing happened. Okay, so that's scenario one, right. Scenario two is every meeting is recorded. The first time said person does something that's not right. The compliance system, which is always listening, sends that person an email and says, hey, by the way, better you didn't do this, talk to that person that way, disclose this piece of information, depending on the severity, second time person does it again, says, hey, I now have to flag this to ir, to hr. I would much rather live in world number two. Right. Because you know what the problem is? There's a system, there's flags that have been raised and eventually, you know, someone kind of gets involved and you either remediate or you terminate the person or whatever. Right?
- [54:07]
A
Yep. So to me, the back and forth that he said, she said all of that.
- [54:12]
B
Yeah, exactly. And who knows, maybe that person, if they had gone that first warning, might have realized, oh, wait, yeah, you're right, I'm being abusive or whatever the case may be of whatever they were, they were violating. Right. So I think to me that's a better world. Right. Than kind of the ignorance of while getting no feedback and then you just learn much later that kind of. You had a problem.
- [54:37]
A
Yeah. I mean, it certainly seems like a trend. I mean, Bridgewater's written about it a lot, Ray Dalio, but then also.
- [54:41]
B
And he did that with no analytics. Right. So that's different, but correct. I think to me what will happen is the analytics are gonna get so much better.
- [54:50]
A
Interesting.
- [54:51]
B
Right. And these systems are gonna know and they're gonna be able to look at your WhatsApp and your messages and your emails and all of your calls and they're gonna be able to just say, hey, by the way, just don't say this. Or did you think about this? Or maybe you could have. They're gonna coach you, they're gonna say, hey, you told this customer to, to fuck off. Be like, well, maybe you shouldn't do that. And here, here's like two other ways you might have mentioned, you know, like whatever this situation is.
- [55:18]
A
Yeah. Your frustration or the situation. Yeah. No, very interesting.
- [55:23]
C
Instead of the token maxing dashboard, you're going to have the social credit score.
- [55:28]
A
Yeah.
- [55:29]
C
The bottom 20%. No, no. There's a lot of environments that it can make.
- [55:33]
A
Yeah. Maybe there's a divide on like the in person versus remote work crowd.
- [55:37]
B
Like. Well, let me be clear. I'm talking about work context.
- [55:40]
C
Yeah.
- [55:40]
B
I'm not talking about after work.
- [55:42]
C
Yeah.
- [55:43]
A
I just wonder if, if that would become something that employees select into or out of for various reasons. Just like some people are huge fans of remote work, some people can't stand it. And there's a variety of.
- [55:56]
B
By the way, I also think there's a clear distinction between transcription and recording.
- [56:00]
A
Okay.
- [56:00]
B
Right. They don't necessarily go hand in hand to Me. Right. So I don't necessarily need a system that transcribes every word that was said and keeps it in some database somewhere. I'm not sure that's necessary, but key takeaways from the meeting. What was said, what was agreed upon, that's useful. That's a good corpus of data. To me, just because someone's recording doesn't necessarily mean that it's transcribed already. I like the option of deleting. In an ideal world, I would recommend. Well, you can delete the transcript. The transcript's not that relevant because maybe you batted an idea back and forth three times and you said something that turned out not to be true, but you figured that out later. So you don't need any of that. What you do need is what was said, what was agreed upon. Was anything done out of compliance or not. Right. So it doesn't mean. It doesn't imply a world where everything you say is recorded.
- [56:50]
A
No.
- [56:51]
B
Right.
- [56:51]
A
No. It's funny because there's so many opinions on this, but we record everything all day and livestream it on the Internet.
- [56:58]
C
What. How have you processed a number of these venture funds that have become publicly listed that are taking a lot of the different names that Code two is in trying to find basically the most in demand secondaries, putting them into funds, as I've watched, I think what I've seen is, yes, it's very obvious there's an incredible amount of demand from the public to invest in these names. But the big issue is that as soon as that demand floods in, supply and demand, price shoots up, and then you have a bunch of people investing at effectively 10 times what the private valuation or the underlying asset value. But how have you processed it? Do you think there's a more elegant solution over time?
- [57:46]
B
So we do have a fund called CTECH that I'm allowed to mention that addresses some of this and people can kind of go online and research more about it. What I would say generally is people want access to these companies. And I think there's a lot of arguments for going public. One of, I think the most powerful, in my opinion, is to democratize access to companies. Let's take an anthropic. Let's take an OpenAI and enabling the retail investor, enabling the Trump accounts, which I think is a marvelous idea that my friend Brad Gerstner really spearheaded. And I give him a lot of credit for this idea of, wow, why can't we have every single new child already have be invested in the market and participate in the value creation of these companies. So I love that democratized nature of it. So what I think it speaks to is there is incredible demand. Right. Let's say you were sitting. You're not a VC investor, you're maybe a dentist, and you're like seeing OpenAI and anthropic and you're like, wow, why am I not able to participate in that? Why is it just like an elite group of funds and accredited investors and so forth and so on? That, to me, will have to change. And I think it should be bipartisan, frankly. Right. And I think there'll need to be some guidelines and stuff put into place. And we don't want bad behavior and, you know, all that kind of stuff. But I think there is incredible demand from the retail investor base to participate in the value creation. And I think what we're learning as a society is the cost of not having broad participation is incredibly high.
- [59:49]
A
Completely agree.
- [59:50]
B
Right. And will be. Right. If you have a whole generation of young people that don't own their house and have student debt and don't feel like they're economically levered to OpenAI or anthropic or even more so, are directly threatened by their technologies, I don't think that's a great future for any of us. So I'm not saying that OpenAI or Anthropic going public is the solution, but it's like the one thing. Right. Obviously there's going to need to be a lot of things that are done, but I do think the transparency that comes with it, the democratized nature of it, will make a huge difference.
- [60:31]
A
Yeah. Makes a ton of sense. Jordan, anything else?
- [60:33]
C
Last question. What are.
- [60:37]
B
Can I make also one point on in person. I'm so glad I got to do this in person. I did not want to do this remote because the tactile feedback you pick up in person as an example for the viewers at home who have not been to this office, the open jar of creatine. Just what a full open jar, just for everybody to describe it. There's a goodie bag.
- [61:03]
A
Okay.
- [61:04]
B
And there's just a jar of creatinine. It's just wide open. The scoops right in there.
- [61:08]
C
It's so inviting.
- [61:09]
B
If you want to power up before you get on. It's just right there.
- [61:12]
A
So it helps if you're. If you're sleep deprived.
- [61:14]
C
Right.
- [61:14]
B
But that's just the kind of tactile feedback you.
- [61:17]
C
Yeah. You don't get that on.
- [61:17]
A
Zoom, zoom. No way. I'm really glad you can be here.
- [61:21]
C
Well, I'll save the next. I'll save the last question for your next appearance.
- [61:24]
A
Okay.
- [61:25]
C
Let's do it again soon.
- [61:26]
A
Well, thank you.
- [61:28]
C
Going way over.
- [61:28]
A
Yeah, yeah, yeah. We went way over.
- [61:30]
B
Delighted to be partners in OpenAI now with you guys. I'm really proud of your success, honestly. I love hustle and people that break into industries. And so congratulations.
- [61:41]
C
Yeah. We bring a very mailroom approach to podcasting.
- [61:44]
A
We do. Yeah. I can think about this. There's a lot of mail room here. A lot of suits here, too. Well, thank you for watching. Tune in tomorrow, 11am Pacific. Leave us five stars on Apple Podcasts and Spotify. Sign up for our newsletter@tvpn.com, and we will see you tomorrow.
- [61:57]
C
Cheers.
- [61:58]
A
Goodbye.
- [61:58]
B
See ya.
- [61:59]
C
We love you.
- [61:59]
A
Goodbye.