Inside Coatue: $70B Hedge Fund’s AI & Retail Strategy

27:06My view broadly, and this is my view, I'm not sure this is everyone's view at Code2, but my view broadly is that any job that exists in the US where you work at a computer at some point will likely can be automated, including my job. And so I think as that starts to play out, there's going to be a lot of revenue opportunities. One thing that we talked about before was positive, negative indicators on if companies are not hiring anymore, if they're doing layoffs, if AI is going to be automating more jobs. So how do you view job automation with picking companies and betting on them? There are two camps here.

27:52There's the camp that says AI will make workers 10x more efficient. And therefore, you probably actually want to hire more workers because, you know, industries are competitive, right? So if your workers are 10 times more efficient, you're going to hire more workers than your competitor because then you're going to be able to do more things. The other camp says, and this is the camp I'm in, says, you're probably going to get more efficiency, adding its orders of magnitude way higher than 10x. I mean, if you think about me, right, like my dream with AI is that instead of having, you know, a few analysts work for me, I have 20 agent analysts doing their same job.

28:40And those two guys that work for me also have 20 or 30 agents that are working around the clock. And so I want to kind of see that world happen. But I think in a lot of industries, what you're starting to see is people aren't... There are some extreme examples, but it's not that people are getting fired today or their jobs are being automated today. It's that the hiring is slowing. You've seen these charts of this kind of college grad software developer chart. And if you think about what's the first obvious use case in the market of an AI application people are using for work, it's software engineering.

29:22And so my view is like that is a little bit of the tell how this plays out. So hiring slows, headcount growth slows. For the stock market, that's good. Because if you think about, let's take the Magnificent Seven, right? Company like Amazon or company like Meta. If they stop growing headcount, these are companies that grew revenues 20 % for years. and headcount kind of grew in line with that. If they stop growing headcount and they just make it flat, the margins are going to increase, the profit, the earnings per share growth is going to accelerate, and the stock is going to go up a lot. So the market will view that positively.

30:00I think that's true across all industries. I think where it gets tricky and the big question that people are asking is, well, if you start, if jobs get replaced across these different industries, like, A, what are they going to do? Like, what new industries, do new industries emerge that, you know, they can work in? And there are debates around that. And then if not, like, you know, Amazon stock price might have gone up a lot, but who's going to buy the goods if, you know, there's unemployment? And so I think people are still trying to kind of figure out those debates. And I'm pretty optimistic that this will happen a little slower than some of the fear mongers think, but there will have to be new industries for a lot of people to work in or other ways of making money.

30:54And I think that's sort of one of the reasons, and you've seen, one of the reasons that it's important for the average American to be investing in the stock market now, right? Because AI is going to benefit all these companies and the stock market's going to go up. And we might be at the front of a multi-year amazing run in the stock market because these companies' revenues are going to grow faster. Their costs are not going to be as much as you would have thought. Your margins are going to go up and the market's going to go up. And so I think it's the most exciting time to be investing in public markets because of that reason.

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32:04I covered Klarna's IPO, and Sebastian was really clear about when they were doing their turnaround, reducing headcount, freezing hiring. Now they're just like waiting on attrition. And same with Open Door. Well, Open Door has to go through a lot more right now to resurrect themselves and do a turnaround. but they're counting on severely slashing headcount and hopefully deploying more AI agents within. And then I'm curious between all of this, how do you price? How do you set long-term and short-term price targets in this environment? Yeah, it's a great question. The last thing, just one thing on your previous point.

32:43Like, I think that the most exciting thing is like these companies are all going to be revolutionized with AI. It's not just forget the revenue growth and the actual workings of the company. The best companies we are seeing today that are going AI native are winning. AppLovin is a great example. Like the way Adam Farooge, I mean, he's notoriously known. I think they've got the highest EBITDA per head of any company in the world. And that he loves this statistic. And his view is, A, everyone at the company needs to figure out how to use AI, like now. And if you're not, you're fired. But I am setting up the company not for what the tech is today, but where the tech is going.

33:28Because it's changing so fast. I want to be ready in two years when the models are significantly better and the applications are significantly better and can automate these different parts of the role or the workforce. I want to make sure that my company is in a place to take advantage of that. And you're seeing other companies maybe less aggressively have this mantra. and those are the ones that are going to win. And this is something that like a big theme of Code 2 is we take a lot of the learnings that we see from the public and private companies and how they're implementing tech and we do it ourselves, right?

34:01So we were, you know, Philippe and Thomas were early investors in the, you know, in the cloud transition, right? And so Code 2 was like became cloud native. All of a sudden you had these best companies talking about, you know, how they were able to use all this data and get it into one place and do data science on it. And so we built out this data science platform like over the course of those years. And it's been amazing. And so kind of like thinking about how this tech will transform Code 2, like that gets accelerated massively in the AI world. And like my belief and something that I spend a lot of my time on is how do we use AI internally and how do we build a workforce and reimagine the workflows, especially in a space where like, you know, as you know, most financial service companies, whether it's a hedge fund or a bank or like the last guys to adopt tech.

34:50Right. And so we think that there's like huge moment where we're able to create kind of the investment fund of the future. And like, it's, it's happening now. Like my views that like, this is, I tell my friends this and they like laugh, but I think that today, 85 % of what I do, like basically can be done by AI. And it's not a question of, is the tech ready? It's how do we implement the tech? And so we're hiring a class of analysts to come in and help me with this problem and basically figure out how to reimagine the workflows that we do every day from I come in the morning, check my email to see all the different sell side notes.

35:29I spent two hours doing that because you have to read everything. But there's only a few important ones to how do I build a model when the click of a button? How do I take disparate data sets and bring it together? How do we do like every single step of the investment process? How can we use AI to almost automate it? But then if you can do that, those six new analysts we hire in three years are basically sector heads with 25 agents working around the clock. So it's this really exciting time. Are you worried for your job? No. So the nice part about the hedge fund industry is it's not that people intensive, right?

36:07So we don't need to cut people costs. There's just a huge prize for becoming exponentially... What we are trying to go and find ideas to invest in, right? And the constraint in ideas is the amount of time you have, the places to look. You only have so much time, right? I can only spend so much time looking and poking around different areas. But if I have 25 agents able to do all that working around the clock, Like I fundamentally believe we are going to be able to find better ideas faster. And even more importantly, like I don't think other firms are going to adopt this that fast and we're going to be light years ahead.

36:49And so the pitch I've been giving to the analysts that we're trying to hire is like, hey, we're going to teach you this investment process, but we're going to go and reimagine it together. How do we do this with AI? And in three years, you know, when you're an analyst, a full-time analyst, you're going to be exponentially more efficient, better at the job and significantly better than your competition because they're just going to start, you know, be picking up these things. So like, I think I timed it perfectly where I, I'm not going to be replaced by AI yet. I just want to like control it. You know, I want to be like the, yeah, the, the last, uh, maybe the last analyst.

37:26The AI captain. The AI captain. Yeah, exactly. Exactly. Exactly. On your point of pricing the stocks though. So it's really tricky because on one hand, the main focus of Code 2 is picking long-term winners. So investing on a multi-year horizon. And what that literally means is take Meta, for example. I have a model for Meta for what I believe they're going to do in revenue, in EBIT, in profit, in earnings, and free cash flow out to 2031 right now. So I'm projecting what they're going to do in the long term and what multiple, I think the business will get assigned to the business in that year and what that stock price is and what the return looks like.

38:18So you have your kind of like long-term view. And the other way we do it is we literally build like you maybe did in college, like a discounted cash flow analysis. So saying like, you know, Meta's market cap should be worth today, you know, the sum of the future free cash flow that's generated discounted back, right? But as you know, like stocks are moving all the time. So you have to have a long-term view of a business. You know, what's going to happen in the industry? Are they gaining share? How are the margins going to evolve over time? How's the company, the earnings profile going to evolve?

38:53But then you also better be damn sure you have a good idea of what's going to happen next quarter. And so what happened in the hedge fund industry is early on, when you think about like Julian Robertson and Philippe, my boss, was an analyst for Julian. He sort of invented this, we're going to do fundamental analysis and invest on a multi-year timeline and over time, we're going to be right. Well, then what happened was you had guys come in who said, we're going to be more short-term focused. We're going to focus on the quarters and really like, you know, and data played a big role in that, right?

39:26All of a sudden you could track credit card data. And early on, like no one had that credit card data. So that was an amazing strategy. And then the idea evolved further into like, we're going to have kind of a bunch of different managers who are hyper-focused on their sector. And within those sectors can pick winners and losers and really focus on the alpha piece. And then as a fund, we're going to control for all the other things, the factors and the shorts and long. We're going to make sure we're running market neutral and we're going to squeeze this alpha out. Well, now we're at a point where I think the winning strategy is, how do you invest in a, how do you have a really good idea of who's going to be a long-term winner and a long-term loser, but then marry that with a real focus on the short-term.

40:16And we spend a lot of time on the short-term. I do, because my view is that the long-term is simply a collection of quarters, right? And so you want to make sure that you have an understanding of how we go from here to here, but also what that path looks like, because it also creates great opportunities to buy a stock lower because Netflix is a good example. You knew what the end state for Netflix was going to be, but at every little hiccup, stock might be down 20%. And so trying to make sure in those moments, you're not massively sized before it goes down 20, because even if you're a long-term investor, let me tell you, that is going to be an ugly day in the office.

41:02but then knowing when these things have overcorrected and being able to size up in those moments when there's a hiccup. Because Code2 is concentrated in technology, how do you balance out these market cycles that is so favorable towards AI and what some would say is a bubble? That's something we think about every day. So broadly, we're investing on the alongside in tech. So, but even within that, like if you think about the NASDAQ, right? So the, the NASDAQ's up, I think maybe 17 % or something this year. And the AI trade has been like a winning trade, but within that, so even, so if you pick the right stocks, you know, within that, maybe you're slightly above the NASDAQ, but within that there's been kind of specific sectors within the net have moved very differently.

41:55So like AI infrastructure, right? Like the build out of AI, the data centers, the constellation energy, the nuclear, the power needed to power these GPUs, those stocks are up like 50. And then you would say, well, Microsoft's like probably an AI winner and Meta is probably an AI winner. Like those stocks are up like 25. And so even in a moment where the market is going up a lot because of excitement around tech, you need to make sure that your book is sized appropriately where you're capturing like the winners even within that, because that's how you kind of drive out performance. So that's sort of when the market's going up, that's how you think about it.

42:39But even this year, there've been crazy moments. And like, I was like, I remember like, I was basically so excited. I was like, we need to like take on more risk. And this is the 30-year-old me saying that. And one of Philippe's amazing qualities is that he is the best risk manager I've ever seen. He has this sense of when something is about to go wrong. It is incredible. And it's really been great for him. In different moments in these drawdowns, he's been able to, we call it cutting gross, but going from, let's say you're 100 % invested to 50 % invested. So you're sitting in 50 % cash very quickly and he gets the timing right.

43:27And then tariffs came. And when Trump came out and put the, you remember like that day where he's got the board with all the tariff prices, I remember like sitting there, you're like, oh God. And so I think one of Philippe's best qualities is he understands how to bet on these tech trends and he's really good at picking stocks, but his single best qualities is risk management. How do you get his buy-in on a new trade? What's the process to get through? I think there are a lot of people that can pick stocks, but what really matters at Code too is you have to be able to do the analysis. You have to be able to pick stocks, you know, that bet on longs that go up and find shorts that go down.

44:13But the key piece is how do you then convince Philippe and Thomas, his brother, and the rest of the group that you're right, ultimately to get that name in the book and then have it play out, right? There's a lot of people who I've seen come through Code 2. And it was true at Melvin too. I mean, this is true at any hedge fund where they're really smart. They're really good at picking stocks. They have great ideas, but they were never able to convince the person above them who's ultimately the decision maker to put that in the book. So this is a bit of an art. And I think the most important thing is, you spend 95 % of your time doing all this deep work and all this deep analysis.

45:04But can you take that thousand line Excel model and all the expert calls and all the nuances around margins and growth rates and sequential growth and all those things, and can you summarize it and simplify it into a three-sentence pitch that when he hears that pitch, he's almost ready to buy the stock before even opening the model because the pitch is so good. And that is a skill that I'm still developing. I mean, I think that Thomas, Fulke's brother is probably the best I've ever seen at this skill. He can take something incredibly complex and get the idea down to three sentences where you hear it and you're like, that's a great idea.

45:53And then you go into the model and you go into the details and show why that's happening. But I spent a lot of time thinking about how do I make a pitch very simple and get it in the book. Sorcery is proudly sponsored by Carta. Carta is transforming the private marketplace, connecting founders, investors, and limited partners through software purpose-built for private capital, trusted by more than 65 ,000 companies in over 160 countries. Carta's platform of software and services lays the groundwork so you can build, invest, and scale with confidence. Carta's fund administration platform supports over 9 ,000 funds and SPVs, representing nearly $185 billion in assets under management, with tools designed to enhance the strategic impact of fund CFOs.

46:44For more information, visit carta.com slash sorcery. That's C-A-R-T-A dot com slash S-O-U-R-C-E-R-Y. Talking about AI and how value is accruing, it's really interesting because there's so much, there's so much innovation happening on the private side, but it's not, you know, it's like open AI, it's anthropic, it's all of these like research labs, it's models, it's a lot of things on the private side that are impacting the public side. but I'm really curious because CO2 does both private and public. How does that inform your decisions? We'll start there. And then I want to ask about valuations.

47:24As I said before, when you're investing in tech, you want to be talking to the practitioners. So one of the best parts about CO2 is that we do both public and private investing. And we spend a lot of time with each other from a team perspective, but we also spend a lot of time, you know, I spend time talking to open AI and these various private companies to kind of get a lay of the land of what's going on. I think that we're at this point, at least in my eight, nine years doing the job, I've never seen a moment where the private companies are impacting the outlooks or said differently. I've never seen a moment where a few private companies are impacting so much public market cap in a way like today.

48:19And so I think just like having an understanding of really what's going on in both areas helps you A, be a better public investor, but B, be a better private investor. And like, there's also this idea where like, it used to be that you could be an investor in one specific sector, right? So you covered restaurants. And when you covered restaurants, the restaurant world wasn't changing that much, or maybe it was, but it was all kind of within that ecosystem, right? Today, you would need to have an understanding of the entire AI value chain to figure out what's going on and who are the winners and losers.

48:56Meaning, I need to understand how many GPUs NVIDIA is planning on selling next year and who they're going to sell them to. And I have an idea of what that looks like because those GPUs go into each of the cloud players' businesses. And we're at a point where your cloud revenues are 100 % dependent upon how many chips you get. So you have to have like an idea of like, okay, how many, and you've, you get a lot of insights from kind of seeing the entire ecosystem. Where do you think value is going to accrue between all those layers? Undoubtedly, there are going to be a lot of public winners or the public, like I think meta is going to be like meta is going to accrue and they already are today a lot of value.

49:37So, and there like are different offshoots. I think the biggest question right now is I like, I was talking to a friend of mine who does reinforcement learning in anthropic. And he kind of laid out this case study of what you're just asking of like, let's take a coding agent, right? Let's take cursor. So you have cursor. it is the most loved, most used coding agent. They figured out this one specific area and they're crushing. Well, then you have the labs. So you've got cursor here, then you have the labs. OpenAI has their own coding agent, but they also have a lot of other things, right? So they're doing coding and then they're going to do a lot of other different things.

50:19And then you've got Google, who basically is the labs, plus the cursor, plus their own cloud, plus their own little mini NVIDIA with their TPUs, plus a search business, plus data on everything. So who wins in this? And I don't think, I don't know the answer yet, but the funny thing is if I just gave you that case study, you'd be like, oh, well, Google's going to win because they have like that plus everything. Well, they're also the slowest and all the people love to use the thing on the far end of that spectrum, the cursor. So I think this is going to be true for a lot of things. Like is cursor and then the next iteration of that for all the different applications or agents that you'd want to use, are they the winners?

51:12Because they're so specialized and they're so, they've gathered this, you know, adoption among the workers. Are they going to be the winners? Or is it going to be open AI and kind of the middle? Or is Google going to be able to take the vast amount of data they have and their ability to do things cheaper? and they can also like, you know, their cloud business, they own it. They don't have to pay for like, or are they going to be the winner? And I think this is going to be the biggest debate over the course of the next few years. But I don't think we're at a point where you need to answer that debate.

51:42Fair. Like I think all three win for a while. I think it's really interesting just seeing how much there's a premium added to these companies, even on the earliest stages. So like Carta, they report series A companies that have AI enablement in their name, or if they're doing that. They get a 30 % premium. You're looking at OpenAI. They just raised a$500 billion secondary. It's insane. But I've had a porv from Altimeter on. He explained OpenAI's valuation. I also asked, I think I asked Alfred about this and maybe Elad, Gil, who's also coming on. And they all have different explanations for this, where it sounds actually more justifiable for open AI versus these younger companies.

52:32And it seems like you can actually see the compounding happen there and the reliability and the predictability of that revenue over the next couple of years versus like these smaller players. And even at the family office level, like we consider investments and we're like, okay, do we think like this, let's take chip. Do we think this new chip company has a chance of beating Nvidia or should we just like do some more Nvidia leaps? Like what should we do? And so it usually just comes down to like, oh, that's like less risky. Like, let's just do that. And like, let's just hedge that one. I don't really know what the question is on this.

53:06No, I mean, I think it's just like, like open AI, you can, we're investors in open AI. The open AI$500 billion around, like it makes sense to me. Like I get why there's a lot of, like, I get why people want to do that because, you know, if you're, when you're, it's, I always look at private investments from, you know, public markets backgrounds. So I have a lot of like analogies in the public markets, right? Opening eyes got what? 800 million weekly active users. It's going so crazy. Spending, like, spending by my estimates close to the amount of time every day that is spent on Instagram.

53:47like Facebook is a, I think like it's close to, it's like a$2 trillion, it's a$2 trillion company. OpenAI is, you know,$500 billion round. They've already got all these users. Like could OpenAI go from a$500 billion company to a$2 trillion company where Facebook is today? And by the way, I think that Facebook is going to become like, you know, much larger market cap. Like I think Facebook is going to be, you know, a three X in five years. So if the two goes, you know, six, what could the 500 go to? Like that makes sense because you've got users and engagement, you know, they're building moats real time.

54:25The more we're talking to chat GPT, the more information it has about us, the more the way, you know, it'll better serve us products and ads. And, you know, Sora, like, you know, was really fun. Like, I don't know, does it become a social company? Like, do they go and build a cloud? There's so many optionality plays with open AI that aren't in the model that you have, that the model like you have works. If they do that, it works. And by the way, we haven't added any of these additional opportunities. Plus the fact you have just such talent density there, and you've got a leader that is going out aggressively, acquiring compute and infrastructure and building the data real time.

55:06And they have this zeitgeist. That makes sense. I think where it gets much harder is investing in, and I say this with, I don't spend my time doing this. So this is just a view from the outside. But I think it gets much harder at investing behind this proliferation of new companies. It's one of the things with AI that's great. We track this. It's never been easier to start a company with AI. The fact that you can, a coding agent alone, two guys in a dorm down the street can build software in a way that they couldn't have built previously, you're seeing a proliferation of new companies, right? And the prize is so big in any of these markets.

55:48I mean, if you think about the TAM for AI, the easiest way I think about the TAM is there's$20 trillion in labor spend. uh software i think it's like a one trillion dollar so one trillion of the 20 is software like that 20 trillion is up for grabs so like the market opportunity is huge um but picking the winners and losers in that is like really difficult

56:16well yeah because also like you just every day you you know you invest in a new startup and then you're just waiting for OpenAI's new launch of like the N8N, right? You saw like, here's our version. And you're like, oh, well, okay. And there's so many examples of that. Yeah. What is it called? Sherlock? Sherlocking or something? Yeah. I think that was with another one. I know we covered this a little bit, but I want to touch upon it again. So Sorcery is sponsored by Brex. They're all about performance, spending smarter, moving faster. We love Brex. For you particularly, what are the metrics that you track within these different companies to determine their success?

56:57It kind of depends on the industry, but broadly, like, you know, we have this sort of, as I told you, like a five, six year view of these companies. But in the near term, what we're tracking is broadly inflections, right? In the digital ad business, that's inflection in growth rates. in the cloud business, inflection in growth rates, inflection in margins, where you have a quarter that is better than people think or worse than people think and helps prove out your thesis faster, right? Because if you think about like, if we have a five-year view of what a stock's going to be, like, ideally, we want the market to figure out that's where it's going as quickly as possible.

57:50And so you'll like, we say like IRRs get pulled forward, right? And so when you have a moment of inflection, that's where your IRR can get pulled forward and the stock reprices higher, kind of more toward your view. But like we're tracking everything. I'll give you an example. We're tracking everything from credit card data to email traffic to, I mean, my analyst sent me this today. Like we go through every Thursday, we sit down and we have KPI tracking using some real-time data set or a mixture of them for every single company we cover. Like even if I'm not looking at, even if we're not invested in the company, I look at that tracking every single week because that tells you something might be changing and that might be a source of a new idea or it kind of gives you, it gives you an understanding of like where we are in the broad economy.

58:44And so those are sort of like table stakes and basic, but you take all that together, you're looking at the ad market and e-commerce and payments, and you have an understanding of where you are in the economy. Are things getting faster, things slowing? Ads have been really great in the third quarter, but about a week ago, they started to slow. Is that consumer spending slowing or is that just a weird shoulder period in the time? But where I think the data science gets like really interesting is when you can take differentiated data sets and piece them together to get a unique view of something happening that other people can't see.

59:23So like a good example of this was one of the companies that we invest in is Reddit. We love Steve Huffman. We love the team. We think that Reddit is going to be a much bigger business over time, that it's going to be this great ad platform. And that really like in the AI era, there's really only one place where actual human-generated content exists. And the value of that content is super valuable, like really valuable because it helps train the models. If OpenAI wants to have a shopping assistant, right? All the reviews in the world are on Google. They're not on OpenAI on ChatGPT today. So where do they go?

59:59They have to go to Reddit. What are they willing to pay Reddit to be able to use that data to ultimately build the shopping assistant that's going to take over the market? The answer is like probably a lot. But there was this moment where, you know, search, as you know, is being like re-architected, right? Like you have AI overviews and now you have chat GPT and Reddit at the time was growing users. And then there was a little bit of a hiccup and the hiccup was related to AI overviews being showed. So if you think about like old world, you type in something in Google, Reddit was like one of the top links.

1:00:34Well, now you got an AI overview that is like taking up your screen. So Reddit's now down here and you're like, okay, they, they just like missed this metric. The market's freaking out because it's very easy to say, well, oh, they were only growing because of, you know, Google. And now AI overviews just took their entire slot. Like this thing will never grow again. Like that's how the public markets react. Like this will never grow again. So the stocks, you know, plummeting. But what we figured out was we were basically able to figure out that in the old world of Google, when you typed in a Google search, Reddit came up maybe, I'm just going to use fake numbers, but 10 % of the time.

1:01:14And within AI overviews, when they first started showing them, when AI overviews were like 5 % of search, they were showing up like 2 % of the time. You're like, that's not great. Well, then AI overviews became 50 % of the search in like two months. And within that though, it went from 2 % to 15%. So it's actually higher than in the old world, but because it went from zero to 50 and that was the disruption. The second we saw that, our takeaway was A, those problems are going to be fixed. and B, that's proof that Reddit's actually more valuable in an AI world because they're showing it more because consumers wanna see it.

1:01:59They like seeing the answers and the citations of Reddit. So that's where you have the confidence with that data science to say, I figured out the tech change and now we like to stock even more and this is how the user numbers are gonna be fine now and now the narrative as this gets out is going to be not their unclear AI winner loser. No, like they're going to be an AI winner camp. And that means your multiple goes higher. And the stock went up, you know, a lot when the market figured this out. Reddit is one that I find fascinating to watch because I didn't understand why anybody was paying them that much money for their data.

1:02:37Like it just didn't make any sense. Well, and the funny thing about that is that's actually changed. Like the thinking, even from Reddit, is changing a lot. What happened was OpenAI went out and basically trained on Reddit data, ChatGPT, right? And it sounds like they may have not asked for permission or done that. At that time, it was just you were going out and trying to build this model. And so what Reddit did, and Google did the same thing in all of this. And what Reddit did was basically say, okay, you guys, hey, we're not going to sue you, but you took our data. So just pay us a licensing fee.

1:03:13I think it's ballpark$50 million. for Google. So Google pays Reddit$50 million a year to kind of A, have trained in the past on it, but B, have updated data and chat CPT does the same thing. So the view was like, okay, Reddit's corpus of data is growing, but like the incremental conversations that are happening are pretty small in the comparison of the whole thing. So whatever that deal was in the beginning, It's not going to get better, right? That$50 million isn't going to go up. That was the view. I think that's what everyone thought, including the companies. But then as AI evolved, you started to realize that incremental data that happens is actually way more valuable because like the shopping assistant example, if you want to build a shopping assistant and Reddit is one of the primary sources on the internet where people are talking about products and what's good and what's bad.

1:04:13There were rumors that Zuckerberg goes and is hiring people for$100 million a year to build this model. So if he's willing to do that, what do you think Google or OpenAI is willing to pay Reddit for the key piece of data that may determine the success of the entire shopping egentic TAM? My guess is higher. I have one last question. This one is going to be really difficult. Are you ready? I'm ready. You're ready? There is some confusion around the name KOTU. I know Philippe and Thomas are French, but a previous partner I used to work for would call it KOTU. Oh yeah, that's wrong. That's just factually wrong.

1:04:57Sorry, Mark. Sorry to call you out too. Can you please explain to the class where the name KOTU comes from yes so i'm glad i know this one uh co2 is a beach in nantucket so it's a beach in nantucket um i believe philippe spent time there so yeah you know it's funny i in my entire time at co2 i've never like heard anyone talk about the beach but actually that that is that is not true We're redoing our office. We're basically building a second floor because the firm's expanding. We need more room. And so people are coming up with names of the new conference rooms. And Thomas's idea was to name them other beaches in Nantucket.

1:05:47Yeah. You kind of do need a beach and a hedge fund in Midtown. Why not? Yeah. Well, as you know, Midtown is like a stormy sea. And every day in the market feels like a stormy sea. So any, any, you know, beach would be, would be good. So. Okay. Well, it's a good way to end it. Yeah. Well, I appreciate you having me. Thank you so much. Of course. And hopefully you get a podcast studio in this new office. I think we might do that. Do it. If we do. And if we do anything, we're going to have you on. Thank you. I was going to just show up, but I appreciate the invitation. Of course. Thanks, Michael.

1:06:21Awesome. Thank you. Hey, it's Molly. If you enjoy our interviews, check out our newsletter, sorcery.bc, where we deliver a once a week top deals and tech headlines email and also go deeper on our podcast interviews. Subscribe to Sorcery today and don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up.