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

the best companies we are seeing today that are going AI native are winning. Before I worked at Code 2, I was working at Melvin Capital. Many of you guys probably heard of Melvin as the hedge fund that was short game stock. We went from probably the best performing hedge fund in the world to basically down 50% in two weeks. And the reason was we didn't realize how powerful retail could be when they focus all their energy on the single stock. The way to source ideas now and come up with new stocks to invest in, a lot of that is coming from the internet. If you go on Wall Street bets, people are posting real work there. And now there's just been this kind of proliferation information. It was a company called App Love. I had the CEO Adam Fruge coming to our office. I messaged at the time my boss and I said, "Hey, you have to get in here right now meet this guy." He's like, "I'm busy." And I'm like, "Trust me." The first use case of AI truly is driving these advertising businesses to grow faster than you would have felt. Any job that exists in the US where you work at a computer at some point can be automated, including my job. So I think as that starts to play out, there's going to be a lot of revenue opportunities. Michael, welcome to Sorcery. Thanks for having me. We have so much to cover today, but to start, let's talk about you. Who are you? What do you do? Well, I'm from Cincinnati, Ohio, and I live in New York now. I work at Co2, which is an asset manager with you both public and private. And my main focus is I work on the public equities. And now we have a retail product that we're also working on. Kerry has had a lot of fun with tech over the last year or so. One of the most fun podcasts we did in the last couple of weeks was with Keith Roboi. And this was when he just rejoined Open Door as board chair. The funny thing about that was specifically the cult sentiment behind it. How is the public market evolved? If you look back maybe six, seven years ago, the idea of retail investors was not a thing. What I love about the public market is that anyone can invest in it. So I would debate, it's actually kind of how I got started. I used to debate stocks with my grandfather. He worked in the plumbing industry, so he wasn't a professional stock picker, but he loved investing. So he would invest in companies that he thought were long-term compounders. He loved Warren Buffett and the idea of value investing. Well fast forward to a few years ago with companies like Robin Hood and retail trading. And just the internet broadly, more and more people have gotten into investing and the impacts on the market have been huge. And so before I worked at Co2, I was working at Melvin Capital. And so many of you guys probably heard of Melvin as the hedge fund that was short game stock. And so I lived through this period where we went from at the time probably the best performing hedge fund in the world from a return perspective, like single manager, long short equity, to basically down 50% in two weeks. And the reason was we were at the time betting against game stock and we didn't realize how powerful retail could be when they focus all their energy on the single stock. And so you've seen that same excitement with OpenDore. They've got a great team and there's been just a lot of excitement around what they could do in the stock. I was 700% or something on that excitement. So the market dynamics have very much evolved and it has created both new opportunities and new risks. On the wrist side, the idea of a game stop going up what it did because of the internet and Reddit and people getting excited was not a thing that existed up until that point. Like any point when people were short of stock, there were squeezes. But it was always catalyzed by something. Volkswagen Porsche was a potential acquisition. This was just a lot of guys and girls on the internet deciding like they were going to buy it. And it went up and people had to cover and it created completely changed investing and the risk that people think about. How did that change your role and like what kind of data and information you pull from? I think one of the best parts about the public markets is that because anyone can invest in it, ideas can come from anywhere, right? And so I think what you've seen over the last few years is the emergence of all these different channels of information. So if you think about like investing in the public markets 15 years ago, right? You would get quarterly earnings reports, eight case annual earnings reports, 10 case, and then you would management would speak. But that was kind of it. Like other than that, you're in the Wall Street Journal and the New York Times. And like that has completely evolved. And now a lot of people, including all the retail investors have opinions on stocks and are doing interesting analysis. And if you go on Wall Street bets, like people are posting real work there. People like you having amazing guests on the podcast offering interesting insights. And so it we are tracking today, like a lot of different data, right? So like, you know, we look at how often stocks are mentioned on Reddit. And we look at Twitter and we look at, you know, how things are trending on the internet all the time on Reddit, all these things. But we also like the way to source ideas now and come up with new stocks to invest in or new analyses to do is like a lot of that is coming from the internet now. And so that's sort of the world we live in. So in terms of co-tos fund, how big is the fund and what's your main portfolio that you cover? Yeah. So the co-tos as a whole is probably it's around 60 billion of assets under management. And the public equities. So we basically have public equities, which is around 25 billion. And then you've got a private business and then a credit business too. So I focus almost all my time on the public equities. The nice part about doing both is I also follow open AI and anthropic and and very in tune to what's going on in the private markets. A, because a lot of those are impacting the public stocks, especially today, but also because when our private team is looking at a private investment, there's a lot of times often interesting insights from the public markets. My knowledge of how digital ad works might impact some business or how they think about it. So but mainly I focus on TMT investing in the public markets trying to find stocks that are going to go up and then trying to find stocks that are going to go down to internet, trying to internet, cloud, and then we have a pretty tight knit team. So we all work together kind of a core group of us. Any particular names? I know Jack Griffin, thank you to Jack for the intro. But I know he mentioned that you found app loving for them? App loving. Yes. It's a pretty crazy story and it kind of like goes into how you find ideas. What ended up happening was there was a company called App Lovin. I think at the time it was like a $20 billion market company. And the name is amazing, right? Like App Lovin. It's like, it's almost like it's like a meme name to begin with. And this business was, they do mobile gaming ads, right? So whenever you're playing like Candy Crush or like pick the best way to describe is when you walk on an airplane and you see everyone looking and playing the solitaire or these various games, they are the guys that serve the ads in those games. And I never had heard of the company. I didn't know what they did. And a buddy of mine called me and was like, hey, you should take a look at this thing. It's pretty small, but something's happening here. It's starting to grow really fast. And so I had the CEO, Adam Fruge come to our office and I met him and I literally knew nothing about this company at that point. Besides they do mobile gaming. And I met this guy and I will never forget this moment. I messaged at the time my boss and I said, hey, you have to get in here right now and meet this guy. And he's like, you know, I'm busy. And I'm like, trust me. In five minutes of meeting, Adam, you knew that there was something really special here. I mean, this guy was the most locked in person I ever met. And so after I walked out that meeting, I was like, okay, we need to figure this out. And what ended up happening was a lot of what we were seeing in the digital ad market at the time was basically pure play happening with app love. And so the idea is like, like AI is this big thing, right? And one of the places we're seeing revenues actually happen are digital advertising companies. And what's happened is over the course of time, if you think about Facebook, right? Their goal is to serve you the right at the right time. And all of the AI learnings from L.O. Lams and everything that we've seen over the past couple of years is directly impacting their ability to serve those ads better. And so when I first joined Code 2, I remember one of the first things I had to do was explain why Facebook could probably grow 10% or more, right? Because they're going through this period where they, you know, with IDFA and kind of Apple, they lost their ability to track. And so there were questions around whether they could really grow above 10%. Well, fast forward two years, they're growing like, you know, mid to high 20s right now, right? Like an impossible thing to kind of imagine at the time. But what happened was the underlying ad engines got better with AI. And so the way Adam, and so you kind of knew that when you had met Adam. And the way he was talking about what they were doing and that basically they had used GPUs on their advertising business. And they were going from, you know, they were growing. I think 15% before and all of a sudden the ad business is growing 15, 50, 70. And the stock at the time was $20 billion market cap company. And I remember I was like, okay, so if you kind of believe this to be true and if you just listen to him and just believed what he was telling you and you put that in a model, one of the things we do is we make discounted cash flow analyses to try to see what a company's worth. You literally could not make the discount cash flow analysis in your worst case scenario be less than like a 3x. And it was the most like remarkable thing I've ever seen. And so then we got to know him better, developed a really close relationship with him. - Sorcery is brought to you by Brex, the financial stack trusted by more than 30,000 companies, including one in three venture back startups in the US. Nearly 40% of startups fail because they run out of cash. Brex is literally built to help founders avoid that. 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Well, I mean, I think broadly, we're at this point in the market right now where there's been a lot of committed spending to build out this AI infrastructure, right? You're seeing new announcements every day, open AI, doing a deal with Nvidia, or doing a deal with AMD, or there's some new data center build. And there's all this money going into it, right? Because like in order to run these GPUs and do whatever the use cases are, you first need to like build the infrastructure and do that. And so worth this, we're at a point where there are a lot, the market's a little like unnervy, worried that we're in a bubble. Like we're spending all this money, you know, hundreds of billions of dollars. And yet like where's the revenue, right? Like, you know, you can't get your PC charges, like what, like a hundred bucks a month or whatever it is, right? Like that's not enough to offset that. But I think when you actually dig deeper, what we've kind of come to the conclusion is there's actually a lot of AI revenue today happening. Now it's not enough, you have to believe there's going to be more. But like a big part of that is advertising. So like the first use case of AI truly is driving these advertising businesses to grow faster than you would have thought. And so if you think about like two years ago, what did I think Meta's revenues were going to be? Maybe I thought they would grow 15%. Well, they're growing 25. So that kind of incremental revenue growth, that's AI. Now it's not generative AI, but that is GPUs accelerating machine learning to find and serve you and add for a snowboard that you might not otherwise have seen. So that's sort of, we're seeing that happen. And it's happening across the board. Apploven is growing really fast because of this. Meta's growing, call it 20 high 20s percent. Google search, which grew, search, maybe people thought it was going to grow sub 10% is now growing mid teens. And so that's kind of the first impact. What's also happening is the recommendation engines of all these companies are getting a lot better. Like you may have noticed this. But like when you're on Instagram, the wheels they're serving you are like, they're more addicting. And if you look at Instagram time spent, basically it was flat for, I don't know, maybe 18 months to six months ago. And now you would spend 40 minutes a day. And now that's gone up like 15%. And just the past six months because they basically took those GPUs and put it at the recommendation engine. And now people are spending more time, right? And that's also more add dollars. So advertising is like a huge place. I think the big question, and it's what you're getting to, is like, well, what happens in a gentic commerce? And what happens to these ad models when you have a shopping agent doing everything for you? And my view is that it's early. It's very early. Like we had OpenAI announced the Shopify and Etsy integration a couple weeks ago. The product today is not at a place to really be that useful. But you can kind of see where it's going. I think that from a shopping perspective, we are going to be in a world where the old world was, I want to buy something. I go on Google and type in red shoes to go skateboarding. And it would come up with a list of results. The next step is going to be I go to Gemini or ChatGBT and say the same thing. But it knows a lot more about me. And it will suggest better products. In that world, there's probably no advertising. And then the ultimate end, which I think is the most exciting, is a lot of the products that you end up buying. If you think about Instagram, the more and more I'm getting advertisements for things, I never even knew I wanted. And then I clicked the button and it shows up. I sort of Toby at Shopify kind of made this comment. I actually agree with it, which is those were actually not impulse purchases. I actually secretly wanted those things, but no one had ever shown them to me. So I would never have gone on Google search and just looked up that interesting steak knife. But when it was showed to me, I bought it. Now imagine a world where you're in ChatGBT or you're in Gemini. And instead of asking it for something, it's just telling you, hey, you're going on this trip. I think you need a new ski coat. Or like, hey, I just found this interesting product because of a conversation that you and I were talking about in a separate chat. And I think that's going to drive consumer spending for these goods a lot higher. And I think when you think about advertising, there might not be like an ad per se. But from the merchant's perspective, instead of spending 20% of my revenue on marketing in the form of ads and 2% of that is going to Shopify, that split probably changes. So that 20% that I'm spending on marketing to maybe it was meta is now going to, like, that profit pool is going to be more going toward a Shopify or the actual agent players themselves, open AI or Gemini. But this is like early in the end. People are debating this literally every day. This one hasn't come out yet. But I had Alfred Linon. I think this will come out before it. He spoke and also Reid Hoffman spoke at four runners AI conference back in like summertime that Kiercing Green throws. And one interesting thing that Alfred said was, things that happened three months ago are not relevant today. Things that are happening today are not going to be relevant in three months. And things are moving so fast. It's really hard to predict. But you have to be active. You have to be watching what's going on. And like gathering as many data points as possible to like adjust accordingly. And then another thing-- and I'm curious for your perspective on this-- was Reid Hoffman was talking about how business models define different generations of technology. Advertising was majority of the last one. We don't really know what the AI business model is yet. Do you have any idea? On your first point about things changing. I mean, that could not be more true. And it is also like, this has been the longest year of my life. Like, I feel like we're at this point in tech where the implications of AI, like, they are going to be big. It's not a question of like, how big they're going to be. It's what is actually going to be impacted. And who's winning from this and who's losing from this. And that changes literally every day. And so you-- like, part of a big part of my job is, you know, I'm not making an investment closing my eyes and waking up in five years, right? Stocks are priced every single day. I'll give you a great example. Like, Open Eye did their Dev Day a couple days ago. Yeah. Oh my god. Like, this was so insane. They get up. And if you got named in the presentation-- like, by the way, you could argue a lot of these companies that are kind of named in the presentation to go and be part of this like agent layer. Like, it might not actually be a good thing. Like, if everyone's using chat GPT, and now you just got like sort of you added an additional layer of like, maybe, disintermediate-- like, it might actually be good. But if your name got mentioned, bang, you're up five. And the best was like, Mattel, like, the toy company, right? Like, not even a tech company. Like, they got mentioned in this thing, Mattel, the toy company, stock went up 6% like in a second. And so, but that just like highlights sort of where we are, because we don't know how this all plays out. And everyone's trying to figure it out. Any sign of you being a AI winner or AI loser, like gets priced in the stock very, very fast. And so part of our job is to stay at the forefront of what's happening and figure out the implications, like, real time and do analysis around that. And the way we sort of do that-- And I think it's a unique. thing with Cotu, but I actually think it's underappreciatedly the most important part of tech investing, is that the best way to figure out what's going to happen in tech is to actually talk to the practitioners of that tech. And so what I mean by that is we spend a lot of time not only talking to the company CEOs and the management of the actual and having relationships with the management of the public companies, but we talk to the private companies. We talk to OpenAI and Anthropic. We talk to the researchers because these are the people every day living and breathing this sort of tech, this AI that is going to change a lot of things and they all have super interesting insights. But if you don't do that and you're just sitting in your computer and trying to build a model or forecast the next five years or the next quarter, you miss these big waves. And I think the part of what makes Cotu really successful over the last 20 years and today is that by being at the front of tech, tech goes out in different forms whether it's web one, web two. There have been winners and losers in new markets and all these tech waves. We think the big one right now is AI and that's not a hot take, but we think it's actually bigger than any of these previous waves. But we spend a lot of time focused on actually meeting the people in the industry because that's where you're going to get these insights. They'll tell you, they will tell you what they think. The way Adam Frugey at App Love and was telling you, hey, this revenue is going to grow a lot faster than people think. And often, those are the best tidbits of information to get because these are the people doing this every single day. And the last thing I'll say on this is that when you have these big tech waves, every single time when things are inflecting positively. So think about like when people got excited about AI, they realize in order to do AI, you needed Nvidia GPUs. Even the most like bullish person in the world about how big AI could be, probably under predicted the amount of GPUs you needed. And the same is true on the inverse side. Like when things are when companies are getting disrupted, that pace of disruption normally happens faster than you think. And so if you can find those big trends and the winners within those trends, you can do all the modeling in the world and the valuation work and the DCFs and the analysis. But normally it ends up being better than you think to the upside and worse than you think to the downside. A question I'm interested in is in the early inning of like this AI cycle maybe in the last year or so, like none of this really existed. Like I feel like now it's like actually taking adoption and there's actual applications. But in the beginning, it was a lot of marketing hype. And I'm curious how you like deduce down who Israel, who is not and like how do you determine whether or not like they're actually making progress or it's like a consulting presentation. Totally. Well, what happened was you got to the point where if you as a public company came out and didn't say how you were going to benefit from AI, like no matter what industry you're in, people instantly were like, oh, they're behind. So then yeah, you saw a lot of companies basically talk about AI like before it was actually being implemented. I actually think you see the same thing at like even in the hedge fund industry today. But I think where we are in this cycle is that we're now actually starting to see revenues from these companies. I mean like a year ago, right, you could point it. There was a big AI scare in the summer of 2024, right? Where there was all this build out happening. And there was a moment where everyone kind of woke up and we're like, okay, so we got Chatchee PT. What else do we have? And like literally, I remember like this and it became like public discourse around the investing world. Like you couldn't really point to anything else. And I mean, these stocks at this point were like tanking like the power and utilities companies, the infrastructure companies like any a lot of the tech companies. These stocks went down. I mean, some of them were going down 15 to 20% in the course of three weeks, right? That's like a disaster if you own these companies and nothing's like changed. You just there were nerves in the market and you didn't have like a lot of things to point to you to say, no, like revenues coming from there and revenues coming from there, it all makes sense. And I remember in that moment, we had this really amazing conversation with one of the head engineers at X AI. And he's like, guys, here's how I think about it. The tech two days. So this was summer of 2024. The models today, where we are today, and you know, where we are fast forward, you know, 14 months is a very different spot. But where we were in the summer of July of 2024, the models are good enough to have a lot of applications that will generate revenue. And the way he talked about it, he was like, each model is like a child, right? So you with each kind of breakthrough in the model architecture or you're training on, you know, more GPUs, the IQ level of that child goes up. And I remember at the time he was like, today, I think the IQ is about of the child is about 100. Well, you know, 100 IQ in this economy, like, there's a lot of work for 100 IQ person to do. But remember, it's a child. And the child can't work right away. So you have to go, the child has to sort of grow up, be, you know, figured out how to use and and his point behind that was the tech's good enough. But we just then need to spend the time. There's going to be a lag to developing applications for that tech. And so fast forward a year, you've seen that right? There've been early breakouts of use cases. The first one's coding, like that's the big one, right? You have these companies that are generating a lot of revenue today, cursor, you know, Windsor before they got acquired, like there are companies cognition, like there are companies generating a lot of revenue. And that's sort of the first use case, okay? Agente coding. My view is that the reason that's the first use case is a lot of the guys that are working on building AI in these labs, they come. So like, what's the first thing they're going to try to figure out? It's going to be how to make their jobs better. That is now starting to broaden out to a lot of other industries. We're now starting to see companies start to generate revenue and products that actually look pretty good for a lot of other things, whether it's building an Excel model, right? So going after the financial services space or, you know, we're seeing this with obviously call centers, my view broadly, and this is my view, I'm not sure this is everyone's view at Cotuba, 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. There'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, companies, industries are competitive, right? So if your workers are 10 times more efficient, you're going to hire more workers in 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, you're 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. And those two guys that work for me also have 20 or 30 agents that are working around the clock. And so like we, I want to kind of see that world happen, but I think in a lot of industries where you're starting to see is people aren't, there are some example, 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 slowing, you know, 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. And so my views, 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 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 EPA, the earnings per share growth is going to accelerate. And the stock is going to to go up a lot. So the market will view that positively. I think that's true across all industries. I think where it gets tricky and the big question that people are asking is, well, if jobs get replaced across these different industries, like, "Hey, what are they going to do?" What new industries do new industries emerge that they can work in and their debates around that? And then if not, Amazon stock price might have gone up a lot, but who's going to buy the goods if there's unemployment? I think people are still trying to figure out those debates. I'm pretty optimistic that this will happen a little slower than some of the fear monger is think. But there will have to be new industries for a lot of people to work in, or other ways of making money. And I think that's 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 is 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 go 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 is going to go up. So I think it's the most exciting time to be investing in the public markets because of that reason. In today's high-speed business world, staying ahead means using the smartest tools possible, including the powerful capabilities of artificial intelligence, meet Turing intelligence. Turing builds customizable AI systems designed to solve your mission critical challenges no matter your industry. From expert guidance to tailored projects, Turing helps top companies realize AI that's more capable, more adaptable, and more effective. With Turing, discover how AI can accelerate your business growth. To learn more, visit Turing.com/sorcery, spelt-sour-r-c-e-r-y. That's Turing.com/sorcery. I covered Clarna's IPO and Sebastian was really clear about when they were doing their turnaround, reducing head count, freezing hiring. Now they're just like, waiting on attrition. And same with Open Door, we'll 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 head count and hopefully deploying more AI agents within. 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, I think the most exciting thing is these companies are all going to be revolutionized with AI. It's not just forget the revenue growth and the actual workings of the companies. App Love is a great example. The way Adam Farougey, 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. His view is, A, everyone at the company needs to figure out how to use AI. 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. Because 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. 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 a big theme of CO2 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. So we were, Philippe and Thomas were early investors in the cloud transition. And so CO2 became cloud native. All of a sudden you had these best companies talking about 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 over the course of those years. And it's been amazing. And so kind of like thinking about how this tech will transform CO2, like that gets accelerated massively in the AI world. And like my belief in something that I spent 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 space where like as you know, most financial service companies, whether it's hedge fund or a bank or like the last guys do a down tech, right? 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 happening now. Like my views that like this is I tell my friends this and they like laugh. But I think that two day 85% of what I do. And so basically can be done by AI and it's not a question of it is the tech ready. It's how do we implement the tech? And so like we're hiring a class of analysts to come in and like 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 cell side notes. You know, I spent two hours doing that because you have to read everything. But like there's only a few important ones to like how do I build a model in the click of a button? How do I like 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? I know. I was part about the hedge fund industries. It's not that people intensive, right? So we don't need to cut people costs. There's just a huge prize for becoming like exponentially like what we are trying to go and find ideas to invest in, right? And the constraint and ideas is the amount of time you have, the places to look like 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. And 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'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 last, maybe the last analyst. The AI captain. Exactly. On your point of like pricing the stocks though. So it's really tricky because on one hand, the main focus of CO2 is like picking long-term winners, so investing on a multi-year horizon. And like what that literally means is like, take meta for example, right? I have a model for meta for what I believe they're going to do in revenue and EBIT. And profit and earnings and free cash flow out to 2031 right now. So I'm projecting like what they're going to do in the long term and what multiple I think the business, what we'll get assigned to the business in that year and what that stock price is and what the return looks like. So you know, you have your kind of like long term view. And the other way we do it is we literally build like you maybe didn't college like a discount of cash flow analysis. So saying like the meta's market cap should be worth today, the sum of the future free cash flow that's generated discount of 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. 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, you know, the earnings profile going to evolve? But 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 you know, fully my boss was an analyst for Julian. Like 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? All 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 of all 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 you know, the shorts and 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 we're going to be able to do that. 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. And we spend a lot of time on the short-term idea because my view is that the long-term is simply a collection of quarters, right? And so you want to make sure that you, you have an understanding of how, you know, 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, you know, like Netflix is a good example. Like you knew what the end state for Netflix was going to be, but at every little hiccup, stock might be down 20%. And so, you know, trying to make sure in those moments you're not, you know, massively sized before it goes down 20 because even if you're a long-term investor, let me tell you, like that is going to be an ugly day in the office. But then knowing when these things have over corrected and being able to size up in those moments when there's a hiccup. Because Co2 is concentrated in technology, how do you balance out these market cycles that is so favorable towards AI and what somebody's is above all? That's the thing we think about every day. So broadly we're investing on the long side in tech. So, but even within that, like if you think about the NASDAQ, right? So 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 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 that have moved very differently. So like AI infrastructure, right? Like the build out of AI, the data centers, the constellation energy, the nuclear, the power needed to, you know, power these GPUs. Those stocks are up like 50. And then you would say, you know, well, Microsoft's like probably an AI winner and Meta's probably an AI winner. Like those stocks are up like, you know, 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. But even this year, there have 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 you know, this is the 30 year old me saying that. And one of Flip's amazing qualities is that he is like the best risk manager I've ever seen. He has this sense of when something is about to go wrong. Like it is incredible. Like he, and it's really been great for him. I mean, and you know, in different moments in these draw downs, he's been able to basically we call it cutting gross, but going from let's say you're 100% invested to 50% invested. So you're sitting at 50% cash very quickly and he gets the timing right. And then tariffs came. And when Trump came out and put the, you know, that you remember like that day where he's got the the board with all the tear prices. And I remember like sitting there, you're like, oh God. And so I think one of Flip'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 train? What's like the process to get through? I think there are a lot of people that can pick stocks. But what really matters at Co2 is you have to be able to do the analysis. You have to be able to pick stocks, you know, that on longs that go up and find shorts that go down. But the key piece is how do you then convince Flip 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 Co2 and it was true. 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 know, you spend 95% of your time doing all this deep work and all this deep analysis. But can you take that, you know, 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, fully spread, there's 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. And 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 CARDA. CARDA 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. CARDA's platform of software and services lays the groundwork so you can build, invest and scale with confidence. CARDA'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. For more information, visit CARDA.com/sorcery. That's C-A-R-T-A.com/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 innovation happening on the private side, but it's not open AI, it's anthropic, it's all of these 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 publics. How does that inform your decisions? We'll start there and then I want to ask about valuations. As I said before, when you're investing in tech, you want to be talking to the practitioners. One of the best parts about CO2 is that we do both public and private investing. We spend a lot of time with each other from a team perspective, but we also spend a lot of time, 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. And so I think just having an understanding of really what's going on in both areas helps you a be a better public investor, but be a better private investor. There's also this idea where 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 need to have an understanding of the entire AI value chain to like figure out what's going on and like who are the winners and losers, meaning like, you know, I, I need to understand how many GPUs and videos planning on selling next year and who they're going to sell them to. And I'm 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 they're 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. So and they're like our different offshoots. I think the biggest question right now is I like I was talking to a friend of mine who does reinforcement learning and 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 like coding agent. They figured out there's one specific area and they're crushing. Well, then you have the labs. So you've got cursor here, then you have the labs. Open AI has their own coding agent, but they also have a lot of other things, right? So they have, they're doing coding and then they're gonna do a lot of other different things. And 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, that you'd be like, oh, well, Google's gonna win, 'cause 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 gonna 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 wanna use? Are they the winners because they're so specialized and they're so, they gathered this adoption among the workers. Are they gonna be the winners or is it gonna be open AI in 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, they're club business, they own it, they don't have to pay for like, or are they gonna be the winner? And I think this is gonna be the biggest debate over the course of the next few years, but I don't think where at a point where you need to answer that debate. - There. - I got in all three wins 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 Karda, they report series A company is that have AI and A-woman in their name or if they're doing that, they get a 30% premium. You're looking at open AI, they just raised a $500 billion secondary, like it's insane, but like I've had a poor from altimeter on, he explained open AI'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, and 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, we consider investments and we're like, okay, do we think this, let's take Chip. Do we think this new Chip company has a chance of beating Nvidia, or should we just do some more Nvidia leaps? What should we do? And so it usually just comes down to like, that's less risky, let's just do that and let's just hedge that one. I really know what the question is on this one. - No, 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, it makes sense to me. I get why there's a lot of, like I get why people want to do that, because you know, when you're, it's, I always look at private investments from public markets background, so I have a lot of like analogies in the public markets, right? Open AI's got what, 800 million weekly active users. - It's so crazy. - Spending, spending by my estimates close to the amount of time every day that is spent on Instagram. Like Facebook is a, I think it's close to, it's like a $2 trillion, it's a $2 trillion company, open AI's, you know, $500 billion around, they've already got all these users. Like, could open AI go from a $500 billion company to a $2 trillion company where Facebook is today? And by the way, I think that Facebook is gonna become, like, you know, a much larger market cap. Like I think Facebook is gonna be, you know, a $3,000 and $5,000, so if the $2 goes, you know, $6,000, what could the $500 go to? Like that makes sense, 'cause you've got users and engagement, you know, they're building modes real time. The more we're talking to chat, GPT, the more information it has about us, the more the way, you know, it'll better service 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? Like, 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. Like, if they do that, it works. And by the way, we haven't added like 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, you know, acquiring compute and infrastructure and building the data real time and they have like this, like, I said, 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, like proliferation of new companies. So one of the things with AI, that's great. Like, we track this. It's like, it's never been easier to start a company with AI, right? Like, the fact that you can code agent alone, two guys in a, you know, and a dorm down the street can build software in a way that they couldn't have built previously, like, you're seeing a proliferation of new companies, right? And the price is so big in any of these markets. I 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, software, I think it's like a $1 trillion, so $1 trillion of the 20 is software. Like, that 20 trillion's up for grabs. So like, the market opportunities huge. But picking the winners and losers and that is like, really difficult. Well, yeah, because also like, you just, every day, you know, you invest and you start up, and then you're just waiting for open ads new launch of like, like the N8N, right? Like, like, you saw like, DevDeep, here's our version, like, and you're like, "Oh, well, okay." So, and there's so many examples of that. Yeah. - What is it called? Sherlock, Sherlocking or something? - Yeah. - It's with another one. I know we covered this a little bit, but I wanna touch upon it again. So, so we're sure you sponsored my 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? - It kind of depends on the industry, but broadly, like, the, 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? Infliction in the digital ad business that's inflection in growth rates. In, you know, 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? 'Cause if you think about like, if we have a five year view of what a stock's gonna be, like, ideally, we want the market to figure out that's where it's going as quickly as possible. And 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 in the stock reprices higher, kind of more toward your view. So, but like, we're tracking everything, like, you know, I'll give you an example, we're tracking, you know, everything from credit card data, to email traffic to, I mean, we, my analyst said 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 investing 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. And 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 like, where you are in the economy, are things getting faster, things slowing, like ads have been really great in the third quarter. But like, about a week ago, they started to slow. Is that like consumer spending slowing? Or is that just a weird shoulder period, you know, 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. So like a good example of this was, one of the companies that we invest in is Reddit. We love Steve Hoffman, we love the team. We think that Reddit is going to be a much bigger business over time that is 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 open AI wants to have a shopping assistant, right? All the reviews in the world are on Google. like they don't, they're not on open AI on chat. She'll be tea today. So where do they go? They 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 search, as you know, was being like re-architected, right? You have AI overviews and I 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. Well 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 just like miss 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. And 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 you know, it went from zero to 50 and like that was the disruption. The second we saw that our takeaway was a, like 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 want to see it. They liked seeing the answers and the citations of Reddit. So you, that's where you have the confidence with that data science to say, I figured out the tech change. And now like we like to stock even more. And this is how, you know, the narrow, you know, the user numbers are going to be fine now. And now the narrative is 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. Like it just didn't make any sense. Well, and the funny thing about that is it's, that's actually changed. Like the, the thinking even from Reddit is changing a lot. Like what happened was opening I went out and basically trained on Reddit data, uh, chat GPT, right? And they, it sounds like may have not asked for permission or done that, you know, at the time it was just a, you know, you were going out and trying to build this model. And so what Reddit did and, you know, Google did the same thing and all this. And what Reddit did was basically say, okay, you guys, hey, you know, we're not going to sue you, but you took our data. So just pass a licensing fee. I 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 be have updated data and read and, and, uh, uh, chat GPT does the same thing. So the view was like, okay, Reddit's corpus of data is growing, but like the incremental, you know, conversations that are happening are pretty small in the comparison of the whole thing. So whatever that deal was in the beginning, like 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, like, for like, there were rumors that Zuckerberg goes and, you know, is hiring people for $100 million a year to build a small. So if he's willing to do that, what do you think, you know, Google or open a eyes willing to pay Reddit for the key piece of data that may determine the success of the entire shopping egentic tab? My guess is higher. I've 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 co-2. I know Philippe and Thomas are French, but a previous partner I used to work for would call it co-a-2. Oh, yeah. That's wrong. That's just fact. Sorry, Mark. Sorry to call you out too. Can you please explain to the class where the name co-2 comes from? Yes. So I'm glad I know this one. Co-2 is a beach in Nantucket. So it's a beach in Nantucket. I believe Philippe spent time there. So, yeah, you know, it's funny. I, in my entire time at Co-2, I've never, like, heard anyone talk about. The beach. But actually, that is not true. We're building a, so we're redoing our office. We're basically building a second floor because the firm's expanding. We need more room. And so, where people are coming up with names of the new conference rooms. And Thomas's idea was to name them other beaches in Nantucket. You kind of do need a beach and a hedge fund in Midtown. Why not? Yeah. Well, it's a, as you know, Midtown is like a stormy sea. And so, 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. 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. 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. Awesome. 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.