Adam Foroughi, Applovin CEO: Surviving a 92% Drawdown, Ads as ML 1.0 & the $50B Game Ad Market

Adam is probably the best founder and no one's ever heard of. There's an athleteful hiding inside 100,000 mobile games and is quietly out performing Facebook ads for e-commerce brands. Of all those thousand plus IPOs, the number one most valuable is AppLeaven. AppLeaven CEO, Adam Purgey. The founder mentality's got to be chase-winning. They're going to print something like $6 billion in cash a share. In a world where things don't make sense, people think you're cheating. Instead of realizing you built one of the cooler technologies the world's ever seen. Please welcome, Adam Purgey. Welcome, Adam. Hey, man. How you doing, bro? Good to see you. Adam, thanks for being here. We thought it'd be really great to chat because a lot of people don't talk as much about you're not in the headlines all the time with your business. You're operating your business almost like absent media. You don't do a lot of press. You don't get out there a lot talking about the company, but it's such an incredible business. Can you just tell the audience what AppLeaven is and maybe also frame up the market a little bit for us? Yeah, totally. I think the fact that we were able to build a very big company without having VC funding at the early stage created this world where we just had to build quietly. And then obviously the goofy name didn't help us all that much as well, but what we are ultimately is an advertising company that's helping mobile game developers monetize that space. Now what people don't realize is just how big the mobile gaming universe has become. You've got over a billion people a day playing mobile casual games. These are all adults, heads of households, and the scale of the opportunity is just humongous. We disclosed last January, so nearly two years ago, that on our own platform there was $11 billion a year of ad spend. Since then, we've grown 60% year over year roughly, and so if you gross that up to a nice round number today, you get 20 billion. Now we're not the only player in this marketplace. This is a market that's monetized by a lot of other ad companies as well. So then you'd probably more than doubled out again and round it off and say there's probably about $50 billion of advertising being spent every single year in this mobile gaming ecosystem. It was not very long ago that social was a $50 billion opportunity. Spaces growing really quickly, a lot of audience, these people watch ads, a lot of times they watch the ads to get rewards, and so that dynamic creates this possibility to create intent. For the most of the companies life, we've been creating that intent to drive a user to take one game's experience and go to the next game's experience, and what's really gotten investors excited about our company and just us excited about the opportunity that we have in front of us is that deep learning models have gotten so powerful now that you could take that same space and try to take that adult and give them a shopper behavior experience. And that allows us to tap into much larger economies, big more of an economic impact in the world, and that's why our team's just really pumped up on what we're doing. The first wave of internet advertising was, in many ways, the spark for a lot of critical technologies that then sort of diffused out into the world, so if you think what Google was able to do with AdWords, AdSense, Applied Semantics, all of that whole range of technology, is that true in this generation of internet advertising, are there technologies and things that are being birthed here that are consequential and foundational now to the rest of the internet? Yeah, advertising is like ML1.0, but really was the first implementation of all these technologies that now are driving AI today, and the economic value of a large language model and what it's doing in our society today is much greater than advertising, but advertising is a very profitable implementation of a deep learning model. Now, recommendation systems are structured differently than large language models, but in a lot of ways, they follow the same trajectory. So a lot of the research that's being done in the space in a large language model space can port to recommendation systems, and vice versa, a lot of the researchers in the large language model space might have started early in their careers looking at advertising systems. So these two spaces are really related. The nice thing about our business and any advertising business is that when you build a model, you're predicting a future outcome and advertisement, or if you're building a social network and engagement post, or a sequence of them, but you can translate the value of that prediction immediately. Is it true that there's just a broad-based behavior around humans' reaction to ads in 2026 versus 2006? Like, has there been an evolutionary arc that's very predictive? Yeah, it's interesting. I started my career in 2005, so I saw the ads back then, complete garbage. It was all spam, and the technologies just weren't powerful enough, and your old company Facebook did a really good job of realizing if you can take all the data we have in front of us, and pair it with good technology, the ads can become really relevant. And if you talk to most people who shop today, most of their shopping recommendations are coming from Instagram. The ads have become very much like content, and in our domain as well, people love the ads that we show, and you would think people wouldn't like them, but we see tons of engagement on little mini-games that are appearing in other games, and people are playing these previews because the technologies have gotten so good at recommending something relevant to someone. There's been a lot of hand-ringing about the impact AI will have on the ad networks, specifically Google's interface, and OpenAI has an ad product now. I'm sure you've been monitoring it and trying to learn from it. What is advertising going to look like when people are doing five or six queries with a chat bot, because it's pretty obvious, 95% of the world are not going to pay 20 bucks a month for this technology. They're going to expect it to be free, chat GPT has already said they're going to make it free. Tell us what they're doing in advertising, and is it going to be less effective each time, but in aggregate, people are going to use it more, or is it going to just be even better than Google's search is franchise? Yeah, I mean, there's two sides of advertising. One part of it is bottom of funnel advertising, where a consumer's sort of knows what they want to buy, but they're doing research to go complete the transaction. And that's Google's search business. If I wanted to buy a pair of dress shoes, go to Google historically, do some research, and they direct me to where I need to go based on the ads. And today you can go to a large language model and close the loop on that same thing. So that ads model is almost going to exclusively compete with the Google search business. What we operate in is a world where we're showing a user an ad, and we don't know what their intent is. So we're trying to create something that didn't exist before. Show them a recommendation, and get them to go, wow, that looks really cool. Let me go transact on that and do it really quickly. That's what drives Facebook's ad business, too. And so the reason that's interesting to me is that the transaction via search or LM was going to happen anyways. If the LM didn't exist and Google ads had never come to existence, but Google search existed, that transaction the closed loop would have happened. So there's not actually a whole lot of economic expansion that happens from that. But when you show a consumer an ad for something that they had no idea existed, they didn't know they needed to buy. Basically. Totally, complete discovery. And this is what makes Metasome amazing in their ad business, and what we aspire to do. You create that discovery moment. Not only is it a really fun moment for the consumer, because then they're excited about what they bought, they wait for the package, they're excited to open it up, but you create economic expansion. But the arms race that develops over time, where some people say, you know, I mentioned something with my friends at lunch and all of a sudden they show up and there are these ads on Meta or wherever. Is that just us overreacting or is that actually happening? And is there a push not to be more, not intrusive, but you know, like the tendency to want to sell more. That's creepy. Yeah. It feels creepy when it does. Or just to push the boundaries at them, like what is actually happening when people say I say something at lunch and all of a sudden an ad for that same thing appears. I mean, I think you've done other actions that are trackable, like do a search, browse the website, do a product search, and you don't realize it. And then you say something related to and you start seeing ads that are relevant. So it's not like the mic is on or there's an app that has actually taken steps. Well, wait, there's a theory though that if we were all at lunch, especially with these apps, you know our geolocation, you've kind of put us into a group. So we might be talking about this new car we're all interested in or watch. And then Freiberg, when he's leaving, searches for the watch to bookmark it after the conversation. But you're tracking all of our locations and then you say, okay, let's give all four of them the ads for the watch and your mix and matching based on what's happening. That's what I told you. I don't think advertising companies can track locations. So we don't track location at all. It's a really heavy concept to track people's precise location, to then render an ad. And then to imagine the amount of data that's transferring if you're mic on, to then parse the mic on content to try to translate to an ad. Not realistic. But what about us being friends and being connected together? Like groups. So we wouldn't have that data. But if you're on a social network, of course your relationships together might drive an ad experience if Jamal searches for something, then you might see something relevant to it. There's nothing wrong with that. I mean, the one thing that people lose, there's a creepy factor that scares people somewhat. But all of the data collected at this point, given the scale of advertising across all these companies, is pretty much controlled in a lot of ways. What people then forget is the economic value that's created from these ads becoming that relevant. That ad that you saw, you recognize that ad. 20 years ago you would not have recognized the ad. And there's a big part of GDP that's now coming from this digital ad economy. I mean, the better these technologies get, faster GDP growth. - Adam, let's just go back to the, 'cause what I find so fascinating about the business is the way you've operated it, you're based in LA, is that right? - I'm based in LA, a company started in Silicon Valley where in Palo Alto. - Palo Alto, but you're here, and then you have a lot of developers in China, is that right? - We have, our engineering offices are in Palo Alto, Beijing and Singapore. And then the company didn't raise a lot of venture money. You take the company public, 2021, it went public, like 20 billion market cap advocate? - Yeah, we were COVID IPO, we went out in April 2021, it was about $28 billion. - $28 billion, and then in 2023, what did the market cap collapse do? - Well, this is the funny thing about the public market, so we went out in 2021, $600 million of you, but $28 billion market cap, we got as high as 40 billion, and then in '22, the stock went down literally every day. We got to about a $3.8 billion market cap, and that year, we did a billion dollars in EBITDA. - That's incredible. - So hold on, so let's just go through this. So the markets and disbelief, for some reason, about the business, and what do you do? - Yeah, what, you learn pretty quickly, and I'm a finance background, so I had good education on this, is that your price in the markets is determined by the quality of your investors, and we had private market investors, about private equity, and ex co-founders, and other team members, that we're gonna sell when we want a public, and because there were so many companies going public during COVID, by the time we went out, blue chip investors weren't doing the research to figure out what is this goofy named company. So we ended up with no demand, and a lot of supply, and that construct created this world where we just tanked, and multiple went from fairly high, I mean, I wouldn't really value companies on 50 times EBITDA, but to something that was absurdly low, sub four times, so being that finance-minded person, you have to remember, with that kind of abashing, you do have an opportunity on the other side of it, and I turned internal to the team and said, I'm not gonna talk to investors all anymore, they're not buying our stock, it's a waste of time, but guess what, we generate a ton of cash, let's start buying our own stock, let's become our best investor, so we kicked off a super aggressive buyback program, and over the, since then, I think we bought roughly $6 billion of the company's stock, retired 20 to 25% of the shares outstanding, at peak, that $6 billion was worth over 50 billion, and so you can take that moment, which does feel super depressing, and turn it into a huge amount of money. - How did you manage, sorry, did you feel that way from the whole time, or was there this period of depression, where you're like, oh my gosh, that's what I was gonna do. How do you manage the internal culture, when the stock is off 92%. - It's tough, I mean, I'll tell you, I would get phone calls from family members, friends, or you sue a title, and I'm like, we got stock at a penny, the stock's still like 10 bucks, it's still up a lot, but it's very tough then, 'cause you realize as a CEO, your team is getting those same phone calls, from their family members. - Yeah, exactly, and they don't have the gravitas that you do, nor the ownership. - So we built it by just saying, look, it's an us against the world mentality, everyone's turned against us, we're gonna buy back shares, and we implemented a performance stock plan, which typically goes to CEOs, but we did it across key people in the company, and said, we understand it's tough right now. We understand you thought you had a house, and now you don't. But if you dig in and we recover, you're gonna make a ton on the upside. - And then what happened, investors started showing up and saying, hey, we're paying attention again? - It was interesting, 'cause for us, what happened was, we went from ML1.0, like we talked about a couple minutes ago to ML2.0, we went from a regression model to a deep learning model, and the outcome was, we're driven by our advertising algorithm. The better it works, the better advertiser return, return is on our platform, and everything is performance-based, so we're selling revenue to advertisers, the more they scale. And so the company just started growing really quickly. Now, we turned into 23 and launched that model in April, we still weren't talking to investors, so people hadn't found out, and then it was somewhere around, I think September of 23 that I went to New York, and I said, the stock's now like 80 bucks, and we'd recovered quite a bit, 'cause performance was good, but I said, I'm gonna start talking to investors, 'cause Mark Capscanning high enough, and we can't really buy back all that aggressively anymore, and in that week, the stock went from 80 to 150, and I think it was like 28 billion, the 55 billion. - From you being in New York. - From me just going out and saying, hey, our company still exists, we survived this. - Epidogue grew. - Yeah, and then people like, I'd send the meetings and do, it's pretty easy to read the other side of the room, if you do that kind of thing, sell your company. I send the meetings and I'm like, these people are literally calling their friends and the room going, bye, bye, bye, bye, bye. And I was like, ah, it's pretty good. - What's the opposite side of that? Once they're long, are they now asking you, okay, Adam, how do we expand? How do we grow faster? Why just games? Why not e-commerce? Why not this? Why not that? - Absolutely. - Dan, if you do Dan, if you don't. - So, unfortunately not a lot of people are contrarian, so you can go to the extreme down, and then on the other side of it, you can go extreme up too. We ended up going from nine to $750 a share in a matter of two and a half years. And so, like, it was like a $3.8 billion markup, some people bought some options back then, they were probably living in some massive homes, and we got to $250 billion markup caps, so extreme on both ends. We've now settled into a place where we have a lot of excitement about our growth opportunities, but I've found public market investors are not all that different than private market investors. They follow trends, but a lot of times later than you'd want. The most sophisticated hit those trends early, and that's why you have really good VCs, and you have average VCs, you have really good public market investors, you've average public market investors. - Maybe you could talk a little bit about privacy, Apple, the EU, really, or look at companies like yours, and this is a little too aggressive in terms of the data you're collecting. Some video game developers don't like having data collected on their users, and they've tightened the screws a bit. Zuckerberg had to deal with it specifically, so what's the headwind on this business, and how do you manage privacy? When Apple really is trying to, let's call it what it is, they're trying to neuter your business. - Look, in any of these spaces, you want the regulations to be clear. So once they're clear, technology can deal with them, and so if you could precisely target a user five years ago on iOS, and today, someone says, "I don't want you to precisely target me, "you grouped them in a bunch." And you serve them a worse advertisement. Now, the funny outcome of that is, we'll get a lot of complaints after that change that Apple made from users to say, "Serve me more relevant ads." You're showing me a bunch of spam. So there is this notion that you need privacy regulation so that technology companies can do exactly what's expected of them. On the other side, consumers do want relevant ads. It helps them discover products. If you're sitting there in a game, and you're watching an ad for 30 seconds to get a free life, you're getting something that has monetary value. Now, if you're doing that, do you want to sit and watch garbage for 30 seconds or do you want to watch something relevant? And so, what's happened since a lot of the privacy noise is a lot of calm. The rules were written, technology companies have adapted, and deep learning networks are really powerful. They can do it quickly. - What happened with this consumer, I just came back one quick follow up. With this amazingly profitable business, I think you dabbled in buying some of the games, and we have bending spoons coming on today to talk about their aggressive acquisition of not bad businesses, but let's call them slower growth businesses that maybe venture isn't interested in. Is that going to be a sustainable plan for you to become a game studio? And does that put you in conflict with the partners? - Yeah, we sold all those games. We bought them originally as a data play. When we built our first deep learning model, we needed to have data to train it. And game studios don't tend to want to share data to third party companies. So we bought our own studios. We seeded the training data in our first model. We built a model that was really successful in market. We started growing really quickly. Once we started doing that, third parties were coming in, and that was that. And we divested that. - What's the world of advertising look like where there's agents everywhere? Agents are servicing you. Maybe the human interface to compute changes, so it's not necessarily a computer that you're typing on or a phone that you're browsing. Maybe it's the meta-glasses or some other device. What role does the ad play? And what happens in this agentic commerce that so many other people are trying to now push into existence? - Yeah, I mean, I think the reality is part of the world will start using things like agents to optimize certain shopper behavior that's consistent. For instance, I might put my supplement subscription into an agent and have it optimized in every single month and deliver on time. But these discovery platforms aren't that. And the typical shopper is not the person who's deep into agents and sitting on Twitter and adopting the latest technology. I sort of say like our audience is a New York Times audience. There's still a ton of people using Yahoo properties every single day. The typical shopper wants to find a product and wants to actually go through that shopper behavior. They want a window shop. They want to go through the transaction experience. - They want to compare, probably. - Totally, they want to track it. And if you told them after the fact a pain agent could have done this for you and saved you 20%. I don't think that matters on a $50 transaction because the dopamine hit from going through it is what they enjoy. So I think there is this part of the world that is technologically advanced that's going to adopt these technologies. I just don't think, I think we really over index on the Twitter verse and forget that the average shopper is not that. Let me just try to understand how you want. Because the two of the smartest companies in the world with the best engineers met a alpha that Google make most of their revenue from advertising. They've built their own models. They've been doing it now for one to two decades. How did a small company compete in this particular domain and win? And what's the operating model that you think gives you an advantage to continue winning? Yeah, I mean, here's something that helped us get to this point. We never think we want. We think every day we wake up, and we're probably going to get screwed right now, and we better work hard. And so you've got a company that's lean with a lot of subject matter experts who are really, really focused on this thing. This mobile gaming experience and translated it into transactional behavior on the other side. And so I think there's this ability to take on giants. If you're very focused, you remain lean, and you can just move faster than that. What's the leakage in the business then? So meaning, when you look at a P&L, we did this-- I did this thing with Amazon a decade ago where it's like, you look at all of these places in which they were leaking in our big insight as well. They just absorb these things, and they'll become the new businesses. And that was our long thesis for Amazon. What's that version for you? There must be-- is it payment infrastructure? Is it other kinds of things Jason asked you about apps? But I guess you've divested that. So where's the leakage? Or said differently, where's the opportunity for margin expansion so that people underwrite this thing? Well, I even thought margins, I think, are number one in the market, it's 84%. So I don't know how much leakage we have. Yeah, given the metric. But the way it's think about it is, Avertagia comes into our platform, and they have a transactional model. Let's say they're selling lipstick. We give them an arbitrage. They from us are buying the consumer. That consumer transacts. And they cover the cost of the consumer immediately. So the consumer buys the lipstick for $20. They pay us less than the $20 minus cost of goods sold. They're happy, they scale up. And that performance model is very scalable. Now, our leakage is we're not the full chain. We're not the advertiser in the equation. But we want to power the advertisers to meet the consumer. And we've run extremely lean and been so algorithmically focused and automation focused that we haven't had a lot of points of leakage. The other side, then, is when you have 85%, EBITDA margins, people say, wow, they could be over earning. That's the classic phrase. And then you have competition that says, I can compete, Adam's margins away. I'm willing to do this at 60% or 50%. But sort of maybe as a corollary to David's question, that hasn't really happened. And it's been incredibly sustained. And why do you think that is? Because these technologies are really complex. And if you can innovate and you have differentiated data, you can build an advantage. I mean, by that token, Anthropic shouldn't be running away with the large language model space. But the power of a model that then reaches a point of scale and gets adopted by a large scale community becomes something that is a mode that is hard for other people to overcome. And talk to us about the team in China and how big of an edge these folks are. I mean, Chinese people are very humble. They're very, very hardworking. They're very sharp. And if you can work with them whether out of China or United States or any other part of the world, you're working with some of the brightest minds in the world. And so when I started the business, one of my goals at this company was just work with great people and figure things out. And so when I sit in a room with some of the people on my team, I know I'm probably the dumbest person in that room. And that gets me excited to show. - Got him. - It gets me to show up for the part of you when we hear that. - Yeah. - Work for me. All right, let's give it up for Adam. - Thanks for having me. - Thanks, bro. - That was great. - Thank you. - Thanks, that was great seeing you. [BLANK_AUDIO]