Adam Foroughi, Applovin CEO: Surviving a 92% Drawdown, Ads as ML 1.0 & the $50B Game Ad Market
- AppLovin sits on a mobile-gaming ad market Foroughi sizes at roughly $50B a year — “it was not very long ago that social was a $50 billion opportunity.” Over a billion adults play casual mobile games daily; AppLovin disclosed $11B of annual ad spend on its own platform nearly two years ago, has grown roughly 60% year over year since (“gross that up to a nice round number today, you get $20 billion”), and the thesis now is extending game-to-game ad intent into e-commerce shopper behavior.
- The host framed the internal-culture episode around a 92% stock drawdown; Foroughi’s capital-allocation response was a masterclass: IPO in April 2021 at about a $28B market cap on $600M of EBITDA, reaching $40B before collapsing to $3.8B in 2022 while producing $1B of EBITDA — sub-4x. He stopped focusing on investors and bought roughly $6B of stock, retiring 20–25% of shares outstanding; at peak, those repurchases were worth over $50B, and shares later ran from $9 to $750 in 2.5 years, touching a $250B market cap.
- The re-rating catalyst was ML 1.0 → ML 2.0: swapping a regression model for a deep-learning ad model launched in April 2023. Because the model improved advertiser performance and let customers scale, the company grew quickly; investors took notice when Foroughi resumed meetings in New York around September 2023. “In that week, the stock went from $80 to $150” — roughly $28B to $55B — after he told investors the company had survived.
- Foroughi’s ad-market split is the key framework for the OpenAI-ads debate: LLM advertising “is almost going to exclusively compete with the Google Search business,” while discovery advertising creates net economic expansion. Bottom-of-funnel transactions would have happened anyway; showing a consumer something “they had no idea existed” is what powers Meta’s business and what AppLovin aspires to.
- On competitors compressing his stated 84% EBITDA margins, his answer is that complexity, differentiated data and scale create a moat: “if you can innovate and you have differentiated data, you can build an advantage.” He analogizes to Anthropic’s position in large language models despite the theory that it should not be running away with that market.
- On surveillance, he says he does not think advertising companies can track location, says AppLovin does not track it, and calls parsing microphone audio for ads unrealistic. He attributes many seemingly uncanny ads to trackable actions people forget. On agents, he sees them fitting repeat behaviors such as supplement subscriptions, but says the typical shopper still wants to browse, compare and enjoy the transaction; “we really over-index on the Twitterverse.”
1. A $50B ad market hiding inside 100,000 mobile games
- Foroughi’s framing of why few people know the company: no VC funding at the early stage meant “we just had to build quietly,” and “the goofy name” did not help. What it actually is: an ad platform monetizing a universe where over a billion people play casual mobile games daily — “these are all adults, heads of households.”
- The sizing chain: $11B/year of on-platform ad spend disclosed nearly two years ago, roughly 60% year-over-year growth since then → about $20B today; more than doubling that across other monetizers → roughly $50B annually. The reward mechanic — users watch ads to receive rewards — creates the possibility of intent, and the growth opportunity is extending that game-to-game recommendation behavior into shopping.
2. Advertising was ML 1.0 — and discovery ads survive the LLM era
- Ads as an early deep-learning implementation: “advertising is like ML 1.0.” Recommendation systems and large language models are related and, in many ways, follow similar trajectories; research can port between them, while advertising can translate the value of a prediction immediately.
- The load-bearing distinction, prompted by the host’s question about OpenAI’s ads: bottom-of-funnel search ads close a transaction that “was going to happen anyway,” so LLM ads “almost exclusively compete with the Google Search business.” Discovery — Meta’s model and AppLovin’s aspiration — creates a recommendation and transaction that did not previously exist, plus “a really fun moment for the consumer” as they wait for the package.
- On ad quality’s arc since 2005 — “complete garbage. It was all spam” — Facebook paired available data with better technology so ads became more like content. In AppLovin’s domain, Foroughi says “people love the ads”: users engage with playable minigame previews appearing inside other games.
3. Surviving the collapse: buybacks as offense
- The host framed the culture challenge as a 92% drawdown. Foroughi’s diagnosis of the collapse was that market prices depend on the quality of the investors owning a stock. A COVID-era IPO wave meant blue-chip investors did not research “this goofy-named company,” leaving little demand against substantial supply; the stock fell “literally every day” in 2022, from a $40B peak to $3.8B while EBITDA reached $1B.
- The response was to stop focusing on investor relations and deploy cash into roughly $6B of buybacks, retiring 20–25% of shares. “You do have an opportunity on the other side of it,” he said.
- The human cost was real: “I would get phone calls from family members and friends — are you suicidal?” His team received similar calls without his ownership cushion. The response was “an us-against-the-world mentality,” plus a performance stock plan extended across key employees: “we understand you thought you had a house and now you don’t.”
- The recovery mechanics: the April 2023 deep-learning model improved advertising performance; because the business is performance-based, better advertiser returns enabled customers to scale and drove rapid company growth. Foroughi resumed investor meetings around September 2023, when the stock moved from $80 to $150 in a week. His takeaway from the full $9-to-$750 cycle is that public and private investors often follow trends later than ideal; the best investors identify them early.
4. Privacy, “creepy” ads, and why agents won’t replace discovery
- The host’s theory — geolocation grouping friends at lunch, then targeting them after one person searches — gets a qualified rebuttal: “I don’t think advertising companies can track location. We don’t track location at all.” Parsing microphone audio and turning it into ads is “not realistic.” Foroughi’s mundane explanation is that users performed trackable actions such as searches or browsing and forgot them. He allows that social-network relationships can influence ad experiences: if one person searches, friends may see something relevant, and “there’s nothing wrong with that.”
- On Apple’s tightening of privacy rules, his view is that regulations should be clear so technology companies can adapt. After users are grouped more broadly, some complain that the resulting ads are spam and ask for more relevant ones. He also argues that relevant ads create economic value: the digital ad economy contributes to GDP, and better technology can accelerate GDP growth.
- On agentic commerce, agents fit consistent repeat behavior such as his supplement subscription. But the typical shopper “wants to window-shop,” compare, track the order and enjoy the transaction; even saving 20% on a $50 purchase may not outweigh that experience. “We really over-index on the Twitterverse.”
5. Competing with Meta and Google through an 84% margin, lean machine
- Asked how a small company competed with a decade or two of Meta and Google advertising expertise, Foroughi says, “We never think we won.” The company stays lean, concentrates subject-matter experts on mobile gaming and transactional behavior, and tries to move faster than larger competitors. The game studios were a data play: AppLovin bought them to seed training data for its first deep-learning model, then divested them once third parties began supplying data.
- The host’s leakage question mostly bounces off AppLovin’s stated 84% EBITDA margin. In Foroughi’s example, a lipstick advertiser pays AppLovin less than the $20 purchase price minus cost of goods sold; the advertiser covers the customer-acquisition cost immediately, then scales under a performance-based model. The admitted gap is that AppLovin is “not the full chain” and is not itself the advertiser.
- Why competitors have not competed the margins away: these technologies are complex, and differentiated data plus a model that reaches scale and adoption can become a moat that is difficult to overcome. Foroughi compares that dynamic to Anthropic’s position in the large-language-model market.
- On the engineering offices in Palo Alto, Beijing and Singapore, he describes Chinese colleagues as humble, hardworking and sharp, and says that when he sits with some team members, “I know I’m probably the dumbest person in that room. And that gets me excited to show up.”
Full transcript
Adam is probably the best founder no one's ever heard of. There's an ad platform hiding inside 100,000 mobile games and is quietly outperforming Facebook ads for e-commerce brands. Of all those thousand plus IPOs, the number one most valuable is AppLovin. The founder mentality has got to be Chase winning. They're going to print something like $6 billion in cash this year. 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 Foroughi.
All right. Welcome, Adam. Hey, man.
Big man.
How you doing, bro? Good to see you.
Likewise.
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. You're not in the headlines all the time with your business. You're operating your business almost absent media. You don't do a lot of press or get out there talking about the company, but it's such an incredible business.
Can you tell the audience what AppLovin is and maybe 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. Obviously, the goofy name didn't help us all that much, either.
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 1 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 2 years ago, that on our own platform there was $11 billion a year of ad spend. Since then, we've grown roughly 60% year over year. 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 double that 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. The space is growing really quickly. A lot of the audience watches ads. A lot of times, they watch the ads to get rewards, and that dynamic creates this possibility to create intent.
For most of AppLovin's 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. What's really gotten investors excited about our company—and us excited about the opportunity 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 shopping experience.
1. Discovery vs search & is your phone listening?
That allows us to tap into much larger economies and make more of an economic impact in the world. That's why our team's just really pumped up about what we're doing.
The first wave of internet advertising was, in many ways, the spark for a lot of critical technologies that then diffused out into the world. If you think about what Google was able to do with AdWords, AdSense, Applied Semantics, and that whole range of technology, is that true in this generation of internet advertising?
Are there technologies and things being birthed here that are consequential and foundational to the rest of the internet?
Yeah, advertising is like ML 1.0. It was really the first implementation of all these technologies that are now 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. A lot of the research being done in the 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 2 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—an advertisement, or, if you're building a social network, an 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' reactions to ads in 2026 versus 2006? 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. They were complete garbage. It was all spam, and the technologies just weren't powerful enough.
Your old company, Facebook, did a really good job of realizing that 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. 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.
In our domain as well, people love the ads that we show. You would think people wouldn't like them, but we see tons of engagement on little minigames that are appearing in other games. 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-wringing about the impact AI will have on the ad networks, specifically Google's interface. 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 5 or 6 queries with a chatbot? It's pretty obvious that 95% of the world are not going to pay $20 a month for this technology. They're going to expect it to be free. ChatGPT has already said they're going to make it free.
Tell us what they're doing in advertising. 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 Search's franchise?
There are 2 sides of advertising. One part of it is bottom-of-funnel advertising, where a consumer sort of knows what they want to buy, but they're doing research to go complete the transaction. That's the Google Search business.
If I wanted to buy a pair of dress shoes, I'd go to Google historically, do some research, and they would direct me to where I needed to go based on the ads. 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. 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. The reason that's interesting to me is that the transaction via search or an LLM was going to happen anyway. If the LLM didn't exist and Google Ads had never come into 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—
Discovery, basically.
Totally. Complete discovery. This is what makes Meta so 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, and they're excited to open it up, but you create economic expansion.
What about the arms race that develops over time, where some people say, “I mention something with my friends at lunch, and all of a sudden I show up and there are these ads on Meta or wherever”? Is that just us overreacting, or is that actually happening? Is there a push not to be more intrusive, but the tendency to want to sell more?
That's creepy.
Yeah, it feels creepy when it does happen, or just to push the boundaries. 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 think you've done other actions that are trackable, like doing a search, browsing a website, or doing a product search, and you don't realize it. Then you say something related to it and start seeing ads that are relevant. So it's not like the mic is on or there's an app that has actually taken your speech.
There's a theory, though, that if we were all at lunch, especially with these apps, our geolocation has kind of put us into a group. We might be talking about this new car we're all interested in, or a watch. Then Friedberg, 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 4 of them the ads for the watch.” You're mixing and matching based on—
Is that what's happening? That's what I'm told.
I don't think advertising companies can track location. 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 imagine the amount of data that's transferring if you're mic'd, then parse the mic content to try to translate it into an ad. That's not realistic.
But what about us being friends and being connected together as groups?
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 Chamath 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 is that 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.
There's a big part of GDP that's now coming from this digital ad economy. The better these technologies get, the faster GDP growth.
2. IPO tumble & becoming your own best investor
Adam, let's just go back, because what I find so fascinating about the business is the way you've operated it. You're based in L.A. Is that right?
I'm based in L.A. The company started in Silicon Valley. We're in Palo Alto.
Palo Alto. But you're here, and then you have a lot of developers in China. Is that right?
Our engineering offices are in Palo Alto, Beijing, and Singapore.
And then the company didn't raise a lot of venture money. You took the company public in 2021. It went public at about a $20 billion market cap out of the gate.
Yeah, we were a 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 to?
Well, this is the funny thing about the public market. We went out in 2021 with $600 million of EBITDA and a $28 billion market cap. We got as high as $40 billion. Then, in 2022, the stock went down literally every day. We got to about a $3.8 billion market cap. In that year, we did $1 billion in EBITDA.
That's incredible.
So hold on. Let's just go through this. The market's in disbelief for some reason about the business. What do you do?
Yeah, what you learn pretty quickly—and I'm a finance background, so I had a good education on this—is that your price in the markets is determined by the quality of your investors. We had private-market investors, both private equity and ex-cofounders and other team members, that were going to sell when we went public. 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.
That construct created this world where we just tanked, and the multiple went from fairly high—I mean, I wouldn't really value companies at 50 times EBITDA—to something that was absurdly low, sub-4 times. Being that finance-minded person, you have to remember that with that kind of a bashing, you do have an opportunity on the other side of it.
I turned internally to the team and said, "I'm not going to talk to investors at all anymore. They're not buying our stock. It's a waste of time. But guess what? We generated 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 since then, I think we've bought roughly $6 billion of the company's stock and retired 20% to 25% of the shares outstanding. At peak, that $6 billion was worth over $50 billion. You can take that moment, which does feel super depressing, and turn it into a huge opportunity.
How did you manage—sorry. Did you feel that way the whole time, or was there this period of depression where you're like, "Oh my gosh, what—"? That's what I was going to ask: How do you manage the internal culture when the stock is off 92%?
It's tough. I mean, I would get phone calls from family members and friends: "Are you suicidal?" And I'm like, "Look, we got stuck at a penny. The stock's still like $10. It's still up a lot."
But it's very tough, because you realize, as a CEO, that your team is getting those same phone calls from their family members.
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 going to buy back shares." 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 going to make a ton on the upside."
And then, when investors started showing up and saying, "Hey, we're paying attention again"—
It was interesting, because for us, what happened was we went from ML 1.0, like we talked about a couple of minutes ago, to ML 2.0. We went from a regression model to a deep-learning model, and the outcome was that we're driven by our advertising algorithm. The better it works, the better the advertiser return is on our platform, and everything is performance-based.
We're selling revenue to advertisers; the more they scale, the more we make. The company just started growing really quickly. We turned into 2023 and launched that model in April. We still weren't talking to investors, so people hadn't found out.
Then, somewhere around September of 23, I went to New York. The stock was now around $80, and we'd recovered quite a bit because performance was good. I said, "I'm going to start talking to investors, because the market cap's getting high enough and we can't really buy back all that aggressively anymore." In that week, the stock went from $80 to $150. I think it went from about $28 billion to $55 billion.
From you being in New York?
From me just going out and saying, "Hey, our company still exists. We survived this." Yeah, and then people—I'd sit in the meetings, and it's pretty easy to read the other side of the room if you do that kind of thing. When you sell your company, I sit in the meetings and I'm like, "These people are literally calling their friends in the room going, 'Buy, buy, buy, buy, buy.'" And I was like, "Ah."
And then 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. Damned if you do, damned if you don't. Unfortunately, not a lot of people are contrarians, 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 $9 to $750 a share in a matter of 2.5 years. It was a $3.8 billion market cap. Some people bought some options back then and were probably living in some massive homes, and we got to a $250 billion market cap. 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 find public-market investors are not all that different from private-market investors. They follow trends, but a lot of times later than you'd want. The most sophisticated hit those trends early.
That's why you have really good VCs and average VCs. You have really good public-market investors and average public-market investors.
3. Privacy rules, Apple's crackdown & how agents change people’s shopping
Maybe you could talk a little bit about privacy. Apple and the EU really are looking at companies like yours, and they think 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.
What's the headwind on this business, and how do you manage privacy when Apple really is trying to neuter your business?
Look, in any of these spaces, you want the regulations to be clear. Once they're clear, technology can deal with them. If you could precisely target a user 5 years ago on iOS and today someone says, "I don't want you to precisely target me," you group them in a bunch and serve them a worse advertisement.
The funny outcome of that is we'll get a lot of complaints after the change Apple made from users who 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. If you're doing that, do you want to sit and watch garbage for 30 seconds, or do you want to watch something relevant?
What's happened since a lot of the privacy noise is that the rules were written, technology companies have adapted, and deep-learning networks are really powerful.
Just one quick follow-up: With this amazingly profitable business, I think you dabbled in buying some of the games. 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 capital 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 with third-party companies.
So we bought our own studios, seeded the training data in our first model, built a model that was really successful in the market, and started growing really quickly. Once we started doing that, third parties were coming in, and we divested them.
What does the world of advertising look like when there are agents everywhere? Agents are servicing you. Maybe the human interface to computing 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 push into existence?
Yeah, I think the reality is that 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 every single month and delivered on time.
But these discovery platforms aren’t that. The typical shopper is not the person who’s deep into agents, sitting on Twitter, and adopting the latest technology. I say our audience is the New York Times audience. There are still a ton of people using Yahoo properties every single day.
The typical shopper wants to find a product and actually go through that shopper behavior. They want to 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, “Hey, an 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 think we really over-index on the Twitterverse and forget that the average shopper is not that.
4. How a lean team beats the giants, margin moats & building in China
Yeah, let me just try to understand how you won, because 2 of the smartest companies in the world, with the best engineers—Meta, Alphabet, Google—make most of their revenue from advertising. They’ve built their own models. They’ve been doing it now for 1 to 2 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. Here’s something that helped us get to this point: We never think we won. We think every day we wake up and we’re probably going to get screwed right now, and we’d better work hard.
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. I think there’s this ability to take on giants if you’re very focused, remain lean, and can just move faster than them.
What’s the leakage in the business, then? Meaning, when you look at a P&L—you know, I did this thing with Amazon a decade ago, where you look at all of these places in which they were leaking, and our big insight was, wow, they just absorb these things and they’ll become the new businesses. That was our long thesis for Amazon.
Yeah.
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, our EBITDA margins, I think, are number 1 in the market. It’s 84%, so I don’t know how much leakage we have, given the metric.
But the way to think about it is that an advertiser comes into our platform, and they have a transactional model. Let’s say they’re selling lipstick. We give them an arbitrage: They’re buying the consumer from us. 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 that 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. 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, as a corollary to David’s question, that hasn’t really happened, and it’s been incredibly sustained.
Yeah.
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. 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 a moat that is hard for other people to overcome.
Talk to us about the team in China and how big of an edge these folks are.
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, the United States, or any other part of the world, you’re working with some of the brightest minds in the world.
When I started the business, one of my goals at this company was just to work with great people and figure things out. 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 up.
Got him.
How does it feel when you hear that?
Yeah.
Works for me.
All right, let’s give it up for Adam.
Adam, thank you. Thanks, bro.
That was great. Thanks, man. Thanks, man. Great to see you.