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

Likewise.

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 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. Space is growing really quickly. A lot of audience these people watch ads. They 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 company's life, we've been creating that intent to drive a user to take one game's experience and go to the next games 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 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, make more of an economic impact in the world. And that's why our team's just really really pumped up on what we're doing.

Yeah, I mean advertising is like ML 1.0 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 a lot in a lot of ways they follow the same trajectory. So a lot of the research that's being done in the space 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 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, 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.

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.

Yeah, I mean there's two 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. And that's Google 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.

Totally complete discovery. And 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 cuz 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.

That's creepy.

I mean I think you've done other actions that are trackable like do a search, browse a 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 space.

I don't think advertising companies can track location. So, we don't track location at all. It's a really heavy concept to track people's precise location to then run render an ad. And then to imagine the amount of data that's transferring if you're mic to then parse the mic on content to try to translate to an ad. Not realistic.

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 someone 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. The better these technologies get, faster GDP growth.

I'm based in LA. Company started in Silicon Valley. We're in Palo Alto.

We have our engineering offices are in Palo Alto, Beijing and Singapore.

Yeah, we were co IPO. We went out in April 2021. It was about $28 billion.

Well, this is the funny thing about the public market. So, we went out in 2021, $600 million of EBITDA, $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. In that year, we did a billion dollars in EBITDA.