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

Chamath Palihapitiya (0:01)

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.

Jason Calacanis (0:10)

Of all those thousand plus IPOs, the number one most valuable is Applovin.

Chamath Palihapitiya (0:16)

Applovin CEO, Adam Foroughi.

Adam Foroughi (0:18)

The founder mentality has got to be chase winning.

Chamath Palihapitiya (0:20)

They're going to print something like $6 billion in cash this year.

Adam Foroughi (0:24)

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 has ever seen.

Chamath Palihapitiya (0:31)

Please welcome Adam Foroughi.

Chamath Palihapitiya (0:45)

Hey, Ben. How you doing, bro? Good to see you.

David Friedberg (0:48)

Adam, thanks for being here.

We thought it would 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 Applovin is and maybe also frame up the market a little bit for us?

Adam Foroughi (1:11)

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.

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 percent year over year, roughly.

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.

A lot of times they watch the ads to get rewards. And so that dynamic creates this possibility to create intent. For most of the light company'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. 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, 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.

Chamath Palihapitiya (3:08)

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 like if you think what Google was able to do with AdWords, 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?

Adam Foroughi (3:34)

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 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.