PodcastAll-In PodcastAdam Foroughi

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

The discussion examines how AppLovin applies deep learning to mobile advertising, including discovery ads, targeting, privacy adaptation, proprietary training data, and the potential expansion into shopper behavior. Foroughi also addresses agentic shopping, competition with Meta and Alphabet, AppLovin's 84% EBITDA margin, and the technology and data advantages that may support its competitive moat. The company's valuation collapse from $28 billion at its 2021 IPO to about $3.8 billion in 2022, followed by roughly $6 billion in buybacks, provides the market context.

Artificial IntelligenceTechnology StocksCorporate EarningsMarket ValuationsAutomationInvestment Strategy

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Key Takeaways

Foroughi sees mobile gaming's roughly $50 billion annual advertising market as a foundation for broader discovery advertising: stronger deep-learning models could use gaming's large, reward-driven audience to identify shopper behavior and introduce consumers to products they were not already seeking. He also links advertising, recommendation systems, and large language models through related research trajectories.

Foroughi argues that highly relevant ads can be generated from observable actions such as searches, browsing, product research, and social-network relationships, rather than microphone access or precise location tracking. He says clear privacy regulations may initially reduce targeting precision, but deep-learning systems allow companies to adapt after new rules take effect.

AppLovin's acquisition of game studios gave it proprietary training data when studios generally would not share data with third parties; after its first deep-learning model succeeded and outside data became available, the company divested those studios. Foroughi presents differentiated data, complex technology, and scale-driven adoption as potential sources of a durable moat.

TECHNOLOGY

Mobile Advertising — Mobile gaming supports roughly $50 billion in annual advertising spend, fueled by its large audience and users who often watch ads for rewards. Foroughi argues that stronger deep-learning models could extend the ecosystem beyond game-to-game promotion into shopper behavior.

“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…”—

Discovery Advertising — Foroughi distinguishes discovery advertising from search-based tools. He argues that ads can introduce consumers to products they did not know existed, creating economic expansion rather than simply completing an intended transaction.

“That ads model is almost gonna exclusively compete with the Google search business…”—

AI Research Links — Foroughi describes advertising as an early, highly profitable application of deep-learning models and says recommendation systems and large language models have related research trajectories. He says research and researchers can move between the two areas.

“Yeah. I mean, advertising is like ML 1 dot o, but really was the 1st implementation of all these technologies that now are driving AI today…”—

MARKETS

Valuation And Buybacks — AppLovin's market capitalization fell from $28 billion at its 2021 IPO to about $3.8 billion in 2022, even as EBITDA rose from $600 million to $1 billion. Foroughi says the company responded by repurchasing roughly $6 billion of stock and retiring 20% to 25% of its shares; at one point, those purchases were worth more than $50 billion.

“So we went out in 2021, $600,000,000 of EBITDA, $28,000,000,000 market cap. We got as high as 40,000,000,000, and then in '22, the stock went down literally every day…”—

Adam Foroughi joins the Besties!

Adam Foroughi explains that AppLovin quietly built a large mobile-game advertising business, with deep-learning recommendations expanding beyond game installs toward broader shopping discovery.

Discovery vs search & is your phone listening?

Foroughi distinguishes discovery advertising from search, argues that relevant ads create economic expansion, and says AppLovin does not use microphones or precise location tracking to target users.

IPO tumble & becoming your own best investor

He recounts AppLovin's collapse from a $28 billion IPO valuation to $3.8 billion, then describes aggressive buybacks, employee incentives, and the deep-learning upgrade that drove its recovery.

Privacy rules, Apple's crackdown & how agents change people’s shopping

Foroughi says clear privacy rules let technology adapt, explains why AppLovin bought and later sold game studios for training data, and argues most shoppers will still enjoy human-led commerce despite agents.

How a lean team beats the giants, margin moats & building in China

He attributes AppLovin's competition with Meta and Alphabet to focus, speed, automation, proprietary data, an 84% EBITDA margin, and a highly capable global engineering team.

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Important Disclaimers

Not Investment Advice: For education only — not financial, tax or legal advice.

AI-Generated: Made by AI from source transcripts and may contain errors.

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