Entrepreneurs
3 Curated Dialogues
Adam Foroughi
Frontier Insights
Core Thesis: AppLovin turned an existential 92% drawdown into an ad-tech juggernaut by refactoring its core engine around Axon 2—replacing rigid branch logic with semantic embeddings to drive unmatched targeting accuracy at lower unit compute costs.
Strategy: Treating AI throughput and token budgets strictly as financial inputs, Foroughi built a hyper-efficient machine delivering ~70% growth, 84% EBITDA margins, and immense free cash flow conversion.
Risks & Next Frontier: Hitting a $1T valuation requires cracking non-gaming verticals like e-commerce. Looming threats include terminal SaaS compression, stock dilution, platform dependencies, and potential talent disruptions from aggressive automation.
Key Views & Dialogues
Adam Foroughi, Applovin CEO: Surviving a 92% Drawdown, Ads as ML 1.0 & the $50B Game Ad Market
AppLovin is extending a roughly $50B mobile-gaming ad market, with platform ad spend growing from $11B to about $20B, into e-commerce discovery. Its April 2023 deep-learning model improved advertiser performance and enabled scaling, while roughly $6B in buybacks retired 20–25% of shares after a 92% drawdown. Discovery may expand activity beyond Google Search, but stated 84% EBITDA margins depend on differentiated data, complexity and scale.
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- 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.”
Bootstrapping a Business to $5 Billion in Free Cash Flow | AppLovin’s Adam Foroughi
AppLovin turned a 92% drawdown into a $6B levered buyback negotiated directly with private-equity holders, while Axon 2 made performance advertising an arbitrage where customers can scale until their bank accounts constrain spending. The resulting $5B+ cash-flow run rate with only 350-400 core employees supports expansion from gaming into e-commerce and SMBs, but thin IPO float, CFIUS history, and trust damage from vertical integration remain risks to monitor.
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- AppLovin’s response to a 92% drawdown was what Foroughi called one of the more successful buybacks in the company’s history: with the stock at a $3.8B market cap against $1B+ of EBITDA, he levered up and deployed ~$6B repurchasing shares — negotiated directly from the private-equity holders he knew would sell — ultimately generating “somewhere in the neighborhood of 50, 60 billion dollars of actual proceeds.” His logic was brutally simple: “You look at a multiple of five times cash flow, and you go, ‘Okay, why don’t we just buy all the shares?’” — and “I never believed in saving cash for a rainy day.”
- Every top VC rejected AppLovin in spring 2012 at $1M for 25% of the company — then funded competitors with the identical idea, which Foroughi turned into fuel. He calls it “a failure in seeing who the founder was” (this was his third advertising business), and says “we had it as a goal to put those companies out of business.” Today the company is worth roughly $140B; the funded rivals “either sold for very little or are gone.”
- The core product thesis is “make the advertiser an arbitrager”: performance ads where the client provably makes more than they spend, so “the only constraint on them scaling in our system is the money that they have in their bank account.” Foroughi deliberately avoided brand advertising from day one — “I wanted to be in the business of no sales, high-value product” — and still runs an ad giant with no CRO, no COO, and no sales force.
- To train ML models without advertiser data that clients refused to share, AppLovin vertically integrated — buying 14-15 game studios to obtain data and understand developers’ needs, temporarily damaging developer trust, then selling the whole portfolio to Tripledot roughly a year ago once advertisers were increasingly connecting to the platform. The lesson he admits: the trust breakdown was “a communication error, not a desire to deceive” — he never explicitly told the community why he was entering gaming.
- Axon 2, the deep-learning model rolled out in April 2023, coincided with the stock moving from $9-11 to a $750 peak (~$250B valuation) and marked the inflection: any advertiser can “plug in, get going, spend money, get revenue on the other side, and make a spread.” The company now generates ~$1.3B of cash in a single quarter — $5B+ run-rate — with only 350-400 people in the core business.
- The talent doctrine is the operational moat: in a year the business grew nearly 100% while Foroughi fired ~40% of the team, concentrated equity in a little over 100 critical people, and still personally approves every single hire — killing automatic “backfills” took annual hiring from hundreds to tens. “A players don’t like to work with B’s, C’s, D’s… eventually you end up churning out your A’s.”
- AI is compounding the leverage asymmetrically: over 80% of code was LLM-written a year and a half ago (“now it’s much higher”), and “your 10X engineer might be 100X more efficient” — which is why he resists hiring ahead of the e-commerce and broader expansion opportunity. The stated ambition, wired into a comp plan with a top trigger at a $1 trillion market cap: expand beyond gaming across SMB categories, down to “the local laundromat discovered by someone playing a game.”
- The governance lessons are candid and tradeable: no board for six years let him walk from a $600M offer (right call) but also led him into a $1B, 70%-control deal with a partially state-owned Chinese PE fund that was restructured after a year-plus CFIUS ordeal (wrong call), and IPO with only a 7-8% float — “probably a mistake in hindsight” because a thin float at a pushed valuation buys volatility and worse investors instead of long-term blue chips.
AppLovin CEO: Why Founders Shouldn’t Angel Invest & Why the Best Don’t Need Mentorship
AppLovin combines roughly 70% growth, 84% EBITDA margins, and over $10M EBITDA per core employee, after rebuilding its recommendation technology from the 2022 trough. Its targeted buyback removed a fragile cap table overhang, but AI-driven layoffs, SBC dilution, and frontier-model competition make cash flow, moat durability, and terminal value the central risks.
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- AppLovin’s financial profile has no comparable, and Foroughi knows it invites suspicion: ~$150B market cap, 84% EBITDA margins, rule of 40 running near 150, ~70% year-over-year growth, and reaching or over $10M EBITDA per head across the ~400-person core business. “There’s not another comp in the world that looks like it” — and his explanation for the short attacks follows directly: “in a world where things don’t make sense, people think you’re cheating.”
- The turnaround was a central conviction bet at the bottom: after falling 92% in 2022 to under $4B (under 4x EBITDA, while growing ~40%), he declared the old recommendation-system ML dead, slowed basically all R&D on it, turned over the people committed to it, and rebuilt on cutting-edge techniques — Model Axon 2, launched April 2023 — with the stock later running from $9 to $750 in roughly two and a half years.
- The 2022 buyback created “roughly a third of the company’s value… call it 50 billion around”: he shut down investor relations, raised some debt, and bought back specifically from the flimsy COVID-era cap table that needed to sell, removing the overhang. But he’s explicit that buybacks don’t usually pan out — “you sort of trade where you deserve to trade” — citing Wix’s big buyback followed by a ~25% weekly drop.
- The org call: he cut 40-50% of staff in most departments during a near-triple-digit growth year, rebuilding “as if we were building it knowing what technologies were available to us today” — HR went from 70-80 people to ~15. Same logic applied to AI spend: “token quotas and token budgets are no different than hiring quotas,” and he expects “a lot more tech layoffs over the next couple of years.”
- The SaaS apocalypse is fair and “I’m not sure it’s actually done yet” — LLM shipping speed makes terminal value “dicier,” and the SBC death spiral (3% dilution becomes 10% after a 66% fall) compounds it. Judge everything on cash flow minus SBC; AppLovin holds its grant flat at ~$300M a year. And on startups: “I would be very very nervous if I was building a business as an interface on top of” the frontier labs.
- Management heterodoxy throughout: no product org (engineers are the product managers; 80-90% of code is AI-written but “that discounts quality over quantity”), no one-on-ones, no reviews, little mentorship — “really good people figure out a way” — a four-person exec team, and Claude Code as the shop standard with Cursor “less so these days.”
- The personal ledger, stated without varnish: founders shouldn’t angel invest (the distraction losses “can compound”), kindness has a speed cost (“if you’re too kind and not as direct, not as aggressive, you’re wasting time”), and the price of the grind was presence — his kids’ childhood was “sort of a blur”: “I was there, but I wasn’t there mentally.”