Advertising as ML 1.0

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• The Duke of AI — Living Handbook

  NEW (v14) — Advertising is described by an operator at very large scale as "ML 1.0", which gives the model-improvement flywheel a track record predating the current cycle. AppLovin's CEO argues ad systems were the first commercial implementation of the techniques now driving AI, that recommender systems and large language models are two branches of the same research with people and methods porting in both directions, and that advertising's structural advantage is immediate feedback — predict an outcome and you learn at once what the prediction was worth. He concedes an LLM's economic value to society is far greater, while claiming advertising is deep learning's most profitable implementation (DK-120). All figures in that entry are self-reported about his own company on a friendly show [...] | 24 Sep 2026 | DK-120 | Big Tech, Legal & Business; AI Model Landscape | v14 merge — advertising as ML 1.0, giving the model-improvement flywheel a track record predating this cycle; the moat question answered in the affirmative by an operator and applied to Anthropic by name; discovery against bottom-of-funnel as the frame for what an LLM ad product can take. | | 24 Sep 2026 | DK-121 | AI Ethics & Safety; AI Model Landscape | v14 merge — the regulatory-capture counter-narrative in its conspiratorial form, and the archive's clearest worked example of a real fact narrated into something it does not support: the Sanders bill is real, the conduct said to trigger it is not, and the claim survives only by dropping six words from a definition. |

• Adam Foroughi | All In Summary

  ## 1. Key Themes ### Advertising as "ML 1.0" — the Original Application Layer for Deep Learning Foroughi frames advertising not as a marketing function but as the first real-world proving ground for the deep learning technology now powering AI broadly. This reframes AppLovin as an applied AI company rather than an ad-tech company. > "Advertising is like ML 1.0, but really was the first implementation of all these technologies that now are driving AI today... 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." 00:03:34 ### The Real Value of Advertising is Discovery, Not Closing Loops [...] POD // EPISODE ALL IN # Adam Foroughi, Applovin CEO: Surviving a 92% Drawdown, Ads as ML 1.0 & the $50B Game Ad Market DATE September 20, 2026SOURCE ALL INPARTICIPANTS ADAM FOROUGHI, CHAMATH PALIHAPITIYA, DAVID FRIEDBERG, JASON CALACANIS Deep LearningCompetitive StrategyCapital EfficiencyFounder StoriesGrowth & ScalingProduct & Design // KEY TAKEAWAYS6 ITEMS 1. 01Advertising as "ML 1.0" 2. 02The Real Value of Advertising is Discovery, Not Closing Loops 3. 03Mobile Gaming Ad Inventory is a Vastly Underestimated, ~$50B Market 4. 04Public Market Mispricing Can Be a Gift, Not a Crisis 5. 05Privacy Regulation, Once Codified, Becomes a Solvable Engineering Problem 6. 06Agentic Commerce Will Be Real But Overestimated by Silicon Valley ## 1. Key Themes

• Adam Foroughi, Applovin CEO: Surviving a 92% Drawdown, Ads as ML 1.0 & the $50B Game Ad Market｜All-In with Chamath, Jason, Sacks & Friedberg — BigGo Finance

  ## Advertising as ML 1.0 and the discovery-versus-search distinction Chamath presses on whether this generation of ad tech is birthing foundational technologies the way AdWords, AdSense, and Applied Semantics did. Foroughi agrees advertising was "like ML 1.0" — the first implementation of techniques now driving AI — and notes the two research communities are porous: recommendation systems and large language models follow similar trajectories, and researchers move between them. His key structural point is that advertising models predict a future outcome whose value can be translated immediately, which makes them a very profitable implementation of deep learning even if the economic value of an LLM in society is greater. [...] ## Advertising as ML 1.0 and the discovery-versus-search distinction Chamath presses on whether this generation of ad tech is birthing foundational technologies the way AdWords, AdSense, and Applied Semantics did. Foroughi agrees advertising was "like ML 1.0" — the first implementation of techniques now driving AI — and notes the two research communities are porous: recommendation systems and large language models follow similar trajectories, and researchers move between them. His key structural point is that advertising models predict a future outcome whose value can be translated immediately, which makes them a very profitable implementation of deep learning even if the economic value of an LLM in society is greater. [...] Adam Foroughi, co-founder and CEO of AppLovin, joins Chamath Palihapitiya, Jason Calacanis, and David Sacks for a rare on-the-record conversation about a company that has spent most of its life operating "absent media." Foroughi frames AppLovin as an advertising company that monetizes mobile casual gaming — a universe of over a billion daily players, mostly adults and heads of households — and describes how a shift from regression models to deep learning ("ML 1.0 to ML 2.0") turned the business into one of the most profitable ad platforms in the market, with EBITDA margins he cites at 84%. The episode's spine is the contrast between that operating performance and the public market's violent mispricing of it: a 2021 IPO at roughly a $28 billion market cap, a collapse to about $3.8 billion

• Adam Foroughi, Applovin CEO: Surviving a 92% Drawdown, Ads as ML 1.0 & the $50B Game Ad Market — All-In with Chamath, Jason, Sacks & Friedberg Transcript

  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 2 spaces are really related. The [...] It was interesting because for us, what happened was we went from ML 1.0, like we talked about a couple minutes ago, to ML 2.0. We went from a regression model to a deep learning model. And the outcome was, we're driven by our advertising algorithm. The better it works, the better advertiser return is on our platform, and everything is performance-based. So we're selling revenue to advertisers, the more they scale. And so the company just started growing really quickly. Now, we turned into '23 and launched that model in April. We still weren't talking to investors, so people hadn't found out And then it was somewhere around, I think, September of '23 that I went to New York and I said, the stock's now like $80, and we'd recovered quite a bit because performance was good. But I said, I'm [...] Yeah, there's 2 sides of advertising. One part of it is bottom-of-funnel advertising where a consumer knows what they want to buy, but they're doing research to go complete the transaction. 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

• Applovin CEO: The "Darkest Hour" of a 92% Stock Price Crash and "Self-Salvation" | Bitget News

  Host: The first wave of Internet advertising lit the spark for countless key technologies that spread worldwide, such as Google’s AdWords, AdSense, application semantics, etc. Is a similar wave underway now? Are there foundational technologies being created here that could shape the internet’s future? Adam Foroughi: Yes. Advertising is ML 1.0—it’s the initial implementation of all these technologies now powering AI. LLMs today generate much more economic value than ads, but advertising remains an incredibly profitable deep learning application. [...] But the company still refrained from making public statements. It wasn’t until September 2023 that Foroughi went to New York to meet investors for the first time. By then, the stock had rebounded to about $80. "That week, the stock price rose from $80 to $150. The market cap jumped from $28 billion to $55 billion." "Just because I went to New York and told everyone: Our company is alive, we made it back." ## Advertising as "ML 1.0": The Technical Logic Foroughi has a clear framework for AppLovin’s technical positioning. Advertising is ML 1.0. It’s the first commercial implementation of all these technologies, which now drive AI. He distinguishes between two types of advertising logic: [...] Foroughi frankly said during the conversation: "Advertising is ML 1.0; it is the first commercial implementation of all the technologies now powering AI." AppLovin, following this very technology curve, made the leap from mobile game advertising to generating e-commerce shopping intent. Applovin CEO: The Applovin CEO: The ## An "Advertising Empire Hidden in 100 Million Games" AppLovin is not exactly a household name. Foroughi himself admits, "That silly name certainly didn't help much." But its scale shouldn't be underestimated. Foroughi explained during the conversation that AppLovin is essentially a mobile game advertising company—helping game developers monetize their traffic. He gave some figures: Over 1 billion people worldwide play mobile casual games every day