A year after Mercor hit $500 million in annualized revenue, the 22-year-old founders of the data-labeling startup just became billionaires, joining an unexpected gold rush. While frontier AI labs like OpenAI and Anthropic burn billions chasing superintelligence, the only companies actually making money off AI right now are the ones selling them the raw material: human expertise. Roughly $10 billion is flowing annually into training data providers, turning a sleepy corner of the AI infrastructure world into the hottest startup category.
Mercor started as something almost boring. When Brendan Foody was 19, he and two high school friends launched it in 2023 to help their other startup-founding friends hire software engineers overseas. Language models screened resumes. Models did the interviews. By the time Scale AI came knocking with a request for 1,200 specialized coders in early 2024, the startup was already pulling in $1 million a month.
Then Scale called. At the time, Scale AI was nearly the only household name in AI training data, having grown to a $14 billion valuation by orchestrating hundreds of thousands of people worldwide labeling data for autonomous vehicles, e-commerce algorithms, and coding tasks. But when OpenAI and Anthropic started pushing their chatbots toward actual programming ability, Scale needed software engineers to produce the training data—the kind of work that requires real expertise, not just crowdsourced button-clicking.
Foody saw something bigger unfolding. When the engineers he recruited complained about missed payments and chaotic platform management at Scale, he pivoted. By September, Mercor announced $500 million in annualized revenue. Foody's most recent fundraising round valued the company at $10 billion. At 22 years old, he and his two cofounders are now the youngest self-made billionaires.
Mercor's success isn't an outlier. It's the clearest signal yet of a wholesale reshuffling in how frontier labs approach AI development. While everyone obsesses over data center buildouts and chip shortages, an analogous race is happening for something equally critical: training data that actually works.
Labs have exhausted the easy stuff. They've already fed their models centuries' worth of publicly available text. When that didn't produce the superintelligence investors were promised, the labs pivoted to something different: teaching models specific skills through , a technique where models get rewarded for producing outputs that humans prefer. But unlike traditional crowdsourcing where you pay someone $3 to label images of dogs, this requires hiring lawyers, consultants, physicists, and surgeons to define what "good" means in their respective domains.
