Key Notes
- AfterQuery, an 18-month-old AI training-data startup, reportedly hit a $3.2 billion valuation in a Series B — a 10x jump from its $300 million Series A valuation just five months earlier.
- The company pays doctors, lawyers, engineers and financial analysts to generate expert-reasoning training data, reporting over $100 million in annualized revenue by April, growing to "hundreds of millions" by July, on roughly $30-34 million in total disclosed funding.
- AfterQuery says it trains its own models on the data before selling it, to prove the material improves performance rather than asking labs to take its word for it.
AI training-data startup AfterQuery has reportedly reached a $3.2 billion valuation in a new funding round, Forbes reported, citing two people with direct knowledge of the deal. The figure is more than ten times the $300 million valuation the company carried just five months earlier, when it closed a $30 million Series A in April.
That jump makes AfterQuery the fastest startup in Y Combinator’s history to go from inception to unicorn status, according to YC partner Gustaf Alströmer, surpassing the previous record held by satellite-data-center startup Starcloud, which reached unicorn status 17 months after its demo day in March. AfterQuery’s founders, Spencer Mateega and Carlos Georgescu, now 23 and 22, started the company in February 2025, 18 months ago, after joining Y Combinator’s Winter 2025 batch as high school friends with no finished product and no fixed idea. One of Forbes’ sources said the company is already profitable and has lined up a lead investor for the round; AfterQuery declined to comment on the report.
AfterQuery’s business centers on paying knowledge professionals, including doctors, lawyers, engineers and financial analysts, to generate training data by working through real professional tasks and capturing their reasoning. Rather than simply verifying whether a model’s answers are correct, the company says it is “encoding the patterns, decisions, and reasoning of the world’s best practitioners,” aiming to teach AI systems to work through complex problems the way skilled professionals actually do. The company has named Nvidia, Legora, Thinking Machines Lab and the Korean AI lab Motif Technologies among its customers.
The financial trajectory has been unusually steep. AfterQuery reported an annualized revenue run rate of $100 million in April, a figure the company said had grown to “hundreds of millions” by July. With total disclosed funding of only around $30 million to $34 million prior to this round, that revenue growth implies notably high capital efficiency relative to typical venture-backed AI startups.
What’s Driving the Valuation
AfterQuery is part of a fast-growing category of startups, following Mercor and Scale AI, built around the premise that freely available internet text is no longer enough to train frontier AI models, and that labs increasingly need curated data reflecting genuine expert judgment. Mateega has said the company’s edge over rival Mercor, which relies on an AI interviewer to staff a large contractor pool, is proprietary software that screens submitted work for a “Goldilocks” difficulty level, hard enough to meaningfully challenge a frontier model but not so hard that the model can’t learn from the resulting answer.
AfterQuery also says it trains its own models on the data before selling it, intended to demonstrate concretely that the material improves performance rather than asking AI labs to take that claim on faith.
The pattern of extremely young founders reaching outsized wealth in this specific niche is now well established. Scale AI’s Alexandr Wang became the data-labeling industry’s first billionaire in 2021 at age 24, before Meta paid $14.3 billion for a 49% stake in the company and installed Wang atop its own AI research lab.
Mercor’s founders surpassed that early benchmark last October, becoming billionaires at 22. AfterQuery’s rapid ascent suggests the underlying market, AI labs racing to secure proprietary, hard-to-replicate expert data, remains intensely competitive and richly funded, even as the specific tenfold valuation jump in five months stands out even by that market’s fast-moving standards.
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