AI Startup AfterQuery Reaches $3.2 Billion Valuation in Rapid Funding Round
AfterQuery, an artificial intelligence startup founded just 18 months ago, has achieved a valuation of $3.2 billion following a new funding round, according to a report by Forbes. This valuation represents a more than tenfold increase from its $300 million valuation just five months prior, when it secured $30 million in a Series A funding round.
The rapid ascent makes AfterQuery the fastest startup in Y Combinator’s history to reach “unicorn” status, a term for privately held startups valued at $1 billion or more, according to YC partner Gustaf Alströmer. AfterQuery declined to comment on the Forbes report. One source cited by Forbes indicated that the company is already profitable and has secured a lead investor for the current funding round.
Spencer Mateega and Carlos Georgescu established AfterQuery in February 2025. The company’s trajectory began 18 months after they joined Y Combinator’s Winter 2025 cohort, initially as high school friends without a defined product or business plan. Mateega posted on X in July that the company’s annual recurring revenue had grown to the “hundreds of millions,” a significant jump from $100 million in April.
Pivoting to Expert Reasoning Data
The founders initially aimed to develop AI agents for the finance sector. However, testing revealed that leading AI models struggled with nuanced, professional-level decision-making. The issue was not a lack of raw capability, but rather that the models had not been trained on how experts approach complex problems.
This realization led to a strategic pivot. AfterQuery now compensates specialists for producing reasoning data—detailed, step-by-step accounts of how professionals tackle problems. This data is used to train AI models to develop judgment, rather than merely factual knowledge. Nvidia has utilized this data to train its open-source Nemotron models. AfterQuery’s client roster also includes Mira Murati’s Thinking Machines Lab and the legal AI firm Legora, Forbes reported.
Addressing the Demand for Expert Judgment
The demand driving this pivot is industry-wide. Companies developing the most advanced AI systems, often referred to as “frontier labs,” have largely exhausted the readily available text data on the open web. While synthetic data, generated by AI, offers some utility, it falls short in replicating the nuanced judgment derived from human experts.
Other companies are attempting to fill this gap through different approaches. South Korean fintech Toss, for instance, recently opened its 30 million users to the AI data economy through a partnership with data infrastructure firm Poseidon. This initiative pays ordinary users to record real-world data that is difficult to find online.
The Competitive Landscape of AI Data
AfterQuery is not the sole entity capitalizing on the shortage of expert AI training data. Alexandr Wang of Scale AI became the industry’s first data-labeling billionaire in 2021 at the age of 24. Meta later acquired a 49% stake in the company for $14.3 billion, placing Wang at the helm of its AI lab. Rival Mercor’s founders surpassed Wang’s early achievement, becoming billionaires last October at the age of 22.
Mateega has highlighted AfterQuery’s competitive advantage over Mercor, which employs an AI interviewer to manage a large pool of contractors. AfterQuery utilizes custom software to screen submissions for a “Goldilocks” level of difficulty—challenging enough for frontier models to learn from, but not so complex that they cannot grasp the solutions. Furthermore, AfterQuery trains its own models on the data before selling it, demonstrating its effectiveness to clients rather than relying solely on their trust.
Mercor is reportedly in discussions with Nvidia for a funding round that would value the company at $20 billion, doubling its $10 billion valuation from October. AfterQuery’s current funding round has not yet closed, and the company has not publicly identified its lead investor.
