Mercor Raises $750M at $20 Billion on an Unaudited $3B Claim

By Nathan Brooks

Mercor raised $750 million in a Series D round announced on November 10, at a $20 billion valuation. The price doubles the roughly $10 billion mark Reuters reported after the company's $350 million Series C in October 2025. Mercor's model is unusual: it pays human experts to write the training data and evaluation tests that tell AI labs whether a model can actually do a job.

Who wrote the check?

Mercor's press release names 13 investors: Benchmark, Felicis, Fundomo, funds managed by affiliates of Apollo, G Squared, General Catalyst, Glade Brook Capital, NVIDIA, Salesforce, Sapphire Ventures, StepStone Group, XTX Ventures and Zoom. No lead investor is identified, so there is no single name to point to as the anchor of the round.

The company says the round "included an employee tender," meaning part of the $750 million bought shares from staff rather than funding operations. Mercor does not disclose the split, which leaves investors guessing how much new money actually reached the balance sheet.

What the $3 billion actually is

The most quoted number in the announcement carries the weakest sourcing. The press release and Mercor's blog post contain no revenue figure. The $3 billion comes from a post by co-founder Brendan Foody on X at 15:52 UTC on announcement day, where he wrote that Mercor "scaled from $1M to $3B in run-rate revenue in 27 months" and called it the fastest any company has reached that mark. No independent verification exists.

Mercor's own earlier disclosure is narrower. In a May 8 engineering blog post, the company said it had "crossed $1B in annualized revenue run rate earlier this year" and went from nothing to a $500 million run-rate in 17 months. That same post said Mercor paid "over $2 million every day to more than 30,000 weekly active contractors."

Molly O'Shea, who interviewed Foody for the announcement, summarized "$5M+ in daily expert payouts" in her notes — an interviewer's figure, not a company filing.

Three cautions sit on top of the $3 billion run-rate:

• Run-rate is not revenue. It annualizes a recent period, and neither source says which period.
• Nothing is audited. Mercor is private, so these figures appear in no regulatory filing.
• Most of it goes back out. At $5 million a day, expert payouts total roughly $1.8 billion a year — about 60% of a $3 billion run-rate, by simple arithmetic on two figures from different sources.

At $20 billion, the valuation implies roughly 6.7 times the claimed run-rate. That is a multiple applied to a number the company has not audited and that largely leaves the building as contractor pay.

What Mercor actually sells

Mercor describes "more than five million experts in our network getting paid to train frontier AI models." That's a cumulative network size, not weekly active workers. In May, the company's own weekly-active figure was a little over 30,000 — on simple arithmetic, more than 160 registered experts for every weekly active one.

Mercor began as an AI-powered hiring platform, backed by Jack Dorsey and Peter Thiel, per Reuters. Today its experts write tasks, grading rubrics and reference answers that show whether a model can do real work. The company publishes a public benchmark series called APEX. Earlier this year it acquired Deeptune, whose team "spent two years recreating hundreds of enterprise applications" as practice environments for agents.

The pitch

Co-CEOs Foody and Adarsh Hiremath split the argument in the release. Foody focuses on demand: "Every job in the economy will need its own AI agents, and every company will need a way to train and evaluate them on its own work." Hiremath argues public benchmarks are insufficient: "A model can score well on a public benchmark and still fail on the systems, policies, and edge cases that define how a specific business runs."

Sapphire Ventures' Jai Das supplies the market view: frontier labs "have been the primary buyers of expert-driven training data, which is about to change."

Foody's blog post adds a fact about Mercor's own cost base: "We already spend 3x as much on inference as on employee salaries." He then projects that ratio across the economy to forecast double-digit GDP growth — the first part a single-company datapoint, the second an extrapolation.

What does this change for AI buyers?

For an enterprise or lab negotiating a training-data contract, the reference price just moved: a vendor that most critics treat as a contractor marketplace now carries a $20 billion mark, and the buyer base is shifting away from frontier labs. Ask what the contractor line does to your gross margin — at Mercor's own ratios, the cost of supply eats most of the revenue. And ask which number you're buying, run-rate or revenue.

What to watch next

• Whether Mercor publishes audited revenue rather than only run-rate. O'Shea's interview notes list an IPO timeline among the topics discussed.
• The customer mix. Foody's chart shows foundation-model customers flat since 2025, with enterprises driving the rise to nearly 120 total customers.
• Competition. O'Shea's summary names Scale AI as a direct rival.

The next test for the $20 billion mark will be documentary: an audited revenue line, an IPO filing, or another round priced below 6.7 times run-rate. Until one of those lands, treat $3 billion as a founder's claim rather than a reporting standard.