Mercor Eyes $10B Valuation as AI Data Marketplace Expands

Mercor nears Series C with ambitious $10B-plus target

Mercor, the startup that connects top AI labs with domain experts for model training, is in talks with investors about a Series C that it is pitching at a $10 billion or higher valuation. The fundraising chatter follows rapid reported growth and a previous February Series B that priced the company at $2 billion.

Growth signals and revenue claims

Mercor says its annualized run-rate revenue has surged toward $450 million and above, with public posts from leadership citing $75 million monthlyized annual revenue earlier and later an ARR of $100 million. Sources tell reporters Mercor is approaching or exceeding $450 million ARR and is on a path to $500 million faster than some peers.

Unlike many high-growth AI companies that remain cash-burning, Mercor reported $6 million in profit in the first half of the year, according to coverage. The company invoices customers for the full service amount and treats contractor payouts separately, a gross-revenue approach it says is standard among similar firms.

What Mercor actually does and product roadmap

Mercor matches specialized human experts — scientists, doctors, lawyers and other domain specialists — to label and curate training data and to support reinforcement learning workflows. The company says it supplies contractors to big AI players including Amazon, Google, Meta, Microsoft, OpenAI, as well as to firms like Tesla and Nvidia.

To diversify beyond human-in-the-loop matching, Mercor is pitching software infrastructure for reinforcement learning and plans an AI-powered recruiting marketplace to scale and automate parts of the sourcing and verification process.

Competition, client concentration, and legal headwinds

Mercor faces a crowded field. Competitors like Surge AI, Scale AI, Turing Labs and others are expanding RL services and hiring marketplaces. Observers also note the risk that large AI labs could internalize specialized human workflows or offer competing platforms.

On top of market competition, Mercor is defending against an allegation from Scale AI that a former employee misappropriated confidential documents — a legal matter that could complicate growth or client trust if it escalates.

• Client concentration risk: heavy reliance on a few large customers could affect revenue stability
• Competitive pressure: rivals and in-house platforms from major AI labs threaten margins
• Legal and IP exposure: lawsuits can distract leadership and affect partnerships

Why this matters to investors, labs, and enterprises

Fast-growing vendors that combine human expertise with tooling are critical to model quality and RL feedback loops. For investors and customers, the key questions are whether revenue claims are durable, how concentrated the customer base is, and whether product expansion (RL tooling, recruiting marketplaces) can meaningfully increase margins.

Mercor’s recruitment of experienced executives and its young founder team signal ambition, but valuation conversations remain fluid. The company reportedly has multiple offers and has used SPVs to bring in new investors while debating final terms.

Actionable takeaways

For CIOs and procurement teams: assess supplier concentration and contract terms to avoid single-vendor exposure. For investors: validate ARR math and examine gross-versus-net accounting. For AI labs: evaluate whether building in-house capabilities or partnering yields a better total cost of ownership for RL data pipelines.

Mercor’s next moves — sealing a Series C, scaling RL software, and resolving litigation — will be a useful case study for the sector. As human expertise remains a bottleneck for high-quality models, the market will reward companies that combine scalable human networks with robust software and governance.