Mercor Raises $750M Series D at $20B Valuation to Expand AI Training

Mercor announced a $750 million Series D on November 10, 2026, valuing the AI training company at $20 billion. The financing included an employee tender and participation from NVIDIA, Salesforce, Zoom and other investors.

Benchmark, Felicis, Fundomo, funds managed by affiliates of Apollo, G Squared, General Catalyst, Glade Brook Capital, Sapphire Ventures, StepStone Group and XTX Ventures also participated. The funding will accelerate infrastructure and research for model evaluations and post-training, or additional training of an existing model, while supporting more environments where agents can learn complex workflows. Mercor also opened New York and London offices in 2026, expanding beyond San Francisco to be closer to enterprise and laboratory customers and recruit employees.

Enterprise Evaluations and Expert Training

Mercor spent the past two years building infrastructure to train and evaluate frontier models on real-world work using human expertise. It is now extending those capabilities to enterprises, with evaluations designed to establish what agents can do, what they cost and how their performance can improve. Its custom evaluations use an employer’s actual workflows to measure models against company requirements and identify where additional training or practice in realistic environments could help.

Mercor reports that its network includes more than five million experts paid to train frontier AI models and that the United States has more than 800 distinct occupations containing workflows against which models have yet to be evaluated. Its enterprise playbook starts with a capable open model, builds public evaluations and uses post-training to reduce costs before training models for customers.

The approach distinguishes between knowing how a task is generally performed and knowing how a particular employer handles it under its own systems, policies and quality standards. Mercor describes post-training as requiring a fraction of the resources needed at the frontier. Its frontier-lab and enterprise work draws on the same expert network, evaluation methods and post-training infrastructure, allowing lessons from each side to inform the other.

Deeptune’s Role in Agent Training

Earlier in 2026, Mercor acquired Deeptune, a reinforcement-learning environment company. The July 9, 2026 announcement outlined the planned acquisition and Deeptune’s two years of work recreating hundreds of enterprise applications, from spreadsheets to Salesforce, for agent-training environments. Those environments let agents practice professional workflows.

Each environment has three components: the software where work occurs, tasks defining what the agent must accomplish and verifiers determining whether it succeeded. Mercor’s experts create tasks and verifiers that translate human judgment into learning signals. The July announcement says its APEX system measures model performance on economically valuable tasks across thousands of real-world workflows, while Deeptune supplies the software layer needed to turn that expertise into training environments.

Internal AI Spending

Mercor reports that it spends three times as much on inference, the computing used to run models, as on employee salaries and that this spending pays for itself by making employees better at their jobs.

Co-founder and chief executive Brendan Foody said that if the ratio holds as AI spreads through the economy, it points to something closer to double-digit growth in gross domestic product over the next five years, nearly three times the rate he described as customary.

The funding supports a single enterprise proposition: build the environment, supply the experts and measure whether an agent is ready. For Mercor, the practical test is reliable performance inside a customer’s own workflows, where general task knowledge alone is insufficient.