Mercor AI Research Fund Grants: Economics of AI

About Mercor

Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.

Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.

Mercor AI Research Fund Grants: Economics of AI

One of the biggest challenges facing the industry today is determining whether frontier AI is capable enough to deliver value in real-world settings. A model can perform well on benchmarks and still struggle in production. That’s why investing in research, realistic evaluations, and robust verification is critical.

Mercor is seeking to fund AI research on the economic impacts of AI. This is separate from the Mercor Research Fellowship, which funds individual experts (apply HERE), our $5m of safety funding awards (apply HERE), and our funding of benchmarking research.

The grant supports:

• The time of experts from Mercor's platform

• Researcher hours

• API costs

• Stipends for event and conference attendance

Budget Categories

Applications are grouped into three budget tiers. Please state your tier clearly in your application and ensure your overall budget ask falls within it:

• Tier 1: under $50,000: Suited to focused, well-scoped projects such as pilot studies, evaluations, replications, or extensions of existing work. These applications undergo a streamlined review.

• Tier 2: under $200,000: Suited to substantial projects requiring dedicated personnel time, meaningful compute, or data collection. Reviewed with proportionally greater attention to the budget breakdown and implementation plan.

• Tier 3: under $500,000: Reserved for ambitious, multi-component research programs. These applications receive the highest level of scrutiny: reviewers will expect a detailed justification of each spend category, a clear account of team capacity to execute at this scale, and milestones against which progress can be assessed.

The level of review scrutiny scales with the size of the ask, so applicants should request the smallest budget that credibly supports the proposed work.

Research Agenda

We are interested in submissions in the following area:

1. Measuring AI’s impact on labor

• How should we measure AI’s effect on real economic and labor outcomes — and what methodologies are up to the task?

• To what extent do benchmark scores map onto real economic outcomes?

• What is the right methodology for measuring AI’s impact on labor outcomes (employment, wages, task composition)?

• How does occupational exposure to AI evolve over time, and how can novel datasets improve its measurement?

2. Human–AI collaboration and productivity

• How do humans actually work with AI systems, and when does that collaboration make them more productive?

• What is the efficiency gain of human experts when paired with AI models, and under what conditions?

• How can we measure the “review cost” that model outputs impose on humans — the time and effort spent checking, correcting, and verifying AI work?

• Which collaboration patterns improve output quality rather than merely output volume?

3. Enterprise adoption and returns

• What does AI adoption look like inside organizations, and what returns does it generate?

• How are organizations actually using AI, and how is that usage changing?

• How can the effect of AI and agent deployments be measured causally, rather than through self-report or correlation?

• What are the right enterprise-level variables for quantifying AI-driven productivity gains?

Eligibility

• Researchers at universities and non-profit research organizations, as well as public benefit corporations and small startups (