Anthropic commits $200 million to research economic impacts of AI

On July 22, 2026, Anthropic announced the Economic Futures Research Fund, a $200 million commitment to support ambitious external research and pilots aimed at preparing society for the economic effects of AI. The company says the fund will finance work to identify which interventions make the economy more flexible and resilient, ensure AI benefits are broadly shared, and reduce harms from AI-driven disruption.

Purpose of the fund

Anthropic notes that AI capabilities continue to improve, but there is substantial uncertainty about how quickly those capabilities will diffuse across the economy and what the aggregate economic effects will be. In its June publication, the Economic Policy Framework (EPF), Anthropic proposed a range of policies and programs for different scenarios. The new fund is intended to generate empirical evidence on which of those interventions—or other novel approaches—actually work in practice. The firm acknowledges that the coming period could lack historical precedent and that promising solutions might be untried; it is therefore prepared to fund creative, large-scale pilots that go beyond what randomized controlled trials alone might reveal.

This fund represents an evolution of Anthropic’s Economic Futures program, launched a year earlier. Based on experience, the company is shifting toward larger grants and bolder projects, arguing this is where it can have the greatest impact. Anthropic also says it is difficult to scale internal capacity to manage many small grants, so the new fund will prioritize large RCTs and pilots while remaining open to partners able to scale effective small-scale pilots.

Funding parameters and eligible applicants

Anthropic plans to primarily fund projects in the $5–30 million range, while remaining flexible to award larger sums for well-scoped, high-potential projects. The fund will not directly finance proposals below $1 million. Eligible applicants include accredited universities and other degree-granting institutions, independent research institutes and policy research organizations, and nonprofits with experience running field experiments at scale. Individual researchers may act as principal investigators through their institutions but the fund will not accept proposals from individuals applying solely in a personal capacity.

The fund is global in scope, though the initial funding directions reflect a U.S.-centric framing in part because Anthropic is headquartered in San Francisco and its Claude model is used more in the U.S. than elsewhere. Anthropic expects to finance projects worldwide.

Five research priorities

Anthropic has identified five priority areas for funding and invites ambitious proposals both within and outside these categories.

1. Shaping AI’s impact on workers at the firm and workplace level

• Objective: study how workplace systems, organizational design, and training protocols shape AI integration, productivity gains, and the distribution of benefits across workers.
• Example fundable work: field experiments randomizing AI systems or integration designs at firm or team level; comparisons of worker- and union-co-developed designs versus top-down approaches; estimates of how organizational choices influence the incidence of AI-driven productivity gains; evaluations of retention tax credits and employer co-investment requirements.

2. Equipping people to navigate AI-driven transitions

• Objective: evaluate retraining, job placement, licensing reform, and educational models that could help people transition amid rapid, AI-driven structural change.
• Example fundable work: evaluations of innovative retraining and reemployment packages (including AI-enabled matching, credentialing, and learning); field experiments on early-career pipelines (apprenticeship, mentorship, rotation models); curriculum and delivery pilots in K–12 and higher education linked to later labor-market outcomes; tests of mobility instruments such as paid leave tied to retraining and portable benefits.

Anthropic notes existing evidence for some sectoral training programs and seeks to determine whether promising approaches can be rapidly scaled—e.g., large-scale state-level pilots that expand selected programs quickly for jobseekers.

3. Modernizing income support for AI-driven displacement

• Objective: design and test income-support instruments suited to displacement that may be broader or more persistent than historical patterns assume.
• Example fundable work: UI reforms tailored to AI-driven displacement (alternative eligibility thresholds, automatic extension triggers linked to industry/occupation, UI integration with wage insurance and retraining); basic needs support for those who exhaust UI or never qualified; longer-duration unconditional income pilots at livable levels measuring labor supply, consumption, wellbeing, family stability, child development, civic participation, and time use.

4. Building worker stakes in AI-driven growth before disruption arrives

• Objective: pilot mechanisms that give workers a stake in AI-generated gains—pre-distributive capital accounts, equity-sharing, AI-sector dividends, or public ownership stakes—and test revenue-raising designs.
• Example fundable work: RCTs testing pre-distributive account designs at scale; pilots of equity-sharing or dividend-style mechanisms, including community-level pilots where local residents receive returns from AI infrastructure or AI-using firms; evaluations comparing revenue bases (corporate profits, capital gains, compute, automation taxes) and distribution mechanisms to see which lead to better labor-market and household outcomes.

5. Generating new evidence on public investments

• Objective: produce evidence on which forms of public spending deliver the greatest public benefit—particularly investments in human- and community-facing work that private markets may undervalue.
• Example fundable work: large pilots funding human- and community-facing service positions (teaching, after-school programs, libraries, community health, parks, arts), measuring employment, educational attainment, crime, and wellbeing; pilots broadening access to AI-enabled public services (legal aid, medical guidance, financial advice) for underserved populations; guaranteed-jobs pilots for displaced or long-term unemployed workers; place-based interventions in communities most exposed to AI-driven displacement or hosting major AI infrastructure build-outs, bundling workforce, public services, infrastructure, and amenities.

Publication and scaling expectations

Anthropic emphasizes that rapid learning and public sharing at key milestones will be valuable because early signals can enable action. The company is particularly interested in pilots that, if promising, could be scaled substantially.

Anthropic has issued a request for proposals and invites interested, eligible institutions to apply.

Summary

The Economic Futures Research Fund is Anthropic’s $200 million effort to generate empirical evidence about how to manage AI-driven economic change. The fund prioritizes large, ambitious projects across five areas—workplace design, transitions and retraining, income-support modernization, worker stakes in AI gains, and public investments—and plans primarily to award grants in the $5–30 million range, with a $1 million minimum for direct funding.