Anthropic's Research Agenda for the Economic Futures Research Fund

Anthropic is publishing the research agenda for its Economic Futures Research Fund, committing $200 million to support ambitious external research on interventions that prepare society for the economic impacts of AI.

The fund aims to study which programs could make the economy more flexible and resilient, ensure that AI's benefits are widely shared, and reduce the harm caused by AI-driven disruption.

Five research areas will be prioritized:

1. Shaping AI's impact on workers at the firm and workplace level
2. Equipping people to navigate AI-driven transitions
3. Modernizing income support for AI-driven displacement
4. Building worker stakes in AI-driven growth before disruption arrives
5. Generating new evidence on public investments

What is the fund?

AI capabilities continue to advance, but the pace at which AI will spread through the economy-and the resulting economic effects-remain uncertain. In Anthropic's Economic Policy Framework (EPF), published in June, programs and policies were proposed for a range of scenarios. However, more empirical evidence is needed on which interventions might actually work in an AI-transformed economy-specifically, which ones increase economic flexibility and resilience while distributing gains broadly. Given this uncertainty, the goal is to build an evidence base so that workers, firms, and governments have room to adapt. This $200 million fund will support external research on interventions proposed in the EPF and on other open questions. This may be a moment without historical precedent, where the most promising solutions are ones nobody has tried yet. Anthropic is willing to fund creative and ambitious pilots that can offer guidance on questions where randomized control trials alone might yield only incremental evidence.

This represents a significant evolution of Anthropic's Economic Futures program, launched a year ago. The focus is shifting toward ambitious projects and large grants, where the highest impact is expected. Anthropic has always believed in funding big external research bets, and this shift enables that. The Economic Futures program also revealed the difficulty of scaling capacity to manage many small grants simultaneously. In addition to large-scale RCTs and pilots, Anthropic is interested in working with partners that could scale up a program of effective small-scale pilots.

The kinds of research being funded

Fundamentally, the goal is to fund the most ambitious proposals possible. The fund aims to support large-scale RCTs or ambitious, creative pilots or program evaluations that expand shared understanding of what shows promise and in which contexts, fill gaps where evidence is thin, and inspire new solutions. The fundable directions outlined below represent a set of possibilities, but Anthropic acknowledges it has not come up with all the good ideas in this space and welcomes proposals that may not be captured below.

AI could transform society faster than traditional research funding and publication cycles can keep pace with. Anthropic is looking to partner with research organizations that are willing to share what they learn publicly at key milestones, because a signal that arrives early enough to act on can be worth more than an answer that arrives too late. Pilots that can be scaled up dramatically if they show promise are of particular interest.

This is a global fund. The funding directions outlined below are somewhat US-centric, in part because Anthropic is headquartered in San Francisco and Claude is used more in the US than any other country. But the need to prepare for disruption will be necessary worldwide, and projects are expected to be funded in a way that reflects that.

Proposals will be accepted from accredited universities and other degree-granting institutions, from independent research institutes and policy research organizations, and from nonprofits with a track record of running field experiments at scale. Individual researchers may serve as principal investigators on proposals made by their institutions, but proposals from individuals applying in their personal capacity will not be considered.

Projects fitting one of the research priorities are more likely to receive funding, but ambitious proposals outside those priorities are welcome, as long as they are calibrated to the scale of the problem and opportunity.

The five research priorities

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

AI's impact on the labor market depends on the systems, workplaces, training protocols, and institutional choices built around it. The existing evidence on AI's workplace integration is observational and short-term. Field experiments can help clarify which collaborative patterns develop human expertise alongside AI, how organizational design choices affect both productivity and who captures the gains, and what difference worker voice makes in those design choices.

Without this evidence, both firm-level decisions and policy levers like incentives for worker augmentation, retention tax credits, employer co-investment requirements, or apprenticeship programs will be poorly informed.

Fundable directions include:

• Field experiments randomizing AI systems and AI integration designs at the firm or team level, including comparisons of designs co-developed with workers and worker organizations against top-down approaches.
• Estimates of how organizational choices around AI workplace integration and usage affect the distribution of AI productivity gains.
• Evaluations of retention tax credits and employer co-investment requirements.

2. Equipping people to navigate AI-driven transitions

The evidence on retraining and job placement is mixed, and it may not generalize to AI-induced economic disruption and rapid structural transformation.

Fundable directions include:

• Evaluations of innovative skill retraining, job placement, licensing reform, and sectoral transition packages, including newer AI-enabled matching, credentialing, and learning models, and the bundling of income support with intensive reemployment services, retraining, and relocation assistance.
• Field experiments on the early-career and professional pipeline, e.g., what apprenticeship, mentorship, or rotational models can build expertise if junior tasks are absorbed by AI.
• Evaluations of curriculum and educational delivery models in K-12 and higher education that aim to prepare students for a transformed labor market, including longitudinal pilots linking educational interventions to later labor market outcomes.
• Tests of ambitious mobility instruments, for example paid leave tied to retraining programs and portable benefits that follow workers across employers.

There is existing evidence on many such efforts, including some especially effective sectoral training programs. The fund seeks to determine whether promising programs could scale quickly across a broader population. For example, a large-scale "fire drill" where selected programs are scaled up rapidly for job seekers in a given state could provide evidence on how well these programs work in the face of major disruption.

3. Modernizing income support for AI-driven displacement

Like similar insurance programs around the world, the US system for supporting displaced workers is built almost entirely around the assumption that joblessness is temporary. AI may lead to displacement that is broader and more persistent. In that scenario, instruments calibrated to a new equilibrium-one with no modern precedent-will be needed.

Fundable directions include:

• Unemployment Insurance (UI) reforms suited to AI-driven displacement, including alternative eligibility thresholds, automatic extension triggers linked to industry or occupation, and integration of UI with wage insurance, retraining, or other transition supports.
• Basic needs relief for workers who exhaust UI, never qualified, or are persistently underemployed.
• Longer-duration unconditional income pilots at livable levels, designed to speak to scenarios where income and work are decoupled for sustained periods of time, with analyzed outcomes spanning not only labor supply and consumption but also wellbeing, family stability, child development, civic participation, and how recipients structure their time.

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

In unprecedented scenarios where AI delivers large aggregate gains, those gains may not be broadly shared by default. The EPF discusses universal pre-distributive capital accounts and adjacent mechanisms, like equity-sharing, AI-sector dividends, and public ownership stakes. But these mechanisms have limited direct empirical precedent at scale, and they also need a funding source. Many proposals to generate revenue exist, including taxing AI-driven returns through corporate, capital gains, or token taxes. However, evidence is lacking on who would bear the economic incidence of such taxes, and how different designs would affect collected revenue and adoption.

Fundable directions include:

• RCTs testing the design of pre-distributive capital accounts at scale.
• Pilots testing equity-sharing or dividend-style mechanisms, including community-level pilots where AI infrastructure or AI-using firms generate direct, ongoing returns to local residents.
• Evaluations comparing different mechanisms for raising and distributing revenue-which tax base (corporate profits, capital gains, compute, automation taxes, etc.) and which mechanism (pre-distributive accounts, equity stakes, dividends, or equivalent direct transfers) lead to the best labor market and household outcomes.

5. Generating new evidence on public investments

The EPF calls for both modernizing the income safety net and substantially expanding public investment in human- and community-facing work. Policymakers need a consistent way to compare these instruments against one another, and against direct transfers. This research would generate evidence on what forms of spending generate the most public benefit, especially in sectors that might be undervalued by the private market.

Fundable directions include:

• Large-scale pilots that directly fund human- and community-facing service positions (in e.g., teaching, after-school programming, libraries, community health, parks, infrastructure, the arts), measuring outcomes including employment levels, educational attainment, crime, and wellbeing.
• Pilots broadening access to AI-enabled public services (legal aid, medical guidance, financial advice) for underserved populations, testing whether such investments can narrow the divide in access.
• Guaranteed-jobs pilots for displaced or long-term unemployed workers, in which participants are offered employment in public good roles in the spirit of the Civilian Conservation Corps but spanning a broader range of roles.
• Place-based interventions in communities most exposed to AI-driven displacement or hosting major AI infrastructure build-outs, including bundled investments in workforce, public services, infrastructure, and amenities, and pilots of regional development authorities that coordinate these investments under unified governance.