Anthropic Commits $200 Million to Economic Futures Research Fund

Anthropic Commits $200 Million to Economic Futures Research Fund Anthropic has committed $200 million to its Economic Futures Research Fund to back external, empirical research on interventions designed to help society prepare for the economic effects of AI. Anthropic is committing $200 million to fund ambitious external research, primarily targeting grants between $5 million and $30 million. The fund shifts away from managing numerous small grants toward large-scale randomized control trials, ambitious pilots, and program evaluations. Grants will be awarded globally to accredited universities, degree-granting institutions, independent research bodies, and nonprofits with field experiment experience, excluding direct individual applicants. The research agenda focuses on workplace integration, retraining scalability, worker support systems for persistent displacement, revenue and equity mechanisms, and public investments in community-facing work. Funding Strategy and Eligibility The fund marks an evolution of the Economic Futures program launched a year prior, pivoting from hard-to-scale small grants to large bets, primarily within the $5 million to $30 million range, with a minimum threshold of $1 million. Anthropic will accept institutional proposals from accredited universities, independent research organizations, and nonprofits capable of running large field experiments. While based in San Francisco, the initiative is global in scope and seeks partners committed to publishing findings at key milestones to provide early, actionable insights. Workplace Integration and Worker Retraining A key research priority addresses the labor market, focusing on field experiments that explore collaborative patterns between humans and AI, workplace organizational design, and worker voice. Another priority examines retraining and placement systems, aiming to test whether successful initiatives, such as sectoral training models, can scale quickly during rapid economic transitions, including through large-scale regional simulations. Safety Nets, Capital Distribution, and Public Investment Given that existing displaced-worker programs assume temporary unemployment, the fund seeks research into safety nets suited for persistent displacement without modern precedent. It will also examine mechanisms for distributing aggregate AI gains, such as capital accounts, equity-sharing, AI dividends, and potential tax designs like token or capital taxes. Finally, the agenda prioritizes evaluating the effectiveness of public investments in community-facing work compared to direct transfers and modernized income safety nets.

Anthropic设立2亿美元经济未来研究基金并发布研究议程 Anthropic宣布为经济未来研究基金承诺出资2亿美元，用于支持外部机构开展大规模研究与试点，探索应对人工智能潜在经济冲击、提升经济韧性并促进收益共享的干预措施。 Anthropic承诺向经济未来研究基金注资2亿美元，重点资助500万至3000万美元的大型项目，不再直接资助100万美元以下的小额申请。 申请资格面向认证大学、独立研究机构、政策研究组织以及具备大规模现场实验经验的非营利组织，不接受个人申请。 基金重点资助职场人机协同、大规模再培训、长期失业保障体系、收益分享与税收机制，以及公共投资与安全网现代化五个方向。 该基金为全球性基金，要求合作机构在关键节点公开发布研究发现，以便在政策和市场窗口期内及早提供决策依据。 资助定位与申请机制调整 该基金是Anthropic一年前启动的“经济未来”计划的演进，核心转变在于从管理分散的小额资助转向支持高影响力的大额项目。单笔资助区间主要设定为500万至3000万美元，对高潜力项目可进一步增加额度，不再直接资助100万美元以下的项目。基金面向全球高校、研究智库和非营利机构开放，寻求开展大规模随机对照试验（RCT）或具有扩展潜力的大型试点，并要求在关键里程碑公开研究结果。 劳动力整合与快速再培训验证 针对当前AI职场整合证据多为短期和观察性的局限，基金将资助现场实验，以厘清人机协同技能培养、组织设计对生产率与收益分配的影响，以及员工话语权的作用。在再培训方面，由于既有经验未必能应对AI引发的结构性颠覆，基金计划探索具有前景的行业培训项目能否快速向更大规模人群推广，例如在特定州开展应对重大冲击的大规模快速扩展测试。 持久性失业应对与资本收益分配 现行失业救济机制普遍假设失业属于暂时现象，而AI可能带来前所未有的广泛且持久性失业，亟需设计适应新均衡的政策工具。在整体收益分配方面，基金将研究全民预分配资本账户、股权共享、AI部门分红及公共持股等机制的可行性，同时探索企业税、资本利得税或Token税等税收设计的经济归宿与潜在影响。 安全网现代化与公共投资评估 依据其此前发布的经济政策框架，基金还将资助对比收入安全网现代化与扩大公共投资（特别是私人市场估值不足的面向人际与社区的工作）的研究。该方向旨在为政策制定者提供可比较的实证框架，评估不同支出形式与直接转移支付相比所能产生的最大公共效益。

We’re sharing the research agenda for the Anthropic Economic Futures Research Fund. We’re committing $200 million to the fund to support ambitious external research on interventions to prepare society for the economic impacts of AI. With the research the Fund supports, we want to study what programs could make the economy more flexible and resilient, ensure the benefits of AI are shared, and minimize the harm that AI-driven disruption could cause. In the fund, we’ll prioritize five research areas: AI capabilities continue to improve. But we don’t yet know how quickly AI will diffuse throughout the economy while also becoming more capable, and what the economic effects will be. In our [Economic Policy Framework](https://www-cdn.anthropic.com/files/4zrzovbb/website/9ea607a5dd67c168093829b701f3a0a6d21156d5.pdf) (EPF), published in June, we proposed programs and policies for a range of scenarios. But we need more empirical evidence on which interventions might actually work in an AI-transformed economy—which ones make the economy more flexible and resilient, and spread the gains broadly. In the face of this uncertainty, our aim is to build this 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. We may be entering a moment without historical precedent, where the most promising solutions are ones nobody has tried yet. We’re willing to fund creative and ambitious pilots that can provide guidance on questions where randomized control trials alone might only provide incremental evidence. This is a significant evolution of our Economic Futures program, launched a year ago. We’re updating our focus to ambitious projects and large grants, because we think it’s where we can have the highest impact. We’ve always thought that it’s important to fund big external research bets; this shift will let us do so. We also learned through the Economic Futures program that it’s hard for us to scale capacity to manage many small grants at once. In addition to large-scale RCTs and pilots, we’re also interested in working with partners that could scale up a program of effective small-scale pilots. Fundamentally, we want to fund the most ambitious proposals possible. We aim to fund large-scale RCTs or ambitious, creative pilots or program evaluations that expand our shared understanding of what shows promise and in which contexts, fill gaps where evidence is thin, and inspire new solutions. The fundable directions below lay out a set of possibilities, but we know that we have not come up with all the good ideas in this space. We welcome proposals that may not be captured below. AI could transform society faster than traditional research funding and publication cycles can keep pace with. We’re looking to partner with research organizations that are willing to share what they’re learning 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. We're especially interested in pilots that can be scaled up dramatically if they show promise. This is a global fund. The funding directions that we’ve outlined below are somewhat US-centric, in part because we’re 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 we expect to fund projects in a way that reflects that. We plan to primarily fund projects in the $5-30 million range, though we’re flexible upward for well-scoped, high-potential-impact projects. Based on what we learned from the Economic Futures program, and the ambition and scale we’re seeking in proposals, we won’t directly fund anything below $1 million from this fund. We’ll accept proposals 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 we won’t consider proposals from individuals applying in their personal capacity. We’re more likely to fund projects that fit one of our research priorities, but we welcome ambitious proposals outside them, as long as they’re calibrated to the scale of the problem and opportunity. [See our request for proposals and apply here.](https://docs.google.com/forms/d/e/1FAIpQLSfQySlKGRi_xqRRz2ZGy-1aC5eUf-fK-j0iJun2m6Fu5pqSOg/viewform?usp=dialog) AI’s impact on the labor market depends on the systems, workplaces, training protocols, and institutional choices that are built around it. The existing evidence on AI’s integration in the workplace is observational and short-term. Field experiments can help us understand 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: The evidence on retraining and job placement is mixed, and it may not generalize to AI-induced economic disruption and rapid structural transformation. There’s existing evidence on many such efforts, including some especially effective [sectoral training programs](https://www.journals.uchicago.edu/doi/abs/10.1086/717932). We want to find out 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. 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, we’ll need instruments calibrated to a new equilibrium, one with no modern precedent. In unprecedented scenarios where AI delivers large aggregate gains, those gains may not be broadly shared by default. In the EPF, we discuss 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. But we lack evidence on who would bear the economic incidence of such taxes, and how different designs would affect collected revenue and adoption. 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.