Economic Policy Responses

Preparing for AI’s economic impact: exploring policy responses \ Anthropic Policy Preparing for AI’s economic impact: exploring policy responses Oct 14, 2025

How will the arrival of powerful AI systems change the structure of the economy? We are uncertain, and so are external experts. But as AI systems continue to improve, and are adopted at an ever-larger scale, it’s crucial there is more discussion about the tools policymakers could use to respond to AI's economic impacts—whatever their nature. To help with this, we’re sharing several economic policy ideas that merit further study.

Since launching the Anthropic Economic Index , we've observed an important shift in AI use. Users are becoming increasingly likely to delegate full tasks to Claude , “collaborating” with Claude less. As AI models continue to work independently for longer periods of time, and as more employers adopt AI to improve their productivity, we expect this trend to accelerate. The implications for the workforce are uncertain. How should policymakers respond? This is not an easy question, nor is it one that any single actor can answer. There is great uncertainty about the scale of the transition ahead, and a wide range of views about how to manage it. But it is imperative to begin formulating ideas now for the economic scenarios we might find ourselves in. Over the past year, we’ve worked with economists and policy experts from around the world (including members of our Economic Advisory Council and participants in our first Economic Futures Symposium ) to move this discussion forward. To generate a broad range of ideas, we’ve engaged with both non-partisan thinkers and those from across the political spectrum. Below, we briefly explore nine of these categories of ideas, covering workforce development, permitting reform, fiscal policy, and social services. While we don’t know what the optimal policies will prove to be, we’re committed to sharing ideas in the open, and to being transparent about the economic effects of advanced AI. Matching policies to scenarios The rate, scale, and form of AI's economic effects will determine the policy responses that are necessary across the world. Accordingly, we've organized these initial ideas into three broad categories: Policy ideas for nearly all scenarios , including those where negative effects on the labor market remain modest. These are policies that their advocates argue merit consideration almost regardless of how significant the disruption of AI proves to be. Given this, many of these proposals have been suggested in other contexts before. They include upskilling workers and students for emerging jobs, and reforming permitting processes to enable the construction of energy and computing infrastructure to improve productivity. Policy ideas for scenarios with moderate acceleration , where AI leads to measurable wage declines and job losses for large portions of the workforce. Here, more substantial fiscal support for displaced workers might be needed. To offset negative externalities imposed on displaced workers from rapid automation, taxes on automation might be considered in this scenario. Policy ideas for faster-moving scenarios, potentially involving dramatic job losses and worsening inequality. These proposals are much more ambitious, and are designed to respond to a starkly different economic picture. So far, ideas include using sovereign wealth funds to give citizens stakes in AI revenues, and finding new ways to generate government revenue.

The proposals below don’t necessarily represent Anthropic's own policy positions. But we’re excited by the breadth of proposals we’ve received, and we hope they encourage further research and debate. Policies for nearly all scenarios 1. Invest in upskilling through workforce training grants At our DC Symposium, Abigail Ball, Executive Director of American Compass , presented the Workforce Training Grant —a proposal she developed with colleague Oren Cass to direct public resources toward on-the-job training. Under this model, governments would provide substantial annual subsidies (Ball and Cass suggest $10,000 per year in the US) directly to employers who create formal trainee positions with structured training programs. This training could take multiple forms: programs operated by individual employers, by employer consortia or industry associations, through partnerships between employers and organized labor, or by technical schools and community colleges working alongside employers. American Compass proposes redirecting existing higher education subsidies to fund this program. But a range of other funding mechanisms might also deserve consideration—including the possibility of using taxes on AI consumption to support workforce development initiatives. 2. Reform tax incentives for worker retention and retraining Tax policy can, on the margin, incentivize employers to retrain and retain employees rather than reducing headcount. Revana Sharfuddin of the Mercatus Center argues that the US tax code creates a bias favoring physical capital investment over human capital investment. Businesses can immediately expense AI systems through bonus depreciation, yet face numerous restrictions when deducting worker training costs. She proposes reforms to the Internal Revenue Code, including eliminating the $5,250 cap on tax-free educational assistance, and extending full and immediate expensing to all job-related training. These changes would aim to reduce the cost of retraining relative to the cost of layoffs, helping those workers whose positions might otherwise be on the margins of workforce reduction decisions. 3. Close corporate tax loopholes Tax policy expert David Gamage has outlined reforms designed to prevent AI transformation from straining government budgets. Several of his proposals involve closing the “partnership gap” that allows large businesses to avoid entity-level taxes, and modernizing tax allocation to combat profit shifting and better capture value from digital and intangibles-based business models. The second reform would allocate business taxes based on customer locations through market-based apportionment, while requiring worldwide combined reporting to treat multinationals and subsidiaries as single entities. This approach is designed to limit artificial profit-shifting to tax havens—a practice that could become more prevalent as AI potentially further increases the economic…