Economic Futures in the Age of AI

May 27, 2026<br>Economic Futures in the Age of AI<br>The OpenAI Foundation is committing an initial $250M to building secure and abundant economic futures.

By Divya Siddarth and Wojciech Zaremba

The OpenAI Foundation is committing an initial $250M to grants, partnerships, and direct work aimed at building secure and abundant economic futures.<br>Economic systems exist, in principle, to give people security, autonomy, and the ability to build purposeful lives. Too often they fall short. AI is going to lead to huge economic changes as it makes previously scarce capabilities far more widely available, and there is deep uncertainty about how far and how fast they will go. The breadth of possibilities makes this an extraordinary opportunity to build systems that enable better lives for people now and in the future. But the current pace of change means the window to get this right is shorter than we're used to, and the cost of getting it wrong is immense.<br>We don’t need to know exactly how the future will unfold to prepare for it. The purpose of this program is to help resource concrete institutional options that can be tested, governed, revised, and scaled. We will work across three areas:<br>Understanding the shift: Investing in independent measurement and forecasting infrastructure to create a clearer picture of AI’s impacts on the economy.

Supporting the transition: Resourcing workers and communities through near-term disruption.

Building economic security: Supporting new approaches to organizing post-AI political economies and sharing economic gains broadly for people around the world.

AI’s economic effects will be widely felt, and people’s experiences are an essential input into our thinking. Alongside this post, we are inviting people to share what they’re seeing in their work, communities, and economic lives. Those perspectives will help us understand what formal research may miss. This is an early step towards building broader channels for collective input as the Foundation’s work develops.<br>Understanding The Shift<br>We still don’t have good ways to answer fundamental questions about how AI is changing and will change the economy. The systems society relies on to measure and interpret economic change were built for a different era. Our goal is to help build what comes next.<br>A central question is not only what AI can do, but where that value accrues: to workers through wages, to firms through margins, to consumers through lower prices and better services, to governments through the tax base, or to capital owners through rents. For example, if AI generates value as digital goods rather than higher wages, income statistics won't capture it. If labor share goes down, workers’ ability to bargain may decrease and GDP may become a worse proxy for welfare. We need measurement that tracks what people can actually do and access, not just what they earn.<br>Many current approaches to studying AI’s economic impacts focus on which tasks could be automated. This is useful, but incomplete. The economic effects of AI will depend on how tasks are bundled into jobs, whether automation displaces human labor or creates new labor-complementary roles, how task distributions shift as model capabilities improve, and how firms and states reorganize around those changes. Understanding these shifts requires better labor market public infrastructure worldwide: BLS-like capacity to measure employment, wages, transitions, and firm behavior, alongside modernized O*NET-like systems for mapping work. These systems should be globally relevant and linked, where appropriate, to demographic, geographic, career-stage, and job-level information.<br>Every country will experience the AI transition differently. Beyond directly measuring AI’s impacts on local economies, we will also fund economic evaluations to understand how AI can help people in different contexts. This is especially urgent in low- and middle-income countries, where AI could quickly expand capabilities, strengthen public goods, and contribute to economic mobility. We are interested in approaches that can inform the building of regionally specific infrastructure, local institutions, and diffusion models to make AI useful on countries’ own terms.<br>Supporting the Transition<br>Economic transitions are lived before they are fully understood. We intend to fund approaches that support people now while helping society prepare for longer-term change.<br>People may need support while they search for jobs, easier access to unemployment insurance, expanded wage loss insurance, help translating their experience into new roles, and pathways into growing sectors. Retraining may be part of the answer, but traditional retraining programs have mixed evidence, and an AI transition agenda will likely need to be broader. Evaluating these efforts must be rigorous–measured by whether they lead to better work, more stability, broader capabilities, and more real choices in people’s economic...