Economic Futures in the Age of AI

The OpenAI Foundation is committing an initial $250M to grants, partnerships, and direct work aimed at building secure and abundant economic futures.

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. 

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:

1. Understanding the shift: Investing in independent measurement and forecasting infrastructure to create a clearer picture of AI’s impacts on the economy.

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

3. 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.

Understanding The Shift

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.

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.

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.

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.

Supporting the Transition

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.

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 lives.

The goal is more than reemployment. We are also interested in approaches that give workers agency over AI deployment and citizens real voice in the institutions shaping economic change. As work changes, we want to better understand when it provides meaning, purpose, and satisfaction, and how more people can have access to those conditions. 

To make these efforts possible, we will also invest in the capacity of governments and public institutions to actually deliver. The best-designed program fails if the infrastructure to run it doesn't exist. AI itself may be a powerful tool for accelerating state capacity and public services around the world, and we will fund ambitious efforts to make that real.

We are especially interested in making AI work well for people least served by existing systems. AI that helps people make career decisions, handle legal and financial questions, access healthcare guidance, and solve problems that used to require scarce expertise could be a genuine equalizer, particularly in parts of the world where these services are scarce or nonexistent.  But this only works if the tools are accessible, deployed carefully, and designed with the people who will use them. What works will vary across sectors and geographies. We invite innovative ideas, and will fund pilots at a meaningful scale across multiple approaches and learn from what we find. 

Building for Long-term Economic Security

There is wide disagreement about the pace and scale of change that AI will bring. But we cannot afford to waste time waiting for certainty. 

The transition measures above are not designed for worlds in which automation accelerates, economic gains dramatically concentrate, or the share of income flowing through wages shifts significantly. In those scenarios, society will likely need new approaches that give people durable stakes in the systems creating value. We want to help move promising approaches from ideas into testable designs: clarifying how they would be financed, what institutions would administer them, what risks they might create, and what evidence would tell us whether they are working. 

On the revenue side, there are serious proposals worth studying and exploring through pilots: shifting taxation from labor toward capital and economic rents, windfall or excess-returns mechanisms, and approaches to public or sovereign wealth funds, drawing on models like the Government Pension Fund of Norway and Alaska’s Permanent Fund. Under deep uncertainty, fiscal mechanisms may need to be adaptive. Tax rates, contribution rules, or dividend formulas could respond to observable indicators such as concentrated gains, changes in labor share, displacement, or extraordinary returns.

On the distribution side, the questions are equally important: how to give people durable claims on broad economic growth through income, capital, public goods, essential services, jobs or public works programs, access to compute, or new forms of data governance. The goal is not only to support people through economic change after decisions have already been made, but to give them a stake and a voice in shaping how that change unfolds. 

Much of the work ahead is not only empirical but architectural, and will require imagining systems that don’t exist yet. We will support the research infrastructure that can inform decision-making across this work. We are particularly interested in multi-agent economic simulations that use AI to model how economies might evolve as capabilities improve, paired with scenario planning across a range of possible futures.

Conclusion

We are looking for ambitious work that matches transformative change, including ideas we have not thought of yet and work we can help scale. We welcome input on what is most needed.

The $250M will support external organizations through grants, open calls, and institutional partnerships, while the Foundation builds a team to advance work directly and helps seed ambitious new projects in this space. We expect to announce our first initiatives later this year, and we will share what we learn as we go. We want to understand which approaches actually work, and strengthen an independent, well-resourced ecosystem that can make options for economic security real before they are urgent.  

We are at the beginning of what is likely to be the most significant economic shift in generations. We believe the work of making this change benefit all of humanity is among the most important things the Foundation could be doing right now, and we intend to treat it that way.