The conversation around artificial intelligence has spent years obsessed with benchmarks, token windows, and the race toward AGI. But in the corridors of power and the quiet offices of economic think tanks, the dialogue has shifted. The question is no longer whether a model can pass the Bar exam, but who actually captures the value when a million white-collar tasks are automated overnight. There is a growing tension between the exponential growth of AI productivity and the stagnant structures of the 20th-century social contract, leaving a void where a new economic blueprint should be.

The Architecture of a Global Policy Experiment

OpenAI is stepping into this void by providing $2 million in funding to 14 independent institutions to research economic and social policies tailored for the AI era. This initiative is not a simple philanthropic gesture but a strategic execution of the external validation and idea-expansion plan promised in the Industrial Policy for the Intelligence Age, announced in April 2026. The funding is split precisely: $1 million in direct cash grants and up to $1 million in API credits, ensuring that researchers have both the financial runway and the computational tools to test their hypotheses.

The scale of interest in this program was significant, with over 400 individuals and organizations submitting proposals. The final 14 projects are distributed across five key regions: the United States, the European Union, Brazil, Singapore, and South Korea. OpenAI's rationale is rooted in the demographics of its own user base; with over 1 billion ChatGPT users spanning every age group and income bracket, the company argues that the policy response to AI must be as ambitious as the technology itself. The goal is to move beyond mere technology adoption and determine exactly how the resulting benefits should be distributed across society.

From Efficiency to Distribution: The Strategic Pivot

While most AI companies focus on the efficiency of the model, this initiative focuses on the resilience of the human economy. The shift is evident in the specific mandates of the selected partners. In the United States, the American Enterprise Institute (AEI) is developing a policy playbook that maps three distinct disruption scenarios—low, medium, and high—to observable economic indicators. Simultaneously, the Progressive Policy Institute (PPI) is prototyping a livelihood insurance system. This is not a traditional unemployment check but a personal benefit system covering retirement, health, education, and disability, designed to function even as the very definition of a job evolves.

The European projects push the boundary further toward systemic ownership. The Centre for European Policy Studies (CEPS) is benchmarking productivity gains against U.S. models to build an adaptive safety net for European workers. Meanwhile, the European Centre for International Political Economy (ECIPE) is constructing a Right to AI framework. This framework explores radical shifts in capital ownership, including employee ownership schemes, citizen investment funds, and social wealth funds, suggesting that the only way to survive AI-driven displacement is to give the displaced a literal ownership stake in the intelligence producing the wealth.

This represents a fundamental reversal in how tech giants typically engage with government. Instead of lobbying for deregulation, OpenAI is funding a decentralized laboratory to determine how it should be regulated and how its wealth should be taxed. The Tax Foundation is already conducting quantitative research on the shifting balance between labor income, corporate profits, and capital gains, with the intent to publish a white paper on fiscal resilience based on neutrality, simplicity, and economic efficiency.

Infrastructure and the Global Coordination Layer

The experiment extends into the physical and clinical realms, recognizing that AI policy is useless without the power to run it. The Abundance Institute is analyzing how U.S. state governments can expand power generation and transmission to meet data center demand, creating a Data Center Atlas to visualize regional benefits. In Brazil, the focus is on turning access into progress. The Instituto de Matemática Pura e Aplicada (IMPA) is establishing a measurement framework to calculate the actual engineering and infrastructure costs required to turn AI access into scientific advancement. In tandem, the Hospital das Clínicas da Faculdade de Medicina da USP (HCFMUSP) is building an AI-driven clinical information infrastructure for Brazil's Unified Health System (SUS), using virtual and anonymized data to test deployment before a full-scale rollout.

To ensure these regional experiments don't remain isolated, the Windfall Trust is managing a global working group network connecting the U.S., UK, Canada, EU, and Latin America. This network acts as a coordination layer, reviewing fiscal, labor, and competitiveness issues across different AI scenarios. By publishing these findings in comprehensive open reports, the trust aims to transform local policy successes into global standards.

For countries like South Korea, the stakes are particularly high. The research here focuses on adapting employment structures and tax reforms to the specific legal and corporate governance landscape of the region. The core challenge is determining if models like AI dividends or employee ownership can actually function within the rigid hierarchies of Korean labor law. The ultimate success of these 14 projects will be measured by the creation of numerical benchmarks for employment disruption and the Right to AI framework, providing governments with the objective triggers needed to activate livelihood insurance or wealth redistribution mechanisms.

This transition from theoretical AI safety to practical economic engineering suggests that the industry has realized a hard truth: the greatest risk to AI adoption is not a rogue agent, but a societal collapse triggered by an obsolete economy.