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Schmidt Sciences' AI Impact on Jobs program supports rigorous interdisciplinary research on how generative AI and AI agents are reshaping employment, occupational tasks, wages, productivity, and labor markets. Funded scope includes empirical measurement of AI adoption across industries and occupations, task-level analyses of AI capabilities and displacement, longitudinal studies of worker outcomes, AI's effects on inequality and labor market polarization, evaluation of reskilling and transition programs, AI's role in entrepreneurship and small business productivity, and policy-relevant work on social safety nets and labor regulation in an AI economy.
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Search similar grants →According to the current listing, eligibility includes: Universities, non-profit research institutions, and think tanks in the U.S. and internationally. Open to economists, sociologists, labor scholars, computer scientists, and interdisciplinary teams. Junior and senior investigators encouraged. Confirm the full requirements in the official notice before applying.
The current listing shows total program commitment over $3 million distributed across grantees. Individual awards typically range from $250,000 to $750,000 per project over 2-3 years. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Applications for Schmidt Sciences AI Impact on Jobs and Labor Markets Awards 2026 are due September 30, 2026. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
Schmidt Sciences AI Impact on Jobs and Labor Markets Awards 2026 is funded by Schmidt Sciences. Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
Schmidt Sciences' 2026 Science of Trustworthy AI RFP closes May 17 with two funding tiers — up to $1M (Tier 1) and $1–5M+ (Tier 2) over 1–3 years, with a 10% indirect cost cap. The three research aims target misalignment under distribution shift, predictive-validity evaluations, and oversight of superhuman systems. Here is why the structure favors team-based proposals.
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