Addressing AI-Induced Labor Market Risks Through Enhanced Individual Capital Income

While rapid advances in generative AI (GenAI) present significant opportunities for productivity and growth, they also risk displacing workers and deepening income inequality, particularly by increasing the relative returns to capital compared to labor. In this paper, we review current policy proposals to address these risks, noting their potential benefits as well as fiscal and implementation challenges. We complement this review with simulations estimating the fiscal impact of GenAI across OECD countries, under various unemployment and growth scenarios.

We then propose a complementary policy approach: enhancing individual capital income through increased household savings and targeted financial tools. We argue that this approach can help workers manage economic transitions, reduce fiscal pressures, and promote a more equitable distribution of AI-driven capital gains. Using comparative data from OECD countries, we identify nations likely to benefit most from such policies—especially those with low household savings, limited social protection, and aging populations. We also highlight key target populations, including workers with limited access to welfare, such as the self-employed in certain countries.

Additional authors: Merav Kaplan, RAND School of Public Policy; Guy Lichtinger, Harvard University