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Apr 28, 2025
Redistributing the AI Dividend: Modeling Data as Labor in a Transformative Economy
Sneha Maria Rozario, Srishti Dutta,
Summary
The economic transformations induced by artificial intelligence (AI) raise pressing distributional concerns. This paper examines the allocation of the AI Dividend—the surplus value generated through AI advancements—and proposes mechanisms to ensure equitable redistribution. Anchoring our analysis in the Distribution track, we focus on the Data as Labor (DaL) framework, inspired by Lanier and Weyl, wherein individuals' data contributions are treated as productive labor. We simulate and compare two paradigms: Data as Capital (DaC), in which data is aggregated as corporate capital, and DaL, wherein individuals are compensated for their data. Using comparative economic simulations, we highlight systemic inequalities emerging under DaC and demonstrate the stabilizing potential of DaL structures. We further subdivide the DaL paradigm into two mechanisms: a corporate taxation regime and an individualized data compensation model, proposing a novel formula for micro-level redistribution. The implications of these models for labor markets, inequality, and societal stability are discussed, with a focus on designing incentive-compatible and scalable economic policies. Our findings suggest that recognizing data as labor not only promotes distributive justice but also enhances the long-term sustainability of the AI-driven economy.Message @vi
Cite this work:
@misc {
title={
Redistributing the AI Dividend: Modeling Data as Labor in a Transformative Economy
},
author={
Sneha Maria Rozario, Srishti Dutta,
},
date={
4/28/25
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}