This is an outdated version published on 2025-11-30. Read the most recent version.

Vol. 1 No. 2 (2025)

Articles

The Impact of Artificial Intelligence on Labor Market Income Inequality

Published 2025-11-30

  • Yuhao Gu
    • ,
  • Yanhui Wang

Yuhao Gu
International Institute of Management and Business image/svg+xml

Yanhui Wang
University of Auckland image/svg+xml

Abstract

As the development and application of Artificial Intelligence (AI) technologies accelerate, their disruptive impact on the labor market has become a central issue in economic research. This paper aims to investigate the multifaceted mechanisms through which AI influences income inequality. It begins by reviewing the established theoretical frameworks linking technological progress to income distribution, followed by a survey of contemporary literature on AI's labor market effects. Building on this foundation, the paper develops a theoretical framework that delineates four primary channels through which AI may exacerbate inequality: enhanced skill-biased technical change (SBTC), capital-labor substitution, job polarization, and the "winner-take-all" effect. The analysis suggests that while AI substitutes for routine tasks performed by low- and middle-skill workers, it simultaneously complements the cognitive, social, and creative skills of high-skill workers, thereby widening the wage gap. Furthermore, as a form of capital, AI deepens capital-labor substitution and may contribute to the rise of "superstar firms," potentially leading to a decline in the labor share of income and amplifying inequality between capital owners and labor. Based on these findings, the paper concludes by proposing a comprehensive suite of policy recommendations, including reforms in education, modernization of the social safety net, and adjustments in labor market regulation and technology governance. These policies are designed to mitigate the adverse distributional consequences of AI while harnessing its potential for productivity growth, ultimately fostering a more inclusive society.

Keywords

Artificial Intelligence, Income Inequality, Labor Market, Job Polarization, Superstar Firms, Labor Share of Income

PDF

References

  1. Acemoglu, D., & Restrepo, P. (2019). Automation and New Tasks: How Technology(2), 3–30. DOI: Journal of Economic Perspectives, 3310.1257/jep.33.2.3 DOI: https://doi.org/10.1257/jep.33.2.3
  2. Brynjolfsson, E., & McAfee, A. (2014). . W. W. Norton & Company.The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
  3. Katz, L. F., & Murphy, K. M. (1992). Changes in Relative Wages, 1963–1987: Supply and Demand Factors. (1), 35–78. DOI: The Quarterly Journal of Economics, 10710.2307/2118323 DOI: https://doi.org/10.2307/2118323
  4. Autor, D. H., Levy, F., & Murnane, R. J. (2003). The Skill Content of Recent Technological Change: An Empirical Exploration. (4), 1279–1333. DOI: The Quarterly Journal of Economics, 11810.1162/003355303322552801 DOI: https://doi.org/10.1162/003355303322552801
  5. Agrawal, A., Gans, J. S., & Goldfarb, A. (2018). . Harvard Business Review Press.Prediction Machines: The Simple Economics of Artificial Intelligence
  6. Acemoglu, D., & Restrepo, P. (2020). Robots and Jobs: Evidence from US Labor Markets. (6), 2188–2244. DOI: Journal of Political Economy, 12810.1086/705716 DOI: https://doi.org/10.1086/705716
  7. Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., ...(14), 6531–6539. DOI: Proceedings of the National Academy of Sciences, 11610.1073/pnas.1900949116
  8. Autor, D., Dorn, D., Katz, L. F., Patterson, C., & Van Reenen, J. (2020). The Fall of the Labor Share and the Rise of Superstar Firms. (2), 645–709. DOI: The Quarterly Journal of Economics, 13510.1093/qje/qjaa004 DOI: https://doi.org/10.1093/qje/qjaa004
  9. Acemoglu, D., & Autor, D. (2011). Skills, Tasks and Technologies: Implications for Employment and Earnings. In *Handbook of Labor Economi (Vol. 4, Part B, pp. 1043–1171). Elsevier. DOI: Handbook of Labor Economics10.1016/S0169-7218(11)02410-5 DOI: https://doi.org/10.1016/S0169-7218(11)02410-5
  10. Autor, D. H. (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. (3), 3–30. DOI: Journal of Economic Perspectives, 2910.1257/jep.29.3.3 DOI: https://doi.org/10.1257/jep.29.3.3
  11. Autor, D. H. (2019). Work of the Past, Work of the Future. . DOI: NBER Working Paper No. 2558810.3386/w25588 DOI: https://doi.org/10.3386/w25588
  12. Rosen, S. (1981). The Economics of Superstars. (5), 845–858.The American Economic Review, 71
  13. Gu, Y., Pan, D., Yang, N., & Wang, X. (2025). Research on Storage and Transportation Cost Control and Technological Breakthroughs from the Perspective of Global Hydrogen Energy Developmen(5), 33-38.Journal of Sustainable Built Environment, 2 DOI: https://doi.org/10.70731/9ygthr89
  14. Gu, Y., Lin, H., & Zhao, W. Li, M..., & Wang, X.(2025). The Ethical Balance Reconstruction of Green Finance Empowered by Computer Technology. International Journal of Accounting and Economics Studies, 12(6), 580-586. DOI: https://doi.org/10.14419/ca6cas51