Theoretical Mechanisms and Coping Strategies of Generative AI's Impact on Employment Structural Polarization

Abstract

The rapid integration of generative artificial intelligence is deeply reshaping the labor market, but existing theories of employment polarization are facing challenges in their explanatory power. This paper, based on classic task models and skills-biased technological progress theory, introduces the variable of "cognitive automation" to analyze the essential characteristics of generative AI that distinguish it from previous technological changes and its impact mechanism on employment polarization. The study finds that generative AI drives employment polarization through a three-pronged, progressive mechanism: the accelerated substitution of routine cognitive tasks constitutes a direct impact; the structural expansion of skill premiums leads to labor market restructuring; and the technological gap in occupational transitions creates a solidification effect. These three mechanisms jointly drive employment polarization from the traditional "expansion at both ends and collapse in the middle" to a new form of "cognitive layer collapse." The response strategy should target each mechanism with intervention, with the core being to narrow the adaptive gap between technology diffusion and human capital accumulation.