The Economic Impact of Generative Artificial Intelligence: A Review of Research from the Perspective of Task Content

Abstract

The rapid iterative development of generative artificial intelligence technology is fundamentally reshaping production methods and labor forms. Its ability to penetrate non-routine cognitive tasks breaks the impact boundary of traditional automation technologies, and also promotes task content analysis method to become the mainstream research paradigm in this field. This paper systematically sorts out the theoretical evolution and research progress of the economic impact of generative AI from the perspective of task content. From the two dimensions of industrial development and labor market, it integrates the research results of core topics such as enterprise adoption, productivity effect, market structure, labor complementarity and substitution, and changes in employment structure. It focuses on clarifying the sources of divergence and applicable boundaries of different research conclusions. Studies show that existing literature has formed a basic consensus that generative AI exerts impact mainly through the reconstruction of intra-occupational tasks. However, there are still significant divergences in the total employment effect, the implementation degree of productivity gains, and the mechanism of cross-country heterogeneity. The main limitations of current research in this field include the short research time span, lack of evidence from developing economies, and insufficient research on long-term dynamic mechanisms. The contribution of this paper is to restore the seemingly divergent literature conclusions to differences in methodological premises with a unified three-effect coordinate, and put forward corresponding testable designs for each type of research gap. Future research needs to be further deepened around the two core dimensions of industry and labor market, improve localized measurement systems, and expand long-term dynamic analysis.