With the exponential development of artificial intelligence (AI) technology, its core impact on the global labor market has penetrated from the macro employment structure to the micro job search. This technological dividend will bring new changes to the choice and combination of job search channels for young workers. In addition, due to the individual differences in the cognition and efficiency drive of young workers, the internalization of technology will also have different impacts on their job search methods. Based on data from the Chinese General Social Survey (CGSS) from 2015 to 2021, this study empirically analyzes the impact of artificial intelligence development on the choice of job search channels and the richness of job search channel combinations of young workers. The results show that the level of AI development significantly promotes young workers to adopt more diversified channel combinations. The mechanism analysis shows that AI technology enhances the ability of workers to obtain job search information by increasing the strength of social networks. At the same time, the study identifies a notable inverse relationship between technology attention and application: the higher the level of AI development, the lower the active attention to technology, yet the higher the channel use efficiency. This shift is attributed to a transition from “cognition-driven” to “efficiency-driven” behavior after technology has been deeply internalized. In addition, the impact effects exhibit significant heterogeneity, with AI technology demonstrating a more pronounced effect on the diversity of job search channel combinations among young workers in western regions, those with higher education levels, and those with work experience. This study provides a new perspective for understanding how AI technology reshapes job search behavior through social capital and cognitive pathways, and has implications for the formulation of differentiated employment promotion policies.

Artificial Intelligence, Young Workers, Job Search, Technological Internalization, Active Attention Level, Social Network Intensity