Impact of artificial intelligence innovation on labor structure: a study based on staggered DID method

In recent years, breakthroughs in computing power and algorithms have driven profound evolution in Artificial Intelligence (AI) applications. Beyond replacing repetitive manual labor, AI has penetrated into complex cognitive labor fields once deemed hard to automate, reshaping industry work paradigms, blurring traditional occupational boundaries, and triggering an unprecedented structural transformation in the labor market. Against this backdrop, exploring AI's far-reaching impact on employment patterns and its mechanisms has become a core concern for academia and policymakers, with vital theoretical and practical value for guiding workers to adapt to change and formulating forward-looking talent strategies. This paper uses the Difference-in-Differences method for empirical research. The study finds that AI innovation exerts a significant positive impact on the labor structure, optimizing the proportion of high-skilled and low-skilled labor. This indicates AI is not a simple labor replacement but a powerful enabler, pushing the overall labor structure toward higher skills and added value. It also provides strong empirical support for the "skill-biased technological change" theory, revealing a significant complementary synergy between technological progress and high-skilled labor in the AI era.