The Impact Effect of AI Washing on Corporate Human Capital Structure: An Empirical Analysis Based on Corporate Artificial Intelligence Disclosure and Investment

While Artificial Intelligence (AI) technology is advancing rapidly, some firms overstate their AI capabilities. What are the economic consequences of such behavior for the labor market? Based on the annual report text data and financial data of Chinese A-share listed companies from 2010 to 2024, this paper constructs an AI washing index that measures the gap between AI information disclosure and AI substantive investment, and investigates the impact of AI washing on corporate human capital structure. The results show that AI washing significantly worsens corporate human capital structure, manifested as a decline in the proportion of highly educated workers. From different dimensions, substantive AI investment intensity significantly improves human capital structure, while the effect of AI disclosure intensity is insignificant. Mechanism analysis reveals that AI washing impairs firms' talent allocation efficiency through three market punishment channels: exacerbating financing constraints, reducing institutional ownership, and decreasing analyst coverage. Heterogeneity analysis indicates that the negative effect of AI washing on human capital structure is more pronounced in high-tech industries and highly competitive industries. Further discussion finds that AI washing also negatively affects total employment and skill structure, exhibiting a pattern of decline in both quantity and quality.