Artificial intelligence (AI) is reshaping labor markets, with particularly disruptive effects in capital-intensive sectors like energy. This study investigates the impact of AI on employment scale and skill composition in China's energy industry, using panel data from 112 listed firms (2011–2023). We construct a novel “industry–region coordinated exposure index” that combines regional AI development with industry-level task automatability, and embed this within a comparative advantage-based double machine learning (DML) framework to identify causal effects. The research results indicate that AI has a significant overall impact on the workforce, leading to a significant substitution effect among college graduate workers; while the labor market for basic education and postgraduate students shows an upward trend, indicating a clear polarization of skills. Significant heterogeneity exists across industries and regions: fossil fuel companies experienced more drastic workforce reductions, while renewable energy companies demonstrated a complementarity between AI and human capital. Regionally, AI led to workforce shrinkage in eastern provinces, promoted moderate skills upgrading in the central region, and had a limited impact in the west. These findings provide valuable evidence for policy considerations regarding the management of AI-driven workforce transformation. Emerging economies should achieve a balance between technological progress and employment structure driven by AI by strengthening workforce retraining, improving vocational education systems, and promoting coordinated regional development. © 2026 Elsevier Ltd