Artificial Intelligence and Labor Market Transformation in China and Spain: A Comparative Institutional Analysis

Artificial intelligence (AI) is reshaping labor markets, but its effects are not determined by technology alone: similar technological capabilities can produce different employment outcomes depending on national systems of labor regulation, training, welfare, and governance. This article examines AI-driven labor market transformation in China and Spain from a comparative institutional perspective, asking how AI changes occupational tasks and skill demand, how institutions mediate its consequences, and how the two countries differ in AI governance, worker protection, and skills development. Drawing on academic literature, policy documents, and international reports, the study applies qualitative document analysis within a most-different-systems comparative design and analyzes the material thematically along three dimensions: task transformation, institutional mediation, and policy response. In both countries, AI operates less as a force of wholesale job replacement than as a process of task reorganization and skill reconfiguration, but through contrasting institutional logics. China’s state-led, innovation-oriented pathway accelerates AI deployment and industrial upgrading but leaves platform, migrant, and low-skilled workers unevenly protected; Spain’s EU-embedded, regulation-oriented pathway offers stronger statutory protection, yet a dualized labor market leaves young, temporary, and other outsider workers with weaker access to protection and training should they be affected. Distinguishing the strength of worker protection from its reach, the analysis shows that the two vary independently and that in both countries protection attaches to positions (registration and employment status in China, the employment contract in Spain) rather than to persons, so that AI-related risk is likely to fall where these eligibility gates exclude workers; differences in economic structure and level of development also contribute and cannot be fully separated in a two-case comparison.