Occupational Convergence or Divergence? Mapping Labor Market Structural Shifts Driven by AI Penetration
Artificial intelligence (AI) is rapidly becoming a defining feature of contemporary labor markets, yet it remains unclear whether its diffusion is producing a common set of competencies across occupations or deepening occupational divisions. We investigate how AI related skill demand is reshaping labor market structure using large scale online vacancy data from ten countries spanning the Global North and Global South. Combining natural language processing, a large language model, and multilevel bipartite network analysis, we map relationships between occupations, required skills, and career stages in the emerging AI economy. We find that AI demand is overwhelmingly concentrated within a narrow technical core, with approximately three quarters to four fifths of AI related vacancies located in STEM occupations across all countries. AI intensive jobs consistently emphasize Python, SQL, machine learning, and data analysis, generating convergence among highly exposed occupations. However, this convergence does not extend across the wider labor market. Instead, AI competencies remain largely confined to technical domains and are most strongly demanded at labor market entry. These findings reveal convergence within an AI exposed core but divergence between that core and the rest of the occupational structure. Rather than democratizing opportunities, AI appears to reinforce occupational stratification, raising barriers to entry and concentrating the benefits of AI adoption among workers and occupations with prior technological advantages.