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Artificial Intelligence and Skills: Evidence from Contrastive Learning in Online Job Vacancies

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  • Hangyu Chen
  • Yongming Sun
  • Yiming Yuan

Abstract

We investigate the impact of artificial intelligence (AI) adoption on skill requirements using 14 million online job vacancies from Chinese listed firms (2018-2022). Employing a novel Extreme Multi-Label Classification (XMLC) algorithm trained via contrastive learning and LLM-driven data augmentation, we map vacancy requirements to the ESCO framework. By benchmarking occupation-skill relationships against 2018 O*NET-ESCO mappings, we document a robust causal relationship between AI adoption and the expansion of skill portfolios. Our analysis identifies two distinct mechanisms. First, AI reduces information asymmetry in the labor market, enabling firms to specify current occupation-specific requirements with greater precision. Second, AI empowers firms to anticipate evolving labor market dynamics. We find that AI adoption significantly increases the demand for "forward-looking" skills--those absent from 2018 standards but subsequently codified in 2022 updates. This suggests that AI allows firms to lead, rather than follow, the formal evolution of occupational standards. Our findings highlight AI's dual role as both a stabilizer of current recruitment information and a catalyst for proactive adaptation to future skill shifts.

Suggested Citation

  • Hangyu Chen & Yongming Sun & Yiming Yuan, 2026. "Artificial Intelligence and Skills: Evidence from Contrastive Learning in Online Job Vacancies," Papers 2601.03558, arXiv.org.
    • Handle: RePEc:arx:papers:2601.03558
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