Generative AI and the Evolution of Skill Requirements in Job Postings across Labor Markets
Diana Maria Popa, Simona-Vasilica Oprea*, Adela Bâra
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania
* Corresponding Author: Simona-Vasilica Oprea. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.084792
Received 29 April 2026; Accepted 24 June 2026; Published online 20 July 2026
Abstract
This paper investigates how generative-artificial intelligence (AI) is influencing job requirements, skill compositions and sectoral dynamics across global labor markets. It examines the evolving frequency and framing of AI-related competencies in job postings, exploring whether generative-AI functions primarily as an augmentative or substitutive component in the workplace. A large-scale, multi-source corpus of over 150,000 English-language job postings (2018–2025) is compiled from twelve open-access datasets and one public API. The analytical framework integrates lexical skill extraction, semantic framing, topic modeling and time-series forecasting. Skill mentions are categorized into five dimensions: AI_Data, Routine, Soft_Meta, Domain_Specific and Leadership, while cross-sectoral analyses and correlation matrices quantify interdependencies between competencies. Sentence-transformer embeddings and cosine similarity are used to compute a Framing Index, distinguishing augmentation- vs. automation-oriented discourse. Results reveal a statistically significant increase in AI-related skill mentions after 2021 (t = −4.70,
p = 0.016), alongside a decline in routine-task skills such as data entry and manual coding. Forecasts indicate potential continued growth in AI_Data and Soft_Meta skills through 2025, signaling a structural convergence toward hybrid human-AI expertise as a new foundation of employability. However, the results do not imply direct evidence of workforce transformation beyond what job-posting data can support. Investigating job postings, our research contributes a replicable, data-driven methodology for mapping the diffusion of AI-related skills across industries and time.
Keywords
Generative-AI; labor market evolution; skill evolution; semantic framing; topic modeling; workforce adaptation
Open Access
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