Generative-AI and the transformation of workforce. A job postings-driven analysis
Diana Maria Popa, Simona-Vasilica Oprea, Adela B\^ara

TL;DR
This study analyzes over 150,000 job postings from 2018 to 2025 to understand how generative AI is transforming skills and job requirements across sectors, revealing a shift towards hybrid human-AI competencies.
Contribution
It introduces a data-driven, replicable methodology for mapping AI-related skill diffusion in labor markets using advanced NLP and statistical techniques.
Findings
Sharp increase in AI-related skills post-2021, such as prompt engineering.
Decline in routine tasks like data entry and manual coding.
Forecasts indicate continued growth in AI_Data and Soft_Meta skills through 2025.
Abstract
This paper investigates how generative-artificial intelligence AI is reshaping 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 force 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, BERTopic, LDA, KMeans, and time-series forecasting ARIMA. 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.…
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