[2604.06906] The AI Skills Shift: Mapping Skill Obsolescence, Emergence, and Transition Pathways in the LLM Era

Computer Science > Computation and Language arXiv:2604.06906 (cs) [Submitted on 8 Apr 2026] Title:The AI Skills Shift: Mapping Skill Obsolescence, Emergence, and Transition Pathways in the LLM Era Authors:Rudra Jadhav, Janhavi Danve View a PDF of the paper titled The AI Skills Shift: Mapping Skill Obsolescence, Emergence, and Transition Pathways in the LLM Era, by Rudra Jadhav and 1 other authors View PDF HTML (experimental) Abstract:As Large Language Models reshape the global labor market, policymakers and workers need empirical data on which occupational skills may be most susceptible to automation. We present the Skill Automation Feasibility Index (SAFI), benchmarking four frontier LLMs -- LLaMA 3.3 70B, Mistral Large, Qwen 2.5 72B, and Gemini 2.5 Flash -- across 263 text-based tasks spanning all 35 skills in the U.S. Department of Labor's O*NET taxonomy (1,052 total model calls, 0% failure rate). Cross-referencing with real-world AI adoption data from the Anthropic Economic Index (756 occupations, 17,998 tasks), we propose an AI Impact Matrix -- an interpretive framework that positions skills along four quadrants: High Displacement Risk, Upskilling Required, AI-Augmented, and Lower Displacement Risk. Key findings: (1) Mathematics (SAFI: 73.2) and Programming (71.8) receive the highest automation feasibility scores; Active Listening (42.2) and Reading Comprehension (45.5) receive the lowest; (2) a "capability-demand inversion" where skills most demanded in AI-exposed jo...