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

• URL: http://arxiv.org/abs/2604.06906v1
• Date: Wed, 08 Apr 2026 10:05:08 GMT
• Title: The AI Skills Shift: Mapping Skill Obsolescence, Emergence, and Transition Pathways in the LLM Era
• Abstract summary: We present the Skill Automation Feasibility Index (SAFI)<n>SAFI measures performance on text-based representations of skills, not full occupational execution.<n>All data, code, and model responses are open-sourced.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 jobs are those LLMs perform least well at in our benchmark; (3) 78.7% of observed AI interactions are augmentation, not automation; (4) all four models converge to similar skill profiles (3.6-point spread), suggesting that text-based automation feasibility may be more skill-dependent than model-dependent. SAFI measures LLM performance on text-based representations of skills, not full occupational execution. All data, code, and model responses are open-sourced.

Related papers

• OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation [57.505743202759646]
  OccuBench is a benchmark covering 100 real-world professional task scenarios across 10 industry categories and 65 specialized domains.<n>Our multi-agent synthesis pipeline automatically produces evaluation instances with guaranteed solvability, calibrated difficulty, and document-grounded diversity.
  arXiv  Detail & Related papers  (2026-04-13T00:27:32Z)
• Generative-AI and the transformation of workforce. A job postings-driven analysis [7.765662835417495]
  This paper investigates how generative-artificial intelligence AI is reshaping job requirements, skill compositions and sectoral dynamics across global labor markets.<n>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.
  arXiv  Detail & Related papers  (2026-04-07T17:02:43Z)
• Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption [1.066082625513843]
  We extend the Acemoglu-Restrepo task exposure framework to address the labor market effects of agentic artificial intelligence systems.<n>Unlike prior automation technologies that substitute for individual subtasks, agentic AI systems execute end-to-end using multi-step reasoning.<n>We introduce the Agentic Exposure score, a composite measure computed algorithmically from O*NET task data.
  arXiv  Detail & Related papers  (2026-03-31T19:36:36Z)
• How AI Forecasts AI Jobs: Benchmarking LLM Predictions of Labor Market Changes [5.848712585343904]
  This paper introduces a benchmark for evaluating how well large language models (LLMs) can anticipate changes in job demand.<n>Our benchmark combines two datasets: a high-frequency index of sector-level job postings in the United States, and a global dataset of projected occupational changes due to AI adoption.<n>Results show that structured task prompts consistently improve forecast stability, while persona prompts offer advantages on short-term trends.
  arXiv  Detail & Related papers  (2025-10-27T14:08:27Z)
• LIMI: Less is More for Agency [49.63355240818081]
  LIMI (Less Is More for Intelligent Agency) demonstrates that agency follows radically different development principles.<n>We show that sophisticated agentic intelligence can emerge from minimal but strategically curated demonstrations of autonomous behavior.<n>Our findings establish the Agency Efficiency Principle: machine autonomy emerges not from data abundance but from strategic curation of high-quality agentic demonstrations.
  arXiv  Detail & Related papers  (2025-09-22T10:59:32Z)
• OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks [52.87238755666243]
  We present OmniEAR, a framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks.<n>We model continuous physical properties and complex spatial relationships across 1,500 scenarios spanning household and industrial domains.<n>Our systematic evaluation reveals severe performance degradation when models must reason from constraints.
  arXiv  Detail & Related papers  (2025-08-07T17:54:15Z)
• AutoMind: Adaptive Knowledgeable Agent for Automated Data Science [70.33796196103499]
  Large Language Model (LLM) agents have shown great potential in addressing real-world data science problems.<n>Existing frameworks depend on rigid, pre-defined and inflexible coding strategies.<n>We introduce AutoMind, an adaptive, knowledgeable LLM-agent framework.
  arXiv  Detail & Related papers  (2025-06-12T17:59:32Z)
• Small Language Models are the Future of Agentic AI [42.62162575221445]
  We lay out the position that small language models (SLMs) are sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems.<n>We discuss the potential barriers for the adoption of SLMs in agentic systems and outline a general LLM-to-SLM agent conversion algorithm.
  arXiv  Detail & Related papers  (2025-06-02T18:35:16Z)
• TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks [55.03911355902567]
  We introduce TheAgentCompany, a benchmark for evaluating AI agents that interact with the world in similar ways to those of a digital worker.<n>We find that the most competitive agent can complete 30% of tasks autonomously.<n>This paints a nuanced picture on task automation with simulating LM agents in a setting a real workplace.
  arXiv  Detail & Related papers  (2024-12-18T18:55:40Z)
• Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs [10.844598404826355]
  One-third of U.S. employment is highly exposed to AI, primarily in high-skill jobs requiring a graduate or postgraduate level of education.<n>Even in high-skill occupations, AI exhibits high variability in task substitution, suggesting that AI and humans complement each other within the same occupation.<n>All results, models, and code are freely available online to allow the community to reproduce our results, compare outcomes, and use our work as a benchmark to monitor AI's progress over time.
  arXiv  Detail & Related papers  (2024-07-27T08:14:18Z)
• WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
  Large language models (LLMs) can mimic human-like intelligence.<n>WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
  arXiv  Detail & Related papers  (2024-07-07T07:15:49Z)
• Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models [50.11814354654953]
  Key ability of an AI agent is to flexibly combine, as needed, the basic skills it has learned. This work introduces Skill-Mix, a new evaluation to measure ability to combine skills.
  arXiv  Detail & Related papers  (2023-10-26T16:55:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.