arXiv:2605. 17086v2 Announce Type: replace-cross Abstract: Automation can displace or complement labour, but this need not be constant across economies.
arXiv:2607. 05404v1 Announce Type: cross Abstract: Frontier AI's labor-market effects matter to workers, firms, and policymakers, but current evidence generally comes from a handful of high-income economies.
The paper introduces the Agentic Adoption Index (AAI), a new measure of delegated exposure that captures whether workers actually commit tasks to AI within structured workflows. Using semantic embeddings of 888,000 agent skill specifications from GitHub and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those most vulnerable to pre-AI automation, that AAI correlates more with technical capability than with current LLM use, and that for lower‑educated occupations AAI rises with wages while it falls for higher‑educated, high‑earning workers. These patterns also appear in an independent corpus from the Manus Skills Marketplace.
arXiv:2412. 19754v4 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) is transforming the nature of work, yet there is limited empirical evidence on how it affects demand for human skills.
The paper introduces the Agentic Adoption Index (AAI), a new metric that captures whether workers actually delegate tasks to AI within their workflows, rather than merely measuring potential AI applicability. Using 53,000 agent skill specifications and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those previously deemed most at risk, that AAI aligns more closely with AI’s capabilities than current usage, and that adoption peaks at mid‑wage, bachelor’s‑level occupations while declining at both ends of the wage and education spectrum. The study highlights that technical availability explains much of the variation, but other factors—such as resistance to specification or professional discretion—also influence who adopts AI. whyItMatters":"The findings suggest that actual AI adoption patterns differ from prior risk assessments, indicating that factors beyond technical feasibility shape who delegates to AI, which has implications for workforce planning and policy."
arXiv:2604. 01363v2 Announce Type: replace Abstract: We propose that AI automation is a continuum between: (i) crashing waves where AI capabilities surge abruptly over small sets of tasks, and (ii) rising tides where the increase in AI capabilities is more continuous and broad-based.
A new framework analyzes 921 occupations and 148 million U. S.
arXiv:2308. 05201v4 Announce Type: replace Abstract: Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape labor market dynamics.
arXiv:2607. 19375v1 Announce Type: cross Abstract: Language models perform economically valuable work, yet they are not currently assessed for how well they perform every economically valuable task.
arXiv:2608. 05172v1 Announce Type: cross Abstract: The task-based framework in economics models occupations as bundles of tasks.
The paper presents an AI-based methodology that uses embedding-based similarity search and large language model classification to map Dutch job profiles to harmonised ESCO occupations. It introduces a Digital Semantic Score, which quantifies the association of job titles and skills with digital concepts relative to a non-digital reference, moving beyond keyword-based approaches. The study finds that digitalisation is unevenly distributed across occupations, with managerial, professional, and ICT roles showing the strongest digital language, while career-transition and skill analyses reveal pathway-dependent movement toward digital work and a multidimensional nature of digital capability.
arXiv:2606. 09833v1 Announce Type: cross Abstract: AI agents are reshaping the workspace, leading to drastic change of how humans work.
arXiv:2608. 08601v1 Announce Type: new Abstract: To anticipate socio-technical risks from AI agents, organizations need taxonomies to classify them.