Newest papers

How exposed is work to robotics? Occupational, industry, and geographic measures

Sebastian Sartor, Robert Seamans, Neil Thompson · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.24267 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

職業能力に基づく技術能力を地図化する露出性単一指数は、AIが仕事を再構成する方法を予測するための標準ツールとなっているが、既存の措置は、過去数十年の産業ロボットを反映する特許とインストールデータを通じてソフトウェアAIまたはタスクロボティクスをカバーするものではありません。我々は、ロボティクスと物理人工知能のための最初の能力ベースの露出量の測定方法を構築し、職業、産業および地理をカバーするための最初の標準ツールとなっています。 Felten et al. (2018、2021)の枠組み合わせに従って、我々は、78の学術、産業および標準源から8つの分野でロボティクス露出性能力

Machine translation (Meta M2M100); the English original follows.

Abstract

Exposure indices that map technical capabilities onto occupational abilities have become a standard tool for anticipating how AI may reshape work, but existing measures either cover software AI or proxy robotics through patents and installation data that reflect the industrial robots of past decades. We construct the first abilities-based exposure measures for robotics and physical AI, and the first to span occupations, industries, and geographies. Following the framework of Felten et al. (2018, 2021), we build a robotics capability taxonomy of 16 capabilities in eight domains from 78 academic, industry, and standards sources, use a large language model to score the relatedness of each capability to the 52 ONET work abilities, and aggregate to Robotics Occupational Exposure (ROE) for 774 occupations, Robotics Industry Exposure (RIE) for 250 NAICS industries, and county-level Robotics Geographic Exposure (RGE). We find that robotics exposure concentrates in physically intensive work such as construction, farming, transportation, and production, and in industries such as logging, mining, trucking, and warehousing. Unlike generative-AI exposure, ROE is higher in occupations with lower wages, lower education requirements, and less creative-ability content. Physical collaboration and assistance, terrain-based locomotion, grasping and handling, and task and functional reasoning move the exposure ranking most, yet no single capability carries the index. Because the measures capture technical relatedness rather than readiness, cost, or adoption, they should be read as an upper bound on where physical automation could affect work.

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Topics: Robotics, Artificial intelligence, Automation, Robot, Computer science, Ranking (information retrieval)

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