Robotics exposure is highest in manual labor jobs and industries
How exposed is work to robotics? Occupational, industry, and geographic measures
RoboticsComputers and Society
Summary
This paper looks at how much different jobs and industries could be affected by robots and physical AI. The authors created a new way to measure how closely robots’ skills match various occupations, industries, and locations. They found that jobs involving physical work like construction, farming, and transportation are most exposed to robotics, especially those with lower pay and education. The measures show where robots could potentially take over tasks but don’t reflect how soon or cheaply this will happen.
What this means in practice
- •For labor market analysts: Identify which occupations and regions are most exposed to physical automation for workforce planning and policy making.
- •For industrial automation planners: Target industries and tasks with highest robotics exposure for prioritizing automation investments and deployment.
Authors
Sebastian Sartor, Robert Seamans, Neil Thompson
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.