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

Sebastian Sartor, Robert Seamans, Neil Thompson · arXiv · 16 Oct 2026

Where this sits in the debate

Will AI destroy more jobs than it creates?

Direction of the finding as read from the abstract by an AI model (AI-written).

Key takeaway AI-written

The authors build the first abilities-based measures of exposure to robotics and physical AI, scoring 16 robotics capabilities against O*NET work abilities with a language model and aggregating to 774 occupations, 250 industries and US counties. Exposure concentrates in physically intensive work such as construction, farming, transportation and warehousing, and unlike generative-AI exposure it is higher in lower-wage, lower-education jobs. The measures capture technical relatedness, not cost or adoption, so the authors present them as an upper bound.

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

Which occupations, industries and places are most exposed to robotics and physical AI?

The approach

Following Felten et al., the authors build a taxonomy of 16 robotics capabilities from 78 sources, use a large language model to score each against 52 O*NET work abilities, and aggregate to exposure scores for 774 occupations, 250 industries and US counties.

The findings

Exposure is highest in physically intensive work such as construction, farming, transport and production, and industries such as logging, mining, trucking and warehousing. Unlike generative-AI exposure, robotics exposure is higher in lower-wage, lower-education jobs.

How to read it: dataset-or-tool. Introduces data or methods others build on — its impact shows up in the studies that use it.

Limitations and counterpoints AI-written

The measures capture technical relatedness, not readiness, cost or adoption, so the authors present them as an upper bound; scores rely on a language model's ratings.

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.

About this publication

Preprint. A preprint — posted publicly before or without journal peer review; findings may still be revised.

Published: 2026 (recently)
Citations: Not recorded for this source

Citation counts come from OpenAlex; none are recorded for this source — absence of a count is not absence of citations.