Who Delegates to AI? Evidence from Agent Configurations in Github

A growing body of literature measures the extent to which occupations are exposed to AI, yet existing measures capture where AI could perform tasks rather than whether workers have actually adopted it. We introduce a distinct tier of exposure, delegated exposure, which records whether a worker has committed a task to AI by embedding it into a structured workflow. We operationalize this concept through the Agentic Adoption Index (AAI), measuring how closely an occupation’s tasks align with the agentic routines that practitioners have built and shared. Using semantic embeddings of roughly 888,000 agent skill specifications from public GitHub repositories, we compute their similarity to nearly 18,000 O*NET task statements and aggregate these scores to the occupational level. We present three main findings. First, the occupations where task delegation concentrates differ sharply from those identified as most vulnerable by pre-AI automation frameworks. Second, the AAI aligns more closely with measures of technical capability than with measures of current conversational LLM use.

Introduction. While artificial intelligence now sits at the center of technological change, accurately measuring its labor market impacts remains an ongoing challenge. Several factors compound this difficulty: the technology is evolving rapidly, its reach is exceptionally broad—extending to unstructured tasks across diverse occupations, and we still lack a clear picture of who is adopting these tools and for what purposes [Bick et al., 2026, Frank et al., 2026]. These characteristics make AI’s labormarket impacts harder to predict than those of earlier automation [Frank et al., 2019], motivating a growing body of research that measures how exposed the tasks within occupations are to AI, and more recently, how far that exposure has translated into use. We group the extensive literature on occupational exposure to AI or automation technology into three lines of research, ordered by how closely each comes to observing the realized adoption of AI.

Discussion / Conclusion. We extend research on AI exposure by adding the concept of delegated exposure, as LLM use moves beyond isolated conversations toward reusable agentic workflows. We operationalize it through the AAI, which matches skill descriptions from the public GitHub repositories to O*NET task statements. The resulting measure reveals a distinct occupational gradient. Delegated exposure concentrates in information-intensive work and remains minimal where work depends on manual dexterity or direct intervention in the physical environment. This distribution departs from pre-AI accounts such as computerization-risk estimates, which located automation risk in routine tasks and ranked occupations differently from what we observe. The AAI also aligns more closely with measures of technical capability and application availability than with prompt-level conversational use, suggesting that it tracks what AI can in principle perform rather than what usage records currently capture. Yet the availability measure does not explain the pattern fully: adoption increases with wages among occupations requiring a bachelor’s degree or less, but declines among higher earners requiring advanced degrees.