Execution and Evaluation: A New Occupational Measure and Long-Run Employment Gradients

Abstract

Artificial intelligence automates execution more readily than evaluation: producing output is cheap, judging whether it is correct is not. Exposure measures rank tasks by whether AI can perform them, not by which function the human supplies. I score all $19{,}265$ O*NET task statements under fixed rubrics to build occupation-level execution and AI-capability shares. The execution share is reproducible across model coders and O*NET vintages and distinct from AI capability and routine-task intensity; it is a model-based measure, not human-validated ground truth, and adds only modest power beyond O*NET's evaluation activities. In a harmonized panel, employment growth is lower in execution-heavy white-collar occupations in every window since 2012, and equality of slopes cannot be rejected: the gradient is a secular trend rather than an AI-era event, largely between occupational families. The vintage-valid capability gradient steepens after 2022, a change that is dated but not causally attributable. The evidence establishes a measure and a chronology, not an AI-caused effect.