Measures of occupational AI exposure ask whether machines can perform a task, not which function the human supplies. I distinguish execution, producing a first-pass output, from evaluation, judging whether it is correct, and make the distinction measurable by scoring all 19,265 O*NET task statements under fixed rubrics. The execution score is reproducible across model coders and O*NET vintages. In a 200-task random audit it correlates 0.81 with the mean of four blinded human coders. It predicts each held-out coder's peers better than that coder does, though the coders disagree substantially among themselves. It adds only modest power beyond O*NET's evaluation activities. Employment growth is lower in execution-heavy white-collar occupations in every window since 2012; the gradient shows no post-2022 break and lies largely between occupational families. The Eloundou exposure gradient is steeper in 2022--2025 than in the pandemic window, but the design does not establish an AI effect. A near-term seniority test is null. If evaluative judgment is itself produced by execution practice, the supply of evaluators falls only after a cohort lag; that prediction remains untested.

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