The article argues that as AI automates data queries, pipelines, and models, the real value shifts to “measurement engineers” who decide if we’re measuring the right things and interpret ambiguous results. It breaks down why judgment—construct validity, reliable metrics, and decision theory—is a teachable skill that organizations must build into hiring, training, and structure.

• As AI automates SQL, pipelines, and modeling, the bottleneck shifts to judgment: deciding whether you're measuring the right thing.
• Teams that track hundreds of metrics tend to cherry-pick supporting ones instead of narrowing to a few that predict real outcomes.
• Rising internal model evals can coexist with falling user satisfaction because evals often measure fluency, not usefulness.
• Borderline A/B test results demand power analysis and decision theory, not just p-values, to judge if effects like a 1.5% retention drop are real.