Human-AI productivity claims should be reported as time-to-acceptance under explicit acceptance tests

This position paper argues that human-AI productivity claims should be treated as scientific claims requiring standardized, complexity-normalized, uncertainty-aware reporting rather than ad hoc "speedup" numbers. In the pre-AGI transition, general-purpose AI is becoming cognitive infrastructure across tasks yet remains unevenly reliable, producing jagged performance frontiers and non-comparable results across studies. Reported gains can silently trade quality for speed and often omit verification, rework, integration, and governance costs that dominate real deployments. We argue that productivity should be reported as Time-To-Acceptance (TTA) under a documented acceptance test, with task complexity held invariant (or explicitly normalized) and heterogeneity and uncertainty treated as first-class quantities. We propose a quantitative framework that reallocates (rather than erases) complexity across human/AI workflow stages, makes rework explicit via acceptance probabilities, and captures non-linear scaling through effective parallelism and collaboration efficiency. Finally, we introduce the Human-AI Productivity Card (HAP Card), a lightweight reporting artifact to support cumulative and comparable evidence on human-AI collaboration in the pre-AGI transition.