What Capital After Labor? Forecasting the Talent ROI Transition in the Human-AI Era

AI augmentation breaks the accounting link between labor time and productive contribution, yet firms continue to evaluate talent through time-based overhead bundles. This paper develops a forecasting framework for the transition from time-based talent accounting to output-based talent ROI in the human-AI era, organized around five theorems: Theorem 3 (ROI Inversion at {\tau}*) carries the central transition claim, with overhead non-additivity, augmentation-saved-time pathways, innovation-premium amplification, and human-AI dyad attribution uncertainty as the mechanism architecture. Korea's staged 52-hour workweek mandate provides the early-warning case. In a DART panel of 365 firms (2,281 observations), the SG&A-to-revenue ratio rose from 18.26 percent (2018) to 20.06 percent (2020) and peaked at 20.10 percent (2024). Under the revenue-percentile cohort proxy, two-way fixed effects (+1.56 pp, p = 0.049), pooled event-study estimates (+4.21 pp at t = +3), and Callaway-Sant'Anna estimates (+4.51 pp at t = +4) converge on a positive overhead-pressure pattern. Institutional cohort evidence separates the two readings: under the statutory employee-size cohort the coefficient is indistinguishable from zero, weighing against a pure 52-hour-law interpretation and supporting the secular regime reading; a 2015-2017 backward extension (224 firms) argues against pre-existing trends. We read the Korean evidence as, to our knowledge, the first publicly documented signature of a secular pre-{\tau} overhead-pressure regime in which time-based accounting still dominates while AI augmentation raises firm-internal overhead. Output-based firms are forecast to outperform time-based peers by 1.5-2.0 percentage points in TFP growth by 2032. The contribution is a forecasting model and planning tool for AI-augmented talent ROI accounting.