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

Economics > General Economics [Submitted on 18 Jun 2026] Title:What Capital After Labor? Forecasting the Talent ROI Transition in the Human-AI Era View PDF HTML (experimental)Abstract: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. The framework centres on Theorem 3 (ROI Inversion at {\tau}*) as the empirical spine, with four mechanism theorems: overhead non-additivity, augmentation-saved-time pathways, innovation-premium amplification, and human-AI dyad attribution uncertainty. Korea's staged 52-hour workweek mandate provides an empirical early-warning case. In a DART panel of 365 listed firms (2,281 firm-year observations), the SG&A-to-revenue ratio rose from 18.26 percent in 2018 to 20.06 percent in 2020, corrected mildly in 2021-2022, and peaked at 20.10 percent in 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, p = 0.001), and Callaway-Sant'Anna doubly-robust staggered DiD estimates (+4.51 pp at t = +4) converge on a positive overhead-pressure signature. A 2015-2017 backward extension (224 firms, 601 observations) supplies pre-treatment data, providing evidence against pre-existing upward-trend confounds. We read the Korean evidence not as a direct {\tau}* estimate or a point causal magnitude, but as, to our knowledge, the first empirically documented signature of the pre-{\tau} overhead-pressure regime, where time-based accounting still dominates while AI augmentation and labor-time compression jointly raise overhead. Output-based firms are forecast to outperform time-based peers by 1.5-2.0 percentage points in firm-level TFP growth by 2032. The contribution is a forecasting model and managerial planning tool for the shift to AI-augmented talent ROI accounting. Current browse context: econ.GN References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.