AI as Coordination-Compressing Capital: Task Reallocation, Organizational Redesign, and the Regime Fork
arXiv:2602.16078
Abstract
Task-based models of AI and labor hold organizational structure fixed. We introduce agent capital: AI that reduces coordination costs, expanding spans of control and enabling endogenous task creation. Five propositions characterize how coordination compression affects output, hierarchy, manager demand, wage dispersion, and the task frontier. The model generates a regime fork: the same technology produces broad-based gains or superstar concentration depending on who benefits from coordination compression. Simulations with heterogeneous workers confirm sharp regime divergence. Economy-wide inequality falls in all regimes through employment expansion, but the manager-worker wage gap widens universally. The distributional impact hinges on who controls organizational elasticity.
v3: Tightened Gini proof (explicit Lorenz quotient-rule argument), qualified economy-wide claims to within-firm scope, added L_eff cancellation at capacity discussion, corrected negative-beta analysis, added proportional allocation definition, expanded PAM robustness discussion, clarified CES limitation, style edits. 23 pages, 5 figures