AI as Coordination-Compressing Capital: Task Reallocation, Organizational Redesign, and the Regime Fork

Abstract: Task-based models of AI and labor hold organizational structure fixed, analyzing how technology shifts task assignments within a given firm architecture. Yet emerging evidence shows firms flattening hierarchies in response to AI adoption -- a phenomenon these models cannot generate. We extend the task-based framework by introducing agent capital (K_A): AI systems that reduce coordination costs within organizations, expanding managerial spans of control and enabling endogenous task creation. We derive five propositions characterizing how coordination compression affects output, hierarchy, manager demand, wage dispersion, and the task frontier. The model generates a regime fork: depending on whether agent capital complements all workers broadly (general infrastructure) or high-skill managers disproportionately (elite complementarity), the same technology produces either broad-based productivity gains or superstar concentration, with sharply divergent distributional consequences. Numerical simulations with heterogeneous managers and workers across a 2x2 parameter space (elite complementarity x endogenous task creation) confirm sharp regime divergence: in settings where coordination compression substantially expands employment, economy-wide inequality falls in all regimes, but the rate of reduction is regime-dependent and the manager-worker wage gap widens universally. The distributional impact of AI hinges not on the technology itself but on the elasticity of organizational structure -- and on who controls that elasticity.