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

TL;DR

This paper models how AI reduces coordination costs, enabling organizational redesign and task reallocation, leading to divergent economic regimes with different impacts on inequality and concentration.

Contribution

It introduces the concept of agent capital and a regime fork, analyzing how AI-driven coordination compression affects organizational structure and economic outcomes.

Findings

Coordination compression can lead to broad gains or superstar concentration.

Wage gaps widen universally, but inequality dynamics depend on organizational control.

Simulations confirm regime divergence based on heterogeneous worker responses.

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.