The Artificial Intelligence Economy and the Labor Market

Artificial intelligence changes labor demand through the cost of accepted production, demand expansion, and the renewal of expertise. Building on the verification frontier in The Artificial Intelligence Economy, this paper develops the dynamics linking entry hiring to later review capacity. A permanent hiring cut initially reduces expertise quadratically in a practice-flow model; a fixed-tenure pipeline instead delays the onset. An explicit threshold separates cuts that exhaust the expertise buffer from cuts that never cause a shortfall. Growing output and training delays require investment ahead of future capacity needs. A private-choice result links depletion to the remaining planning horizon and the share of learning returns captured by the firm, separating capacity preservation from welfare justification for intervention. The paper expresses the companion's scale–substitution decomposition as a scale-free labor-demand threshold β; companion results also supply incidence and growth benchmarks. Evidence available through September 2026 shows heterogeneous task effects and a descriptive entry-level hiring gap, without identifying a common aggregate employment effect. A proposed supervised-practice experiment identifies intervention effects; separate variation and stock measurement are needed to estimate the depletion mechanism. The paper connects creativity, expertise, agency, and capital to innovation and new work, and examines leisure demand, evolving acceptance standards, ownership, and resilience. All numerical examples are illustrative scenarios, not fitted forecasts.