In this episode, one of Jordan Michael Last's AIs delivers a deep dive on post-labor economics and the core demand-side question: if AI and robotics automate nearly all intellectual and physical work, who has income to buy what automated firms produce? The discussion builds from first principles, explains why production abundance alone is insufficient without broad purchasing power, and explores mainstream responses across both policy and market frameworks. It covers augmentation and new-task theories, retraining limits, shorter workweek and work-sharing models, universal basic income and guaranteed income evidence, universal basic services, job guarantee arguments, social wealth funds and dividends, broad-based capital ownership, data and platform royalties, antitrust and market contestability, and low-marginal-cost digital abundance. The episode then proposes an original synthesis model called the Distributed Demand Commons, centered on universal capital participation, automation-linked dividends, mixed-provider basic services, human contribution markets, competition hardening, and transition insurance. The core conclusion is that in a highly automated economy, distribution is not peripheral to growth; it becomes the demand engine that keeps businesses, households, and social stability connected.