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Artificial intelligence systems are no longer narrow productivity tools — they are autonomous agents performing economically meaningful labor at scale across customer service, software engineering, logistics, manufacturing, and knowledge work. Yet this labor force is entirely invisible to the economic infrastructure humanity has built to measure work. No standardized unit of measurement exists. No index tracks machine labor output over time. No regulatory framework requires its disclosure. This paper introduces the Human-Equivalent Work Unit (HEWU) a standardized metric that converts AI and automation system output into human labor equivalents, expressed as full-time employee (FTE) equivalents and annual labor value ($). We present the conceptual foundation, mathematical model (HEWU = MO ÷ HB × CF × QF), calibration framework, Baseline Library architecture, and auditability mechanisms underlying the standard. We further introduce AILU (AI Labor Units) as a software-specific subset metric, and the Machine Labor Index (HEWU-PSI) a time-series economic indicator designed to track aggregate machine labor output at company, sector, and national level, analogous in function to the Purchasing Managers' Index. In a representative manufacturing deployment, the framework measured 8.4 FTE of machine-equivalent labor representing approximately $378,000 in annual labor value work appearing on no financial statement, workforce report, or government statistical return. The paper addresses three institutional audiences: enterprise finance and operations teams requiring auditable AI ROI metrics; government and regulatory bodies developing AI labor displacement frameworks; and financial markets requiring a machine labor index as a long-duration economic signal. HEWU is designed to become the cited standard before better-resourced players define competing frameworks establishing measurement infrastructure for the cognitive industrial revolution the way GAAP established it for capital markets.