AI'S 'DARK OUTPUT' POSES HISTORIC MEASUREMENT CRISIS

As artificial intelligence systems produce increasing volumes of value, much of it remains uncaptured by traditional GDP and economic metrics. This hidden productivity spans everything from internal business process improvements to algorithmic decision-making that generates measurable but untracked returns. The challenge differs from prior statistical blind spots. Unlike the informal economy or digital services that eventually integrate into official measures, AI's dark output operates across sectors in fragmented ways. A single model might boost efficiency in supply chains, customer service, and product development simultaneously—each contribution difficult to isolate and quantify. National accounting systems weren't designed for intangible, distributed economic gains. Standard methods struggle to assign value to marginal improvements across millions of workflows. Without reliable measurement frameworks, policymakers lack accurate pictures of productivity growth, economic health, and AI's true macroeconomic impact.