Judgment-as-Product: The Binding Constraint of AI-Mediated Production

Abstract

As artificial intelligence systems rapidly expand access to information and automate large parts of knowledge work, a fundamental question emerges: if information is no longer scarce, what becomes the limiting factor of production? This article argues that the constraint has shifted to the human side-specifically, to the structure and quality of judgment. It introduces the concept of Judgment-as-Product (JAP), proposing that judgment is not merely a cognitive step preceding action, but a core productive output that completes value in AI-mediated systems. Within this framework, the binding constraint of production moves from information acquisition to judgment formation, integration, and articulation. While AI systems dramatically increase the volume and accessibility of generated outputs, they simultaneously raise the demands placed on human judgment. As a result, productivity is increasingly determined by the ability to produce coherent, context-sensitive, and decision-relevant judgments. This reframing positions human-AI systems as co-constructed processes in which value emerges from the interaction between computational generation and human evaluative integration. The paper outlines implications for AI-mediated production, decision-making, and the distribution of advantage in environments where judgment, rather than information, becomes the primary bottleneck.

Keywords: human-AI interaction, AI-mediated production, large language model, decision-making, cognitive load, judgment, judgment-as-product, binding constraint, value completion