Brain Capital Management: A Firm-Level Theory of Cognitive Capability, Its Three Constraints, and an Agenda for Its Measurement in the Age of AI

Abstract

This working paper develops a firm-level theory of brain capital for the age of artificial intelligence. While existing brain capital research has primarily treated brain health and brain skills as national- or population-level assets, this paper defines enterprise brain capital as the stock of cognitive capability that a firm can actually bring to bear on the redefinition of its future. The framework identifies three distinct constraints on that capability: Belonging, which determines whether cognitive capacity can be voiced and transmitted within the organization; Base, which determines whether capacity remains available rather than being consumed by unresolved material and financial strain; and Build, which governs the accumulation, maintenance, and depreciation of capability over time. The paper distinguishes brain capital as a stock from organizational conditions that determine its utilization, develops fourteen falsifiable propositions, and proposes a three-tier measurement architecture combining administrative indicators, validated workforce-state measures, and structural mechanisms. It also distinguishes AI-assisted productivity from unassisted capability, arguing that productivity gains from AI adoption do not necessarily imply capability accumulation. A central finding is a measurement asymmetry: existing human-capital disclosure regimes predominantly report inputs, costs, demographics, hazards, and policies, while validated measures of workforce cognitive and psychological states remain largely outside mandatory disclosure. The paper therefore positions Brain Capital Management as a diagnostic and research agenda rather than a completed measurement framework, and specifies the empirical studies and instruments required for further validation.