The Zero-Click Economy: AI Intermediation, Computational Transmission Failure, and Dynamic Enterprise Risk

For two decades, digital economics operated under a stable transmission mechanism: search engines indexed and ranked content, users clicked through to websites, and firms converted visits into revenue. Traffic reliably approximated access to economic demand. This model is being supplanted by AI-mediated allocation, where conversational interfaces interpret user intent, synthesize answers internally from multiple sources, select entities for consideration, recommend options without guaranteeing outbound clicks, and in some cases execute actions directly without rendering supplier interfaces. This paper examines the economic consequences of this transition. It advances the Current Reporting-Period Hypothesis: AI-mediated demand allocation may already be affecting corporate and national accounts, although causal attribution remains empirically unresolved. It introduces the Computational Transmission Gap and the Law of Computational Transmission Attrition to describe how economically valid demand may be lost across visibility, selection, recommendation, verification, actionability, and conversion. The paper develops a consolidated measurement architecture covering firm readiness, zero-click exposure, platform dependency, computational business risk, dynamic computational risk, enterprise adaptation velocity, asset computability, national computability, and sovereign adaptation capacity. It also examines financial transmission through revenue pressure, customer-acquisition costs, EBITDA compression, asset impairment, cost of capital, and enterprise-value deterioration. The framework is intended for investors, firms, boards, policymakers, regulators, and central banks assessing economic resilience in AI-mediated markets. HomeSelf is included solely as an illustrative case of agent-ready market infrastructure. All equations, indices, and relationships proposed are theoretical and require empirical validation.