The shared blind spot: why diverse AI governance approaches fail for the same reason
TL;DR
This work argues that anticipatory regulatory governance rests on three operational premises—categorical stability, epistemic accessibility, and manageable pace—and that AI’s emergence, opacity, and velocity violate all three in compound, and proposes three premise-level substitutions each replacing a violated precondition with a weaker, design-addressable one.
Abstract
AI governance instruments are proliferating, and so are their difficulties. Across major jurisdictions and international bodies, reform efforts built on substantially different premises encounter a recognizably similar pattern of failure. I argue that anticipatory regulatory governance rests on three operational premises —categorical stability, epistemic accessibility, and manageable pace—and that AI’s emergence, opacity, and velocity violate all three in compound. These premises form a distinct layer of operational preconditions, not a complete theory of governance. Reform within the existing premises reproduces the violations they produce. I call this configuration the reform trap : a paradigmatic lock-in at the level of operational preconditions, distinct from path dependence and policy paradigm rigidity. The pattern is convergent across five strategies in active reform—categorical regulation, process-based management, information disclosure, normative guidance, and adaptive experimentation—and persists even in the most adaptive of them. I propose three premise-level substitutions —outcome observability, causal attributability, and enforcement capability—each replacing a violated precondition with a weaker, design-addressable one. These differ from outcome-based and performance-based regulation, which swaps instruments within an architecture whose premises remain stable.