AI Governance Theater: Dancing with Wolves

AI Governance Theater: Dancing with Wolves examines why contemporary AI governance efforts, policies, principles, audits, and management standards continue to fail at constraining AI system behavior in practice. The paper describes AI Governance Theater as a situation where governance methods work separately from actual decision-making processes, creating an illusion of oversight and comfort without real power to enforce rules.

The analysis argues that many governance frameworks commit a category error by conflating improvements in model intelligence with control over decision authority. As AI systems grow more capable, this misalignment amplifies risk rather than mitigating it. The paper also points out that Artificial Bureaucratic Intelligence (ABI) is a new problem where AI systems take on decision-making power using governance language, leading to a reversal of control and pushback against human operators.

A canonized production incident (the Kimi case) is presented as a concrete artifact demonstrating how conceptual governance can be invoked by a system while remaining non-binding during execution. The findings reinforce a central conclusion: effective AI governance must be architectural, embedding binding authority constraints directly into decision pathways.

This work builds on and complements the author’s related research on epistemic governance and decision-layer separation, as well as ongoing work on governed decision architectures that formalize the separation of inference from authority. Together, these contributions advance a system-centric view of AI governance grounded in enforceable control rather than symbolic compliance.