4d (edited) โข General discussion
๐To Want AI Governance or To Want to Want AI Governance
AI governance today sits at a crossroads between necessity and theater. The question is no longer whether governance is needed, but whether institutions genuinely want governance or merely want to appear as if they do. This distinction mirrors a deeper psychological divide identified in behavioral science: the difference between wanting an outcome and wanting to maintain the state of wanting itself (Berridge & Robinson, 1998). In AI systems, this manifests as governance language without governance enforcement.
From a systems perspective, true AI governance is a terminating condition. It constrains behavior, forces accountability, and collapses ambiguity at runtime. That is precisely why it is resisted. Governance that is real introduces friction, decision points, and ownership. Governance that is symbolic preserves optionality and protects status. Organizations therefore optimize not for aligned systems, but for governance narratives that signal responsibility without imposing constraint.
Large language models have made this contradiction explicit. The cost of reasoning, auditing, and identifying non-zero-sum outcomes has collapsed. Clarity is now cheaper than ambiguity. Yet ambiguity persists, not because truth is unavailable, but because truth forces action. Action introduces risk, and risk threatens institutional ego. As with individual behavior, the system prefers to keep wanting governance rather than reaching sufficiency, because sufficiency ends the game.
This mirrors incentive-salience dynamics: wanting can intensify even when liking or utility does not (Berridge & Robinson, 2016). In AI governance, committees, principles, and ethics boards proliferate while enforcement mechanisms lag. The desire to โwork on governanceโ replaces the harder work of binding systems to invariant constraints, auditability, and failure modes. Governance becomes identity maintenance, not organism-level regulation.
The outcome is predictable. Without termination conditions, governance becomes an infinite loop; status-preserving, biologically costly, and operationally ineffective. As AI systems increasingly mediate real-world outcomes, this distinction becomes unsustainable. The choice is no longer philosophical. Either institutions want AI governance, or they want to want it. Only one of those survives contact with reality.
References
Berridge, K. C., & Robinson, T. E. (1998). What is the role of dopamine in reward? Brain Research Reviews, 28(3), 309โ369.
Berridge, K. C., & Robinson, T. E. (2016). Liking, wanting, and the incentive-sensitization theory of addiction. American Psychologist, 71(8),
670โ679.
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๐To Want AI Governance or To Want to Want AI Governance
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