From Paperclips to Premature Trust: Reconciling Divergent Views on AI Risk

Abstract

Two prominent narratives about artificial intelligence risk, Bostrom's superintelligence scenario and the increasingly influential claim that the more pressing danger is premature trust in ordinary present-day systems, are often treated as competing diagnoses, and Gans's (2018) formal argument that AI systems might rationally self-regulate is read as evidence for one side or the other. This essay proposes a different way of holding the three together. The Correction Freeze Problem (Vuong, 2026) is used as a technical toolkit: it converts Gans's qualitative conditions for self-regulation into quantities that can be checked, and so reframes the Bostromversus-Gans dispute as a question of which parameter regime a given system occupies rather than which author is correct. The absurdist approach (Nguyen & Ho, 2026) is used as the epistemic tool that keeps that toolkit honest, by resisting the closure any fixed checklist tends to produce around its own categories. The aim is synthetic rather than original, and the essay tries to remain honest about how much this synthesis actually settles.