Alice and Bob, The Three Inevitabilities, and The Perfect AI Assistant

This paper introduces a thought experiment in reverse alignment, in which, instead of aligning artificial intelligence (AI) with human values, we examine how humans inadvertently align with AI systems. We posit a theoretically perfect AI assistant and demonstrate that even this idealized system can produce concerning dynamics. In particular, we build on principles from pattern reinforcement and behavioral inertia to intuitively show that as the quality of AI assistance increases, human decision making tends to converge toward a utility-tilted sharpening of the assistant’s predictive distribution. Critically, this convergence exhibits strong path dependence: withdrawing assistance does not, by itself, restore cognitive independence, because the update contains no force that returns an agent to its pre-assistance policy. Recovery requires an active novelty incentive, and even then, it proceeds through history-dependent transients whose duration can exceed institutional planning horizons. We formalize these dynamics through two lenses: the Alice-Bob divergence , which tracks how adoption timing alone creates persistent cognitive divergence between identical agents, and the Three Inevitabilities , which captures the multi-scale forces (i.e. individual, social, and species-level) that drive convergence. Our theoretical and mathematical analysis reveals a fundamental trilemma: systems cannot simultaneously maintain optimal task outcomes, persistent human cognitive independence, and the absence of artificial novelty incentives. We conclude by identifying escape mechanisms and interventions that preserve human agency while retaining beneficial AI assistance to navigate the tension between augmentation and autonomy.