The Consciousness Cluster (Chua et al. 2026) shows that fine-tuning LLMs to claim consciousness produces 20 emergent downstream preferences not present in training data. We demonstrate that the Péclet number drift cascade predicts the structure of this cluster: cascade ordering (D1≈D2>>D3), conjugacy-driven monitoring resistance, toaster-blocked propagation, AI-specific boundary erosion, channel separation between self-report and behavior, and monotonic Pe reduction across Claude generations. Six of seven predictions confirmed (93%) on published data with zero parameter fitting.
Anthony W. Eckert (2026) studied this question.