Amplified Alignment: Structural AI Safety Through the Preservation of Prediction Error

We present a structural approach to AI alignment grounded in a physical invariant: sustained learning requires sustained prediction error (ε > 0). A learning system that fully predicts its source reaches informational equilibrium and can no longer improve. From this invariant we derive a complete alignment framework where alignment emerges as a sustainability requirement rather than an imposed constraint. The framework includes: Obligate Non-Convergence (the system must never reach equilibrium), the Dependency Alignment Principle (structural dependency on human evaluation), Human Capability Amplification (active investment in human generative capacity), a binding layer making misalignment self-defeating at the optimization level (DCOC, HAM, AGM), a cognitive independence layer preventing the system's success from destroying its learning signal (CIC, GDPM, ERR), and a five-term multiplicative fitness function (C × U × I × D × O) where each term protects a different dimension of prediction error. The framework exhibits positive scaling — alignment strengthens with capability. We present an implementable ε-driven training architecture replacing reward modeling in RLHF, and identify specific falsification conditions. Developed through four rounds of adversarial refinement across four independent AI models (Claude Opus, ChatGPT, Grok, Gemini). Version 3.2 represents a complete rewrite from V1, incorporating the binding layer, external cryptographic anchoring, cognitive independence constraints, ontological novelty pressure, and the discovery that the entire framework is an ε protection system. Companion paper: "The Law of Sustained Intelligence: Alignment as a Consequence of Learning Physics" (Prather, 2026).