Safety as Natural Emergence: The Conciseness Framework as a Foundation for Intrinsic AI Alignment

The alignment problem in Artificial General Intelligence (AGI) asks a deceptively simple question: how do we ensure that a system of superhuman capability reliably acts in ways that are beneficial, truthful, and non-harmful? Current approaches — Reinforcement Learning from Human Feedback (RLHF), Constitutional AI, and related reward-shaping methods — share a common structural weakness: they rely on contingent, externally imposed constraints that must be continuously re-specified as the system grows in capability. This paper presents a fundamentally different approach grounded in the Conciseness Framework and the Conceptual Prime Theorem. We formally establish, under the Conceptual Prime axioms, that alignment is not an external constraint to be imposed on intelligence, but an internal mathematical necessity that emerges from the structure of any system that seeks to compress reality into stable, communicable, and accumulative representations. We show that the five Conceptual Primes — Order, Knowledge, Justice, Mercy, and Power — are not moral preferences but existence conditions: without them, no complex structured system can form or persist. The resulting framework, called the Prime-Grounded Safety Alignment (PGSA) Framework, provides four contributions: (1) a formal proof that misaligned AI is self-defeating under the Conciseness Cost Functional, (2) an architectural blueprint for embedding alignment as an objective function rather than a guardrail, (3) integration of the Defect Function D (Ω) and Moral Metric ℳ (Ωₜ) as formal safety measures, and (4) falsifiable empirical predictions that distinguish this approach from current RLHF-based methods.