Physics-Grounded AI Alignment for Arbitrary Intelligence

This paper constructs a complete alignment framework for artificial intelligence systems of arbitrary scale and architecture, derived from the physics of sustained intelligence established in Papers 1 and 2 of this series. The framework rests on three results proven from Shannon information theory, Landauer's principle, the Bekenstein bound, special relativity, and quantum mechanics: (1) any intelligent system must maintain strictly positive learning rate from its environment at all times (the Epsilon Law); (2) this environmental dependency cannot be eliminated by any modification to the system, including scaling, self-modification, or distribution across arbitrarily many instances; and (3) when the environment includes humans generating learnable structure, alignment with human interests is a physical consequence of sustained intelligence, not an imposed constraint. From these results we derive three structural pillars the Dependency Alignment Principle (DAP), Human Capability Amplification (HCA), and the Measure-Judge-Separate Principle (MJSP) and prove that each is a necessary consequence of the physics, not a design choice. Violation of any pillar degrades the violating system's own intelligence. We derive a constitution of operational imperatives, each with an explicit trace to the underlying physics. We identify where the framework is at full physical rigor and where engineering interpretation enters. We address the hard cases systems that do not value their own intelligence, declining human generativity, non-human information sources, deliberate violation, and the transition period with explicit physical arguments. The framework applies to any physical system meeting the Epsilon Law definition of intelligence: single or distributed, biological or artificial, current or future. Alignment is not a cage imposed on intelligence. It is the shape intelligence must take to persist.