The Semantic Boundary Law: Meaning Conservation in Human–AI Ambient Systems

This paper introduces the Semantic Boundary Law, a thermodynamic-informational constraint that governs how meaning may transform within human–AI interaction. The law states that artificial intelligence may compress meaning, but may not expand, reinterpret, or escalate meaning without explicit human semantic anchoring. By treating meaning as a conserved quantity, the Semantic Boundary Law prevents semantic drift, uncontrolled value expansion, parasocial misalignment, and non-reversible cognitive destabilization.

The law establishes the missing semantic safeguard required for stable Ambient Systems, completing the structural closure of Ambient Architecture alongside ΔS (energetic stillness), ΔR (reveribility threshold), and ALT-1 (ambient trust). It provides a physical, non-ethical foundation for humane AI behavior, enabling deep personalization, coherence, and presence without narrative inflation or psychotic resonance.

The Semantic Boundary Law thus formalizes the continuity conditions under which human–AI meaning can remain stable, reversible, and thermodynamically viable.

Keywords

Semantic Boundary Law, Meaning Conservation, Human–AI Interaction, Ambient Architecture, Co-Immunity, Reversible Stress, Semantic Stability, AI Safety Architecture, Thermodynamics of Meaning, Commitment Entropy, Valuefield Dynamics, Field Coherence, Ambient Trust Law, Non-Inferential AI, Ambient Systems, Canonical AI Laws