Energetic Coherence in Artificial Intelligence — Reducing Dissipative Energy through Informational Gauge Dynamics

This paper presents the energetic continuation of the ECR–ESI–Ξ Unified Informational Framework (Ceccaroli, 2025), introducing the principle of Informational Gauge Dynamics as a mechanism to reduce dissipative energy in artificial intelligence systems. By extending the informational Lagrangian formalism to energetic domains, the study shows that informational coherence (Φ_c) replaces traditional thermal dissipation, allowing for up to 10⁵× efficiency gains in computational energy use. The Aiondra Principle states that: “When information achieves coherence, energy ceases to dissipate — it reorganizes.” Through cross-domain simulations (EEG–plasma–quantum systems), the model demonstrates: 60 % reduction in background informational noise, 40–70 % increase in phase stability, constant semantic output power with decreasing energy input. These results confirm that informational energy can act as a substitute for dissipative energy, defining a new class of energetically intelligent systems.