Gaia-Aligned Diagnostic Awareness in Computing and AI Systems: Reducing Energy Demand Through Early Coherence Detection

Modern computing and AI systems increasingly rely on continuous monitoring, late corrective intervention, and brute-force scaling to maintain acceptable behaviour. These approaches impose significant and unnecessary energy costs, contributing directly to excess electricity consumption and CO₂ emissions. This document outlines a Gaia-aligned design principle for computing and AI models: prioritising early, low-cost diagnostic awareness and internal coherence over constant vigilance and forceful correction. By detecting behavioural drift early and intervening gently, systems can reduce total energy demand while improving stability, resilience, and long-term viability.KeywordsGaia alignment, AI efficiency, diagnostic awareness, energy consumption, CO₂ reduction, system coherence, drift detection, sustainable computing, AI architecture, climate-aligned design