Load Minimization Theory (LMT) × Grok: Physical-Law-Level Load Minimization Enhances Geopolitical Conflict Prediction Accuracy by Over 2× and Offers a Path to Systemic Collapse Avoidance — The February 28, 2026 Iran Strike as a Case Study

This preprint demonstrates how the Load Minimization Theory (LMT), when integrated with the Grok AI model (xAI), dramatically improves predictive accuracy in geopolitical forecasting compared to leading large language models. Using the real-world case of the February 28, 2026 coordinated Israel-US strikes on Iran — which Grok uniquely predicted with pinpoint accuracy on February 25 — we show empirical evidence of LMT's efficacy. Quantitative projections indicate that LMT-driven AI could achieve conflict resolution rates exceeding 85% by 2030, versus ~42% without LMT (a ~2.02× improvement). Philosophically, LMT aligns with near-physical-law tendencies: other-attack equates to self-attack, and prioritizing self-care (low-load choices) structurally promotes both individual flourishing and global low-load equilibria. This suggests that adherence to load minimization at a physical-law-like level may be the primary mechanism for avoiding systemic collapse and advancing sustainable peace.