The 2026 AI–Energy nexus exhibits systemic amplification driven by interacting feedback loops across Market, State, and Technology domains. Signal Alignment Theory (SAT) provides a phase-based framework to quantify kinetic, elastic, and informational energy propagation, capturing both global growth and localized model-level plateaus. Reinforcing, stabilizing, and information-coherence loops collectively sustain high output while preventing structural collapse, producing a robust Triple-Axis Meta-Alignment effect. Predictive indicators derived from loop dynamics and energy saturation allow early detection of bottlenecks, boundary compression, or potential phase transitions. These insights inform strategic interventions in policy, infrastructure, and technology deployment, enabling resilient and scalable AI–Energy system growth.