The AI infrastructure bottleneck is increasingly about power, not GPUs, and existing data centers already offer the fastest path to scale. Recent studies indicate that 30–50% of installed data center power capacity in the U.S. sits unused, creating large pools of stranded, paid-for capacity. An investigation into colocation and enterprise facilities in the UK finds a lower, 20–30% average utilization. At the same time, live AI inference environments reserve substantial headroom for rare demand spikes, leaving GPUs idle for significant amounts of time. The paper reframes this as a systemic, stack-wide opportunity and positions latency-tolerant AI workloads as the ideal way to fill these demand valleys. Its core implication: operators who implement cross-layer orchestration of power, cooling, and workloads can unlock gigawatt-scale AI readiness, cut cost per million tokens, and secure durable competitive advantage without waiting for new grid capacity.