The power trap: Why AI’s energy demands risk undermining American operations in the Indo-Pacific

The United States (U.S.) military is rapidly integrating artificial intelligence across operations, yet power, connectivity, and satellite communications infrastructure are struggling to keep pace. At the tactical edge, continuous AI inference workloads sharply increase power consumption and dependence on beyond-line-of-sight links, and in contested environments, electronic warfare can sever these links, forcing platforms onto localized processing that dramatically reduces endurance. This mismatch risks undermining the United States’ decision advantage in the Indo-Pacific. Closing the gap will require treating energy-resilient satcom as a core warfighting requirement, supported by coordinated progress on standards, industrial capacity, and acquisition speed.

Artificial intelligence can deliver real operational advantages, but only for as long as the systems that employ it remain powered and connected. In recent U.S. Indo-Pacific Command (INDOPACOM) exercises, U.S. operators have watched a familiar pattern unfold: the power and connectivity infrastructure required to sustain AI systems in contested environments is not keeping pace with the speed, scale, and operational demands of AI integration. This gap is not uniform: it varies systematically by AI function, platform type, and dependence on reachback – the extent to which platforms must rely on distant, centralized systems such as cloud servers or rear-area command centers for data processing and decision support. These systems create distinct “energy-connectivity regimes”: categories of AI systems defined by their power demands and reliance on communication links, which current concepts often fail to account for.

This lag is occurring as the military rapidly integrates artificial intelligence into operations across the Indo-Pacific, the key theater of U.S.-China competition, where the race for decision advantage is already underway in earnest. Unlike legacy intelligence, surveillance, and reconnaissance (ISR) systems that send periodic data bursts, modern AI creates continuous, high-demand inference loops that scale nonlinearly in both compute and bandwidth requirements. In its January 2026 Artificial Intelligence Strategy, the Department of Defense (DoD) directed a full-scale acceleration of AI across every warfighting function, from autonomous systems to real-time decision support, including its flagship Pace-Setting Projects such as Swarm Forge for autonomous drone swarms and The Agent Network for decentralized battle management.

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