The U.S. Should Rely on Performance, Not Explanation, When Evaluating AI

Michael Perry

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To stay competitive, the U.S. should evaluate AI tools like large language models based on performance, not explainability. Trust should be grounded in results, not unrealistic expectations of human-like reasoning.

The United States Must Treat AI as a Strategic Asset in Great Power Competition

As the United States enters a new era of great power competition—particularly with a technologically ambitious China—questions about how and when to trust artificial intelligence systems like large language models (LLMs) are not merely technical. They are strategic. These tools will increasingly shape how the United States allocates resources, prioritizes defense investments, and maintains a credible military posture in the Indo-Pacific and beyond.

Focusing on Explainability Could Undermine Strategic Adoption of AI

LLMs are not reasoning agents. They are pattern recognizers trained on vast datasets, designed to predict the next word in a sequence. Like a chess grandmaster making a brilliant but intuitive move, LLMs often cannot explain why they generate a specific output. Yet the Department of Defense, through organizations like the Chief Digital and AI Office, has prioritized explainable AI as a requirement for operational use. This well-meaning mandate risks missing the point.

Explainability in LLMs may not be technically achievable—and chasing it could be a strategic distraction. These models don’t “understand” in the human sense. Their outputs are statistical associations, not causal conclusions. Post-hoc explanations, while satisfying, can be misleading and ultimately hinder adoption of tools that could enhance strategic foresight, intelligence analysis, and operational planning.

The real danger lies in overemphasizing explainability at the expense of performance. Many decisions in national security—from target selection to long-range procurement—already involve opaque but proven processes, like wargaming or expert judgment. LLMs, if properly tested, can complement these approaches by processing volumes of information at speeds that human analysts cannot match.