Building a U.S. National Strategy for the Artificial Intelligence Era

Drawing Lessons from Six Archetypal Strategies

Michael J. Mazarr, Anca Agachi

Expert InsightsPublished Nov 17, 2025

The United States is on the cusp of a transformative techno-industrial transition: an Artificial Intelligence (AI) Revolution. Although the ultimate shape of the transition remains uncertain, it will become a potent new sorting mechanism in geopolitics. This effect may take more time to emerge than some AI enthusiasts claim. However, the full application of AI to thousands of business, government, education, military, and other tasks offers the potential for significant increases in productivity, growth, efficiency, and, ultimately, national power.

In July 2025, the Trump administration issued its AI Action Plan, which includes a wide-ranging outline of short- and medium-term steps that are needed to preserve U.S. AI advantages. Yet even with that release, relatively little attention is being paid to the challenge of developing a comprehensive U.S. national strategy. Whereas strategizing AI technology is still the critical task and an appropriate subject for policy agendas, plans that focus on this goal do not fully consider the challenge posed by this technological revolution.

To address this gap, the authors seek to advance the understanding of the requirements of a national strategy for AI and its ultimate expressions, including artificial general intelligence or artificial superintelligence. They draw lessons gleaned from a survey of existing AI strategy documents, identify major challenges in developing a U.S. AI strategy, offer a set of six archetypal strategies to help advance comprehensive U.S. strategic thinking, and conclude by identifying several no-regret steps that can serve as the initial foundations of a comprehensive strategy.

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Mazarr, Michael J. and Anca Agachi, Building a U.S. National Strategy for the Artificial Intelligence Era: Drawing Lessons from Six Archetypal Strategies. Santa Monica, CA: RAND Corporation, 2025. https://www.rand.org/pubs/perspectives/PEA4297-1.html.
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