Debating the U.S. National AI Strategy: Is Privatized Acceleration Sufficient or a Risky Gambit?

The Harvard Kennedy School Armed Forces Committee (AFC), in partnership with the Defense, Emerging Technology, and Strategy (DETS) Program, convened a closed-door, structured debate to surface candid, well-informed disagreement on a contentious national security question in a disciplined and respectful format. The session brought together two teams of Harvard students to examine America’s national artificial intelligence (AI) strategy.

1. The debate centered on the proposition: Is the current U.S. national AI strategy sufficient to safeguard national security? For the purpose of this exercise, the strategy was defined in advance by four guiding documents from the Trump Administration:
   Executive Order (EO) 14179, Removing Barriers to American Leadership in Artificial Intelligence
2. The July 2025 America’s AI Action Plan
3. EO 14365, Ensuring a National Policy Framework for Artificial Intelligence
4. The January 2026 memorandum, Artificial Intelligence Strategy for the Department of War.

This framework aims to ‘sustain and enhance America’s global AI dominance’ by accelerating both overall capabilities and their integration into the national security infrastructure. By deregulating and centralizing authority, these policies depart from President Biden’s now-revoked Executive Order 14110 Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence and many of the guardrails that came with it. The new strategy acknowledges AI development as a race that can be won by reducing barriers and capitalizing on asymmetric advantages through a ‘minimally burdensome national standard.’

The ‘pro team’ argued that the United States’ current AI strategy is sufficient to safeguard national security, while the ‘opposing team’ argued that it is not. The debate focused on whether the current U.S. national AI strategy is a calculated bet or a risky gambit, judged by whether it can prevent, deter, or rapidly mitigate AI-enabled catastrophic harms through clear authorities, resourcing, and measurable compliance across agencies.

The debate ultimately became a question of whether speed and innovation should take precedence over safety and guardrails, and three themes recurred throughout the discussion:

The debate highlighted implications of a strategy that compresses timelines to secure a decisive strategic advantage. Both teams admitted that expedited development is crucial, but dissented on whether rapid acceleration is an effective national security strategy or would lead to escalation. In other words, could the loosened guardrails be justified if the resulting technology makes the residual risk tolerable?

The pro team argued that the most dangerous risk is strategic technological inferiority, and the most effective deterrence method is first-mover advantage. Development of a decisive strategic advantage could prove so consequential in a great power competition that the U.S. cannot afford to slow progress until proper legislation is developed. In this scenario, an imperfect strategy today is better than a perfect one tomorrow, and the current strategy is sufficient in creating the environment to win this race through regulatory rollback and pushing for the adoption of American models worldwide. The strategy further supplements gains from acceleration by denying adversaries leverage and maintaining deterrence with defensive measures like export controls and supply chain independence.

By contrast, it could be an oversimplification to equate acceleration as national security. As the opposing team argued, innovation, speed, and technological advancements alone do not produce national security outcomes. Winning the race is not a strategy, but must be supported by governance, requirements, authorities, and supply chains. Acceleration may also be logistically unsustainable, as energy and computing resources are not guaranteed to scale with needs. Another risk mentioned is the wider access to tacit knowledge, which eases creation of cyberattacks; chemical, biological, radiological, nuclear, and explosive (CBRNE) weapons; and deepfakes and disinformation. The White House’s AI Action Plan embeds countermeasures to mitigate these risks by hardening the homeland and critical infrastructure protections, but as model capabilities scale, they could outpace institutions’ ability to govern them, scaling the vulnerabilities as well.

Participants identified benefits and liabilities to relying on private firms to develop a potentially existential capability for national security. On one hand, America’s competitive advantage due to a market economy gives it an asymmetric advantage over China. Similar to how the United States industrially outpaced the Soviet Union in the Cold War, America’s innovation ecosystem will enable rapid fielding of frontier AI capabilities at scale. On the other hand, the strategy implicitly assumes that technological leadership automatically translates to military advantages, and it may not do enough to direct defense-specific requirements.

A recurring theme in the debate was whether private companies could be trusted to implement Laws of Armed Conflict (LOAC), while simultaneously enabling the military’s freedom of action. Participants argued that America already has a civilian-controlled chain of command to conduct lawful warfighting. Integrating private companies into the kill chain could hamstring our military by giving veto power to technocrats. No other military technology, whether it is artillery, claymores, or rifles, gives the tech provider a veto power. The rules of engagement are implemented by commanders and their legal counsel, and allowing private industry to determine AI’s deployment undermines the chain of command.

The other side contested this claim, arguing that the method through which AI is deployed in warfare is not a new phenomenon, but an automation of tasks already performed. In certain contexts, AI is a weapon and should be treated as one. No human is ‘in the loop’ after a bullet is fired, but LOAC governs the process up until that moment. It should not be up to companies to hard-code LOAC into their models, but the military needs to do it, either through acquisition requirements or direct control.

Both teams agreed that leaders may face pressure to depart from strategy as priorities and constraints change. The U.S. government should refrain from deviating from its strategy, except as an adjustment to protect national security interests. Guardrails can reinforce strategic discipline without eliminating the flexibility needed to respond to changing threats, constraints, and technological developments.

If the goal is to partner with academic institutions, the government should maintain predictable funding streams and collaborative relationships. If the goal is to enable private firms to develop AI with minimal regulatory friction, it should keep visa pathways reliable, award contracts consistently, and avoid punitive procurement responses during disputes. Likewise, to ensure worldwide adoption of American open-source models, we should prioritize open models in addition to supporting closed model dominance.

Another central criticism was that the strategy is more directive than authoritative. Participants noted that the Department of War memorandum successfully lays out clear implementation strategies with designated action owners, such as a mandatory 30-day deployment of new models, the creation of a ‘Barrier Removal Board,’ and introducing seven ‘Pace-Setting Projects.’ On the other hand, the AI Action Plan contains mainly recommendations. Whether or not the current AI strategy sufficiently lays the groundwork to safeguard national security, the four documents, as written, may need supplemental policies to ensure long-term, stable implementation. Executive orders and departmental directives are an important start, but Congress needs to develop the national standard proposed in EO 14365 to give the strategy teeth to enforce its provisions through appropriations and statutory authorities. The strategy also needs bipartisan political support to retain continuity across administrations.

The debate did not produce consensus on whether the current strategy is ‘sufficient,’ but it did clarify what sufficiency would require. Participants repeatedly returned to three practical imperatives: evaluate whether acceleration produces national security advantages or just leads to vulnerabilities; consider whether reliance on private corporations extends capabilities or limits military advantages; and determine if the strategy is backed by enough requirements and resources. If American AI progress continues on its trajectory, any national security gains could be counteracted by policy failures and implementation gaps. Embracing speed and enabling private actors can be advantageous but can also be multipliers of institutional weakness. A strategy built around acceleration can succeed only if governance capacity scales with it, and if implementation mechanisms are durable enough to sustain it.