The strengths and risks of the White House’s AI Action Plan
The AI Action Plan released by the White House on Wednesday seeks to secure America’s leadership in AI through infrastructure investment, interpretability and safety standards, workforce development, and export controls. Although it has triggered some partisan reactions, many of the provisions it contains are part of a bipartisan consensus emerging on AI policy. However, provisions that restrict funding to states with “onerous” AI regulations or require federal procurement of only “objective” large language models ( LLMs) risk deepening party factions and partisan divides—potentially alienating Republicans who value state autonomy and Democrats concerned about misinformation and bias in AI outputs.
Taking a closer look, this post explores six areas where the White House’s AI Action Plan offers forward-thinking policies that increase oversight and mitigate risks from AI systems while maintaining America’s lead in innovation.
Looking Under the Hood of AI
At the heart of the AI Action Plan is a call for major investment in AI interpretability, control, and robustness. Today’s frontier AI models—like large language models—have immense capabilities to create content and automate tasks. Yet even their creators often can’t explain why the model generated a specific output.
This opacity is more than a technical curiosity—it’s a serious national security risk. Whether AI is guiding defense systems or supporting intelligence operations, unpredictability is unacceptable. By launching new DARPA-led research programs in partnership with the National Science Foundation and the Department of Commerce, the federal government seeks to advance their capabilities to understand and control how AI systems work.
Building Trust
The Action Plan also prioritizes building a robust ecosystem for AI evaluations—something akin to FDA approvals that have long kept Americans safe. AI evaluations can help ensure systems perform reliably and meet regulatory standards. Through new testbeds, transparency guidelines, and multistakeholder coordination led by NIST and the Center for AI Standards and Innovation (CAISI), among other agencies, the U.S. is establishing processes for trustworthy AI development and use.
Securing and Scaling Infrastructure
To fully realize the promise of AI—and safeguard the systems we rely on—the Action Plan prioritizes investment in secure and scalable AI infrastructure. This means not only building resilient data centers but also streamlining the permitting processes that currently delay them. The Plan calls for modernizing our environmental permitting frameworks, making federal lands available for high-security infrastructure, and ensuring that the domestic AI compute stack is free from adversarial technology.
Exporting AI
America’s leadership in AI depends not only on building the most advanced technologies, but also ensuring they are deployed responsibly—and globally. The Action Plan outlines a multilateral, values-aligned approach to AI governance that seeks to strengthen U.S. competitiveness, prevent technological backsliding, and limit adversarial countries’ access to advanced AI by: 1) applying stronger enforcement on existing export controls, and 2) plugging loopholes in the current export control regime.
While enabling BIS to stop the black-market sale of advanced AI chips to China is critical to maintaining the U.S.’s lead on AI, it’s also important to note that the AI Action Plan simultaneously promotes the sale of the rest of the U.S. tech stack, potentially including the Nvidia H20 chips (which are excellent for running advanced models once they’ve been trained), in a way that may enable China or other authoritarian regimes to deploy advanced AI more broadly. Accelerating the export of advanced US chips risks fueling rivals’ AI capabilities.
Empowering American AI Readiness
The AI Action Plan outlines a comprehensive strategy to build a future-ready American workforce by expanding AI education and training from K–12 through higher education, investing in community colleges and technician programs, and strengthening public-private partnerships. It emphasizes growing federal capacity by recruiting top AI talent, appointing Chief AI Officers across agencies, and upskilling civil servants through training and sandbox environments. Additionally, it calls for broadening access to computing resources via the National AI Research Resource (NAIRR), ensuring that students, educators, and smaller institutions can access cutting-edge AI. Together, these efforts aim to close the AI talent gap and ensure all Americans benefit from AI-driven opportunities.
Preparing for the Worst, Planning for the Future
The Action Plan acknowledges what many experts fear: that frontier AI systems may eventually be used to design novel biological or chemical threats. Led by CAISI at the Department of Commerce in coordination with agencies specializing in cyber and CBRNE (chemical, biological, radiological, nuclear, and explosives) risks, these evaluations would assess how powerful AI models might be misused or behave unpredictably. The Plan urges assessments of foreign-developed AI systems used within U.S. critical infrastructure, identifying potential backdoors, security vulnerabilities, and malign influence. New protocols for AI incident response and red-teaming exercises seek to prepare the public and private sectors for AI failures before they happen.
Potential Pitfalls: Politicizing AI Governance and Procurement
While the AI Action Plan advances several critical priorities, some provisions raise concerns about the politicization of AI governance and federal funding. The Plan would allow the Office of Management and Budget (OMB) to limit federal AI-related funding based on a state’s regulatory climate. This could introduce a dangerous precedent where funding decisions are tied not to technical merit or community need but to the perceived ideological alignment of state policy—potentially penalizing states that adopt stronger transparency, safety, or civil rights protections for AI systems.
Similarly, proposed updates to federal procurement guidelines would require government contracts for frontier LLMs to be contingent on developers demonstrating that their systems are “objective” and free from “top-down ideological bias.” On the one hand, compliance with new procurement standards will be tied to developer “disclosure of prompts, specifications, evaluations, and other relevant documentation,” and better disclosure can help the public better understand and trust AI outputs.
But a lot depends on how these procurement guidelines are implemented. While protecting against bias is important, vague or politically charged language could be weaponized to suppress valid ethical guardrails or exclude certain developers based on subjective interpretations of “ideology.” These provisions risk undermining a core strength of American innovation that underpins the AI Action Plan: an open, competitive ecosystem driven by technical excellence, safety, and public trust.