The Federal AI Framework Nobody Asked For

Washington's approach to AI in 2025 and 2026 is a study in political contradictions. The federal government wants American AI companies to lead the world. but also wants to stay out of their way. It wants AI to be safe and trustworthy, but opposes mandatory safety testing. It wants to protect consumers, but won't fund the agencies that would do the protecting. The result is an AI framework that technically exists but functions more like an absence of policy than an actual strategy.

This isn't accidental. The framework reflects a deliberate choice: trust the market to sort out AI's problems rather than imposing top-down regulation. Whether that's wise depends entirely on your faith in the market's ability to self-correct — and on your tolerance for the collateral damage that accumulates while it figures things out.

What's Actually in the Framework

The federal AI framework — cobbled together from executive orders, agency guidance, and Congressional proposals — rests on a few core principles. First, voluntary industry standards rather than mandatory government rules. Second, federal preemption of state regulations to create a single national market. Third, reduced reporting requirements for AI developers. Fourth, aggressive promotion of AI use across government agencies. Fifth, significant R&D investment through programs like the National AI Research Resource.

None of these are inherently bad ideas. Voluntary standards can be more flexible than government rules. Federal preemption reduces compliance complexity. R&D investment builds American capabilities. But the framework conspicuously lacks anything resembling enforcement, oversight, or accountability. There's no federal AI regulator. No mandatory safety testing. No meaningful consequences for companies that develop and deploy harmful AI systems.

Voluntary standards — Industry groups like NIST develop best practices, but compliance is optional

• Federal preemption — A national standard would override state-level AI regulations
• Reduced reporting — Companies don't need to disclose training data, capabilities, or safety evaluations
• Government AI adoption — Federal agencies directed to accelerate AI integration into their operations
• R&D investment — Continued funding for AI research through NSF, DARPA, and other agencies

The Enforcement Gap

The biggest problem with the federal framework isn't what it includes — it's what it doesn't. There's no mechanism for enforcing any of the voluntary standards. If a company builds an AI system that's biased, unsafe, or harmful, the federal framework provides no tools for addressing that problem. The FTC can act in some cases under existing consumer protection authority, but it's chronically underfunded and lacks AI-specific expertise.

This enforcement gap creates a moral hazard. Companies that invest in responsible AI development face the same regulatory environment as companies that cut corners. There's no competitive advantage to safety because there's no penalty for ignoring it. Over time, this dynamic favors the most reckless actors — exactly the opposite of what good policy should achieve.

The State Backfill

Because the federal framework lacks teeth, states are filling the void. Colorado, California, Illinois, and others are passing their own AI regulations with real enforcement mechanisms. This creates exactly the regulatory patchwork that federal preemption is supposed to prevent — but it's happening because states don't trust the federal framework to protect their residents.

The irony is painful. A framework designed to create regulatory clarity is instead creating regulatory chaos. Companies face different rules in different states, consumers in some states have protections that others don't, and the overall governance space is more fragmented than it would be with a strong federal regulator.

What Would a Real Framework Look Like?

A meaningful federal AI framework would include mandatory safety testing for high-risk applications, a dedicated regulatory body with technical expertise and enforcement authority, transparency requirements for AI systems that affect people's lives, and a mechanism for consumers to seek redress when AI causes harm. It would also include international coordination — because AI doesn't respect borders, and unilateral American regulation is only effective if other major AI powers agree to similar standards.

What we've instead is a framework that prioritizes industry comfort over public safety. Whether that's a sustainable approach for the most powerful technology of the 21st century is still to be seen.

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