A fully loaded wooden cart moves with a quiet, heavy hum. The weight absorbs the shock.
An empty one rattles. It bounces. It makes noise.
My grandmother had a proverb for this: 빈수레가 요란하다.
An empty cart rattles loudly.
She deployed this phrase not as an insult, but as a diagnostic tool. The noise was the proof that nothing was inside.
Last week, the White House attempted to roll a very loud cart past us as quietly as possible.
There was no major press conference. Just a classic Friday news dump for the administration’s national AI legislative framework. On paper, the claims are grand: seven structural pillars designed to secure American leadership in artificial intelligence.
Then you read the text. Four pages. No new enforcement body. No concrete legislative text. No mechanism to verify anything.
The framework doesn’t fail to govern AI—it actively proposes to dismantle the distributed governance experiments already underway at the state level while building nothing to replace them. The vacuum isn’t an oversight. It is the policy.
I. What the Framework Actually Proposes
The document centers on three major moves:
First, it proposes federal preemption of state AI laws concerning the development and deployment of models. Second, it explicitly rejects the creation of a new federal oversight agency, instead routing governance through existing agencies with “sector-specific expertise.” Third, it proposes liability shields, preventing states from holding frontier AI developers accountable for third-party misuse.
What is conspicuously absent is the infrastructure required to make governance real. There is no audit mechanism. There is no enforcement architecture. There are no technical capability requirements for the existing agencies that would inherit this massive oversight burden.
It reads less like legislation and more like a blueprint—one that names the pillars but omits anything load-bearing.
II. What is Being Dismantled
This framework is not being introduced into a vacuum. It is being dropped into an ecosystem where states were already doing the hard work of building governance.
In 2025 alone, over 1,000 AI-related bills were introduced at the state level. California’s SB 53 and New York’s RAISE Act created transparency and whistleblower requirements for frontier labs. These state-level laws are closer to early-stage clinical trials than finished regulation—imperfect, iterative, but generating real data. Preempting them now is like shutting down clinical trials mid-experiment and declaring the drug safe anyway.
The framework proposes to preempt these state efforts while offering no federal replacement. This exact legislative maneuver—attempting to federally ban states from regulating AI—has already failed twice in Congress. Broad preemption clauses were stripped from the recent reconciliation bill by a 99-1 vote and rejected in the National Defense Authorization Act (NDAA). Over 50 Republican state legislators publicly opposed the move.
This isn’t a partisan split; it is a federalism split.
You do not get to call this “light-touch regulation.” Light-touch regulation is still regulation. This is the active removal of governance infrastructure marketed as a design philosophy.
III. Why “Existing Agencies” Can’t Do This
The framework assumes the SEC can oversee AI in finance, the FDA can oversee AI in health, and the FTC can handle consumer protection.
But which existing agency can audit whether a Large Language Model (LLM) actually trained on your proprietary data? Which agency has the technical infrastructure to verify an opt-out claim on a massive training pipeline?
I wrote that exact policy for an AI-native platform. The enterprise world operates on an “unverifiable promise” when it comes to AI data security. The answer is: none of them. Trying to govern frontier AI by distributing it across legacy agencies is the equivalent of trying to audit a black box system with a checklist designed for transparent ones.
Sector-specific oversight assumes sector-specific failures. But the failure modes of foundation models are architectural. A model hallucinating in healthcare and a model leaking proprietary data in finance are both manifestations of the same underlying opacity. No existing agency is equipped for that, because the technical capability to audit model behavior at the training level doesn’t exist in any government body today.
The framework’s answer to “who governs AI?” is “everyone who already governs everything else.” That is not a strategy. It is a diffusion of responsibility.
Systems fail not because no one is responsible, but because everyone is partially responsible.
We’ve already seen how this breaks in practice. In healthcare, some of the most dangerous failures don’t come from a single bad decision. They happen during handoffs—when responsibility moves from one team to another, and critical context is lost. Each team does their part. The system still fails.
We see the same pattern in cloud security. Breaches rarely happen because a system has no owner. They happen because responsibility is split across layers—provider, platform, customer—and each assumes the other is covering the gap. The model is called “shared responsibility.” In practice, it often means no one’s responsibility.
That is exactly the structure being proposed here.
IV. The Vacuum is the Policy
The most important sentence in the four-page framework is the one declaring that Congress should refrain from creating any new federal rulemaking body.
That is not a procedural recommendation. It is a structural commitment to ungoverned space.
The framework introduces “regulatory sandboxes” as its mechanism for innovation—exemptions from standard rules to allow experimentation.
But it does not specify which agency runs them. It does not define exit criteria. It does not define what happens when harm occurs inside the sandbox.
That’s not a sandbox. A sandbox has boundaries, monitoring, and exit criteria.
It’s an accountability void with a brand name.
When your primary innovation mechanism has no defined oversight and no accountability for harm, you aren’t building a sandbox.
You’re building a moat.
AI governance at this level of complexity requires a technically fluent, structurally independent oversight body — one that doesn’t yet exist at the federal level and that this framework explicitly refuses to create.
The risk isn’t that this framework fails—it’s that it works exactly as designed.
Seven pillars. No foundation. The cart is loud. There’s nothing inside.
