U.S. AI New Policy: Farewell to the “50 Labs” Era, Washington Opens a New Wide Door

Introduction: From 1887 to the AI Era

A century and a half later, Silicon Valley’s AI companies stand at the same crossroads.

In recent years, fragmented state rules have imposed high costs on entrepreneurs and given competitors like China an opportunity to catch up. On March 20, the White House released the National AI Policy Framework, promising to establish nationwide unified standards—at first glance, it seems like a burden reduction, but in essence, this is not a regulatory retreat but a reclamation of regulatory authority. In other words, Washington is not taking its hands off the steering wheel but is starting to take the wheel back: replacing 50 uneven hands with one larger, steadier, harder-to-avoid hand.

In 1887, American cartoonist W.A. Rogers satirically depicted the scene of Congress passing the Interstate Commerce Act and establishing the Interstate Commerce Commission (ICC) to regulate the railroad industry.

1. 50 Laboratories: When Federalism Meets Economies of Scale

“States are the laboratories of democracy”—this phrase has worked in America for over a hundred years. Minimum wage, healthcare expansion, environmental standards: states experiment first, containing failures locally and replicating successes nationally. Federalism operated like a distributed innovation system, working well in traditional industries.

But AI is not minimum wage, nor is it smokestack emissions. It is not well-suited for “distributed trial and error.”

The core characteristic of AI is increasing returns to scale: more data, a larger market, and broader iterations lead to smarter models, lower costs, and higher barriers. In this structure, compliance is no longer just a cost but evolves into a competitive barrier—small companies bear uncertainty, while large companies bear expenses.

Asking a ten-person startup to navigate 50 conflicting sets of state laws is akin to forcing it to play chess on 50 boards simultaneously: every move could trigger compliance risks in another state. Industry giants, however, can spread audit and legal costs across their budgets, even productizing compliance processes to create entry barriers.

Thus, a counterintuitive outcome emerges: regulatory fragmentation in the AI era does not foster a hundred flowers blooming; instead, it cedes the market to the players best able to handle complexity—often not the most creative, but the most resourceful.

What the White House framework attempts to sever is precisely this logical chain. But its approach may be more worthy of caution than the problem itself.

2. The Counterintuitive Truth: This Isn’t “Less Regulation,” But Taking the Whistle Back to Washington

The core of this framework is not a specific technical standard, but a legal wrench: Federal Preemption.

Simply put, federal law trumps state law. Congress aims to eliminate state-level rules that “impose undue burdens on AI development,” establishing a nationwide minimum burden standard. It appears to be a loosening: compliance manuals shrink from 50 to 1, and entrepreneurs no longer need to repeatedly step on landmines at state borders. But if you zoom out, it looks more like a power reclamation: previously, 50 states blew whistles and issued penalties in segments; now it’s one entry point, one whistle, one chief referee.

The more subtle point is: today’s “light touch” can become tomorrow’s “channel for a heavy fist.”

The tension lies here: a unified entry point can make the market smoother, but it can also make control more centralized. Today it’s packaged as a “light-touch framework”; tomorrow it could become an institutional channel for any administration to “tighten at will”—because the switch is already installed, waiting for someone to flip it.

This script is not unfamiliar in history. In the late 19th century, the railroad industry descended into chaos under fragmented interstate regulation: rate discrimination, differential pricing for long and short hauls, inefficient cross-state transfers. Citing the need for a “unified market and eliminating chaos,” Congress passed the 1887 Interstate Commerce Act, establishing the Interstate Commerce Commission (ICC) and centralizing regulatory power at the federal level. Railroads initially welcomed it: finally free from battling each state. They soon discovered they were facing a stronger, more persistent, harder-to-circumvent regulatory opponent.

The AI industry stands at a similar crossroads. You can view it as a burden reduction, or you can see it as the “establishment of a unified entry point.” And once that entry point is established, who guards the gate, how they guard it, and how strictly are no longer decisions you make.

3. Six Keys: Who Benefits, Who Gets Constrained?

The White House condensed this thinking into six directions. They are not like a hefty legal code but more like a set of gatekeeper keys—each determining who enters more smoothly and who gets stuck.

Federal Uniformity and State Law Preemption

Reducing compliance manuals from 50 to 1 is an immediate boon for interstate products. But simultaneously, your fate becomes more deeply tied to Congress and the federal political cycle: national uniformity means national synchronized swings. You no longer have the “try another state” option.

Child Protection

Requiring platforms to add age verification mechanisms is one of the few areas with cross-party consensus. But it also clearly places costs on consumer-facing products—especially teams working on C-end applications, education, and social platforms, whose compliance budgets will immediately thicken. Age verification is not a technical challenge but a liability challenge: if it fails, who bears the responsibility?

Energy Cost Protection

Data centers cannot pass electricity costs onto residents, sounding “public-friendly,” but in practice, it imposes hard constraints on infrastructure-layer companies. Electricity, site selection, peak/off-peak load, and contract structures with local utilities become more regulatory issues than engineering ones. The subtext of this rule is: you can build data centers, but don’t let residents’ electricity bills get thicker.

Intellectual Property

The White House leans towards the view that “training AI with copyrighted content is not illegal,” but also acknowledges opposing views, leaving key rulings to the courts. Translation: gray areas persist, risks haven’t disappeared, they’re just deferred to litigation and precedent for resolution—and precedent timelines are typically measured in “years.” For entrepreneurs, this means you can continue using data to train models, but you must also be prepared to face lawsuits at any time. What you can often do is risk management, not risk elimination.

Freedom of Speech

Prohibiting AI for censoring lawful political expression draws a red line for content moderation. For platforms, this is both a constraint and a protection: it’s harder to “actively filter,” but easier to use the rules as a shield under political pressure. But where is the boundary of “lawful political expression”? Who 定义nes it? This is another question left to the courts.

Labor and Education

Expanding AI skills training attempts to turn social pressure into retraining programs. It doesn’t directly resolve distribution conflicts but at least acknowledges their existence and tries to use policy to shorten the shockwave. But can training keep pace with the speed of displacement? Historical experience is not optimistic.

The “smartest” aspect of this framework is its deliberate choice not to create a new federal AI regulatory agency: instead, it relies on existing laws, courts, and market self-regulation to operate—light, fast, with low political resistance.

But consequently, it lacks a “dedicated safety net”: once mechanisms fail, there’s no specialized agency to provide unified interpretation, rapid correction, or continuous iteration. The cost of errors may manifest as lawsuits, industry chilling effects, or sudden policy reversals.

4. Three Global Paths: The Choices of the EU, China, and the US

Placing the US framework in a global comparison makes it clearer: AI governance is diverging into three institutional paths.

EU: Safety First

The AI Act categorizes by risk level, requiring strict certification for high-risk systems. The result is higher public trust, but innovation speed and entrepreneurial flexibility are often compressed, especially unfriendly to teams with insufficient resources. The EU chooses “build guardrails first, then let the cars run.”

China: State-Led

Concentrated resources, rapid advancement, enabling synergy in infrastructure, data organization, and industrial mobilization; but transparency, diversity, and room for debate on certain boundaries are smaller. China chooses “state directs, industry follows.”

US: Scale First

This framework bets that the combination of “unified market + court precedent + market self-regulation” will continue to attract computing power, capital, and talent. As David Sacks, Special Advisor to the White House on AI and Crypto Affairs, stated, 50 uncoordinated state regulations are eroding America’s lead in the AI race—and that lead is especially fragile in the face of economies of scale: if you slow down just a bit, you may never catch up.

The three paths have no absolute right or wrong, only different risk structures:

• If the EU fails, it may lose part of its industry, but societal stability is higher;
• If China fails, it may form a “silo effect” in computing power and ecosystems, but internal mobilization capability is stronger;
• If the US fails, the cost is more “nationally synchronized”—because it actively unified the rules. Once the direction is wrong, the cost of correction is higher.

More crucially, these three paths are shaping each other. The EU’s strict standards will force US companies to raise compliance levels for exports; China’s state investment will accelerate technological iteration; the US market size will continue to attract global talent. The ultimate competition is not “whose rules are better,” but “whose rules allow the industry to run faster, more steadily, and more sustainably.”

5. The Real Meaning for Entrepreneurs: A Window, or a New Fence?

For entrepreneurs currently in the AI industry, short-term signals are likely positive: compliance costs decrease, interstate deployment becomes more predictable, fundraising narratives smoother—”We no longer need 50 compliance plans for 50 states” itself makes a business plan look more like a company and less like a law exam.

But behind this benefit lie three unanswered questions:

• Is the Congressional timeline reliable?

Political agendas are always crowded. AI is hot, but legislation is slow. Implementing federal preemption requires sufficient consensus and time windows, and windows aren’t always open. More troublesome, the legislative process itself can introduce new variables: amendments, riders, lobbying by interest groups—the final passed version may differ significantly from the White House framework.

• Can federal standards maintain a “light touch” long-term?

Today’s promise is not a constitutional firewall. The flip side of centralization is greater reversibility: a change in administration, a change in committee composition, and a light touch can become heavy pressure. And once federal preemption is established, you no longer have the “try another state” option.

• When will the intellectual property gray area be resolved?

Court rulings may take years. During this period, “the legality of training data” remains a variable hanging over products and fundraising. You can continue using data to train models, but you must also be prepared to face lawsuits at any time. Investors will ask: if precedent turns unfavorable, is your moat still there?

Entrepreneurs get a wider door, but there are still invisible beams behind it. You can run faster, but you must also be prepared to brake at any time.

6. The Final Question: Laboratories Close, Factories Open

The era of “50 laboratories” is winding down. Back then, each state was a narrow gate: entrepreneurs could find gaps between states, experiment, and accumulate experience, but efficiency was low and the market fragmented.

Now, Washington wants to build a “national AI factory”—more efficient, clearer rules, nationally unified. This is a wide gate: you can enter faster, deploy across states more easily, reduce friction, expand the market, enabling products to truly cross state lines with one click.

The door is open, but the keys and switches are all in Washington’s hands. You can walk in, but whether you pass through smoothly depends entirely on when they turn the lock.

The truly worth-asking question is not “Is federal regulation good or bad?” but: When America chooses “the market is smarter than regulation,” who defines the moment of market failure?

Before that moment, the window is open;

After that moment, the new laboratory—perhaps only this one left in the factory.

And the key to that one laboratory is not in your hands, nor in the hands of 50 states—it’s in Washington.

This is not just regulation. This is consolidation.

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