U.S. AI New Policy: Goodbye to the "50 Labs" Era

Author: On-Chain Revelation

Introduction: From 1887 to the AI Era

In 1887, American railroads received “good news”: Congress passed the Interstate Commerce Act, attempting to end the chaos of fragmented state regulation—different track gauges, disjointed rate systems, and friction in interstate transportation nearly equated to operating between different countries. The business community cheered, but they quickly realized that this was not just about order, but a restructuring of power: no longer needing to negotiate with 50 states, they now had to contend with a single, centralized federal regulator.

A century and a half later, AI companies in Silicon Valley find themselves at the same crossroads.

In recent years, fragmented state regulations have imposed high costs on entrepreneurs and provided opportunities for competitors like China to catch up. On March 20, the White House released a four-page “National Artificial Intelligence Policy Framework,” committing to establish a nationally unified standard—at first glance, it seems like a relief, but essentially, this is not a retreat from regulation, but a consolidation of regulatory power. In other words, Washington is not taking its hands off the steering wheel; instead, it is moving to reclaim the steering wheel: replacing 50 uneven hands with one larger, steadier, and harder-to-dodge hand.

In 1887, American cartoonist W.A. Rogers illustrated Congress passing the Interstate Commerce Act and establishing the Interstate Commerce Commission (ICC) to regulate the railroad industry through satire.

I. 50 Laboratories: When Federalism Meets Economies of Scale

“The states are laboratories of democracy”—this phrase has been useful in the U.S. for over a century. Minimum wage, healthcare expansion, environmental standards—all tested first by the states, with mistakes limited locally and successes replicated nationally. Federalism functions like a distributed innovation system, working well in traditional industries.

But AI is not minimum wage, nor is it chimney emissions. It does not lend itself to “distributed trial and error.”

The core characteristic of AI is increasing returns to scale: the more data there is, the larger the market, and the broader the iterations, the smarter the model becomes, the lower the costs, and the higher the barriers. In this structure, compliance evolves from merely a cost to a competitive barrier—small companies bear uncertainty, while large companies bear expenses.

Asking a ten-person startup to navigate 50 conflicting state laws is akin to asking it to play chess on 50 boards simultaneously: every move could trigger compliance risks in another state. Meanwhile, industry giants can spread audit and legal costs into their budgets and even productize compliance processes, thereby creating barriers to entry.

Thus, an counterintuitive result emerges: regulatory fragmentation in the AI era will not lead to a flourishing of ideas, but will instead cede the market to those best able to bear complexity—often not the most creative, but the most resourceful.

The White House framework aims to sever this logical chain. However, the method it employs may be more concerning than the issue itself.

II. The Counterintuitive Truth: Washington as the Chief Referee?

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

In simple terms, federal law supersedes state law. Congress aims to eliminate state-level rules that “impose undue burdens on AI development” and establish a nationwide minimum burden standard. It looks like deregulation: compliance manuals shrink from 50 to 1, and entrepreneurs no longer need to repeatedly navigate state boundaries. But if you zoom out a bit, it resembles a power reclamation: previously, 50 states individually sounded alarms and imposed penalties; now it has transformed into one entry point, one alarm, one chief referee.

The more subtle aspect is that today’s “light touch” could become tomorrow’s “heavy-handed access channel.”

The tension here is that a unified entry point can smooth market operations while also centralizing control. Today, it is packaged as a “light touch framework”; tomorrow it could become any government’s “grab it if you want” institutional channel—because the switch has already been installed; it just depends on who flips it.

This kind of script is not unfamiliar in history. At the end of the 19th century, the railroad industry fell into chaos under fragmented interstate regulation: rate discrimination, differential pricing for short and long hauls, and inefficiencies in interstate transport. Congress passed the 1887 Interstate Commerce Act on the grounds of “unifying the market and eliminating chaos,” establishing the Interstate Commerce Commission (ICC) and consolidating regulatory power at the federal level. Railroads initially welcomed this: they no longer had to tussle with the states. They soon discovered they faced a stronger, more enduring, and harder-to-evade regulatory adversary.

The AI industry stands at a similar crossroads. You can view it as a burden reduction or as the establishment of a “unified entry point.” Once that entry point is established, who guards the door, how they guard it, and how strictly they guard it will no longer be your decision.

III. Six Keys: Who Benefits, Who is Restricted?

The White House has distilled this line of thought into six directions. They are not like a heavy codex but rather a set of keys—each deciding who can enter smoothly and who might get stuck.

Federal Unity and State Law Preemption

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

Child Protection

Requiring platforms to increase age verification mechanisms is one of the few areas where bipartisan consensus can be reached. However, it explicitly places costs on consumer-facing products—especially for teams focused on C-end applications, education, and social networking; compliance budgets will immediately thicken. Age verification is not a technical challenge, but a responsibility challenge: if something goes wrong, who bears the burden?

Energy Cost Protection

Data centers are prohibited from passing electricity costs onto residents, sounding “consumer-friendly,” but in reality imposes hard constraints on infrastructure-level companies. Electricity, site selection, peak and off-peak loads, and contracts with local utilities are more regulatory issues than engineering issues. The subtext of this rule is: you can build data centers, but don’t let residents’ electricity bills rise.

Intellectual Property

The White House leans towards the view that “using copyrighted content to train AI is not illegal,” but acknowledges there are opposing viewpoints, leaving key rulings to the courts. In translation: gray areas remain, risks have not disappeared, but have merely been postponed to be resolved through litigation and case law—where the timelines are typically measured in “years.” For entrepreneurs, this means you can continue using data to train models but 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 from censoring lawful political expression draws a red line for content moderation. For platforms, this is both a constraint and protection: it becomes harder to “filter proactively” and easier to use rules as a shield under political pressure. But where do the boundaries of “lawful political expression” lie? Who defines it? This is another issue for the courts.

Labor and Education

Expanding AI skills training attempts to transform social pressure into retraining programs. It does not directly resolve distribution conflicts, but at least acknowledges their existence and attempts to shorten the shockwave with policy. But can training keep pace with the speed of replacement? Historical experience is not optimistic.

The “smartest” aspect of this framework is its deliberate choice not to establish a federal AI regulatory agency: rather, it relies on existing laws, courts, and market self-regulation to function—lightweight, fast, and with little political resistance.

However, this also results in a lack of “dedicated safety nets”: if mechanisms fail, there is no specialized agency to unify interpretation, quickly correct, or continually iterate, and the costs of errors may manifest as litigation, industry paralysis, or sudden policy reversals.

IV. Three Global Paths: The Contest Between China, the U.S., and the EU

Placing the U.S. framework in a global comparison clarifies the picture: AI governance is diverging into three institutional paths.

European Union: Safety First

The AI Act categorizes systems by risk levels, requiring strict certification for high-risk systems. The result is higher public trust, but often compresses innovation speed and entrepreneurial flexibility, particularly unkind to resource-strapped teams. The EU chooses to “build guardrails first, then let the vehicle run.”

China: State-Led

With concentrated resources and rapid advancement, China can form a synergy in infrastructure, data organization, and industrial mobilization; however, transparency, diversity, and the debatable space for certain boundaries will be smaller. China opts for “state command, industry follow.”

United States: Scale First

This framework bets that the combination of “unified market + court precedent + market self-regulation” can continue attracting computing power, capital, and talent. As White House AI and crypto advisor David Sacks has noted, 50 sets of incoherent state regulations are eroding America’s leading position in the AI race—and this advantage is particularly fragile in the face of economies of scale: if you fall behind slightly, you may never catch up.

None of these paths are absolutely right or wrong; they simply carry different risk structures:

• If the EU fails, it may lose part of its industry, but social stability is higher;

• If China fails, it may create a “island effect” in computing power and ecosystems, but has stronger internal mobilization capabilities;

• If the U.S. fails, the cost is more “nationally synchronized”—as it has actively unified the rules. Once the direction is wrong, the cost of correction will be higher.

More critically, these three paths are shaping each other. The EU’s strict standards will force U.S. companies to elevate compliance levels when exporting; China’s state investment will accelerate technological iterations; and the U.S. market scale will continue to attract global talent. The final competition is not about “whose rules are better,” but “whose rules allow the industry to run faster, more smoothly, and more sustainably.”

V. The Real Implications for Entrepreneurs: A Window or a New Barrier?

For entrepreneurs currently in the AI industry, short-term signals are likely favorable: compliance costs are decreasing, interstate deployment is more predictable, and financing narratives are smoother—“We no longer need to prepare 50 compliance plans for 50 states” makes the business plan look more like a company and less like a legal exam.

However, behind this benefit, there are still three unanswered questions:

• Is Congress’s timeline reliable?

Political agendas are always crowded. AI is hot, but legislation is slow. The implementation of federal preemption requires sufficient consensus and a time window, which does not always exist. More troubling, the legislative process itself may introduce new variables: amendments, riders, and lobbying from interest groups—the final version may diverge significantly from the White House framework.

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

Today’s commitments are not a constitutional firewall. The other side of centralization is greater reversibility: a new government, a new committee, and light touch may become heavy pressure. Once federal preemption is established, you no longer have the option to “try a different state.”

• When will the gray areas of intellectual property be resolved?

Court rulings may take years. In the meantime, the “legitimacy of training data” remains a variable hanging over products and financing. You can continue using data to train models, but be prepared to face lawsuits at any time. Investors will ask: if the precedent is unfavorable, is your moat still in place?

Entrepreneurs gain a wider door, but there are still invisible beams behind it. You can run faster, but also be ready to brake at any moment.

VI. The Final Question: Closing the Labs, Opening the Factories

The era of “50 laboratories” is coming to an end. Back then, each state was a narrow door: entrepreneurs could find gaps between states, experiment, and accumulate experience, but with low efficiency and market fragmentation.

Now, Washington aims to build a “national-level AI factory”—more efficient, clearer rules, and a unified national standard. This is a wide door: you can enter faster, deploy across states more easily, reduce friction, expand the market, and truly achieve one-click interstate product capability.

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

What is truly worth questioning is not “is federal regulation good,” but: when the U.S. 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 laboratories—perhaps only one remains in the factory.

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