U.S. AI Policy: Bidding Farewell to the "50 Labs" Era, Washington Wants to Open a New Wide Door

Introduction: From 1887 to the AI Era

In 1887, American railroads received some “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 that was almost equivalent to operating between different countries. The business community cheered, but they soon realized that this was not just about order; it was also a rearrangement of power structures: no longer having to negotiate with 50 states, but now facing 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 the National Artificial Intelligence Policy Framework, promising to establish a nationwide uniform standard—at first glance, it seems like a reduction of burdens, but essentially, this is not a regulatory retreat, but a consolidation of regulatory power. In other words, Washington is not taking its hands off the steering wheel; it is in fact moving to take the wheel back: from 50 uneven hands to a single, larger, more stable, and harder-to-dodge hand.

In 1887, American cartoonist W.A. Rogers used satire to depict the scene of Congress passing the Interstate Commerce Act and establishing the Interstate Commerce Commission (ICC) to regulate the rail industry.

I. 50 Laboratories: When Federalism Meets Economies of Scale

“The states are the laboratories of democracy”—this phrase has been effective in the U.S. for over a century. Minimum wages, healthcare expansion, environmental standards—states try them first, limit losses if they go wrong, and replicate what works nationwide. Federalism operates like a distributed innovation system, functioning well in traditional industries.

But AI is not about minimum wages or chimney emissions. It is not suited for “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, costs decrease, and barriers increase. In this structure, compliance transforms from merely a cost into a competitive barrier—small companies bear uncertainty, while large companies bear the expenses.

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

Thus, an unexpected outcome emerges: fragmented regulations in the AI era will not lead to a blossoming of innovation, but rather hand the market to those best able to endure complexity—often, these are not the most creative, but the most resourceful players.

The White House framework aims to sever this logical chain. However, its approach may be more concerning than the problem itself.

II. The Counterintuitive Truth: This Is Not “Less Regulation,” But a Return of the Whistle to Washington

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

In simple terms, federal law takes precedence over state law. Congress aims to eliminate those state-level rules that impose “undue burdens on AI development,” establishing a nationwide minimum burden standard. It appears to be a loosening of restrictions: compliance manuals shrink from 50 to 1, and entrepreneurs no longer have to repeatedly navigate minefields at state borders. But if you zoom out a bit, it resembles a power recovery: in the past, 50 states sounded the alarm and issued penalties; now, it has changed to one entrance, one whistle, one chief referee.

The more subtle point is that today’s “light touch” could become tomorrow’s “heavy-handed approach.”

The tension here is that a unified entrance can streamline the market while concentrating control. Today it is packaged as a “light-touch framework,” but tomorrow it could become a mechanism for any government to “collect whenever it wants”—because the switch has already been installed; it just needs someone to flip it.

Historically, this script is not unfamiliar. In the late 19th century, the railroad industry fell into chaos under interstate fragmented regulation: rate discrimination, differential pricing for long and short hauls, and inefficiencies in interstate transfer. Congress passed the 1887 Interstate Commerce Act, arguing for a “unified market and elimination of chaos,” establishing the ICC and consolidating regulatory power at the federal level. Railroads initially welcomed this: finally free from tussles with the states. It was only later that they realized they were facing a stronger, more enduring, and harder-to-evade regulatory opponent.

The AI industry stands at a similar crossroads. You can see it as a reduction of burdens, or you can see it as the establishment of a “unified entrance.” Once the entrance is established, who guards the door, how they guard it, and how strictly it is guarded will no longer be up to you.

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

The White House has distilled this line of thinking into six directions. They are less like a heavy code of laws and more like a set of keys—each determining who can enter smoothly and who might get stuck.

Federal Uniformity and State Law Preemption

Reducing the compliance manual from 50 to 1 is an immediate benefit for interstate products. However, at the same time, your fate is more deeply tied to Congress and the federal political cycle: national uniformity means national synchronous swings. You no longer have the option of “trying a different state.”

Child Protection

Requiring platforms to implement age verification mechanisms is one of the few areas where bipartisan consensus can be reached. However, it also clearly shifts the costs onto consumer-facing products—especially for teams engaged in B2C applications, education, and social networking, the compliance budget will immediately grow. Age verification is not a technical challenge but a responsibility challenge: if something goes wrong, who is accountable?

Energy Cost Protection

Data centers cannot pass electricity costs onto residents, which sounds “friendly to the public,” but imposes hard constraints on infrastructure-level companies. Electricity, site selection, peak and off-peak load, and contracts with local utilities are more like regulatory issues than engineering issues. The subtext of this rule is: you can build a data center, but don’t let residents’ electricity bills become more burdensome.

Intellectual Property

The White House tends to believe that “using copyrighted content to train AI is not illegal,” but also acknowledges the existence of contrary viewpoints, leaving key judgments to the courts. In translation: the gray area remains, and the risks have not disappeared; they have merely been postponed to be resolved in litigation and case law—where the timeline for cases is typically measured in “years.” For entrepreneurs, this means you can continue to use data to train models, but you must also be prepared to face lawsuits at any time. What you can often do is risk management rather than risk elimination.

Freedom of Speech

Prohibiting AI from censoring lawful political expression defines a red line for content moderation. For platforms, this is both a constraint and a protection: it becomes harder to “actively filter,” but easier to use rules as a shield under political pressure. But where are the boundaries of “lawful political expression”? Who defines them? This is yet another issue left for the courts.

Workforce 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 that conflicts exist and tries to shorten the shockwaves with policy. But can training keep up with the pace of displacement? Historical experience is not optimistic.

The most “clever” aspect of this framework is that it deliberately does not establish a federal AI regulatory agency: rather, it relies on existing laws, courts, and market self-regulation to operate—lightweight, fast, and with minimal political resistance.

But it also lacks a “dedicated bottom line”: once mechanisms fail, there is no specialized agency to unify interpretation, quickly correct errors, or continuously iterate; the costs of mistakes may manifest as lawsuits, industry silence, or sudden policy reversals.

IV. Three Global Paths: The Choices of the EU, China, and the U.S.

Placing the U.S. framework in a global comparison provides clarity: AI governance is diversifying into three institutional paths.

EU: Safety First

The AI Act categorizes systems by risk, with high-risk systems requiring strict certification. The result is higher public trust, but often a compression of innovation speed and entrepreneurial flexibility, particularly unfriendly to resource-constrained teams. The EU has chosen “first build guardrails, then let the cars run.”

China: State-Led

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

U.S.: Scale First

This framework bets that the combination of “unified market + court cases + market self-regulation” can continue to attract computing power, capital, and talent. As White House AI and Crypto Affairs Special Advisor David Sacks stated, the 50 sets of disjointed state regulations are eroding the U.S.'s leading position in the AI race—where the advantage of being ahead is particularly vulnerable to economies of scale: if you are just a bit slower, you may never catch up.

None of these paths are absolutely right or wrong; they only present different risk structures:

• If the EU fails, it may lose part of its industry, but social stability will be higher;
• If China fails, it may form a “isolated effect” in computing power and ecosystems, but it has stronger internal mobilization capabilities;
• If the U.S. fails, the costs will be more “synchronized nationwide”—because it has actively unified its rules. Once the direction is wrong, the costs of correction will be higher.

More critically, these three paths are shaping each other. The EU’s stringent standards will compel American companies to elevate compliance levels in exports; China’s state investment will accelerate technological iteration; and America’s market size will continue to attract global talent. The ultimate competition is not “whose rules are better,” but “whose rules can make the industry run faster, more steadily, and more sustainably.”

V. The Real Meaning for Entrepreneurs: A Window, or New Barriers?

For entrepreneurs currently in the AI industry, the 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,” which alone can make a business plan look more like a company and less like a legal exam.

But behind this favorable outlook, there are still three unanswered questions:

• Is the Congressional timetable 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 is not always present. What’s more troubling is that the legislative process itself may introduce new variables: amendments, add-ons, and lobbying from interest groups—the final version passed may differ significantly from the White House framework.

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

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

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

Court rulings may take years. In the meantime, the “legality of training data” remains a variable hanging over products and financing. You can continue to use data to train models, but you must also be prepared to face lawsuits at any moment. Investors will ask: if the rulings are unfavorable, do you still have your competitive moat?

Entrepreneurs are presented with a wider door, but behind that door are still several invisible beams. You can run faster, but also be ready to hit the brakes at any time.

VI. The Final Question: Closing the Laboratory, Opening the Factory

The era of “50 laboratories” is coming to an end. Back then, each state was a narrow door: entrepreneurs could seek gaps between states, trial and error, and accumulate experience, but with low efficiency and fragmented markets.

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, and expand the market, enabling products to truly cross state lines at the click of a button.

Though the door is open, the keys and switches are all in Washington’s hands. You can step inside, 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 or bad,” but rather: when the U.S. chooses “the market is smarter than regulation,” who will define the moment of market failure?

Before that moment, the window is open;

After that moment, the new laboratory—perhaps only this factory remains.

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

This is not just regulation. This is consolidation.