Trump's 'One Rule' Will Remake AI Forever

The Tweet That Ignited a Firestorm

Donald Trump did not roll out his AI agenda with a white paper or a Rose Garden speech. He did it with a Truth Social blast promising a “One Rule” executive order that would put Washington, not Sacramento or Albany, in charge of artificial intelligence. In all caps, he warned that AI “will be destroyed in its infancy” if companies must navigate 50 different state rulebooks.

At the core of the post sits a blunt thesis: 50 separate AI regimes will kill U.S. innovation. Trump argued that startups cannot “get 50 approvals every time they want to do something,” framing state attorneys general and ambitious governors as “bad actors” that will smother the industry in red tape. The message fits neatly into his broader “America first” AI rhetoric: centralize, move fast, beat China.

The announcement landed in a policy landscape already primed for a fight. Over 1,200 AI-related bills have surfaced in state legislatures, and more than 100 measures have already passed, with California racing ahead on safety, transparency, and labor rules. Trump’s post signaled that his administration intends to slam on the brakes and impose a single federal standard instead.

Tech circles reacted instantly. Founders and VCs on X cheered the promise of a unified rulebook, seeing it as protection against a compliance nightmare that only giants like Google, Meta, OpenAI, and Anthropic could survive. Civil society groups and some state officials, meanwhile, saw an opening salvo in a campaign to strip states of their traditional consumer protection powers.

David Sacks, Trump’s informal AI and crypto consigliere, quickly translated the tweet into legalese. He argued that AI development, training, and inference already span multiple states and ride on national telecommunications networks, making it classic interstate commerce squarely under federal jurisdiction. The “One Rule” order, in his telling, does not abolish AI regulation; it decides who gets the pen.

That framing matters. Trump is not just promising lighter-touch rules; he is asserting federal preemption over any state that tries to go its own way on AI, setting up a high-stakes showdown over who actually governs the machines.

The 50-State AI Nightmare Fueling This Move

Chaos now defines AI law in America. Instead of one coherent rulebook, companies face a fast‑growing patchwork of state statutes, agency rules, and task-force “guidelines” that increasingly carry legal teeth. More than 1,200 AI-related bills have been introduced in state legislatures, with over 100 measures already passed, according to policy trackers.

California sits at the center of this arms race. Lawmakers in Sacramento have pushed sweeping rules on algorithmic discrimination, automated decision systems, and safety testing, with proposals that would force companies to audit models for bias and document training data practices. Some bills explicitly target hiring, housing, and lending algorithms, exposing violators to state civil-rights litigation and class actions.

Colorado followed with its own AI statute focused on “consequential decisions.” It requires “developers” and “deployers” of high‑risk AI systems to implement risk‑management programs, perform impact assessments, and notify consumers when an automated system materially affects them. Fail to comply, and you face enforcement by the Colorado Attorney General under state consumer‑protection law.

Multiply that by 50 and you get the nightmare Trump is pointing at. A startup building a recommendation engine or HR screening tool could need separate compliance playbooks for: - California’s bias and transparency mandates - Colorado’s high‑risk AI framework - Emerging rules in New York, Texas, and Illinois

Every divergence adds cost. A five‑person team now needs outside counsel in multiple states, parallel documentation regimes, and sometimes different model behaviors per jurisdiction. Training slightly different models to satisfy conflicting definitions of “sensitive attribute” or “high‑risk use” means extra compute, engineering time, and ongoing monitoring.

Large incumbents treat this as a rounding error. Google, Meta, Microsoft, and Apple already run global compliance operations and can lobby to shape each state’s rulemaking. Startups instead face a brutal choice: geo‑block certain states, slow product launches, or accept legal risk they cannot afford.

Foreign competitors quietly benefit too. A Chinese or European lab training frontier models under a single national or regional regime does not juggle 50 sets of discovery requests and audits. Fragmented regulation at home becomes a competitive subsidy abroad.

The Legal Gambit: How 'Interstate Commerce' Unlocks Federal Control

David Sacks’ legal gambit hinges on a familiar constitutional workhorse: interstate commerce. His argument, echoed by Trump’s allies, is that modern AI is so deeply entangled with cross-border economic activity that only the federal government can realistically regulate it. If AI is interstate commerce, then under the Commerce Clause, Washington gets the wheel and state lawmakers move to the back seat.

Start with how an actual large language model lives its life. A startup might design and code its model in San Francisco, using engineers employed in California. That model then ships to a GPU-heavy data center in Texas for months of training, burning megawatts and ingesting datasets sourced from multiple states and countries.

Inference adds another hop. The same model could run on servers in Virginia or Ohio, where cloud providers cluster their cheapest capacity. When a user in Florida, New York, or Iowa hits “submit,” their request pings those out-of-state racks, the model generates a response, and the answer travels back across fiber and wireless networks that span dozens of jurisdictions.

Every step in that chain rides on national telecommunications infrastructure: Tier 1 internet backbones, undersea cables, content delivery networks, and data centers regulated by federal agencies like the FCC and FTC. Sacks’ point is simple: when AI workloads move over these pipes, and when companies bill for those services across state lines, they are engaged in classic interstate commerce—exactly what the framers expected Congress, not Sacramento or Tallahassee, to police.

Trump’s earlier order, Executive Order: Removing Barriers to American Leadership in Artificial Intelligence, already leans into this framing. It treats AI as national infrastructure, tying it to federal priorities like defense, international competition, and critical communications. The “One Rule” order would extend that logic from promotion to preemption.

Critics can still argue states have traditional authority over consumer protection, labor, and civil rights. But Sacks’ model-lifecycle story makes those objections harder to weaponize against a federal takeover. If a single inference call routinely touches three or more states and rides networks regulated in Washington, AI stops looking like a local business and starts looking like railroads, airlines, or telecom: inherently federal by design.

Why Startups Secretly Love This Plan

Startups may not say it out loud on X, but they read Trump’s “One Rule” post like a term sheet finally written in plain English. A single federal standard for AI turns a chaotic legal minefield into something a five-person team can actually map out on a whiteboard.

Right now, early-stage founders face a slow-motion compliance disaster. Over 1,200 AI-related bills have surfaced in state legislatures, with more than 100 already passed, each threatening a different definition of “harmful” or “high-risk” AI.

That environment supercharges regulatory capture. Companies like Google, Meta, OpenAI, and Anthropic can hire battalions of lawyers to negotiate bespoke deals with Sacramento, Albany, and Austin. A seed-stage startup that just raised $3 million cannot afford a 50-state legal tour.

Complex, state-by-state rules quietly lock in incumbents. If deploying an AI agent requires separate compliance work for: - California’s safety audits - New York’s transparency mandates - Texas’s data localization quirks

then only firms with nine-figure legal budgets can ship nationwide products.

A single, clear federal rulebook flips that script. Instead of building three versions of an AI hiring tool to satisfy three incompatible state laws, a startup can ship one product, one time, and know it works from Miami to Seattle.

Capital allocation changes overnight. Every dollar not spent on outside counsel or redundant model retraining flows into GPU time, new features, and better evals. For a small company, redirecting even 10–15% of budget from compliance to R&D meaningfully extends runway.

Speed becomes the real prize. With predictable federal rules, founders can move from prototype to national deployment in weeks, not months, without pausing to decode 50 different attorney general press releases. That kind of regulatory certainty tends to do one thing very quickly: flood the zone with new AI startups.

A Warning From the Auto Industry

California already wrote this script once. In the 1970s and 1980s, the state’s vehicle emissions rules went far beyond federal standards, forcing automakers to treat one state as a regulatory superpower. If you wanted access to 39 million Californians, you built to California’s rulebook.

Automakers initially tried to split the difference. They engineered a “California car” with stricter smog controls and a cheaper, dirtier version for everyone else. That dual-track strategy collapsed under its own complexity and cost.

By the 1990s, the California car effectively became the national standard. It was too expensive to maintain separate manufacturing lines, certification regimes, and supply chains for two sets of rules. When the biggest market demanded cleaner vehicles, Detroit and Tokyo just gave those vehicles to everyone.

That history underpins today’s AI fight. California, New York, and a handful of other states already push aggressive AI bills on bias audits, model transparency, and data provenance. More than 1,200 AI-related bills have surfaced in state legislatures, with over 100 already passed.

Here’s the catch: tailpipe emissions are local. Smog chokes Los Angeles, not Louisville. A tougher catalytic converter requirement mainly affects air quality inside the regulating state’s borders, so letting California lead made pragmatic sense, and other states could free‑ride on the cleaner tech.

AI does not respect borders in the same way. A model trained in San Jose, hosted in Dallas, and inferenced from a phone in Miami can generate misinformation, discriminatory outputs, or deepfakes anywhere on earth in milliseconds. The harms—financial scams, election interference, reputational damage—propagate globally, not just inside the state that wrote the statute.

Trying to replay the auto playbook for AI means this: whichever state writes the most restrictive rules on model training data, safety evaluations, or deployment might end up dictating de facto standards for everyone. But unlike catalytic converters, AI systems update weekly, not on 7‑year model cycles.

A state-led regime that once worked for slowly evolving hardware collapses under the speed, scale, and borderless reach of software that ships as weights and APIs. AI behaves like the internet, not like a car lot, and regulation designed for tailpipes will not survive contact with transformers.

Is This Just Deregulation in Disguise?

Critics see Trump’s “One Rule” push as a Trojan horse: centralize AI policy in Washington, then quietly gut safety and ethics rules. Civil liberties groups warn that preempting aggressive state laws—especially from California and New York—will functionally erase hard-won protections on bias, privacy, and transparency before any robust federal guardrails exist.

David Sacks insists that’s not the play. In his follow-up post, he frames the move as a narrow fight over jurisdiction, not a blanket “AI amnesty” or moratorium on rules. His argument: AI development, training, and inference already span multiple states and ride national telecom networks, so under the Commerce Clause, federal agencies—not Sacramento or Albany—should write the rulebook.

That line matters because the broader Trump AI program already leans heavily pro-business. Executive Order 14179, “Removing Barriers to American Leadership in Artificial Intelligence,” directs agencies to identify and strip out regulations that “unnecessarily impede” AI deployment. The America’s AI Action Plan built on that order focuses on three pillars: accelerating innovation, building infrastructure, and projecting U.S. power abroad.

Read closely, the plan puts a thumb on the scale for deregulation. Agencies must justify any new AI rule against its impact on competitiveness, and the White House explicitly warns against “overly restrictive” standards that might slow domestic champions relative to China. At the same time, it nods to “prudent” state laws, creating just enough ambiguity to keep lawsuits flowing for years.

Supporters argue that federal supremacy does not automatically mean a regulatory vacuum. They point to potential nationwide baselines on model evaluations, critical infrastructure use, and export controls, all coordinated through a single framework like the one outlined at AI.gov – President Trump’s AI Strategy and Action Plan. In their view, uniform light-touch rules beat a maze of conflicting mandates.

Opponents counter that “light-touch” is doing a lot of work. With over 1,200 state AI bills introduced and more than 100 already passed, states are where the most aggressive safety ideas live. Forcing everything through a deregulatory federal filter could lock in a permanently weaker standard just as AI risk—and geopolitical competition—spikes.

The Global Chessboard: USA vs. China vs. Europe

Global AI power politics hang over Trump’s “One Rule” idea like a backdrop no one can ignore. Federalizing AI regulation is not just a domestic clean‑up job; supporters pitch it as a weapon in a three‑way contest: USA vs. China vs. Europe.

China runs AI like it runs everything else: centralized, vertically integrated, and explicitly tied to state power. Beijing’s rules force companies like Baidu and Tencent to register models, submit security reviews, and hard‑code censorship aligned with Chinese Communist Party priorities.

Europe has taken the opposite route, building a dense web of process‑heavy guardrails. Between GDPR, the Digital Services Act, and the EU AI Act’s risk tiers, companies face mandatory impact assessments, documentation requirements, and steep fines that can hit 6% of global revenue.

American AI firms currently race ahead on raw model capability, but they do it while dodging a growing minefield of state bills. Over 1,200 AI‑related bills and more than 100 measures already passed at the state level create a regulatory maze that OpenAI, Meta, Google, Anthropic, and every startup must navigate.

Advocates argue that this fragmentation is not just annoying; it is a strategic vulnerability China can exploit. A company pinned down by conflicting California, Texas, and New York rules ships slower, spends more on lawyers, and takes fewer technical risks than a rival in Beijing answering to a single party line.

“One Rule” fans frame a unified federal standard as the only way to preserve “American AI dominance” that Trump insists is already slipping. They want one national compliance target, one set of audits, one enforcement apparatus, not 50 mini‑Brussels scattered across state capitols.

The pitch is blunt: match China’s centralization without its authoritarianism, avoid Europe’s bureaucracy, and turn federal preemption into a competitive advantage before the gap in model quality and deployment speed starts to close.

Drawing the Line: Where Federal Power Would Stop

Federalization only works politically if it comes with hard limits, and Trump’s allies know it. David Sacks has started talking about a set of carve‑outs he calls the “Four Cs”, designed to reassure governors, mayors, and creators that Washington will not swallow every AI decision whole.

First C: child safety. Under the One Rule concept, broadly applicable state laws that protect kids online—age‑verification rules, restrictions on targeted ads to minors, duty‑of‑care statutes—would still apply to AI products as long as they hit every platform, not just AI. Think California‑style minor privacy laws or Utah’s social‑media curfews, but extended to AI chatbots, recommendation engines, and generative apps.

That distinction matters because it draws a bright line between regulating “AI” as an industry and regulating harms to children regardless of tech stack. A state could still punish an AI‑powered app that serves explicit content to a 13‑year‑old, but it could not, under this framework, impose its own separate licensing regime for training models above a certain parameter count.

Second C: communities. Even if model rules move to Washington, local governments would retain control over land use, zoning, and environmental review for the physical footprint of AI—data centers, substations, cooling infrastructure. A city council in Phoenix or a county board in Iowa could still say no to a 500‑megawatt server farm that guzzles water or overloads the grid.

That means the same patchwork that already governs warehouses, server farms, and industrial facilities would continue to shape where AI infrastructure actually lives. Federal AI rules might decide how a model gets audited, but a planning commission can still block the building that houses the GPUs.

Third C: creators & copyright. Here, Trump’s One Rule order almost deliberately steps back, because copyright already sits squarely in federal hands under Article I and the Copyright Act. Training‑data lawsuits against OpenAI, Meta, Stability AI, and others will rise or fall in federal courts, not in a Truth Social policy thread.

Any attempt by a state to create its own AI‑specific copyright regime—say, a California “dataset licensing” statute—would run headfirst into preemption doctrine. Under the Four Cs framing, the executive order sets jurisdiction for safety and deployment, while judges, not regulators, decide whether scraping your novel to train a model counts as fair use.

The Culture War Comes for Your AI

Culture war politics already shapes how states talk about AI. Red legislatures frame models as potential censors, obsessed with political bias, “woke filters,” and deplatforming. Blue legislatures talk about algorithmic discrimination, surveillance, and labor exploitation baked into training data and deployment.

Colorado’s new AI law shows the blue-state template. It targets “consequential decisions” in housing, credit, employment, and health care, forcing companies to assess and mitigate algorithmic discrimination risks and notify consumers when AI makes or shapes a decision. Lawmakers in New York, Illinois, and Washington are drafting similar rules, with civil-rights groups pushing hard on auditability and documentation.

Conservative states move in the opposite direction. Florida and Texas lawmakers float bills that would punish platforms or models for “viewpoint discrimination,” often naming ChatGPT and other large language models in hearings. Proposals focus less on accuracy or safety and more on ensuring AI will happily generate content aligned with right-wing politics, from election memes to school curricula.

That split guarantees future clashes. A model tuned to satisfy Colorado’s bias rules might face lawsuits in Texas for allegedly suppressing conservative content. Meanwhile, a system designed to avoid “censorship” in Florida could violate New York’s civil rights and consumer protection standards. Legal scholars already sketch preemption strategies in pieces like Eliminating State Law "Obstruction" of National Artificial Intelligence Policy – Part I.

Nothing illustrates the cultural volatility better than the “Black George Washington” incident. After users posted screenshots showing image generators refusing to depict white people while eagerly producing Black versions of America’s founders, right-wing media turned it into a multi-day scandal. Companies scrambled to patch prompts, but the narrative stuck: AI had become another front in the representation wars.

Proponents of Trump’s “One Rule” argue a federal framework could short-circuit this escalation. Instead of 50 ideological experiments, they want a single baseline focused on: - Economic growth and startup viability - National security and critical infrastructure - Clear liability and transparency rules

That promise of a neutral, commerce-first standard sounds appealing to developers exhausted by whiplash. Whether any federal rulebook can stay neutral once Congress starts editing the prompts is a different question.

The Aftermath: What Happens the Day After the Order Is Signed?

Day one after a “One Rule” executive order would feel like a software update pushed to an entire country at once. Lawyers at OpenAI, Google, Meta, Anthropic, and every AI startup in a WeWork would drop state trackers and start combing through whatever federal rulebook replaces them.

Federal agencies would move first. The FCC, already probing whether state AI rules interfere with interstate communications under the Communications Act, would gain explicit cover to say: if an AI system touches national telecom infrastructure, federal rules win.

Expect a wave of preemption moves. Agencies like the FTC, FCC, and possibly the Department of Commerce could begin issuing guidance that any AI service trained, hosted, or inferenced across state lines falls under federal jurisdiction, not Sacramento, Austin, or Albany.

States like California will not just shrug and walk away. Within 24 hours, attorneys general in California, New York, and Massachusetts almost certainly file for injunctions arguing the order tramples state police powers and exceeds the Commerce Clause.

That fight heads straight for the federal courts. Conservative judges sympathetic to state sovereignty, plus a Supreme Court that just clipped agency power with the “major questions” doctrine, could turn this into the most consequential federalism showdown since Obamacare.

Short term, chaos wins. Companies will freeze product launches, pause rollouts in high‑risk states, and spin up parallel compliance plans: one assuming the order stands, another assuming courts gut it.

Startups get a strange kind of breathing room. Instead of tracking 1,200+ state AI bills and 100+ enacted measures, they can bet—at least temporarily—on a single emerging federal standard and stop designing 50 slightly different versions of the same product.

States will hunt for carve‑outs. Expect aggressive arguments that AI used for health care, elections, or education falls under traditional state authority, even if the underlying models run in multi‑state data centers.

If the order survives, the long game looks clear: a unified, pro‑innovation baseline that treats AI more like the internet than the auto industry. One national rulebook, fewer compliance landmines, and a regulatory environment designed less for satisfying California and more for out‑shipping China and Europe.

Frequently Asked Questions

What is Trump's 'One Rule' proposal for AI?

It's a proposed executive order to centralize all AI regulation at the federal level, creating a single rulebook for the entire country and preempting individual state laws.

Why is AI considered 'interstate commerce'?

Because AI models are often developed, trained, and inferenced in different states, and are delivered to users nationwide via the internet, which is a national telecommunications infrastructure.

How would federal AI regulation affect startups?

Proponents argue it would drastically lower compliance costs and legal complexity, allowing startups to compete more effectively against tech giants who can afford to navigate 50 different state regulations.

Will this new rule eliminate all AI safety regulations?

According to its architects, the goal is not to eliminate regulation but to decide jurisdiction. It aims to replace a patchwork of state rules with a single federal framework, while still allowing for generally applicable laws on issues like child safety.