On March 20, 2026, the White House released the National AI Policy Framework, formally placing federal preemption at the center of AI governance debates. According to multiple law firms and policy institutions, the White House subsequently entered intensive negotiations with Congress: the federal government would use "preemptive exclusion of state AI legislative authority" as a condition in exchange for advancing online safety legislation including the Kids Online Safety Act (KOSA), the NO FAKES Act, and federal age verification requirements. The structure of this transaction determines its essence——it is not a regulatory upgrade, but a meticulously packaged elimination of state authority.
The Real Logic Behind the Package Deal
According to reports, Senator Marsha Blackburn (Republican, Tennessee) is leading negotiations on behalf of the White House, with a spokesperson from her office confirming: "Senator Blackburn is leading negotiations with the White House to finalize legislative text." The choice of Blackburn is itself an anomalous signal. In 2025, Blackburn had successfully blocked the Trump administration's earlier attempt to suppress state AI regulation——her own AI bill contained a degree of state-level regulatory provisions. Now she has reversed her position to lead negotiations of the same nature, indicating that the White House offered sufficient political consideration for this deal.
The design of this "consideration" is extraordinarily intricate: it bundles AI deregulation with child protection into a single legislative package. Progressive lawmakers advocating for children's online safety who want KOSA must simultaneously accept federal AI preemption; conservatives supporting AI deregulation who do not want to advance KOSA would find it politically difficult to explain why they are rejecting child protection. This dilemma structure is not a byproduct of policy design but the core of the negotiating strategy.
The age verification requirement further intensifies the contradiction in this logic: it mandates users to prove their age by uploading government identification documents, undergoing facial scans, or behavioral analysis——a regulatory density far exceeding any current state AI regulation——yet it is being advanced within a "deregulation" framework. The hand claiming to reduce AI regulatory burdens is simultaneously installing new federal control gateways in another direction.
Fragmentation Crisis: The Real-World Trigger for Federal Intervention
Numerically, the pressure for federal action is real. As of 2025, all 50 states and territories had introduced more than 1,000 AI-related bills, with California, Colorado, Texas, and Utah having passed state-level AI laws affecting the private sector. According to TechPolicy.Press data, as of July 1, 2026, states had enacted 109 AI laws and 28 data center-related laws. In the first six weeks of 2026 alone, 30 states submitted more than 300 data center regulatory proposals, covering electricity cost pass-through, water usage audits, carbon emission disclosures, and more.
White House AI "czar" David Sacks described the situation as "an increasingly chaotic patchwork of 50 different state regulatory regimes that could stifle innovation and endanger America's leadership in the AI race." This assessment is not entirely without basis: businesses operating under 50 different compliance requirements face real cost accumulation effects. But Sacks's analysis skips a critical question: why have state legislative efforts converged so densely toward tightening in the first place?
The answer is not hard to find. AI data centers' energy consumption is expanding far faster than grid planning cycles, and some states have already seen cases where surging data center electricity demand has driven up residential electricity prices. The real driving force behind state legislation comes, to a considerable degree, from citizens' electricity bills——not abstract "regulatory impulses."
Energy Consumption Oversight: The Most Overlooked Casualty
The National AI Policy Framework explicitly opposes three categories of state action: regulating the AI development process, penalizing AI developers for third-party misuse of AI products, and excessively restricting Americans' use of AI in lawful activities. The framework also specifically opposes state measures restricting energy-intensive AI data centers.
The framework preserves states' authority over "data center siting," yet establishes federal preemption provisions targeting state laws that "excessively restrict" data center energy consumption——the boundary between the two is extremely blurred. According to Multistate data, the mainstream direction of state data center legislation in 2026 has shifted from tax incentives to taxpayer protection and energy audits, requiring data center operators not to pass electricity costs on to residents. If federal preemption takes effect, the legality of such provisions would enter a highly uncertain zone.
AI data centers are currently one of the fastest-growing sources of electricity consumption globally. With the federal level having no intention of establishing an independent AI regulatory agency while simultaneously clearing away state-level regulatory authority, responsibility for energy consumption issues will fall into a substantive regulatory vacuum.
The Framework's Substantive Boundaries: What Is Preserved
The White House framework is not a blank check. It explicitly preserves states' enforcement authority in the following areas: protecting minors, preventing fraud, protecting consumers, and states' decision-making power over procuring AI tools and internal AI use by state governments. The framework also recommends establishing a "regulatory sandbox" at the congressional level to provide controlled testing environments for AI applications, reducing compliance pressure during early development stages.
However, this "limited concession" faces three layers of resistance in political practice.
First, the exemption boundary for "child protection" is unclear. California's AI Transparency Act, effective August 2, 2026, requires generative AI platforms with more than one million California users to provide free content detection tools and add visible disclosure labels——does this fall under "general law enforcement for child protection"? The act currently contains no provisions specifically targeting minors, and if federal preemption takes effect, its legality would immediately face challenge.
Second, even state laws advanced by Republicans may not be exempt. Professor Saurabh Vishnubhakat of Yeshiva University's Cardozo School of Law noted that federal law "could preempt parts of Texas's AI regulations while preserving others"——Texas is a Republican-governed state. In other words, this knife does not only cut toward blue states; it cuts toward any state legislation that touches AI development liability.
Third, the office of California Governor Gavin Newsom responded bluntly: "Trump is once again attempting to repeal California's laws protecting resident safety and consumers——this is a core responsibility of the state." California is the state with the largest GDP in the U.S., and the impact of its AI legislation effectively extends nationwide, because companies tend to design products to meet the strictest requirements. Losing California as the "regulatory benchmark" would mean federal standards become the de facto floor rather than the ceiling.
Congressional Obstacles and Multiple Legislative Tracks
The White House framework cannot automatically become law; it requires congressional legislation. Currently, there are at least two parallel tracks.
The first is the 269-page bipartisan discussion draft, the Great American AI Act of 2026, jointly proposed by Representatives Jay Obernolte (Republican, California) and Lori Trahan (Democrat, Massachusetts). The proposal imposes a three-year preemption period on state laws for "large frontier developers" (companies with annual revenue exceeding $500 million that have trained frontier models), while attaching a certain degree of developer obligation requirements. It is currently still in the public comment phase and has not been formally introduced.
The second is the White House swap package led by Senator Blackburn, bundling AI preemption with KOSA and other bills. This path is more complex in political arithmetic, but if negotiations succeed, legislation could move faster.
According to technology policy institutions, the legislative window for the current Congress is narrowing, and the approaching November 2026 midterm elections will bring congressional agendas to a standstill. Tech companies have invested tens of millions of dollars in lobbying, targeting candidates who support AI regulation. This flow of money is one of the contextual factors behind the accelerated pace of current negotiations.
Independent Assessment
The core of this federal-state power struggle is not the technical question of whether "uniform regulation can promote innovation," but rather: under the premise of explicit federal regulatory absence (no independent AI regulatory agency, relying only on existing institutions), who will fill the regulatory vacuum left by cleared-out state rules?
The White House framework's answer is: market self-discipline plus industry standards. This answer has been historically tested across multiple industries, from the 2008 financial crisis to the 2010 Deepwater Horizon incident. In the AI domain, the difference is that systemic risk propagates far faster than regulators' learning curve.
Using child protection legislation as political packaging is itself a signal. True policy designers would not need to bundle deregulation bills with child safety bills——the purpose of such bundling is to force opponents to pay a reputational cost for "opposing child protection" at the ballot box. When a policy can only advance through bundling, it usually means it cannot withstand public interest scrutiny in independent debate.
As of late August 2026, the specific legislative text has yet to be finalized, and the congressional window is closing. But regardless of whether the current Congress can complete legislation, this battle has already accomplished an important task: it has moved "federal AI preemption" from policy discussion to specific provisions in legislative negotiations. The next Congress will start from this point when facing the same issue, rather than from zero.
© 2026 Winzheng.com 赢政天下 | 转载请注明来源并附原文链接