White House and House GOP Move to Pre-empt State AI Laws Amid Regulatory Confusion

The White House and House Republicans are preparing federal legislation that would block state governments from enacting their own artificial intelligence regulations, according to reporting from The Washington Post, as confusion over who should regulate AI deepens across multiple levels of government.

The move comes as state legislatures nationwide struggle to craft coherent AI policy. A recent survey of Ohio lawmakers found 38 percent remain undecided on whether AI should be used more in government operations, while 39 percent are uncertain about student use of the technology in schools. Despite this ambiguity, Ohio House Bill 524 would impose penalties on developers who deploy AI models that encourage harm, while Tennessee's SB1493 proposes Class A felony charges for certain AI-related violations.

The partisan divide over regulation is pronounced. The Ohio legislative survey revealed sharp disagreement between Democratic and Republican members on regulatory approaches, though specific positions were not detailed. Meanwhile, Rep. Jay Obernolte (R-Calif.) framed federal action as necessary to prevent the United States from falling behind China in AI development, stating in support of separate biodata legislation that "we currently lack common standards for biological data, which limits our ability to fully harness AI and puts us at risk of falling behind competitors like China."

Critics of state-level AI laws argue many proposals are poorly constructed and overreaching. Forbes contributor Lance Eliot highlighted Tennessee's proposed law as a "doozy," noting that its provisions reflect a fundamental misunderstanding of how AI systems function. The legislation assumes AI can only perform actions for which it was explicitly trained, ignoring the reality that pattern-matching algorithms can generate harmful outputs through computational variation during active use, not just from initial training data.

(The federal pre-emption effort may include provisions related to child safety, according to the Washington Post report, though specific details have not been disclosed. The White House did not provide comment for this story.)

The regulatory uncertainty extends beyond U.S. borders. In China, the government has moved to restrict certain AI agent applications amid privacy concerns, despite the country's expansive surveillance infrastructure. "China has surprisingly had a pretty significant privacy awakening over the past half-decade," said Samm Sacks, a senior fellow at New America who studies Chinese tech policy. "It calls into question all of these core concepts of China's privacy law." Major Chinese platforms including WeChat and Alipay have aggressively promoted AI agents to users this year, with Tencent hosting free events in Shenzhen to help hundreds of users install agentic AI tools.

The push for federal pre-emption reflects a broader tension between rapid technological development and the slow pace of legislative response. Robert Herjavec of ABC's "Shark Tank" predicted that regulating AI will present challenges for the next five to ten years because "AI technology is moving so quickly and governments are still figuring out how to respond to it." Investment banker Jeff Cook, whose firm has advised on more than 50 government technology deals, argued that AI's impact on the sector "is not a zero-sum game" and will create value alongside traditional software rather than replacing it entirely.

The debate over federal versus state authority in AI regulation mirrors historical conflicts over technology governance, from telecommunications to data privacy. California's Consumer Privacy Act forced national companies to adopt stricter data practices, demonstrating how state action can shape industry standards even without federal mandates. The current federal pre-emption effort would reverse this dynamic, centralizing regulatory authority in Washington at a moment when neither state nor federal lawmakers have demonstrated clear consensus on appropriate AI governance frameworks.