The Trump administration aims to unify AI regulation: from chaos in multiple states to a federal framework policy shift

White House AI Advisor Saks recently stated that the Trump administration is pushing to establish a single federal AI regulatory framework. This reflects an important policy shift: the government is no longer tolerating the current patchwork AI regulation at the state level, but instead aims to regulate this rapidly evolving industry from a top-down design perspective. Behind this move is a direct response to the long-standing issue of “regulatory fragmentation.”

Why Multi-State Regulation Has Become a Barrier to AI Innovation

According to Saks, one of the biggest threats facing current AI innovation is the “ridiculous” level of regulation. While this statement is concise, it points to a real dilemma:

• Inconsistent Rules: Different states have varying requirements for AI applications, data usage, and algorithm transparency, making it difficult for companies to develop unified compliance standards
• Policy Patchwork: States independently introduce regulations, leading to compliance costs that feel like “patching” when operating nationwide
• Innovation Hindrance: This uncertainty causes AI companies to hesitate in product design and deployment, slowing down innovation
• Competitive Disadvantage: Compared to the EU’s unified AI legislation, the US’s decentralized approach puts domestic companies at a disadvantage in international competition

This situation poses a systemic problem for the AI industry, which generally requires high standardization and cross-state operations.

The Logic Behind a Unified Federal Framework

The Trump administration’s push for a single federal framework is essentially a trade-off:

Benefits of centralized decision-making

• Reduces compliance costs for companies, providing AI firms with clear nationwide standards
• Accelerates innovation, reducing unnecessary regulatory uncertainty
• Enhances the global competitiveness of the US AI industry
• Prevents “regulatory competition” from lowering standards (where states relax requirements to attract companies)

Practical challenges in implementation

• Balancing federal authority and state sovereignty
• Divergent interests among groups (tech companies, consumer protection organizations, unions, etc.)
• Disagreements between Congress’s two parties on AI regulation
• How to design a framework that both protects innovation and manages risks

Policy Context and Signals

This stance is not isolated. According to recent reports, the White House’s Chief of Encryption is also advancing policies related to Bitcoin strategic reserves. These initiatives collectively point in one direction: the current government is more open and supportive of the tech industry, while also seeking to improve governance efficiency through unified regulation.

In comparison, some countries’ approaches offer reference points. For example, Kazakhstan recently introduced a digital asset regulation framework that adopts a classification management model—applying different rules to different asset types, maintaining flexibility while establishing basic unified standards.

From Policy Intent to Practical Implementation

It’s important to note that Saks’s statements represent the government’s policy intent. For a truly unified framework to materialize, the process will require:

• Negotiation and legislative procedures with Congress
• Coordination with state governments
• Sufficient communication with industry stakeholders
• Consensus among different political parties in Congress

This process could take months or even longer. However, the very establishment of this direction is an important market signal—the federal government is actively addressing the fragmentation issue in AI regulation.

Summary

The Trump administration’s push for a unified AI regulatory framework reflects a recognition of the long-standing problem of “regulatory fragmentation.” The core logic of this policy shift is to exchange unified standards for increased innovation efficiency and competitiveness. Regardless of how the final framework is designed, this direction indicates that the US government is rethinking how to balance innovation and regulation. For the AI industry, this could mean a transition from “fragmented governance” to “clear rules”—though the process will face challenges, this shift is a positive long-term signal compared to the current uncertainty.