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U.S. Representative Pramila Jayapal (WA-07) has introduced a legislative framework titled the 'National AI Charter Act.' The proposal mandates that all AI companies operating within the United States must obtain a federal charter, a requirement modeled after regulatory systems used for national banks and the oversight of nuclear materials. Under this framework, companies would be subject to continuous federal oversight, mandatory pre-release safety testing, and structural requirements designed to prevent market concentration and data exploitation.
Representative Pramila Jayapal announced the National AI Charter Act framework on October 1, 2026. The proposal seeks to treat AI companies similarly to entities in highly regulated sectors like banking or nuclear energy, where a federal charter is a prerequisite for business operations.
The framework includes provisions to ban specific practices, such as mass surveillance for data collection and the exploitation of worker rights. It also proposes structural changes to the AI market to prevent the consolidation of power among a few 'Big Tech' firms.
According to the announcement, the charter would not preempt existing federal, state, or local laws. Instead, it is intended to provide a new enforcement mechanism that allows regulators to hold companies accountable for violations before significant public harm occurs.
The framework was developed following a congressional hearing hosted by Representative Jayapal regarding the risks of surveillance-based AI models.
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The proposed framework represents a significant shift in legislative strategy by moving away from single-issue AI regulation toward a comprehensive, structural approach. By conditioning market access on a federal charter, the bill seeks to establish preemptive that hold AI firms accountable for systemic risks, including surveillance, labor exploitation, and the use of private data for model training. This approach aims to address the root economic and structural incentives that drive AI development, potentially altering how major technology companies deploy AI products in the U.S. market.
The proposal is notable for its attempt to address the 'root profit drivers' of AI development. By linking the right to operate to a public charter, the framework attempts to force companies to prioritize public interest over shareholder profit.
The framework has received support from various advocacy groups and legal experts, including former officials from the Federal Trade Commission and the Department of Justice, who argue that current self-regulation models have failed to protect consumers.
If enacted, this would represent a major departure from the current U.S. approach to AI, which has largely relied on voluntary safety accords and sector-specific guidelines. The mandatory nature of the charter would create a high barrier to entry for companies that do not comply with federal safety and privacy mandates.
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다음에 무엇을 볼 것인가
The legislative path for the National AI Charter Act remains uncertain, as it is currently a framework rather than a formal bill introduced for a floor vote. Observers should monitor whether this proposal gains bipartisan support or if it faces significant opposition from industry lobbyists. Additionally, the specific mechanisms for 'structural separation' of the AI stack and the criteria for charter revocation will be critical details to watch as the framework potentially moves toward formal legislative drafting.
The primary unknown is the specific legislative language that will define the 'terms and conditions' of the charter. The framework currently lacks details on the exact metrics for safety testing or the specific thresholds for what constitutes a 'risky' model.
Industry reaction and the potential for legal challenges regarding the scope of federal authority over AI development will be key factors in the bill's viability.
The proposal's emphasis on 'structural separation' of the AI stack suggests a potential conflict with current business models of major AI developers, which may lead to intense lobbying efforts against the framework.