In the absence of a federal moratorium, the American policy on AI is expected to become increasingly fractured.
In the absence of a federal moratorium on state-level AI regulation, the United States is facing a complex and inconsistent regulatory landscape for artificial intelligence (AI). This patchwork of state laws, each with its own unique set of requirements, is causing confusion for consumers and businesses, slowing innovation, and imposing heavy compliance burdens.
One of the most significant issues arises from the broad definition of "high-risk" and wide scope of risks in SB 420, a California bill, which is influencing compliance strategies at the national scale. The extensive pre-deployment requirements and detailed impact assessments before releasing AI systems to the public create significant hurdles and costs for developers.
Similarly, New York’s Responsible AI Safety and Education (RAISE) Act targets advanced AI models and imposes sweeping requirements, such as maintaining detailed documentation, conducting third-party audits, and reporting incidents within 72 hours. Violations can result in civil penalties of up to $10 million.
This regulatory fragmentation is expected to lead to a patchwork of AI laws across states, creating confusion for consumers, complicating compliance for developers, and increasing business costs. Companies might face operational and legal challenges dealing with diverse and potentially conflicting state regulations, which could hinder AI innovation and deployment speed.
The lack of a coherent national AI framework means Congress will need to continue to address AI policy without the breathing room a pause would have provided to develop a consistent federal regulatory framework. The ultimate goal is to effectively coordinate AI regulation at the federal level to foster innovation and establish regulatory clarity across state lines.
China, on the other hand, regulates AI at the national level through centralized standards issued by the Cyberspace Administration of China (CAC). The CAC's 2025 Measures for Labeling AI-Generated Content apply uniformly to all providers, providing a clear rulebook that is lacking in the U.S.
Congress should consider a federal moratorium on state AI laws, particularly those that impact other states, for as long as politically feasible. Such a moratorium could have helped avoid the current confusing legal environment and allowed Congress to design a national framework that protects consumers without unnecessarily restricting innovation.
While large corporations may manage the added complexity, they may also find themselves building out large legal teams instead of investing more in engineering. The state-by-state regulatory framework for AI in the U.S. is particularly challenging for startups and smaller firms to comply with.
In summary, while the rejection of a federal moratorium means the U.S. will pursue a decentralized approach to AI regulation with states independently enacting diverse rules, concerns about fragmentation, regulatory complexity, and slowed innovation in AI technologies persist. A unified approach to AI governance that promotes innovation and establishes regulatory clarity across state lines is crucial for the U.S. to stay competitive globally.
- The inconsistent AI regulations across various U.S. states are influencing data management strategies at a national scale, as the broad definition of "high-risk" and varying requirements create complications for developers.
- The advancement of AI technology is subject to heavy compliance burdens due to the detailed pre-deployment requirements and incident reporting regulations in bills like California's SB 420 and New York's RAISE Act, which increase costs and pose significant hurdles for AI innovation.
- In contrast, China regulates AI centrally through the Cyberspace Administration of China (CAC), implementing uniform standards and providing a clear rulebook that fosters innovation and minimizes regulatory complexity, unlike the current state-by-state approach in the U.S.
