Regulating the Tool or the Trouble? A Survey of State AI Bills
Debates about federal preemption in artificial-intelligence (AI) policy often pose a stark choice: Congress adopts a national framework and states lose the ability to police harmful conduct, or states retain broad authority and businesses face a 50-state compliance patchwork that chills innovation. Our review of state AI bills suggests the debate is aimed at the wrong target. Most state legislative activity does not regulate how AI systems are built.
To clarify terms: an AI “model” is the core software—a set of mathematical parameters (“weights”) trained on large datasets—that produces text, images, predictions, or other outputs. An AI “system” is the broader product that uses one or more models, along with interfaces, data pipelines, guardrails, and other application logic. State bills use both terms, often loosely.
Regulating the “model layer” means imposing obligations on developers about how the core software is designed, trained, or evaluated. Regulating the “use layer” means governing what people and organizations do with AI systems once they exist—how they are deployed and what conduct they enable. Roughly three-quarters of the state AI bills we examined focus on downstream uses, rather than model design or development. As a result, they are unlikely to be preempted.
A federal framework could therefore focus on model-layer obligations while preserving traditional state police powers over uses such as fraud, deception, impersonation, election manipulation, and discrimination. Under that structure, most state laws would likely survive in some form.
Two important caveats apply.
First, labeling a bill as regulating “uses” does not necessarily make it a traditional police-powers measure. A statute targeting fraud that imposes joint-and-several liability on model developers for downstream misuse may read like a conduct rule. In practice, it functions as a model-layer obligation because it forces developers to change how systems are built, not merely how they are used. States considering use-layer legislation should ask whether their proposals effectively require design changes at the foundational level. Requirements that operate this way raise the same interstate-commerce and compliance-fragmentation concerns as direct model-layer regulation.
Second, even well-intentioned use-layer rules can burden interstate commerce. A state’s label does not control the constitutional analysis. Courts look to substance, not characterization.
Our survey does not predict how courts would resolve any particular case. No descriptive coding can. It does clarify the practical stakes: fears that federal AI preemption would eliminate most state AI law do not match what states are actually enacting and proposing.