Ohio has used artificial intelligence to flag roughly 900 obsolete rules and put the state on track to eliminate about 5 million words from its regulatory code—one-third of the total—while saving an estimated $44 million and 58,000 staff hours over a decade, according to a new report from Pioneer Institute. The report, written by research intern Shriya Buche, examines how several states have begun deploying AI tools to identify outdated, duplicative, or contradictory regulations within codes that have grown so large that manual review is no longer realistic. The technology doesn't remove regulations itself but flags candidates for human policymakers to review through standard legal processes.

The scale of the problem is staggering. The U.S. Code of Federal Regulations now runs over 180,000 pages, up from just 10,000 pages in 1950, and contains more than one million regulatory restrictions across roughly 103 million words. Ohio's state code recently surpassed 17 million words. Across states, there's massive variation: California has more than 420,000 restrictions, compared to fewer than 65,000 in Arizona. Research from the Mercatus Center estimates that federal regulatory growth has reduced U.S. economic growth by roughly 0.8 percent annually since 1980—meaning the economy could be approximately 25 percent larger today if regulatory burdens had remained at 1980 levels. Studies also show that a 10 percent increase in regulatory burden is associated with about a 2.5 percent increase in the poverty rate, and nearly 69 percent of small businesses report higher compliance costs per employee than larger firms.

Virginia launched the first statewide AI-assisted regulatory review in 2025, directing the technology across its entire administrative code and all executive agencies simultaneously. The AI tools compare regulations with their authorizing laws, conduct cost-benefit analyses, examine other states' approaches, and scan "incorporated" documents referenced in regulations. About 240,000 of Virginia's roughly 335,000 regulatory requirements are contained in these incorporated documents, many behind copyrighted paywalls, meaning businesses sometimes must pay to read rules they're legally required to follow. The report notes that Virginia's initiative builds on earlier reforms that reduced regulatory requirements by about 27 percent and saved an estimated $1.2 billion annually, though those gains came from multi-year manual review rather than AI. San Francisco partnered with Stanford Law School's RegLab to build a custom AI tool that scanned roughly 16 million words of the city's municipal code, producing a 351-page ordinance proposing the deletion or consolidation of 174 separate mandates.

The report emphasizes that AI's limitations deserve careful consideration. While it can identify patterns and flag potentially problematic regulations, it can't determine whether a rule serves an important public purpose—a regulation that appears redundant may still provide valuable protections. Lt. Governor Jon Husted of Ohio, who led that state's initiative, argued that regulations accumulate over decades because no one is tasked with regularly reviewing them. The report warns that counting restrictive terms provides only a partial picture of regulatory impact, and successful reviews of existing regulations don't address the incentives that produce new rules in the first place. The question is no longer whether AI has a role in regulatory reform, but how it can be used responsibly, the report concludes. Texas has announced an AI-assisted regulatory review modeled on Ohio and Virginia's work, starting with occupational licensing rules, and a federal Senate proposal introduced in 2025 seeks to apply a similar model to the entire federal regulatory code.