In 528 AD, Emperor Justinian inherited a Roman legal system drowning in centuries of accumulated laws, conflicting edicts, and contradictory jurisprudence. His solution was radical: commission the Corpus Juris Civilis, a comprehensive codification and streamlining of Roman law across all provinces. Over 14 months, Justinian's commission reduced approximately 3 million lines of legal text to just 150,000, laying the foundation for the Western legal canon.

America faces its own Justinian moment.

Between regulations, statutes, and case law, our country has accumulated around 260 million lines of law at the federal level alone. Agencies write rules to expand their authority and incumbents lobby for regulations that raise barriers to entry. While many politicians have tried with varied success to streamline modern law, the advent of AI changes the realm of possibility entirely.

AI is capable of writing new laws and is already deployed by some states and federal agencies to identify opportunities for regulatory streamlining. In this new governance frontier, large language models are shaping American policy. Just recently, we learned that the Department of Government Efficiency (DOGE) — which thus far has focused on government spending — is now using AI to propose regulatory reductions.

AI-driven reform is exciting because republics die from institutional sclerosis. Plato and Cicero both observed this in their respective Republics. Indeed, Rome's Senate accumulated powers and procedures until effective governance became impossible, beckoning an emperor. The French Directory multiplied bureaucracies until Napoleon seemed the only solution. The Weimar Republic's financial and regulatory morass made strongman rule attractive.

Modern America exhibits its own early symptoms of regulatory morass and accompanying decay. Since 1980, regulatory accumulation has cost our economy $4 trillion in forgone annual GDP growth. The Code of Federal Regulations spans 185,984 pages containing over 1 million restrictions. Small manufacturers pay $50,100 per employee in compliance costs versus $24,800 for large corporations.

Just like in our predecessor republics, each law and regulation began with good intentions — protecting workers, ensuring safety, preventing fraud. But institutions and policies designed to solve old problems often outlive their goals and yield new, compounding problems. Nobel laureate economist George Stigler contended: "Regulation is acquired by the industry and is designed and operated primarily for its benefit."

An unlikely alliance

In America, an unlikely alliance offers a different path. Progressive economists like Ezra Klein champion abundance while conservative governors slash regulations. As left-leaning Matt Yglesias promotes red-tape reduction to promote homebuilding, President Trump endeavors to eliminate federal bureaucracy. This ideological convergence reflects the recognition that artificial scarcity driven by regulation helps no one except the rent-seekers described by Stigler.

AI offers the first realistic tool to efficiently disrupt, capture and cut the correct amounts and types of modern red tape. Unlike human bureaucrats who resist change, algorithms execute precisely. Where manual review takes decades, AI can process millions of pages in weeks.

State governments and federal DOGE embrace this reality. Virginia Governor Glenn Youngkin's Executive Order 51, issued in July, requires that all agencies use "agentic AI" to regularly review their rulebooks. This builds on the state's recent success: a 26.8% reduction in regulatory requirements, $1.2 billion in annual savings, and a $24,000 decrease in new home costs, all in just four years. Federally, DOGE's announcement referenced 100,000 federal regulations lacking statutory authority.

Tanner H. Jones is the CEO of Vulcan Technologies, the Y Combinator backed company which conducted Virginia’s AI regulatory reduction pilot program.

Patrick A. McLaughlin is a PhD economist and a research fellow at the Hoover Institution; he created the RegData and QuantGov projects, which quantify regulations using data-science tools and have informed reforms in several states.