The United States Code of Federal Regulations, towering at over 145 million words, represents one of the most complex and unwieldy legal systems in human history. However, like poorly maintained software, this vast corpus of legal architecture has become bloated with redundancy, obsolescence, and provisions that are no longer relevant or invoked.
The solution? A sweeping overhaul of the U.S. legal system, leveraging the unprecedented capabilities of artificial intelligence. Today, with the creation of the Department of Government Efficiency (DOGE), America has a chance to “refactor” its code base.
Refactoring is a term well-known in software engineering. It refers to the process of restructuring existing computer code to make it cleaner, more efficient, and easier to understand—all without altering its essential functionality. Applying this concept to America’s legal framework would revolutionize governance. AI can identify “dead code” by analyzing billions of words of court proceedings, legal filings, and administrative rulings to determine which statutes and regulations are never cited or applied in practice.
This approach isn’t about politics—it’s about data. Unlike ideologically charged regulatory overhauls, AI operates apolitically as it works through computation. If a law hasn’t been cited in decades or lacks relevance in modern governance, it can be marked for deprecation, much like unused functions in a software library.
Critics might argue that such a proposal risks throwing out the proverbial baby with the bathwater. What if a law is dormant but still relevant? AI cannot operate in a vacuum; it must work in tandem with legal scholars, lawmakers, and practitioners who understand context. Refactoring is not deletion—it’s an iterative process. Flagged provisions would undergo review, ensuring essential laws remain intact.
Others may worry about entrusting this process to machines. But humans have long been overwhelmed by the scale of the U.S. Code. Every administration promises to cut red tape; few deliver results. The task is simply too vast. AI offers a way forward, not as a policymaker, but as a tool for managing complexity at a scale beyond human capacity.
The private sector is already there. Companies routinely use AI to manage sprawling corporate policies and compliance requirements. Software engineers deploy refactoring tools to keep codebases lean. If these principles work for Fortune 500 companies, why not apply them to the U.S. government?
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