AI to Write Laws and Regulations
We are deleting red tape and replacing expensive consultants with AI tools that empower elected officials to actualize the public will and interest.
Virginia Required Our Product by Law
Elected officials — representatives, senators, governors, and presidents — write laws and executive orders to actualize their campaign promises and, in theory, serve the public will and interest. These newly-minted laws and executive orders are then passed to regulatory agencies in the executive branch, occupied by thousands of bureaucrats and contractors, who write regulations and guidance documents designed to implement laws and executive orders.
In the status quo, the regulatory implementation process is sluggish and highly bureaucratic. Each regulatory change must cohere with all existing state law, federal law, and case law; otherwise, agencies risk litigation and the loss of their budgets. Consequently, agencies spend millions of dollars and years hiring consultancies and law firms just to double-check that their proposed implementation of laws and executive orders is actually lawful.
Each step in this onerous and years-long iteration loop creates new arenas for political conflict, wastes taxpayer dollars, and means that the public will, exercised by elected officials, is not actually implemented.
Our agentic scraper reflects the entire American legal corpus (statutes, regulations, case law, in all jurisdictions) and our proprietary AI knowledge graph maps relationships between legal nodes. Examples: court precedent nullifies federal law, federal law supersedes state law, federal funding demands state compliance, etc.
Our system:
We enable elected officials and their appointees to execute policy changes without wasting time and tax dollars on legal complexities and consultant dependencies.
We came together because we believe AI can and should strengthen America's democratic institutions.
Tanner turned down Harvard Law School and left his job as Technology and Regulatory Policy Director at Joe Lonsdale's Cicero Institute where he worked on AI and regulatory policy in 30 states. Previously he worked for Andrew Yang's presidential campaign and wrote his Dartmouth thesis about the "ontology of American regulation."
Alek dropped out of Dartmouth where he studied Machine Learning, was a chess champion, and a recipient of the Jack Byrne grant for Dartmouth's top math students. Alek also worked as an AI engineer at several startups before founding Vulcan.
Chris left Google where he built ML infrastructure for Gemini and Waymo. Before Google, Chris studied CS and Philosophy at Princeton. Chris became passionate about regulatory reform and leveraging AI to improve policy after following SF politics and the emergent abundance movement.
We see a dialectic in AI and government: totalitarianism (algorithmic control and surveillance undermining freedom) vs. humanism (AI empowering innovation and responsive government, unlocking unprecedented freedom, creativity, and individualism).
At Vulcan, we are building the humanist future — tools that empower elected officials to serve the public interest without bureaucratic capture.
Our core belief is this: as AI gets better, our Republic should get better too.
Contact: founders@vulcan-tech.com
Website: www.vulcan-tech.com