I’m thrilled to sit down with Donald Gainsborough, a political savant and the visionary leader at the helm of Government Curated. With his extensive expertise in policy and legislation, Donald has been a driving force in integrating cutting-edge technology into state governance. Today, we’re diving into Virginia’s groundbreaking initiative to streamline regulations using artificial intelligence, exploring how this technology is reshaping government efficiency, the innovative startup behind it, and the broader implications for public policy. Our conversation touches on the nuts and bolts of AI in regulatory reform, the challenges of implementation, and the potential for this approach to transform governance at every level.
Can you walk us through what agentic AI is and how it stands out from other forms of artificial intelligence in the context of government work?
Agentic AI is a type of artificial intelligence that doesn’t just process data or follow preset rules—it acts with a degree of autonomy to achieve specific goals. Unlike traditional AI, which might analyze data and provide insights, agentic AI can make decisions, adapt to new information, and execute tasks with minimal human oversight. In government work, like Virginia’s regulatory streamlining, this means it can sift through thousands of pages of laws and regulations, identify inconsistencies, and even suggest actionable reforms. It’s like having a tireless, hyper-efficient staff member who can think on their feet.
What inspired the decision to use agentic AI for streamlining regulations in Virginia, and what were the key goals behind this initiative?
The decision came from a recognition that Virginia’s regulatory framework had become overly complex and burdensome over time. Governor Youngkin wanted to cut through the red tape to make the system more efficient for businesses and citizens alike. The key goals were to reduce unnecessary regulations—already down by over 26% thanks to earlier efforts—and to speed up policy implementation without relying on costly external consultants. Agentic AI offered a way to do this systematically, with precision and at a fraction of the usual cost, while ensuring elected officials could directly enact the public’s will.
Can you tell us about the startup leading this effort and how they managed to secure such a significant contract so soon after being founded?
Vulcan Technologies, founded just this year, is at the forefront of this project. They’re a small but incredibly ambitious team of Ivy League graduates who built an AI tool to map and analyze vast bodies of law. Their rapid success comes down to a combination of innovation and hustle. They pitched with a passion for solving structural problems in government, undercutting much larger competitors by offering their services at a significantly lower cost. Their involvement in the Y Combinator program also gave them the mentorship and visibility to refine their product and make a compelling case to Virginia’s leadership.
How does this AI technology actually work to analyze and streamline Virginia’s regulations?
The process starts with pulling all of Virginia’s regulations, statutes, and case law into a centralized database. From there, Vulcan’s AI agents comb through the text, mapping out connections and overlaps. They can spot discrepancies—like a fine in a statute differing from what an agency enforces—and flag whether a regulation is explicitly legislated or just agency-driven. The system uses what’s called an agentic scraper to keep the database current, automatically tracking changes in laws or rules as they happen. It’s a dynamic, ongoing process that ensures nothing slips through the cracks.
What are some of the biggest hurdles faced in rolling out this AI-driven approach to regulatory reform?
One major challenge is the sheer volume and complexity of existing regulations. Getting all that data into a usable format was a huge undertaking, and ensuring the AI interprets legal language correctly requires constant fine-tuning. There’s also pushback from some quarters—agencies can be protective of their authority, and there’s skepticism about relying on AI for something as nuanced as law. Plus, there’s the risk of over-automation; you don’t want to strip out necessary regulations just for the sake of cutting numbers. Balancing efficiency with accountability is a tightrope walk.
How do you see AI tools like this changing the way elected officials navigate bureaucracy and implement policy?
These tools are game-changers. They empower elected officials to bypass much of the traditional bureaucracy—delays, red tape, and the hefty price tags of consultants or law firms. With AI, a governor or legislator can get a clear picture of the regulatory landscape almost instantly and make informed decisions to enact their agenda. It’s about cutting through the noise and getting back to the core of representative governance: translating voter mandates into real action without getting bogged down by unelected gatekeepers or endless process.
What’s your forecast for the future of AI in regulatory reform across state and federal levels?
I’m optimistic but cautious. If Virginia’s experiment proves successful—and early results are promising—I think we’ll see a wave of adoption across other states and even at the federal level. The potential to save billions in costs while making government more responsive is hard to ignore. But it’ll require careful oversight to avoid unintended consequences, like eroding necessary protections or over-relying on tech at the expense of human judgment. Over the next decade, I expect AI to become a standard tool in governance, reshaping how we think about efficiency and accountability in public policy.
