Answer extracted from the 3 Takeaways™ podcast — listen to the full episode below.
How should government rethink AI funding to actually transform public services?
Government must stop treating AI as a simple IT line item and instead allocate resources to real, sizable programs targeting concrete mission problems. The shift transforms how citizens experience everyday services—from the DMV to tax filing—making each interaction incrementally better, which compounds into a fundamentally different relationship with government over time.
From IT budget to mission-driven programs
The core issue is structural: most government agencies fund AI as infrastructure, not as a tool solving real problems for real people. This approach fragments spending across generic tools and pilots that never scale. Mulligan argues for flipping the funding model entirely—allocating resources to specific, sizable programs that directly address citizen-facing pain points.
This reframing matters because it shifts the question from "What AI tools do we buy?" to "What citizen problem are we actually solving?" As Katrina Mulligan explains in the episode, the difference is between acquiring technology and deploying it with purpose.
The cumulative impact of small improvements
Mulligan's vision is not about one breakthrough moment. It's about making each government interaction slightly more positive—reducing wait times, clarifying confusing forms, personalizing responses based on individual circumstances. When multiplied across dozens of interactions over a year, these small improvements reshape how people perceive government itself.
Real citizen-facing services like the DMV, tax preparation, benefit applications, and license renewal are the testing grounds. Each one involves frustration points where AI can reduce friction. The point is not perfection; it's the tangible accumulation of better experiences that changes public trust and perception over months and years.
For a deeper look at how Mulligan sees government and frontier AI competing on vastly different timescales, the full conversation on Listenly covers her transition from the Pentagon to OpenAI and what she learned about organizational speed.
"I honestly think that I am at least 30% more effective, maybe more than I was a year ago, because of how I've matured my use of these tools. I'm a better leader. I'm a better manager."
Katrina Mulligan — Head of National Security Partnerships at OpenAI. Previously served in senior roles at the Department of Defense, National Security Council, and Department of Justice, including as number two at the Pentagon overseeing special operations. Her dual vantage point inside both Washington's national security structures and frontier AI development makes her uniquely positioned to bridge policy and technology deployment.
See also
How far ahead is the United States from China in AI development, and is that lead secure?
Mulligan estimates the U.S. has opened up its lead to a single-digit number of months — approximately six to eight months ahead, up from roughly four to six months previously.
How does China's approach to AI differ from the United States, and could America build the best AI and still lose?
Mulligan identifies three key differences: China has much higher public trust in AI, the Chinese government and CCP have a more direct seat at the frontier of AI development, and China's willingness to deploy AI without the same safety constraints as the U.S. gives it strategic advantages.
What is a 'model garden' and why does Katrina Mulligan consider it a mistake in AI adoption strategy?
A model garden is the practice of making 12 to 20 different AI models available in a single place so employees can choose among them. Mulligan argues this creates confusion, slows adoption, and diverts resources from building real organizational capability with a focused tool.
Key takeaways
- Stop funding AI as an IT line item; allocate resources to real programs targeting specific citizen-facing mission problems.
- Small improvements across multiple government interactions compound over time into a fundamentally transformed public experience.
- Services like the DMV and tax filing are the proving ground where AI can reduce friction and rebuild public trust through incremental, tangible gains.
- Mulligan's own productivity increase of 30% through AI tools demonstrates the personal and organizational impact of mature, purposeful implementation.