Product Insights for Emerging Technology

20 years in emerging tech — ex-Head of Product at Palantir, now founder of two ventures.

At Common Sense, we’re building modern agentic AI systems for local government — 100x increases in productivity across critical workflows that simplify civil service and delight citizens.

At First Principles, we help emerging tech companies generate massive value, ship faster, align teams, and unlock product-market fit or hypergrowth — through fractional product leadership and expert product coaching.

Whether it serves 10,000 users or 10 million residents, the system has to work — and keep working.

Sound like I can help you? Get in touch:

adam@firstprinciples.la or adam@commonsensetech.ai

Adam Judelson

A city once invited us in for an in-person working session.

We aligned the agenda, booked the flights, and landed.

Minutes before the meeting while we prepared in a nearby coffee shop, they cancelled due to an emergency.

Most teams would have stepped back and waited for a new date.

We didn’t.

We went to the building anyway, stood in the lobby, and said, “We’re here. We know the timing shifted. We’ll stay as long as you need.” Turns out, the opportunity to get ahead outweighed the crisis.

This should be the bare minimum when working with public institutions.

Show up. Make it easy. Remove friction. Respect their time.

The difference between us and the incumbents is simple. 

We want to have a conversation. 

We want to understand the work. 

We want to sit with the people responsible for keeping the city running.

And we believe that if the government is going to get faster and more effective, it starts with partners who are willing to be in the room, see the real constraints, and prove what they can do.

Building Common Sense (govtech) | Founder, First Principles | Product Insights for Emerging Tech | Ex-Palantir | 4x Venture-Backed Exec

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I just finished one of the most talked-about books in government right now, “Breakneck: China’s Quest to Engineer the Future,” and it made me rethink something we see every day inside cities.

Wang’s core argument is that China became an engineering state, one that builds at speed and scale, while America became a lawyerly society that regulates, litigates, and creates veto points that block building.

The American “lawyerly” model has crucial advantages in protecting individual rights, constraining state overreach, and fostering a flexible, innovative society, even though it often slows physical building. He sees these legal and institutional strengths as exactly what China most needs to import, just as the U.S. should import some of China’s engineering spirit.

Local government sits right at the collision point of these two mindsets – cities need to build (housing, infrastructure, services) but operate inside a thicket of legal constraints and procedural requirements.

Most of the people I meet in government, across agencies, roles, and floors, genuinely care. They want things to work. They want residents to feel the difference.

Both types of thinking and action are crucial to progress, and the lack of understanding from these opposite optics causes the work to drag on, because every decision becomes defensive or about optics instead of outcomes.

Individuals start protecting themselves instead of the mission.

Wang’s point isn’t just about internal dysfunction – it’s about what happens when a society only optimizes for preventing bad outcomes rather than producing good ones. Local government has become very good at avoiding mistakes and needs work building things and taking bold, calculated risks. 

The question is whether we can recover some of that engineering ethos without losing the legal protections that matter to the world’s most important democratic republic.

Every city leader I meet wants faster service delivery with AI. And increasingly, the teams running those services want to deliver it.

But there’s a gap between enthusiasm and implementation.

The same AI doomerism questions come up in almost every conversation: “What if the AI makes a mistake?” “What if the AI misses something?” “What if the work or answer is incomplete?”

These are the right questions to ask. Public sector teams are accountable to their communities in ways that private companies aren’t, with a litigious public ready to pounce on mistakes. Every strong AI company must be able to explain how accuracy and quality are measured, for sure.

But it’s also worth asking: what’s the error rate in your current workflow? How much work is left entirely undone right now? How long are people waiting for answers or approvals under the status quo?

Because in most cases, the baseline isn’t perfection. It’s human staff working through hundreds of repetitive tasks under time pressure, applying convoluted legal frameworks, with inconsistent results.

The challenge isn’t that teams are being too careful. It’s that we’re measuring AI against an impossible standard while accepting a much higher error rate and set of delays from manual processes.

Government operations are excellent at managing long-tail risk.

But managing opportunity requires a different muscle to be flexed, one that involves controlled testing rather than comprehensive policy first.

AI won’t move in the public sector through white papers or policies alone. It will move through small, measured pilots that prove value in real-world conditions.

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