Insights

This summer, I had the opportunity to be a Colorado Thrives AI Fellow, hosted by the Center for Rural AI (CRAI) alongside a fellow CRAI colleague. The program began with training in Denver with Anthropic's Beneficial Deployment team and CodePath, followed by a ten-week build period with weekly CodePath mentorship meetings to complete our projects. After the training, we set off to develop systems that would help our organization advance its mission.

This summer I was one of twelve Colorado Thrives AI Fellows, a new program run with Anthropic and CodePath that pairs students with Colorado organizations for a summer of real AI work. I did mine through my employer, the Center for Rural AI. My colleague Emaliah Sawyer and I were CRAI's two fellows. The fellowship gave me weekly mentorship through CodePath, hot-seat sessions in front of the other fellows, technical reviews with Anthropic staff, and a showcase in Denver at the end. Most of all it gave me a project to carry from idea to something the team uses every day.

America is not entering the AI economy as one country. It is entering as two.

One economy is concentrated in a handful of metropolitan corridors where AI capital, talent, research, infrastructure and policy influence reinforce one another. The other includes rural towns, small cities, tribal nations, agricultural communities and resource regions that remain largely outside the institutions shaping AI's future.

Five Questions We're Learning to Ask at CRAI

At the Center for Rural AI, we spend a lot of time building with artificial intelligence. That has also meant spending a fair amount of time taking things apart.

We've watched AI make complicated work remarkably easier. We've also watched it generate unnecessary architecture, follow the appearance of a process rather than its intent, confidently report success without adequate evidence, and obscure substantial technical authority behind a simple conversational interface.

An Interview with Jason Strickland, Head of Technology

The conversation that started CRAI's open-source AI grant writing tool for nonprofits happened over coffee. Jason Strickland had recently stepped away from a corporate job, looking to plug into something with more community purpose. A connection through mutual contacts brought him to CRAI, and a specific problem was already waiting: could he build something to help with grant research?

Every AI answer is a claim to be tested, and what better way than using another model for verification?

Rural people learned a long time ago that trust is something you earn, not something you hand over. You check the hours on a used tractor before you buy it. You read the water-rights agreement twice before you sign. You call a neighbor who has done the job before you start it yourself. Verify first, rely second. It is a habit born of distance and thin margins, and it's exactly the discipline the AI economy needs right now.

There's an ice cream shop near where I live in Durango that's been around quite a long time. On a summer Saturday, the line runs out the door and halfway down the block.

When you get to the front, you pay on a register that looks like it was salvaged from a 1940s soda fountain. Cash only. No receipt unless you ask. The owner has been running it this way for decades and sees no reason to change.

Rural Americans represent roughly 14% of the U.S. population — about 46 million citizens. According to Brookings Metro's July 2025 analysis of national AI activity, rural counties account for 0.3% of U.S. AI job postings, 0.3% of AI patents, and 1.5% of AI-related bachelor's degrees.

I started the Center for Rural AI because I believe this is one of the most consequential economic challenges in the country right now, and because I think the conventional framing around it is wrong. The dominant narrative treats rural communities as disadvantaged recipients who need urban tech companies to charitably extend their solutions outward. That framing gets the problem backward.