Insights & Research

Rural Americans represent roughly 17% of the U.S. population — over 60 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. AI startup and venture capital activity in rural counties is characterized as “virtually nonexistent.” However you measure it, rural America is capturing a fraction of AI economic activity proportional to its population — the gap is somewhere between stark and severe depending on how broadly you draw the boundaries.

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. Rural communities have real advantages: 2 to 4 times lower operating costs than tech hubs, stronger talent retention once people can build meaningful careers locally, and environments that generate the kind of real-world edge cases that AI systems need. The issue isn't that rural America lacks potential. It's that the AI industry has been structurally set up to unconsciously ignore it.

The training data problem is more fundamental than most people realize

When an AI system underperforms in a rural context, broadband usually gets blamed. Connectivity matters, but the deeper problem is that most foundation models were built on data that treats rural America as nearly invisible.

Current foundation models draw 60 to 70% of their training data from web crawls that prioritize high-traffic, well-linked sites. Rural businesses, local governments, and community organizations have lower PageRank scores. They generate less digitized content. The curated text collections that make up another 20 to 30% of training data skew heavily toward R1 universities in major metros. The New York Times publishes roughly ten times more content about New York City than about all of Iowa.

A 2025 peer-reviewed study found that poverty-mapping AI performs significantly worse in rural areas than urban ones — not because of a technical flaw, but because those communities generate far less training data. Rural America is, in effect, statistically invisible to models built from, by, and for the web. Indigenous knowledge systems represent less than 0.1% of foundation model training data.

This creates real harm. An AI triage system might recommend immediate transport to a cardiac catheterization lab that is 120 miles from the rural critical access hospital where the patient is sitting. A weed identification tool for small farms — which the AgroBench evaluation published in July 2025 tested thoroughly — performs near random for most open-source vision-language models. Agricultural AI built for enterprise-scale precision farming, requiring $50,000 in sensors, does nothing for a diversified family operation in the San Juan Mountains. A healthcare AI trained on urban hospital data inherits assumptions about equipment, workflows, and demographics that don't transfer.

These aren't edge cases; they're the standard experience of rural AI users today.

What we're building

At the Center for Rural AI, we're working from a thesis that Fort Lewis College's AI Institute helped surface: the rural-urban AI gap isn't primarily a technology problem. It's a structural one. Rural communities lack the organized advocacy, coordinated infrastructure, training, and research programs to enable our citizens, small businesses, and institutional partnerships to claim a seat at the table where AI is being designed and deployed.

Our approach focuses on the 900-plus rural and tribal higher education institutions across the country, along with local and regional businesses and government. Colleges already sit inside their communities. They have trust. They have students who want to stay if there's something worth staying for. We're building the capacity for these institutions to become regional AI hubs, with AI Readiness Assessments designed specifically for rural contexts, open-source curricula that don't assume enterprise-scale infrastructure, and a hub-and-spoke model that lets us support hundreds of institutions and businesses without requiring each one to start from scratch.

The need is well-documented. More than 260 rural community colleges serve approximately 670,000 students annually (ACCT). According to TICAS research building on education geographer Nicholas Hillman's work, 3.1 million Americans live in education deserts — areas without a college within 25 miles — and 75% of those deserts are in rural communities. For a large share of students at these institutions, the nearest alternative is well beyond commuting distance.

Early evidence from AI adoption in higher education points to real opportunity for rural institutions specifically. Georgia State University's AI advising chatbot — studied via randomized controlled trial — reduced summer melt by roughly 4 percentage points among treated students, a meaningful outcome at an institution serving a large first-generation, lower-income population. Rural community colleges, which typically operate with minimal advising staff relative to enrollment, stand to benefit disproportionately from tools that extend institutional reach without adding headcount.

The economic opportunity argument is grounded in the same Brookings data that reveals the problem. Rural counties produce 1.5% of AI-related bachelor's degrees while representing roughly 17% of the population. They hold 0.3% of AI job postings. Applied to a U.S. AI economy that industry analysts project will exceed $1 trillion by 2030, that disproportion represents an opportunity gap well into the hundreds of billions of dollars. AI companies that understand rural markets, rural data, and rural deployment will have significant advantages as AI adoption spreads beyond major metros. Right now, almost no one is building for that.

What needs to change

Three things would move the needle.

First, the training data problem needs to be treated as an industry responsibility, not a charity project. Foundation model developers should actively partner with rural institutions, agricultural cooperatives, tribal nations, and rural health systems to incorporate representative data. This isn't altruism; it's how you build models that work for the full population. Datasets that exclude 17% of Americans will produce products that fail for 17% of Americans, and that failure has market consequences.

Second, federal agencies need implementation partners who understand rural contexts and can work at community scale. The EDA, USDA, NSF, and DOL have the funding authority. What they often lack is the rural-specific capacity to deploy it effectively. CRAI and organizations like us exist precisely to bridge that gap. The policy architecture is in place. The bottleneck is execution.

Third, rural communities need to be in the room where AI gets designed, not consulted after the fact. The RAISE AI Collaborative's approach of sitting down with rural teachers, parents, and students before building anything is a model worth replicating. The co-design principle sounds obvious when stated plainly: tools built without input from the people who will use them don't work as well for those people. But it remains the exception rather than the rule in how AI products get developed and deployed.

The choice before us

Here's what I tell communities, foundations, educational institutions, and tech companies I talk to: either rural communities help shape AI, or AI misses rural communities. The passive path leads somewhere predictable. The AI economy accelerates its geographic concentration. Rural talent pipelines drain faster as young people leave for places where the technology actually works. Communities that host AI data centers get construction jobs and property tax revenue but not economic participation. A gap that already looks like 0.3% versus 17% compounds further.

The active path requires treating this as the structural problem it is, not a connectivity challenge to be solved by the next broadband rollout. It requires AI companies to take rural data representation seriously. It requires federal programs to find implementation partners who can move at community speed. And it requires rural institutions to claim the capacity to lead, not just to receive.

That's what we're working on at the Center for Rural AI. The window to get this right is narrower than it looks.

Andrew Aitken is the Executive Director of the Center for Rural AI (ruralai.org), a 501(c)(3) nonprofit based in Durango, Colorado. CRAI is partnered with the AI Institute at Fort Lewis College.