The AI Economy Is Leaving Rural America Behind. We're Going to Change That.

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. 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 relative to its population.

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: an estimated 3x 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 and generate less digitized content. 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. 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 120 miles from the rural critical-access hospital where the patient is sitting. A weed-identification tool for small farms 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. 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. Our approach focuses on the 1,000-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, and 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 for rural contexts, open-source curricula that don't assume enterprise-scale infrastructure, and a hub-and-spoke model that supports many institutions without requiring each 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. Early evidence from AI adoption in higher education points to real opportunity: Georgia State University's AI advising chatbot, studied via randomized controlled trial, reduced summer melt by roughly 4 percentage points among treated students. 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.

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 — datasets that exclude 14% of Americans produce products that fail for 14% of Americans, and that failure has market consequences. Second, federal agencies (EDA, USDA, NSF, DOL) have the funding authority but often lack the rural-specific capacity to deploy it; CRAI and organizations like us exist to bridge that gap. Third, rural communities need to be in the room where AI gets designed, not consulted after the fact.

The choice before us

Here's what I tell communities, foundations, educational institutions, and tech companies: 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, and a gap that already looks like 0.3% versus 14% 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.

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 Founder and Executive Director of the Center for Rural AI (ruralai.org), a fiscally sponsored project of SW Community Foundation based in Durango, Colorado. CRAI is partnered with the AI Institute at Fort Lewis College.