The administration released its National Policy Framework for Artificial Intelligence in March, offering Congress seven legislative priorities meant to establish the United States as the dominant force in global AI development. For a country moving fast on AI, a coordinated legislative framework is better than no framework at all.
But frameworks reveal priorities through omission as much as inclusion. When read carefully, a stark gap emerges: Rural America, home to 60 million citizens and roughly 17% of the U.S. population and more than 90% of the landmass, is almost nowhere to be seen.
What the framework actually says
The document’s seven pillars address protecting children, safeguarding communities, respecting intellectual property, preventing censorship, enabling innovation, developing an AI-ready workforce and establishing federal preemption of state AI laws. Within those pillars, rural communities receive just three mentions: grants and technical assistance for small businesses, a single reference to land-grant institutions, and a provision that protects residential ratepayers from data center construction costs.
There is no acknowledgment of geographic equity in workforce development. Tribal nations and Indigenous data sovereignty are absent entirely. Community colleges, regional universities and rural health care systems go unaddressed. Agriculture does not appear once.
Why this matters
This is not an inconsequential oversight. According to Brookings Metro research, the top 30 metro areas account for over two-thirds of all AI job postings, while rural counties lag on every measured dimension of AI readiness: talent, innovation and adoption. The framework, as written, does nothing to close that gap. In several respects, it could widen it.
The preemption provision is the clearest example. The framework calls for Congress to override state AI laws that impose undue burdens. Colorado has been among the more forward-looking states on AI governance, with provisions that create accountability for algorithmic decisions affecting communities with limited political and economic leverage. Federal preemption, without replacing those protections with federal equivalents, removes a layer of accountability that rural communities were just starting to implement.
The workforce section is similarly thin. It recommends incorporating AI training into existing education and training programs, which is reasonable guidance for workers with reliable internet, nearby training providers and employers in the same labor market. For a rural health care worker, a seasonal agricultural worker or a student at a tribal college, however, existing programs do not include them and were never designed to do so.
What a real framework would do
Rural America needs to be treated as a full participant in the AI economy. Congress should make connectivity a prerequisite. No AI workforce program, small-business tool or technical-assistance initiative can function without broadband or satellite. Tie AI investment programs to connectivity baselines and enforce them.
Congress should fund land-grant institutions to deliver, not just research. The single reference to land-grants is the most promising element in the document, but it is wildly underspecified. Land-grants, including those with Native American-serving missions, already operate in rural communities nationwide. Fund them explicitly to provide AI technical assistance to small businesses, school districts and local governments.
Congress should recognize tribal nations as sovereign stakeholders with distinct data governance priorities and economic development strategies.
Congress should require that foundation model developers receiving federal contracts or incentives demonstrate meaningful rural data representation in their training sets. AI systems trained overwhelmingly on urban data produce outputs that reflect urban assumptions, and rural communities bear the downstream cost in the form of tools that misread their labor markets, health care needs, and agricultural and financial realities. A federal AI framework serious about geographic equity would set standards for training data provenance, not just workforce programs.
And Congress should set geographic equity requirements for every federal AI investment program. Without them, grants and incentives will flow to the markets of least resistance: urban, coastal, already-resourced.
The bottom line
American AI leadership built on a foundation that excludes 60 million rural citizens is fundamentally inadequate. Rural communities want and deserve full participation in one of the most transformative and significant technological changes ever.
Andrew Aitken is a Silicon Valley and startup veteran with more than two decades of experience advising organizations including the White House, Capital One and Microsoft. He is founder and executive director of the Center for Rural AI, a Durango-based nonprofit, a strategist at Fort Lewis College’s AI Institute, and an adviser to multiple venture-backed AI startups. He lives in Durango with his wife and daughter. Visit ruralai.org