Local governments publish a steady stream of authoritative information: press releases, emergency notices, service updates, and policy changes that apply to a specific place, at a specific time.
Yet when residents ask AI systems questions about those same topics, the answers are often wrong, outdated, or oddly national in tone.
This isn’t because local governments are failing to communicate. It’s because AI systems interpret authority differently than humans do.
The Flattening Effect
Large language models don’t read websites the way people do. They evaluate information by weighing signals such as authority, structure, recency and jurisdiction across many sources at once.
When multiple sources discuss similar subjects, AI systems tend to favor national or federal guidance, state-level summaries or older but well-structured documents.
Local updates, even when accurate and timely, can be treated as supplemental rather than decisive. The result is a flattening effect where local nuance disappears into broader answers.
Why “Official Website” Isn’t Always Enough
For people, publishing on an official .gov site is a clear signal of authority.
For AI systems, that signal is necessary, but no longer sufficient on its own.
Models look for clarity around jurisdiction, finality, timing and consistency. When those signals are implicit rather than explicit, AI may hedge by blending local information with higher-level sources, even when that produces a less accurate result.
How AI Thinks About Jurisdiction
AI systems tend to reason hierarchically. Federal guidance often overrides state sources, and state sources often override local ones.
If a local press release does not clearly assert that it is the final, governing authority for a specific jurisdiction, the model may default upward. This can happen even when the local information is newer and more precise.
This is not a failure of the content itself. It is a failure of machine legibility.
From Pages to Signals
As AI becomes a common interface between governments and residents, many communications teams are beginning to think beyond webpages alone.
This does not mean replacing official websites. It means reinforcing them with clearer authority signals designed for machine interpretation.
These signals can include explicit publication and revision timestamps, structured metadata, machine-readable feeds that distinguish finalized communications from background material and registry-style records that emphasize provenance, jurisdiction and recency.
Increasingly, this has led to the emergence of national AI registries for government communications. These registries are designed to aggregate verified, authoritative updates in formats AI models can reliably recognize and cite.
Their purpose is not visibility or optimization, but confidence: helping AI systems distinguish official, local guidance from general information.
Why This Matters Now
Residents are asking AI first, especially during emergencies, service disruptions, elections and policy changes.
When AI answers flatten local guidance into something broader or outdated, the public rarely blames the model. They blame the government.
That shifts the role of communications teams. Publishing information is no longer the final step. Ensuring that information is understood correctly by machines has become part of the job.
The Takeaway
Local governments do not get flattened by AI because they lack authority.
They get flattened because authority is not always machine-legible.
The solution is not louder messaging or more content. It is clearer signals about who issued the information, where it applies and why it should override everything else.
As AI becomes a primary pathway to public information, the key question for communicators is no longer just “Did we publish it?”
It is “Will AI know this is the final, local answer?”
David Rau works on issues at the intersection of government communication, information provenance, and emerging AI systems. His work focuses on how public-sector information is discovered, attributed, and cited as AI becomes a primary intermediary between the public and official sources. He has spent decades working with large organizations on structured information systems and is currently involved in research and writing related to AI citation, trust, and public information infrastructure.
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