A language model gives you one answer. A recruited population argues. This is the same expert-networks study, rebuilt so the disagreement — the thing ChatGPT cannot produce — is the first thing a buyer sees.
Q4 of 7: "Imagine that instead of a human researcher, an AI conducted the expert interview for you…" counts illustrative — wire to response data
Ten people placed where they actually stand, reject → embrace. Hover any dot for their words. The scatter is the argument: a model that was "just ChatGPT" would put all ten in the same place.
Two real positions from the same study, set against each other. One screen like this kills "it all sounds the same" faster than any methodology paragraph — and it's built from quotes the report already contains.
"Co-pilot" — AI runs scheduling, capture, extraction and contradiction-hunting. The human probes.
An interview is a trust relationship. Guardrails don't change that — she won't delegate it, full stop.
The panel's adoption conditions as a scannable grid — one row per person, one column per demand. Ten different condition fingerprints is what a real population looks like; a single LLM has exactly one. Column totals are the product roadmap, ranked by the market. cells partly inferred — quotes real, wire to response data
| Respondent | Verbatim + timestamps |
Raw audio / attestations |
MNPI / PHI guardrails |
Human sign-off |
Procurement-safe pricing |
|---|---|---|---|---|---|
| Kimberly Straitpublic-sector · rural PA | |||||
| Cleotilde Seymourbuy-side · rural CA | |||||
| Michelle Thaohealthcare · East LA | |||||
| Sonia Danielhealthcare · DC | |||||
| Dale Gomezbuy-side · Fort Worth | |||||
| Jason Talbotbuy-side · Cincinnati | |||||
| Paula Thomascorporate ops · Charlotte | |||||
| Chelsea Chavezcorporate ops · Milwaukee | |||||
| Herbert Proctorpublic-sector · rural GA | |||||
| Anthony Strandcorporate ops · Denver | |||||
| demanded by | 9/10 | 2/10 | 3/10 | 10/10 | 4/10 |
Consensus is a finding; contest is a bigger one. Ranking the seven questions by disagreement gives the reader a map — and tells the client exactly where a follow-up study earns its money. splits illustrative — computable from stance classification
And the split isn't noise — it's structured by sector. Small multiples make that scannable in two seconds: illustrative
The skeptic's first question gets a polling-house answer, one scroll after the argument. Every number here is read from the study record. live data
Every summary claim carries its count, its named respondents, and a verbatim quote — with a route to the full responses. The narrative survives as connective tissue between receipts.
"I read 8–12 transcripts before the first call."
"No bundles, no annuals."
One GLG call killed an entire OEM thesis.
"Co-pilot."
Mock v3 · Jul 10, 2026 · built from the live shared study (fielded Jun 19). Divergence devices: the position spectrum, the head-to-head confrontation, the demand matrix, and the contested index with sector small multiples — plus the ledger and traceable insight cards from v1. All demographics, quotes, segment contrasts and outlier positions are read from the study record; ◆ and "illustrative" mark stance allocations pending wiring to per-question response data. No data output altered — only order, hierarchy, and citation surface.