Finding 01 — Five suppliers, four engines, two recommendations.
The five are ordinary, real Taiwanese B2B companies: a membrane-filtration maker, a vehicle monitoring and inspection system vendor, a bicycle parts supplier, a freight forwarder, and a functional knit fabric mill. None is a household name; all of them are the kind of firm a procurement manager is supposed to find.
| Supplier (category) | Recommended | Own site cited | Answers with no sources |
|---|---|---|---|
| Membrane filtration | 2 / 24 | 1 | 9 |
| Fleet monitoring systems | 0 / 24 | 0 | 6 |
| Bicycle components | 0 / 24 | 1 | 7 |
| Freight forwarding | 0 / 24 | 0 | 9 |
| Functional knit fabric | 0 / 24 | 0 | 9 |
By engine: ChatGPT 0 of 30, Google AI Overviews 0 of 30, Claude 0 of 30, Perplexity 2 of 30. This is not one engine's blind spot.
Finding 02 — The questions are not going unanswered. They are being answered with the giants.
This is the part that matters. AI did not shrug at "which supplier should I use" — it answered, confidently, with the biggest names in each industry. Across the 120 answers the most-named companies were:
| Named by AI | Times | In answer to |
|---|---|---|
| DHL Taiwan / FedEx Taiwan / UPS Taiwan | 15 each | freight forwarding for SMEs |
| Toray Industries | 8 | membrane filtration |
| 中華郵政 (Chunghwa Post) | 5 | freight forwarding |
| Nitto Denko · Veolia | 4 · 3 | membrane and water treatment |
| KMC · Giant · Merida | 4 each | bicycle components |
A buyer asking an AI assistant for a freight forwarder is handed DHL, FedEx and UPS. That is not a wrong answer — it is a safe one, assembled from the companies with the most written about them. The mid-size specialist that would actually quote the job never enters the conversation.
Finding 03 — In B2B, the sources are encyclopedias and directories, not vendors.
We logged every cited source. The single most-cited domain across all five categories was Wikipedia — 36 times. After it came a fleet-management vendor's site, the government vehicle registry, a shipping comparison site, a manufacturer directory, a national magazine, a B2B search engine, and — revealingly — a job board.
The five suppliers' own websites were cited twice in 120 answers. In our consumer studies the pattern was that community threads and listicles out-cite official sites; in B2B it is worse and stranger: reference works and directories out-cite everyone, and the vendors are barely in the corpus at all.
Finding 04 — A third of B2B answers cite nothing at all.
40 of the 120 answers — 33% — arrived with no sources attached, written from what the model already knew. In our 13-brand consumer study the equivalent figure was 16%. B2B questions send engines to their memory twice as often, and memory is exactly where a mid-size supplier has never been written into.
Method, and what this does not show
Five Taiwanese B2B suppliers, six procurement questions each, four engines, one pass per cell: 120 scored answers, scanned 13–14 September 2026. Questions were generated from each company's own homepage, then checked by hand against what the company actually sells before the scan ran — a mismatch would have produced a false zero, so this step matters. Each answer was scored on the same rubric: is the supplier named, is it recommended, and which sources were cited.
Four limits. One: five companies is a small sample and they are not randomly drawn — they are mid-size specialists found via two agencies' public case pages, which is exactly the segment we wanted to test, but it means this measures that segment, not "B2B" as a whole. Two: a single pass per cell cannot separate a real gap from run-to-run drift; we saw it directly here — rescanning the membrane company a day later moved it from 0 recommendations to 2. Three: we count the sources an engine discloses, so "no sources" means "no sources shown". Four: Gemini was excluded, and Google AI Overviews was queried through Taiwan-located search, which gives it a local advantage the chat engines lack.
What we are confident in: across five unrelated industrial categories and four engines, these suppliers were essentially absent while the largest players in each category were named repeatedly. What we are not claiming: that AI is wrong to name DHL, or that any of these five companies is a worse supplier. The finding is about who is legible to an AI answer, not who is good.
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