AI Shopping Answers Draw on 233 Domains and Repeat 13: Source-Pool Depth Across 43 Segments
Measured against the Machine Relations Index, the 43 published shopping-shape segments of the 2026-09-25 release cite a median 233 distinct domains each — and in the median segment only 13 of them appear in at least one answer in ten, while 44.4 percent appear exactly once. The pool a brand joins and the pool a brand competes in are different sizes.
The mechanic measured here is source-pool depth: how many distinct domains an AI answer engine draws on across a fixed set of shopping questions, and how that pool divides between domains the engines return to and domains they touch once. Measured against the Machine Relations Index release machine_relations_index_public_view_v2.0, methodology mri_score_v2.0, release id mri_score_v2.0+2026-09-25+04fcb7fb8f29, generated 2026-09-25, the 43 published shopping-shape segments cite a median 233 distinct domains per segment. In the median segment 13 of those domains are cited in at least 10 percent of observed answers, and 44.4 percent of the pool is cited exactly once.
For a brand's leaders, those two numbers answer different questions. The pool figure says how many domains an engine is willing to reach for in the category. The depth figure says how many it actually returns to — and that is the set a shopper's answer is assembled from on any given day.
This is a cross-section, not an experiment. It reports what six engines cited over a fixed 132-day window. It makes no claim that any page, placement, schema change or campaign caused a citation rate, and it does not compare a before to an after. Para Labs publishes it as a measured baseline a later experiment can move against.
The measurement
| Field | Value |
|---|---|
| Release | mri_score_v2.0+2026-09-25+04fcb7fb8f29 |
| Window | 2026-05-10 to 2026-09-25, 132 days observed |
| Shapes included | best_x best-tools, top_list ranked roundup, x_vs_y head-to-head comparison |
| Segments | 43 published category-and-shape segments across 15 categories |
| Observed answer runs | 4,884 |
| Domain citations counted | 35,308 |
| Distinct segment-and-domain pairs | 10,486 |
| Engines | ChatGPT, Claude, Gemini, Google AI Mode, Google AI Overviews, Perplexity |
| Evidence floor per segment | 10 observed runs across 7 distinct run dates |
Two parts of the release are deliberately left out. The news_topic shape and the legacy-unmapped category run on a different observation sample — 53 distinct run dates against the 7 that a shopping segment carries — so mixing them into a per-segment median would compare a high-frequency monitoring series against a periodic one. Every figure below is from the 43 shopping segments only.
The machine-readable release carries every row, the release manifest carries the window and engine roster, and each segment is readable on its own page — for example Consumer Products best-tools answers, AI Visibility roundups and Family Software comparisons.
Wide pool, shallow contest
Medians below are medians of the per-segment values, not ratios of the medians.
| Shape | Segments | Median domains cited | Median citations per answer | Median cited in 10% or more of answers | Median cited in 5% or more | Median share cited exactly once |
|---|---|---|---|---|---|---|
best_x |
15 | 247 | 7.72 | 13 | 36 | 47.9% |
top_list |
14 | 267 | 8.09 | 13 | 37.5 | 43.6% |
x_vs_y |
14 | 182 | 6.03 | 11.5 | 30 | 44.0% |
| All 43 | 43 | 233 | 7.17 | 13 | 34 | 44.4% |
Read the third and fifth columns together. The pool is 233 domains wide in the median segment and the set of domains cited in at least one answer in ten is 13 — a median 5.5 percent of the pool, and in no segment more than 13.9 percent. Loosen the bar to one answer in twenty and the set reaches a median 34 domains, still a median 15.6 percent of the pool. In 34 of the 43 segments, 15 or fewer domains clear the one-in-ten bar.
The other end of the same distribution is the part a dashboard reports as a win. In the median segment, 44.4 percent of all cited domains were cited exactly once across roughly a hundred observed answers. In 34 of 43 segments that share is 40 percent or higher; at the extreme, 371 of the 558 domains cited in AI Visibility roundups appeared once. A single citation is the most common outcome in the dataset, and it is not evidence of a durable position — it is the shape of the tail.
That distinction matters commercially. A brand that appears once in a hundred observed answers and a brand cited in a quarter of them both register as "cited" in a monitoring tool. One of them is in the set the answer is built from and one is in the pool the engine occasionally reaches into. The gap between a median 13 and a median 233 is the size of that misreading.
Every published shopping segment
Answer runs is the count of observed answers in the segment during the window. Domains cited is the count of distinct domains cited in at least one of them. Citations is the count of segment-and-domain citation observations. The top-slot figure is the most-cited domain and the share of observed answers citing it.
| Category | Shape | Answer runs | Domains cited | Citations | Cited in 10%+ | Cited exactly once | Top-slot domain |
|---|---|---|---|---|---|---|---|
| AI Infrastructure | best_x |
107 | 263 | 872 | 15 | 130 of 263, 49.4% | medium.com, 42.1% |
| AI Security & Privacy | best_x |
136 | 275 | 1111 | 15 | 124 of 275, 45.1% | arxiv.org, 38.2% |
| AI Visibility & GEO | best_x |
113 | 351 | 1115 | 20 | 189 of 351, 53.8% | youtube.com, 26.5% |
| Consumer Finance | best_x |
132 | 236 | 1027 | 18 | 113 of 236, 47.9% | forbes.com, 39.4% |
| Consumer Health | best_x |
132 | 298 | 1090 | 15 | 156 of 298, 52.3% | healthline.com, 43.2% |
| Consumer Products | best_x |
131 | 318 | 1043 | 12 | 157 of 318, 49.4% | forbes.com, 29.8% |
| Cybersecurity | best_x |
105 | 247 | 833 | 13 | 136 of 247, 55.1% | huntress.com, 53.3% |
| Deep Tech & Hardware | best_x |
121 | 328 | 928 | 9 | 158 of 328, 48.2% | reddit.com, 20.7% |
| Education & Training | best_x |
106 | 263 | 818 | 13 | 130 of 263, 49.4% | reddit.com, 34.0% |
| Emergent Prosumer | best_x |
100 | 219 | 641 | 8 | 92 of 219, 42.0% | reddit.com, 21.0% |
| Enterprise Software | best_x |
101 | 167 | 616 | 9 | 48 of 167, 28.7% | erpresearch.com, 33.7% |
| Family Software | best_x |
114 | 118 | 637 | 13 | 37 of 118, 31.4% | techradar.com, 34.2% |
| Fintech | best_x |
108 | 224 | 794 | 16 | 97 of 224, 43.3% | airwallex.com, 38.9% |
| Healthcare Services | best_x |
107 | 199 | 660 | 13 | 83 of 199, 41.7% | omnimd.com, 32.7% |
| HR & Talent | best_x |
107 | 186 | 767 | 17 | 69 of 186, 37.1% | pin.com, 29.9% |
| AI Infrastructure | top_list |
108 | 270 | 930 | 23 | 138 of 270, 51.1% | spheron.network, 37.0% |
| AI Security & Privacy | top_list |
106 | 313 | 987 | 18 | 171 of 313, 54.6% | ovaledge.com, 39.6% |
| AI Visibility & GEO | top_list |
101 | 558 | 1102 | 10 | 371 of 558, 66.5% | youtube.com, 26.7% |
| Consumer Finance | top_list |
132 | 233 | 1096 | 15 | 101 of 233, 43.3% | nerdwallet.com, 38.6% |
| Consumer Health | top_list |
131 | 338 | 1202 | 15 | 143 of 338, 42.3% | forbes.com, 30.5% |
| Consumer Products | top_list |
131 | 380 | 1186 | 14 | 195 of 380, 51.3% | chewy.com, 23.7% |
| Cybersecurity | top_list |
130 | 264 | 1098 | 13 | 100 of 264, 37.9% | hoxhunt.com, 28.5% |
| Deep Tech & Hardware | top_list |
106 | 279 | 834 | 12 | 116 of 279, 41.6% | unmannedsystemstechnology.com, 28.3% |
| Education & Training | top_list |
100 | 295 | 746 | 9 | 149 of 295, 50.5% | reddit.com, 19.0% |
| Emergent Prosumer | top_list |
101 | 211 | 659 | 13 | 82 of 211, 38.9% | reddit.com, 19.8% |
| Enterprise Software | top_list |
114 | 264 | 762 | 6 | 125 of 264, 47.3% | procuredesk.com, 16.7% |
| Family Software | top_list |
108 | 195 | 703 | 13 | 79 of 195, 40.5% | apple.com, 37.0% |
| Fintech | top_list |
102 | 200 | 627 | 11 | 85 of 200, 42.5% | stripe.com, 24.5% |
| Healthcare Services | top_list |
102 | 223 | 661 | 9 | 98 of 223, 43.9% | omnimd.com, 24.5% |
| AI Infrastructure | x_vs_y |
108 | 231 | 678 | 13 | 136 of 231, 58.9% | youtube.com, 33.3% |
| AI Security & Privacy | x_vs_y |
114 | 263 | 762 | 11 | 140 of 263, 53.2% | arxiv.org, 47.4% |
| AI Visibility & GEO | x_vs_y |
132 | 417 | 1021 | 10 | 251 of 417, 60.2% | hubspot.com, 26.5% |
| Consumer Finance | x_vs_y |
138 | 170 | 914 | 16 | 78 of 170, 45.9% | acorns.com, 38.4% |
| Consumer Health | x_vs_y |
131 | 217 | 943 | 18 | 107 of 217, 49.3% | healthline.com, 32.8% |
| Consumer Products | x_vs_y |
131 | 268 | 931 | 10 | 119 of 268, 44.4% | youtube.com, 29.8% |
| Cybersecurity | x_vs_y |
107 | 189 | 661 | 12 | 85 of 189, 45.0% | exabeam.com, 26.2% |
| Deep Tech & Hardware | x_vs_y |
105 | 171 | 616 | 13 | 72 of 171, 42.1% | reddit.com, 22.9% |
| Education & Training | x_vs_y |
100 | 126 | 542 | 16 | 55 of 126, 43.7% | uopeople.edu, 28.0% |
| Emergent Prosumer | x_vs_y |
95 | 118 | 448 | 11 | 48 of 118, 40.7% | youtube.com, 37.9% |
| Enterprise Software | x_vs_y |
114 | 186 | 639 | 7 | 72 of 186, 38.7% | erpfocus.com, 20.2% |
| Family Software | x_vs_y |
108 | 101 | 530 | 14 | 28 of 101, 27.7% | useboomerang.com, 26.9% |
| Fintech | x_vs_y |
102 | 136 | 534 | 10 | 44 of 136, 32.4% | wise.com, 18.6% |
| Healthcare Services | x_vs_y |
107 | 178 | 544 | 5 | 62 of 178, 34.8% | adsc.com, 17.8% |
The top-slot share ranges from 16.7 percent to 53.3 percent of observed answers, with a median of 29.8 percent. Exactly one domain across the 43 segments is cited in a majority of its segment's answers: Huntress, in Cybersecurity best-tools answers, at 53.3 percent. In every other segment the most-cited source appears in a minority of observed answers, and at the median it appears in roughly three answers in ten.
Pool width is not the same thing as concentration
Two adjacent measurements answer different questions and should not be read as this one. Concentration asks how few domains it takes to reach half the citations in a category, published as category citation concentration and source distribution. Rank movement asks whether a given domain's position holds still over time, covered in AI visibility ranking instability. This measurement asks a third thing: across a segment's whole observed answer set, how many domains were reachable at all, and how many were reached more than once.
A brand can be inside a concentrated head, outside it, or — most commonly — in the single-citation tail, and the three measurements will disagree about how well it is doing. The one that maps to a shopper's experience is the depth figure, because a shopper sees one answer.
Limits
The release reports how many observed answers cited a domain, not which ones. Overlap between two domains' citations is therefore not computable from the public artifact, so this measurement says nothing about whether the 13 frequently cited domains in a segment appear together or in rotation.
Domains are counted as registrable domains. The ingest step that writes the citation rows merges subdomains into the registrable domain, so two subdomains each cited once in a segment are recorded as one domain cited twice. That biases the single-citation share downward: the true share of sources cited exactly once is at least the figure reported here, and the pool represents more distinct hosts than the domain count shows.
Segment status matters. Of 157 category-and-shape segments in the taxonomy, 95 are published and the rest are still collecting evidence against the floor of 10 observed runs across 7 distinct run dates. A category absent from the table above is not a category where nothing is cited; it is a category where the observation count has not yet reached the publication floor.
One field in the public artifact is worth reading carefully before recomputing these figures. The per-segment field runs_cited_domains counts distinct domains rather than runs: in AI Infrastructure best-tools answers it reads 263, the domain count, while the citation-observation count for that segment is 872. Sum runs_cited across the domain records to get the event count.
Methodology and sources
Citation rates, answer-run counts and per-segment domain records were read from the public release artifact at https://machinerelations.ai/data/machine-relations-index.json, checksum 04fcb7fb8f290732063c585fa3d37f8aadd2d1bb63ed9082a4f502d6560c877d, with the window and engine roster read from https://machinerelations.ai/data/mri-release-manifest.json. Per-segment figures were computed by grouping every domain record's mri_score_v2.strata entries by category and question shape, restricted to segments with status: published, excluding the news_topic shape and the legacy-unmapped category. Medians are medians of per-segment values. The definition of a citation and of the source-role labels used above is published in the Machine Relations Index methodology and in the AI citations glossary entry maintained by AuthorityTech, which practises the discipline commercially. Named top-slot domains are linked at their own sites for verification: arxiv.org, youtube.com, forbes.com, healthline.com, nerdwallet.com, reddit.com, chewy.com, techradar.com, stripe.com, hubspot.com, apple.com and huntress.com.
A companion cross-section on the same release, measuring six named supplement brands across the same three shapes, is published as a consumer-health shape gap.