Disclosure first: I built citeOS, which does AI citation audits for crypto brands. This is our data and I have a commercial interest in you finding it interesting. Method and counts are below so you can argue with them.
Between April and August 2026 we probed five AI engines with buyer-style prompts about 72 crypto brands. Not brand-name lookups, which guarantee a mention and tell you nothing, but the questions someone types before choosing: which exchange should I use, is this wallet safe, cheapest way to accept crypto payments.
Every source URL in every answer was logged and attributed to the brand the question was about. 39,948 citation events, 17,276 distinct pages, 4,395 source domains.
Five things came out of it.
1. Own-domain pages were 7.78% of citations.
The other 92.22% landed on pages the audited brand does not control.
This is the number that reorganised how we think about the work. Schema, llms.txt, FAQ blocks, structured data: all of it competes inside that 7.78%. It helps. It is not the lever. The lever is what other people published about you.
2. The source pool is enumerable.
4,395 domains sounds hopeless until you sort it. 99 domains carry 50.1% of all citations. 2,218 domains, more than half, were cited exactly once in five months.
Nothing individually dominates, the largest single domain is under 4.5%. But the head is about a hundred sources wide and you can write it down in an afternoon.
3. The most cited single pages are ranked lists.
Top URLs in the corpus, by citations earned:
176 – money. com, best crypto wallets
170 – triple-a. io, best crypto payment gateways
132 – bitcoinfoundation. org, crypto payments feature
131 – coingecko. com, top crypto cards
119 – bitcoinfoundation. org, best crypto wallets
100 – ledger. com academy, best crypto wallets (vendor-published)
98 – forbes. com advisor, best crypto exchanges
17 – most cited YouTube video, any brand, all five months
Six of the seven leading pages are the same object: a ranked list of named products. The seventh is the interesting one. Ledger published its own category listicle and it placed eighth overall, and Ledger is one of the 72 brands we audited. So the pattern is not "third-party pages win", it is "ranked lists win", and a vendor is allowed to publish one.
Caveat I will raise before someone else does: our prompts are buyer-shaped, and buyer-shaped prompts surface buyer-shaped pages. Some of this is designed into the instrument. A corpus built from troubleshooting prompts would put docs and forum threads on top instead. Listicles are 33% of citations across the full corpus and 48% among the most-cited pages we hand-read, and quoting only the second number overstates it.
4. Per page, being the subject beats being listed.
6.22 citations for being the subject of a page. 3.78 for being one name in someone else's list. 3.39 for being mentioned nearby without being in the list proper.
This is the test that nearly killed finding 3, which is why it is here. Subject-of-page wins on rate. Lists win on totals because there are far more of them and one list serves many brands. Both are true and people quote whichever one suits them.
5. The boring finding beat all the clever ones.
Citations scale with the number of pages that mention a brand at all. 1.48 citations per page, r = 0.80.
That is close to definitional and I am not calling it a discovery. What makes it usable is the stability: coefficient of variation of 9.5% across brands of very different sizes. It behaves like a planning constant. Know roughly how many pages mention a brand and you can put a range on its citation volume before running a single probe.
What this does not show.
Crypto sample, so the specific head domains do not transfer. Five months is short. Citation counts are not traffic and not revenue and we have made no causal claim from a cited page to a purchase. Engines with fewer than five usable answers for a brand were dropped rather than estimated. If you want to attack it, attack the prompt set, that is where the most load-bearing assumption lives.
Happy to answer anything, and I will send the raw per-brand tables to anyone who wants to check a number.
Source: r/GEO_optimization · by /u/ss2803