The domains AI cites most across 44 categories

A founder is between calls, checking the most cited websites by AI before deciding where to place the next comparison page. The answer is not one universal winner: Reddit leads the sample, while other sites become important in narrower categories.

The top domains cited by AI assistants reveal two things at once. Some sources recur across many categories, yet the useful page still changes with the question being answered. Here is what the scan shows, and how to use it without treating a domain leaderboard as a content brief.

Reddit leads, but the leaderboard is not a shortlist of universal winners

CiteGraph recorded 64,667 citations across 9,791 AI answers. Those answers came from 77 scans of 44 sites in 48 categories, covering 507 distinct buyer questions. The engines cited 9,192 distinct domains and 21,465 distinct pages across that work. Across the sample, reddit.com was cited 1,885 times across 47 categories, well ahead of linkedin.com at 512 citations across 33 categories and github.com at 509 citations across 27 categories.

That lead matters because Reddit is both large and broad in this sample. It appears across more categories than any other domain in the top group, so it is not simply winning one narrow use case. LinkedIn and GitHub also travel across categories, but their lower totals and narrower category spread make their role different.

The next names are less general. Otterly.ai was cited 430 times across one category, while Landingboost.app was cited 388 times across one category. Medium was cited 385 times across 33 categories, and Virlo.ai was cited 346 times across three categories. Semrush was cited 339 times across 13 categories, Apple 335 times across 23 categories, and Tryprofound.com 329 times across three categories.

A high total can come from broad category coverage, concentrated performance in one category, or both. Reddit combines reach and volume, while Otterly.ai and Landingboost.app each appear in a single category, showing that a high total does not require broad reach. The category count helps separate those cases.

Check the leaderboards to see the recurring sources, but do not stop at the first row. Look at the number of categories beside the citation count. It tells you whether you are seeing a widely reused source or a strong specialist source. You still need to inspect the pages behind the count.

Citation share is broad enough to punish generic advice

The ten most cited domains took 8.4% of all citations in the scan. That is meaningful concentration, but it is not ownership of the answer set. The breadth of the source set leaves a large field outside the leading group. Overall rank is therefore useful for finding recurring sources, not for mapping every answer.

The distribution has a long tail. CiteGraph recorded 1,352 domains with ten or more citations, and 139 domains with fifty or more. A site can matter to a category without appearing near the top of the overall leaderboard. That makes category-level checking necessary when you are planning content for a specific market.

That distinction changes the work for you. If you see Reddit leading, the sensible response is not to copy its format or assume every buyer question is decided there. Find the pages that appear for your own category and query set, then separate recurring sources from single inclusions. The useful comparison is between those pages and the page you want an engine to cite.

There is also a page type pattern behind the source list. Listicles and roundups accounted for 68.3% of all recorded citations, or 44,164 citations. Competitor product pages accounted for 24.1%, or 15,575 citations. Together, those two page types made up 92.4% of citations, while community threads accounted for 3%.

The remaining page types were small in the aggregate: the scanned product's own site accounted for 1.7%, social posts for 0.8%, documentation for 0.8%, review platforms for 0.8%, YouTube for 0.4%, news for 0.1%, and Wikipedia for 25 citations. These figures do not say that documentation or first-party pages never matter. They say that, in this sample, those formats were rarely the main citation path. Your own category may produce a different distribution.

This is where generic AI citation advice falls short. A domain leaderboard tells you who recurs. The page type data tells you what kind of page often carries the citation in this sample. Your category tells you which specific page deserves closer inspection.

The useful source changes with the buyer question

A useful source is the page that repeatedly gives an engine enough material to cite for a particular category or question. It may sit on a broad site such as Reddit, LinkedIn, GitHub, Medium, or YouTube. It may also sit on a specialist site that appears often in one category and nowhere else in the leading group. The scan records these patterns, but it does not turn them into a universal rule.

Broad sources and specialists have different citation-to-category profiles. Reddit has 1,885 citations across 47 categories, while Otterly.ai has 430 citations across one category. That gap means a high overall count can describe either wide reach or concentrated volume. The useful source still has to be checked against the buyer question.

Which page type wins likely varies by question. The scan does not break that down by buyer intent, so you have to check your own categories directly. For each question, record the cited page, its source, and its page type before deciding what to publish. That keeps the analysis tied to observed answers rather than plausible but unsupported assumptions.

The strongest objection to this argument is practical: if the leading domains take only a small share of citations, perhaps domain research is too diffuse to guide a content plan. That objection is fair. A broad source set makes prediction harder, and an overall leaderboard cannot tell you which page will be selected for your next buyer question. But a leaderboard was never meant to replace category-level checking; use it as a filter, not a plan.

You can make that shift quickly:

1. Write down the buyer questions that matter to your category, including comparisons and alternatives.
2. Check which sources and exact pages appear in the answers for those questions.
3. Mark whether each source is a roundup, competitor page, community thread, documentation page, or another page type.
4. Note the single biggest gap between your page and the cited page so you know what to fix first.

That process is more useful than chasing Reddit, which only leads the overall table, not necessarily your category. It also gives you a way to spot a specialist source before its total citation count looks impressive. Keep the category, question, page, and page type together in your notes. Otherwise the analysis turns back into a list of impressive domains.

You can use the weekly research on who owns AI answers to watch the wider pattern, then read how CiteGraph measures before comparing results with your own tracking. The measurement unit matters. A domain count, a page count, a category count, and a citation count answer different questions.

What the domain list can and cannot tell you

The recurring sources are useful evidence because AI answers do not draw evenly from the open web. Reddit, LinkedIn, GitHub, Medium, and other high-ranking sites return across several categories, while specialist sites can dominate a smaller part of the sample. That is enough to guide investigation when you pair the source with its category spread and page type. It is not enough to choose a publishing channel by itself.

Our sample has clear limits. We measured 77 scans of 44 sites in 48 categories, not every AI answer or every market, so we do not know what we have not measured. The list does not prove that an engine will cite a site in every context, or that publishing on a high-ranking site will earn a citation. Treat the figures as a map of the sample, not a census of AI search.

For a founder or marketer, the practical implication is narrow and useful. Start with recurring sources to find likely evidence hubs. Then move down to the category and page level, where the useful page usually sits. Your goal is not to imitate the highest-ranked site; it is to make the page that answers the buyer's question easier to retrieve, understand, and cite.

Reddit tops the table, but which page gets cited is decided category by category, not by the table.