We Analyzed 595,000 AI Citations. Reddit Barely Shows Up.

Reddit dominates Google results, so most teams assume it carries the same weight inside AI answers. A claim making the rounds in SEO circles says the opposite: that AI engines reject Reddit as a citation source most of the time.

Claims like that deserve data, so we checked our own corpus. Citedintel runs live audits across the major AI engines with web search enabled and stores every source each answer cites. That gives us 67,881 AI answers carrying 595,051 cited URLs across 64 buying categories and 14,640 brands, collected between June and November 2026.

The short version: the claim holds. Reddit is under 1% of everything these engines cite in buying conversations. What wins instead is more interesting.

How we measured this

Every number below comes directly from returned answers, not proxies. Each audit sends buyer-intent prompts to the major AI engines with live web search on, then records which brands each answer recommends and which URLs it cites as sources. We counted a citation once per URL per answer and grouped domains into source classes: community (Reddit, Quora, Stack Exchange, Hacker News), review and comparison platforms (G2, Capterra, Gartner, TrustRadius, Trustpilot and peers), social, video, Wikipedia and the open web.

Four limits of this data. Our corpus is commercial by design: these are buying prompts, not general knowledge questions, so the mix will differ for informational queries. Gemini is not yet in this dataset. And a citation is not a recommendation; it is the evidence an engine chose to ground its answer on, which is why it matters for visibility. And the corpus skews B2B: of the citations we can segment by brand type (324,051 URLs across 44,001 answers), 62.6% come from B2B software categories, 20.7% from B2C and D2C brands, 14.8% from services and other categories, and 1.9% from B2B2C software. Aggregate numbers here mostly describe B2B buying conversations; the category-type cut later in this piece shows exactly where the story changes.

Reddit is 0.8% of AI citations in buying conversations

Across 595,051 cited URLs, community content of every kind accounts for 0.88%. Reddit alone is roughly 0.84%. Quora, despite its scale, is nearly invisible at 0.02% or less on every engine.

One nuance: Reddit is still the fourth most cited single domain in our corpus by raw count, at 5,005 citations. The open web is a very long tail, so almost no individual domain holds a large share. The point is not that Reddit never appears. The point is that a source which dominates page one of Google holds well under 1% of the citation evidence inside AI answers, and over 90% of answers cite no Reddit thread at all. Ranking and being cited are different games. One more thing the aggregate hides: Reddit runs roughly five times higher in consumer categories than in B2B software. The category-type cut below has the detail.

What wins instead: the review and comparison layer

The three most cited domains in the entire corpus are G2 (14,757 citations), Capterra (7,588) and Gartner (7,301). As a class, review and comparison platforms take 6.71% of all citations. That is roughly eight times the entire community class, concentrated in a handful of domains you can actually influence.

Video has limited citation value. YouTube accounts for 0.7% of Perplexity citations and near-zero citations across the other major AI engines. If your AI visibility plan leans on video or community seeding, that effort is landing on surfaces the engines rarely treat as citable evidence in buying contexts.

Each engine has a different source diet

The averages hide the most actionable finding. Claude grounds 11.73% of its citations in review and comparison platforms, roughly five times the rate recorded for the other two systems (2.61% and 2.10%). If Claude matters for your category, your G2 and Capterra presence is not optional hygiene; it is a primary citation surface.

The number of sources differs as well. Across the three systems, a cited answer carries 7.4, 8.9 and 10.1 sources on average, respectively. The pattern matches field experience: some systems corroborate each claim across multiple sources, while others attach fewer, more decisive references. The practical reading is that earning a Perplexity citation requires coverage across a wide evidence set, while earning a ChatGPT citation is closer to having the strongest page for that claim.

The same numbers, cut by category type

A fair challenge to the aggregate above: 64 buying categories and 14,640 brands say nothing about the mix behind them. So here is the mix, and the story does change with it. These cuts use the slice of the corpus we can segment by brand type: 324,051 cited URLs across 44,001 answers.

Reddit is mostly a consumer pattern. At 1.69% of B2C and D2C citations, Reddit runs roughly five times its B2B software share (0.34%) and is the seventh most cited domain in the consumer slice. It is still under 2%: even where Reddit is strongest, more than 49 of every 50 citations go elsewhere.

The review layer is a B2B story, full stop. Review and comparison platforms take 12.72% of B2B software citations in this slice and effectively zero (0.01%) of consumer citations. If you sell to consumers, the G2 and Capterra playbook does not transfer. What engines cite instead for consumer prompts: brands' own sites, official and regulatory sources, and app store listings.

Engines split further by segment. Perplexity is the most Reddit-friendly engine for consumer prompts at 2.44%, about one citation in forty. Claude is the review-platform heavyweight for B2B, grounding 23.31% of its B2B software citations in that layer within this slice, while citing essentially none of it for consumer prompts.

Honest scope note: our B2C and D2C slice currently skews toward consumer finance categories, so treat the consumer numbers as an early read rather than a settled benchmark. We will re-cut these numbers as the corpus broadens. The aggregate finding, community content under 1%, has held in every cut so far.

Why engines cite authorities over threads

AI assistants do not crawl the web themselves when they answer; they retrieve pages from search indexes and then decide what is worth citing. A Reddit thread is messy evidence: anonymous, contradictory, undated and hard to attribute. A structured comparison page or a review profile with hundreds of scored data points is liftable evidence. Retrieval brings both in; citation keeps the one the model can defend.

This also explains the Google paradox. Google ranks Reddit because engagement signals reward it. Citation engines optimize for defensibility, not engagement. The two systems reward different content, which is why a strategy copied from classic SEO can quietly miss the surfaces AI answers are actually built from.

What to do with this

Four moves fall directly out of the data.

First, treat your review-platform presence as citation infrastructure. Complete, current G2 and Capterra profiles with real review volume are among the few third-party surfaces engines cite at scale, and they are directly in your control to improve.

Second, build one definitive page per claim you want to win. Engines that cite sparingly attach a single URL to a claim. A scattered set of near-duplicate pages splits your chance of being that URL.

Third, earn corroboration for engines that ground widely. Multiple independent pages agreeing on what you are and who you serve is what heavy-grounding engines assemble answers from.

Fourth, measure your own citation mix instead of borrowing averages. These are corpus-wide numbers across 64 categories; your category will have its own pattern, and the only way to know is to look at what the engines actually cite when buyers ask about your space.

That last one is the reason we built the measurement this post is based on. Every Citedintel audit stores the citation evidence behind every answer, so you can see which sources the engines trust in your category and whether you are on them.