How the Test Was Actually Run
Here's the actual setup, because "AI cites Reddit a lot" is a claim you should be able to check, not take on faith. We ran 32 questions across ChatGPT, Gemini, and Perplexity — 96 question-runs total, zero failures. We reran the ChatGPT batch a few days later on the same 32 questions and got the same zero-failure rate, so this wasn't a one-off glitch in the tooling.
Where the Citations Landed
Out of those 96 question-runs, citations pointing back into reddit.com plus a handful of Reddit-adjacent subsidiary sites added up to about 46 question-runs — meaning something in the Reddit ecosystem showed up as a source roughly half the time across all three engines combined. LinkedIn was the second most common domain specifically within the English-language question set, showing up in 6 of those runs. Wikipedia and arXiv each landed around 6 as well, for reference.
The Three Engines Behave Very Differently
What surprised us more than the Reddit number itself was how differently the three engines behave once you look past the aggregate. Perplexity was by far the heaviest citer — across its 32 question-runs it returned 329 citations total, which tells you it's pulling in multiple sources per answer rather than picking one and stopping. Gemini was the opposite extreme: across its own 32 question-runs it gave out only 21 citations combined. That's not a typo — Gemini's citation surface is just structurally narrow compared to the other two. If you're trying to figure out where to put effort, "get cited by AI" means something very different depending on which engine you're optimizing for.
Why We Never Add the Engines Together
One methodology note, because it matters if you're going to run something similar yourself: we don't add these engines' numbers together into one grand "total citation count." ChatGPT, Gemini, and Perplexity surface sources through different mechanisms, so a citation in Perplexity and a citation in Gemini aren't the same unit of measurement — treating them as interchangeable and summing them is how you end up with a number that looks impressive and means nothing. Anything you see reported here is scoped to the specific engine it came from.
We also weren't tracking this to see if any specific company got recommended — we were tracking domain-level citation patterns, which is a cleaner signal anyway since it isn't sensitive to how any one engine happens to phrase a recommendation on a given day.
What This Means for Where You Put Effort
Practical takeaway if you're thinking about where AI search actually pulls from right now: a well-placed, genuinely useful thread or comment in the right subreddit is doing real work in these answers, at a rate that rivals or beats dedicated content sites. We do GEO measurement work at MaxGrowth (maxgrowth.ai) and this was one of the more consistent patterns across our test set — happy to share more of the raw breakdown if people want specific engine-by-engine numbers.
This piece is written by the MaxGrowth (maxgrowth.ai) team, operated by 北京口袋智创科技有限公司.
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