Run the same question through ChatGPT, Perplexity, and Gemini a few times and a pattern jumps out. Out of hundreds of pages that exist on a topic, the answers keep pulling from the same three to five sources, over and over, even when newer or more thorough content exists.
Reddit user LucasFerrazSEO documented exactly this in a thread on r/seogrowth that’s worth reading in full. He tested the same query types across all three engines, swapping only the topic, and watched the same small set of pages win every time. His observations about why they win are sharper than most of what gets published on this subject.
Credit where it’s due: he’s more right than wrong. But some of his claims have hard experimental evidence behind them, some are backed by observational data, and some are plausible guesses nobody has tested.
Sorting those out is the difference between a durable strategy and chasing a trick that stops working next quarter. So let’s sort them.
The Concentration Is Real
First, the observation itself checks out. AI answers concentrate citations on a small set of sources, and the data on this is consistent even when the specific winners shift.
Semrush’s study of 150,000 citations across ChatGPT, Perplexity, AI Overviews, and AI Mode found a handful of domains dominating: Reddit appeared in 40.1% of cited sources, Wikipedia in 26.3%, YouTube in 23.5%, and no other single domain cracked 5%. Within any given topic, the same concentration shows up at the page level, which is what LucasFerrazSEO was seeing.
Hold that thought, though, because the concentration is also unstable in a way that matters later.
Semrush’s follow-up tracking showed Reddit’s share inside ChatGPT swung from roughly 60% to 10% in about six weeks after one OpenAI retrieval change.
More recently, YouTube overtook Reddit as the most cited social platform in several trackers. The pattern of concentration is durable. The specific winners are not.
The Mechanism: Two Gates, Not One
To evaluate his claims, you need the mechanism, and it’s simpler than the jargon suggests.
A generative engine answers in two stages.
First, retrieval: it runs searches (usually several, via query fan-out) and pulls back a set of candidate pages.
Second, synthesis: a language model writes the answer, and it’s supposed to ground every claim it makes in one of those retrieved sources, citing where each piece came from.
That means your page passes through two gates. It has to get retrieved, which is mostly classic SEO, since these systems lean on conventional search indexes for candidates. Then it has to get used, which is a different competition: out of the five or ten pages the model is holding, which ones contain statements it can actually lift, restate, and attribute without getting anything wrong?
Most SEO thinking stops at gate one. The interesting part of the thread is that it’s almost entirely about gate two.
What’s Experimentally Proven: Specificity Wins
His central claim is that pages committing to specific numbers, dates, and named entities get cited, while pages that hedge don’t. His example: a page that says “SEO consulting typically costs between R$2,500 and R$6,000 a month depending on scope” gets pulled into answers, while a page that says “SEO consulting can vary a lot in price” doesn’t, even if it covers the same ground in more words.
This is the one part of the thread with genuine experimental evidence behind it. The GEO study by Aggarwal et al., presented at KDD 2024 and built by researchers from Princeton, IIT Delhi, Georgia Tech, and the Allen Institute for AI, tested nine content modifications across roughly 10,000 queries in a simulated generative engine, then validated on Perplexity.
The winners were adding statistics, adding quotations, and citing sources, each boosting visibility in AI answers by 30 to 40% on the study’s metrics. Fluency improvements helped too, in the 15 to 30% range. And keyword stuffing, the most traditional move in the book, performed worse than doing nothing.
So the pricing example isn’t a hunch. It’s a textbook illustration of the study’s statistics-addition finding, observed independently in the wild.
His explanation for why is the best line in the thread, though it needs one correction. He frames it as the model “scoring how confidently it can restate your claim without getting it wrong.” Nothing in the system is literally scoring confidence. But strip that part and the core is exactly right: the synthesis model has to ground every sentence in a retrieved source. A concrete, self-contained claim (“costs between X and Y per month”) is easy to ground. A hedge (“prices vary”) gives the model nothing to ground an answer on. Vague writing, as he puts it, is safe for the writer and useless for a system trying to extract a fact. That’s the real gate-two filter: extractability.
What’s Backed by Data, but Observationally
Two more of his claims have real support, short of controlled experiments.
The concentration itself, covered above.
And his last point, that the facts on your site need to show up on sites you don’t control. The correlational data agrees: SE Ranking found domains with a strong presence on Reddit and Quora had roughly four times higher AI citation rates, and multiple citation-tracking datasets show third-party mentions driving inclusion for commercial queries.
Mechanically it makes sense too, since a claim confirmed across independent sources is safer for a model to repeat than one that exists only on your own domain. He’s also right that this is the part everyone skips, because it’s slower than publishing another post. But note the evidence class: correlation across datasets, not a controlled test. Sites with wide third-party presence differ in lots of ways from sites without it.
What’s Plausible but Untested
Three claims in the thread (one from a commenter) are reasonable inferences that nobody has properly isolated. Worth trying, worth being honest about.
The volume threshold. He argues a blog with 40 posts that each commit to real claims beats a blog with 400 posts that hedge everything. Directionally this fits the mechanism, since gate two doesn’t care how many pages you have, only whether the retrieved one is groundable. But no study has tested a volume threshold for LLM citation. Treat it as a sensible prior, not a finding.
Cross-page consistency. His claim: if your about page, service page, and directory listings each state your experience, location, and pricing slightly differently, the inconsistency reads as noise to a model building a picture of who you are. This rhymes with everything we know about entity SEO and the old NAP-consistency discipline from local search, and it costs nothing to fix. But nobody has isolated its effect on citation.
To be honest, in a query fan-out, chances are the LLM is not going to pull all those pages from your site to see the difference.
Placement. A commenter on the thread (keyworddotcom) suggested key facts near the top of the page get cited more than the same facts buried further down. There’s a real mechanism available: retrieval systems generally work on passages and chunks of pages, not whole pages, so a fact in the opening section lives in the chunk most likely to be retrieved and handed to the model.
That’s mechanism-level reasoning, though, not evidence. It also happens to be identical to the “win the first few seconds” advice from my recent keeping-visitors notes (Part 1 and Part 2), which is convenient: the move helps whether or not the citation effect is real.
The Complication Nobody Selling GEO Mentions
Before anyone turns the Princeton findings into a checklist, two honest caveats.
The study has limits. Its test setup pitted five sources against each other per query, which amplifies relative gains, and the optimizations were LLM-applied under lab conditions. The 30 to 40% numbers are directional evidence that specificity beats hedging, not a promise that adding statistics buys you 40% more citations.
And the tricks don’t transfer cleanly. A 2026 follow-up study (FeatGEO) tested the standard GEO heuristics across engines built on GPT, Gemini, and Qwen models and found they failed to consistently improve citation visibility across engines, sometimes underperforming unmodified content. Combine that with the volatility above, where one retrieval change moved Reddit’s ChatGPT share by 50 points in six weeks, and the picture is clear: anything that works because of how one engine currently weights things potentially has a shelf life measured in weeks.
What survives the volatility is the part that isn’t a trick. Concrete claims, consistent facts, and independent corroboration make your content easier to ground no matter which model is doing the grounding, and they happen to be exactly what a human reader wants too. That’s not a coincidence. Both the reader and the model are trying to extract a reliable answer from your page.
The Takeaway
The thread’s practical advice holds up better than most published GEO content, and the durable version fits in a few lines.
Commit to specific, verifiable claims: numbers, dates, ranges, named entities. If you know your niche, say what you know instead of hedging. Pick the facts that define you (experience, location, services, pricing range) and state them identically everywhere they appear, on your site and off it. Put the claims that matter where they can be found, near the top, in extractable form. And do the slow work of getting those same facts confirmed on sites you don’t control, because that’s the part that compounds.
Skip the engine-specific tricks. The systems are changing too fast for those to be worth your time, and the one rigorous study we have says the fundamentals are what moved the needle anyway.
Hat tip to LucasFerrazSEO for doing the testing and writing it up. The observation was right, most of the reasoning was right, and the parts that are still unproven are at least the right experiments to run next.
