The sites AI cites most: Reddit, YouTube, Wikipedia and LinkedIn

When an AI engine answers a buyer's question, it leans on a surprisingly small set of sources. Study after study in 2026 surfaces the same names: Reddit, YouTube, Wikipedia, LinkedIn. If you want to be in the answer, it helps to know where the answer comes from - because that is where your brand needs to be described, accurately, before the model can cite it.

These platforms have quietly become the gatekeepers of AI recommendations. Not because anyone appointed them, but because they are where clear, structured, human-corroborated information about products and brands lives - exactly what a model wants to lean on.

What the studies found

One analysis by Peec AI, covering around 30 million sources across ChatGPT, Google AI Mode, Gemini, Perplexity and AI Overviews, put the most-cited domains in a familiar order: Reddit, YouTube, LinkedIn, Wikipedia and Forbes, with review platforms like G2 and Yelp appearing frequently in recommendation-style queries. Separate research has put Reddit and Wikipedia together at a large share of US ChatGPT citations, with major traditional news outlets notably absent from the top tier.

The consistency is the story. Different researchers, different samples, and the same handful of domains keep rising to the top. These are the sources that disproportionately shape what AI says about your category.

The engines don't all cite the same way

An important nuance: the citation mix differs by engine, so where your buyers ask changes what matters. The same Peec research found:

• ChatGPT leaned toward Wikipedia, Reddit and editorial sites like Forbes.
• Perplexity emphasised Reddit, LinkedIn and G2 - notably for B2B queries.
• Google's AI surfaces leaned toward platforms including Reddit and Yelp.

If your buyers research on Perplexity, your G2 presence and LinkedIn footprint carry weight. If they use ChatGPT, your Wikipedia-grade factual consistency and Reddit reputation matter more. One size does not fit all.

"The sources models trust most are often not your own domain. That is by design."

Why these platforms, and not your website

It stings a little: the sources models trust most are often not your own domain. That is by design. A model has every reason to be cautious about a brand describing itself, and every reason to trust a claim that many independent people converge on. Reddit threads, reviews and community answers are corroboration at scale. Wikipedia is structured, neutral fact. YouTube is demonstrable, watchable evidence. Your marketing page is a single, self-interested source - useful, but not decisive on its own.

This is the same lesson as brand mentions beating backlinks, seen from another angle: AI rewards what others say about you, consistently, in the places it reads.

How to get mentioned where AI reads

1. Participate genuinely in relevant communities. Reddit rewards real contribution, not spam. Be present where your category is discussed, help, and let accurate mentions of your brand accumulate naturally.
2. Earn reviews on the platforms that matter for you. For B2B, that is often G2; for local or consumer, Yelp and similar. Recommendation queries lean on these heavily.
3. Publish useful video. YouTube is one of the most-cited sources across engines. A clear explainer or demo is both content and citation fuel.
4. Keep your entity data consistent. The same clear description of what you do, matched across these platforms and your own site, is what lets a model trust and repeat it.
5. Measure and trace. Track whether the engines actually name you, and which sources are driving the citations, so you invest where it is working.

A per-platform playbook for building genuine presence

The short list of gatekeepers is stable, but each one rewards a different kind of contribution. "Be present on Reddit" and "be present on G2" are not the same job. Here is how to earn a real footprint on each, without pretending to be something you are not.

• Reddit. Reddit rewards helpfulness and punishes marketing. Find the two or three subreddits where your category actually gets discussed, read them for a few weeks, and answer questions where you genuinely know the answer - often without mentioning your product at all. When your tool is a fair answer to someone's question, disclose that you work there and say so plainly. One honest, downvote-proof comment does more for you than fifty deleted plugs. Over months, accurate mentions of your brand accumulate in exactly the threads models pull from.
• YouTube. Publish video that shows the thing working, not video that talks around it. A five-minute screen recording of your product solving a real task is both content and citation fuel, because it is demonstrable evidence a model can point to. Use clear spoken narration and accurate titles, since transcripts are what get read. Answer the specific question a buyer would type, not a vague brand-awareness theme.
• Wikipedia. This one you mostly cannot force, and that is the point. Wikipedia's notability guideline for organizations requires significant, independent, secondary coverage - editors commonly look for several solid third-party references before an article survives. Self-published material, press releases, and paid placements explicitly do not count toward notability. So do not write your own article. Instead, earn the independent coverage that makes a neutral editor able to write one, and make sure the basic facts about your company are consistent everywhere a fact-checker would look.
• LinkedIn. LinkedIn carries weight in B2B answers, and its signal is your people, not just your company page. Founders and subject-matter experts posting specific, experience-based takes build an entity trail that models associate with your brand. Keep the company page's description of what you do crisp and identical to how you describe yourself elsewhere.
• G2 and review sites. For B2B software, this is the highest-leverage surface of the group. Reported analysis from Foundation and G2's own 2026 research describes G2 as roughly the most-cited B2B software source in AI answers, with categories that have more reviews tending to earn proportionally more citations. Ask happy customers for reviews through legitimate, unincentivized channels, keep your profile and feature list current, and respond to critical reviews in public. Volume and freshness both matter.

Why astroturfing backfires - and what to do instead

The obvious temptation, once you see this list, is to manufacture the corroboration: fake Reddit accounts talking up your product, incentivized five-star reviews, a ghost-written Wikipedia stub. Do not. It is the single fastest way to undo the work.

The reason these platforms feed AI in the first place is that they have spent years building detection and moderation against exactly this behavior. Reddit communities remove and permanently ban obvious shilling, review sites strip incentivized or duplicate reviews, and Wikipedia editors delete promotional articles and flag paid editing. When your fabricated corroboration gets removed, you lose the citation - and if a moderator connects the pattern to your brand, you can earn a reputation that follows you across the very threads and profiles models read. The model does not need you to say nice things about yourself in disguise. It needs many independent people to converge on the truth about you. Astroturfing is a bet that you can fake convergence at scale, and that bet loses.

The ethical version is slower and it compounds. Real answers, real reviews, and real expertise accrue trust that survives moderation because it is not trying to beat moderation.

B2B and B2C pull from different corners of the same map

The top-cited domains overlap, but the emphasis flips depending on who you sell to, and that should reshape where you spend effort.

For B2B, the center of gravity is Perplexity-style research behavior: Reddit for candid practitioner opinion, LinkedIn for credibility and expertise, and G2 for verified, structured product comparison. The Peec AI analysis of around 30 million sources highlighted this B2B lean toward Reddit, LinkedIn and G2 specifically. If you sell software, a thin G2 profile is a bigger liability than a thin blog.

For B2C and local, the mix tilts toward Yelp and similar review platforms for recommendation queries, YouTube for hands-on evaluation, and Reddit for unfiltered consumer sentiment. Google's AI surfaces in particular have leaned on Reddit and Yelp. A restaurant, a consumer app, or a physical product lives and dies on review freshness and volume in places a shopper would already check.

The practical read: do not copy a competitor's channel strategy if they serve a different buyer. A B2C playbook applied to enterprise software wastes budget on the wrong surfaces, and vice versa.

Prioritize by where your buyers actually ask

You cannot build a serious presence on all of these at once, and you do not need to. The winning move is to concentrate on the intersection of two things: the engines your buyers use, and the platforms those engines favor. Work it in this order.

1. Find out which engine your buyers research on. Ask them directly, check what referral sources already show up in your analytics, and watch which tool your sales conversations reference. A design agency's clients and a fintech procurement team do not use the same assistant.
2. Map that engine to its favored sources. If it is ChatGPT, your Wikipedia-grade factual consistency and Reddit reputation carry the most weight. If it is Perplexity, your G2 profile and LinkedIn footprint do. If it is Google's AI surfaces, Reddit and Yelp move first.
3. Pick one or two platforms and go deep before you go wide. A strong, current G2 presence beats a shallow toe-hold on five sites. Depth is what produces the repeated, corroborated mentions models reward.
4. Measure whether it names you. Track the actual answers - do the engines mention you for your priority queries, and which sources are driving those mentions - then move budget toward what is working and away from what is not.

The gatekeepers are known and they are stable. The advantage goes to the brands that pick the right two, build there honestly, and check the answer instead of guessing.

The takeaway

The web has new gatekeepers, and they are not who traditional PR would have guessed. Getting cited by AI is, in large part, about being present and accurately described on Reddit, YouTube, Wikipedia, LinkedIn and the review platforms your buyers trust - tuned to the engines they actually use. Build that presence deliberately, then measure whether it is translating into being named in the answer.

Related: Brand mentions beat backlinks for AI citations - the 2026 data →