How often Reddit and YouTube appear in AI shopping answers

One public study reports Reddit at 13.1% of Gemini's citations and 2.2% of OpenAI's (its API model, gpt-5.4 nano); Pew reports Wikipedia, YouTube and Reddit together at 15% of Google AI summary sources, with no separate Reddit figure. Numbers like these can send a brand to hire a community manager. Our October 2026 sample of shopping answers, from OpenAI and Perplexity, found reddit.com in 0.8% of OpenAI answers and 3.3% of Perplexity answers. That counts answers, not a share of links, so it cannot be set directly against those figures. This post gives the Reddit and YouTube counts per engine, sets out what public studies count differently, and sets out how to decide for your own category. Everything here describes answers to prompts we wrote, in an API sample. It does not describe shoppers.

What did our October sample show?

The October 2026 sample covers 160 categories and two engines: OpenAI's API with its web search tool on (model gpt-6-luna), with 800 answers, and Perplexity's Agent API with the Sonar model (perplexity/sonar) and web search on, with 787. For OpenAI, the source list is the pages its answer links. For Perplexity, it is the pages its search retrieved, not all of which the answer uses. The two are different counts, so they stay in separate columns.

With counts this small, the intervals are wide. A 95% Wilson interval puts Reddit's true rate for these prompts at about 0.3% to 1.6% of OpenAI answers and about 2.3% to 4.8% of Perplexity answers. For YouTube it is about 0.2% to 1.3% for OpenAI and up to about 0.5% for Perplexity, where we saw none.

We also watched five other social, video and reference domains: Quora, TikTok, Instagram, Facebook and Wikipedia (in English and Spanish). Together they appeared in one answer, an English Wikipedia page in one Perplexity answer's retrieved list. Our broader "forum or video" measure, a hand-made list of domains, matches the same story: 1.3% of OpenAI answers and 3.3% of Perplexity answers.

In plain terms, Reddit is in roughly one Perplexity answer in thirty and in fewer than one OpenAI answer in a hundred, and YouTube is close to absent. Perplexity's lists are far longer (27.3 URLs per answer against 2.2 for OpenAI) and record retrieved pages, not linked ones, so the two columns are not like for like.

Why do public studies report different Reddit numbers?

They count different things, so the numbers cannot be compared directly; we did not test how much each difference changes the result. Here are three public sources next to ours, with what each one sampled.

The differences that matter for reading these side by side:

• The unit. We count answers that contain a domain. The preprint counts a share of citations (URLs linked in the answer). A domain can be a small share of links and still sit in a larger share of answers, or the reverse, so the two percentages are not comparable.
• The engine. Two of the three sources are not OpenAI or Perplexity. The preprint's highest figure, 13.1%, is for Gemini with Google Search grounding, an engine we do not sample. Pew studied Google's AI summaries.
• The topic. Ours is shopping across 160 categories. The preprint's main dataset is one industry. Pew's is whatever people searched for.
• The date. October 2026, a three-month window ending in September 2026, and March 2025.
• The record. OpenAI's links and Perplexity's retrieved lists are different objects, and a study that records only linked pages cannot be set against one that records everything retrieved.

For OpenAI, both figures are low, though they are different measures. The preprint's 2.2% of citations is small, and the authors also report that, at equal search rank, the OpenAI engine rarely links Reddit threads it has retrieved. That is a statement about their questions and their model, not about ours, but it fits a low Reddit presence in OpenAI's answers.

Should a brand invest in Reddit or YouTube for AI shopping visibility?

On this evidence, not for that reason alone. In October 2026, Reddit was in a low single-digit share of Perplexity's retrieved lists and fewer than one in a hundred of OpenAI's answers, and YouTube was in four answers in all. The same sample cannot speak to Google's AI surfaces, where Pew found Reddit, YouTube and Wikipedia among the most frequently listed sources in 2025, and where the preprint's Gemini result is higher. If those surfaces matter to your sales, your category check should include them.

There are good reasons to be on Reddit or YouTube that have nothing to do with AI answers, such as supporting customers where they already ask questions. Our sample does not measure that value in either direction.

To decide for your category:

1. Draft five shopper prompts for your category: one for the best product overall, one under a price limit, one for a premium pick, one for a specific need, and one asking for buying advice.
2. Ask each engine you care about every question several times on different days, recording each source list.
3. Count answers that contain reddit.com and youtube.com, and divide by answers asked, per engine. Report the number with its denominator. Our free manual method covers the sample size.
4. If either appears in answers beyond the noise in a count this small (our 6 of 800 spans 0.3% to 1.6%), open those pages and see what they say about your products. Accuracy matters more than volume.
5. Only then decide whether participation is worth the time.

If you take part, the platforms' rules set the shape of it. Reddit's rules, as of October 2026, ask people to participate authentically in communities where they have a personal interest and not to spam, and not to impersonate an individual or entity in a deceptive manner. The Federal Trade Commission's guidance for social media endorsements says to disclose any financial, employment, personal or family relationship with a brand. Posting as if you were an unconnected customer conflicts with both. An account that says who it belongs to is more consistent with them, but communities can set their own rules on promotion, so read those and the sources above before posting. This is not legal advice.

What this data cannot tell you

• It is the API, not the app. The consumer apps can show other sources. See what API sampling can and cannot tell you.
• Our prompts do not ask for Reddit. They are plain shopper questions. A prompt such as "what do people on Reddit say about..." would change the lists, and we did not test one.
• Small counts. Six and four answers are counts you should hold loosely. The intervals above show how loosely.
• Source lists, not words. We count domains in source lists, not mentions of Reddit in the answer text. We match exact domains (reddit.com and youtube.com, with www removed), so other hosts such as youtu.be or old.reddit.com are not in these counts.
• One month. Each prompt was asked once per engine in October 2026, and these counts say nothing about change over time.
• Presence is not influence. A domain in a list does not show that it changed a recommendation.

The wider picture is in the October 2026 pillar post and the websites inventory. For review sites and retailers, see reviews and ratings in AI shopping answers and retail listings.

Frequently asked questions

Does a low Reddit count mean assistants never use Reddit?

No. It means few answers in this sample linked or retrieved a reddit.com page for these shopper prompts. Other prompts, other engines or the consumer apps may differ.

Why does Perplexity show more Reddit than OpenAI here?

We did not measure why. Perplexity's retrieved lists are about twelve times longer than OpenAI's linked pages, and the two lists record different things.