Where to get mentions for AI search now that Reddit is out

Reddit's share of ChatGPT Search citations fell from 3.83% to 0.52% in 4 days, between 14 and 17 August 2026. That is minus 86.4%, according to Promptwatch, a platform that monitors AI answers, pulls citations from live interfaces and sells reports to marketers; the piece came out on 19 August 2026 on Search Engine Land. Before that, Reddit had spent years near the top of the list of domains ChatGPT puts into answers as a source. I break down why leaning on one forum collapses in 4 days, which sources to build a mention portfolio from, and which single metric belongs in the report.

What Reddit's drop in ChatGPT looked like day by day

The collapse came in two steps, and that matters more than the headline number. Step one was 8 August 2026: Reddit's share slid from around 3.9% to the middle of the 2% range. On the same day Promptwatch recorded a change in query fan-out in ChatGPT Search. Query fan-out is the mechanic where the model splits a person's question into several search subqueries; it lives inside ChatGPT's search layer and decides which domains even make it into the candidate pool for citation.

Step two came 6 days later, on 14 August 2026, and pushed the share down to 0.52%. So less than a week passed between "everything is fine" and "we are not in the answers", with no advance signal at all.

Google products saw no such one day collapse. In Google AI Overviews, the block with a neural network answer above the regular results, Reddit's share declined smoothly: from roughly 2.5% in early July to about 2.1% in August 2026. In Google AI Mode, a separate Google search tab with a conversational answer, the decline started at the end of July. The same source behaves differently in different engines, and that is the first argument against leaning on a single platform.

Why the overall citation curve hides this drop

A second measurement came from OtterlyAI, a platform that tracks brand visibility in AI search across the US market. They took 16 brand reports across 14 industries, published 27 August 2026, updated 14 September 2026. Citations of reddit.com in ChatGPT fell from 497 to 132 per day, minus 73.4%. Over the same window ChatGPT's total citation volume grew from 36,096 to 37,366 per day, plus 3.5%: the model did not stop citing, it stopped citing one domain.

In the other engines Reddit barely moved: from 2,117 to 2,104 citations per day, minus 0.6%. That covers Google AI Overviews, Google AI Mode, Gemini and Perplexity.

Now the part that hurts reporting most. In the aggregate across all engines reddit.com lost only 14.4%, its share shifting from 2.00% to 1.66%. If you look at one averaged "AI search visibility" number, you will not see this event at all. And in 3 of the 16 reports (American Red Cross, Udemy, Bayer) Reddit citations in ChatGPT hit zero within 4 days after 14 August 2026.

It took me two days to work out why we need separate columns per model in the sheet. This is why: an average across four engines hides a collapse in one of them.

Who took Reddit's place in ChatGPT answers

According to OtterlyAI, the freed up slots in answers went to three types of sources, and that is a ready made map of where to move your effort.

• Encyclopedias and reference sites: up in 4 of the 5 reports where they appeared at all. For Netflix, English language Wikipedia gained ground.
• Official pages of the brands themselves and their competitors: growth in 10 of 15 reports.
• Other commercial sites: growth in 9 of 15.
• News and media: decline in 11 of 12 reports. In the Tesla report, sec.gov (the US Securities and Exchange Commission site, where company filings live) and irs.gov (the US tax agency) grew, while Reuters dropped out of answers.
• Specialist educational and industry organizations: for Udemy, MIT OpenCourseWare, DeepLearning.AI and the Project Management Institute appeared.

The leading hypothesis for the cause: ChatGPT moved to retrieval via the site: operator, meaning it goes to a known list of sites rather than starting from the open web. Gizmodo wrote about this on 18 August 2026. OpenAI published no explanation and did not respond to a request for comment.

For anyone promoting their own site or brand, the conclusion is simple: the model now prefers sources with clear accountability for their data, registries, official sites, reference works, industry bodies. A forum thread with an anonymous opinion fits that logic worse.

Which sources to build a mention portfolio from

A portfolio is not a list of "where else can we post". It is a set of source types, so that one of them falling does not zero out your visibility. Five types the models cite:

1. Industry media and specialist organizations. A piece with your commentary, analysis or numbers, ideally with the company name in the body text, not just in the author byline.
2. Wikis and reference sites. A page about the company, product or industry standard, with facts that can be checked against a primary source.
3. Q and A services. Answers to specific questions rather than filler: the model pulls phrasing straight out of them.
4. YouTube transcripts. The automatic text of a video gets indexed and ends up in answers, so the brand name and the point have to be spoken out loud in the first 60 seconds, not shown on a caption card.
5. Reviews and comparisons. Pieces in the format "A versus B", roundups of solutions, spec tables.

There is fresh field data on formats. On 14 September 2026 two GEO experiments with manual verification were published, 775 recorded citation events in total. The first took 15 commercial queries across 4 platforms: listicles, meaning roundup pieces with a list of solutions, delivered 72.4% of all citations, PR publications 24.1%, and all other formats together the remaining 3.5%.

What interests me in these numbers is not the proportion but the reason. A roundup answers a person's question through its structure: item, name, why people pick it. The model needs exactly that kind of chunk, a ready made paragraph with a named entity that can be moved into an answer without rewriting. A press release about "the launch of a new version" contains no such chunk.

Local platforms and mentions without a link

In non English queries the source list looks different: national tech blogs, local review aggregators, map reviews and marketplace product page reviews carry the weight, with the same logic as in English results, a technical breakdown with numbers, a case study with actual amounts, a review with order details. There are numbers on how concentrated this is. An August 2026 Visiobrand study of 317,045 citations across 9 AI platforms, measured on the Russian market, found that the top 20 domains take 42.2% of all citations and the top 100 take 67.5%. The platforms differ by language, the concentration does not: a short list of sites carries most of the weight.

Map reviews and product page reviews deserve a separate note: they give you not a link but a phrasing. When a person asks a neural network "which service should I choose", what goes into the answer is the substance of the reviews, not the domain.

And that shifts the conversation to mentions without a link. The model pulls a brand as an entity, not only as a domain: a company name in the text with no hyperlink still makes it into the answer. So when you negotiate a publication, it matters more to get the exact spelling of the name and one verifiable number next to it than a link in the first paragraph.

Share of model voice: how to count your share of brand mentions

The metric I put in the report is one: share of model voice, the share of answers mentioning the brand out of 100 model queries. Counted by hand or through a monitoring service, in this order.

1. Collect 100 queries people actually ask a neural network before buying. Not keywords from a semantic map, but questions: "what is the difference between", "what should I choose for", "how much does it cost", "what alternatives are there".
2. Run all 100 through each model separately. Three models minimum: answers diverge more than search positions do.
3. Count two numbers per model: in how many answers the brand is mentioned in words, and in how many a link to your domain is given. These are different indicators, do not add them up.
4. Write down the sources the model cited in those answers. That is your list of platforms worth fighting for, instead of guessing.
5. Repeat on a fixed cadence: weekly or every two weeks, but on the same day.

I keep the table flat: a row is a query, the columns are models, and each cell holds two marks, mention and link. No pivots on the first sheet: a pivot hides a collapse in one model, the same way the average across four engines hid Reddit's minus 73.4%.

Why you need to check at least three models

August 2026 proved this in numbers. One and the same event: minus 86.4% in ChatGPT Search per Promptwatch, minus 0.6% in the other engines per OtterlyAI, minus 14.4% in the aggregate. Three different answers to the question "what happened to visibility", and all three are correct, just cut differently.

The practical minimum: ChatGPT, Google AI Overviews and Perplexity. If the product sells in markets where Gemini or AI Mode are strong, add those too, but as separate columns.

• 100 questions, not keywords
• a separate run per model
• mention and link counted separately
• the list of cited sources saved
• repeat on the same weekday

Why organic traffic and AI answer traffic have to be counted separately

On 8 September 2026 Google told Reuters that the new results layout in the European Union, built to meet the requirements of the DMA (Digital Markets Act, the EU regulation on digital markets that applies to large platforms), produced the largest drop in service quality in the 29 year history of its search. In the new layout one specialized search service is placed at the top, two more sit below it with less detail, and the carousel for hotels, flights and restaurants lost some of its attributes, including real time prices. The same day Google published a help document on regional differences in results.

What this means for the report. If your traffic dipped in September, there are now at least two possible causes: reshuffled results in the region, and model behavior. Added into one number, they produce the conclusion "SEO is broken" and not a single action. Separated, they produce two different work plans.

So my table counts three sources separately: visits from organic results by region, visits from AI interfaces, and brand mentions in answers with no visit. The third line brings no visits, but it is the one that shows first that the model has stopped knowing you.

What to do in an hour

1. Open your 20 most commercial questions and run them in ChatGPT, Google AI Overviews and Perplexity. Record where the brand is mentioned and which sources were cited.
2. From that source list, pick the platforms that are not a forum: an industry outlet, a reference site, a Q and A service, a comparison review, a video with a transcript.
3. Check whether you have a roundup or comparison format piece on your own site. If not, put it first in the plan: roundups accounted for 72.4% of citations in the 14 September 2026 experiment.
4. Set up a table for share of model voice: rows are queries, columns are models, and each cell records mention and link separately.
5. In your analytics, split visits from organic results by region from visits from AI interfaces, so the next change to search results does not look like one general slump.

If you want, I will calculate your share of mentions across 100 queries and show which sources the models pull into answers in your topic, write to support.