You will have seen the chart by now. Reddit's share of ChatGPT citations falling off a cliff in the second week of August, shared all week with varying degrees of alarm.

It is real and we can corroborate it in our own data. What I would argue with is the conclusion people are drawing from it, because Reddit is still doing its job in other engines and its capitulation is confined to one of them.

In our numbers Reddit went from 3.06% of ChatGPT citations to 0.17% between late July and the middle of August, which is a 94% relative fall. The public version had it at roughly 3.8% down to 0.5%, so the same shape with a slightly lower reading on both sides.

The obvious reading is that Reddit stopped being worth citing, so the first thing worth doing is checking that against another engine.

We run the same prompts through Google AI Overviews. Reddit's share there held between 2.7% and 3.1% across the identical window, with no movement whatsoever on 8 August. Same questions, same days, same subject matter, and one engine changed its mind while the other carried on exactly as before.

So Reddit did not get worse. One engine changed how it picks sources, which means anyone treating Reddit as a single lever across all of AI search has just found out it is several levers, and they move independently.

Now, here is the thing. It did not go to the other big user-generated platforms, which is what most of the commentary has assumed.

YouTube barely moved and Wikipedia fell by about three quarters. LinkedIn went from 0.07% to 0.52%, which is a big relative jump and still a rounding error next to what Reddit gave up.

The share went to first-party sites. Companies' own domains and official government and regulator pages (category dependent), which between them took almost everything Reddit lost. That is the same shift I wrote about last week, when ChatGPT started going to named domains directly inside its fan-out queries.

Reddit did not fall alone. Comparison roundups, national newspapers and the big reference sites all lost ground in the same week, with the Guardian, Forbes, TechRadar and Investopedia among them. If your visibility currently depends on being well placed in somebody else's "best X for Y" roundup, that position is worth less than it was in early August, so something to take a look at and see whether it shows up in your conversion reporting (that is, if you are tracking self-reporting referrals).

At the same time the number of unique hosts ChatGPT cited nearly tripled. The lists got longer and they got a great deal more official.

The 94% is a share figure, and share figures have a trap in them. Your share can fall without you doing anything differently at all, simply because the thing you are a share of got bigger.

Think of it as share of voice. If ten brands in your category are talking and another forty turn up next month, your share of voice drops through the floor while your own output has not changed by a single post.

Reddit's share is its own citations set against every citation ChatGPT handed out that day, which means the percentage can drop either because Reddit is being cited less or because everything else is being cited a great deal more.

What happened in August is mostly the second one. Before 8 August a typical answer cited around 12 sources, and afterwards it was citing somewhere between 50 and 70, so an answer that used to name a dozen sites was suddenly naming sixty. Reddit could have held its ground completely, been cited exactly as often as it was in July, and still watched its share fall purely because the list it sat in got five times longer.

That said, Reddit did lose ground.

Its actual citation count fell by roughly 76% between the days before 8 August and the week from the 14th. That is a real and significant drop, and it is a good deal smaller than the number currently doing the rounds. If you are taking this to a board or a client, 76% is the more accurate reflection, because it measures Reddit rather than the size of the list around it.

The best explanation of the architecture I have read is this piece on Search Engine Land by Olivier de Segonzac of Resoneo, and it is worth a sit down and a coffee.

The short version is that ChatGPT is not simply querying a search engine and reading the results. OpenAI runs its own retrieval index, and on instant answers, the fast mode free users get by default and where the overwhelming majority of ChatGPT usage sits, almost everything it cites from a search comes out of that index. Live questions are the exception, and those pull from Google too.

Move to a paid thinking mode and the mix flips towards scraped Google results, so two people asking the same question can end up answered from completely different corpora depending on what they pay.

Underneath it sits a read cache, keyed by URL and shared across everybody. A stored copy stays fresh for around half an hour, after which the next person gets the stale version immediately while a background fetch updates it for whoever comes next. Copies have been served more than 90 days after they were taken.

The part that I found super interesting: how often your pages get refreshed in that cache is driven by how often ChatGPT users ask about them. Wild.

It is worth stating how any of this is known, because it is better evidenced than most claims in this space. ChatGPT's browser stream used to carry a field naming the pipeline behind every single search result, which is how the index was identified in the first place, and OpenAI removed that field on 21 July.

The architecture is documented. Watching it operate live got considerably harder the moment people started paying attention to it. I first read about this when Mark Williams-Cook posted about it a number of weeks ago.

What our own Obsero data adds is what that index looks like from the outside. The source list changed character in a single day due to a model change, with three times as many unique hosts, first-party and official domains climbing, and the editorial middle falling away. That is the pattern you would expect from retrieval leaning on a set of entities the model already recognises.

Five things, and the first one is the big one.

Check the other engines before you change anything. The same Reddit content that lost 94% of its ChatGPT share held its position in Google AI Overviews, so anyone cancelling their community work off the back of one chart is optimising for a single engine while damaging themselves everywhere else.

And it is worth remembering that Reddit was never only an AI visibility play. It sends traffic, it ranks all over classic Google search, it is where a great many people go to research a purchase before they make it, and it remains the best source of unfiltered customer conversation you can get without paying an agency to go and find it. Judging all of that on one engine's citation share is measuring a channel by one of its smallest outputs.

What should actually concern you about Reddit is the strain it is under as marketers pile in trying to growth-hack their way into AI answers. That spam erodes the very thing that made the place valuable to a model in the first place.

Your own website is now the source. The domains that gained were the companies themselves and the official bodies around them, so if ChatGPT is going straight to your domain for pricing, specifications, eligibility and product detail, the accuracy and crawlability of those pages has become a retrieval problem rather than a web team problem.

You can check this (Query Fan-Out) in Obsero by signing up to our free tier.

Getting onto that shortlist is a brand job. A model can only go to your domain by name if it already knows the name, and that recognition is built long before anybody types a prompt. Digital PR, consistent association between your brand and your category, and coverage on the sources models already lean on are what put you into that memory. It is the part most GEO advice skips, because it is slower and considerably harder to sell than a content refresh.

Your citation count went up and means less. An answer that used to carry 12 sources now carries 50 to 70, so any single citation is a much smaller part of what the reader actually sees. Counting citations as a success metric will show you a rise that is mostly inflation, so share of citations, or better still whether you are in the recommendation itself, is the thing to track.

A falling share is not always a falling anything. If your August visibility report shows a drop, look at how many citations you actually picked up before you take it to the board. Plenty of the drops being reported this month are the effect we have just been through, where the total number of sources grew and everybody's slice looked smaller as a result.

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From Monday 24 August, ads start serving inside ChatGPT across 31 European markets, including Germany, France, Spain, Italy, Poland, Ireland, the Netherlands and the Nordics. Buying in Europe runs through OpenAI's sales team and agency partners for now, so there is no self-serve route the way there is in the UK and the US.

Ads have been running inside ChatGPT answers since February. If you sell anything, there is a fair chance a competitor is already paying to appear underneath an answer you spent two years earning and until recently there was no way to check.

Three things worth knowing before we get to what we have built.

We have just shipped ad coverage in Obsero, which shows how often your tracked prompts return a paid placement and exactly who is buying them.

We ran it across 134 Levi's prompts, the sort of questions a person actually asks before buying jeans. Every single prompt returned a sponsored unit. All 134.

Part of that is the category, because apparel is about as commercial as it gets. The rest is a reminder that a single snapshot of one prompt tells you very little, and that coverage in a category you care about is worth measuring rather than assuming.

Across those 134 prompts we captured 550 sponsored units, and the single biggest advertiser held a third of them.

There is no keyword targeting in ChatGPT ads, and no demographic or interest targeting either. What you get instead is a free-text description of the conversations where your product might be relevant, plus geography, audience exclusions and negative keywords, and that is genuinely all of it.

So the matching is being done by a model reading the conversation, your landing page and your ad copy. The same thing that decides whether you get cited organically is deciding whether your ad is relevant, which makes content quality a paid media input. Almost nobody is treating it that way yet.

Where we are today: only one advertiser comes back per response, so this is not the full competitive picture you would get from Google, and the data is country-level with no city signal in it.

Our full explainer is here, with more on how the ads work and how the timeline has played out.

As always, thanks for reading. I do love to hear your feedback, so please reply to this email and let me know what you thought of this edition.

If you want me to run the citation analysis on your category, reply and tell me the brand. I will have a look.

Until next week,

Andy