Google cites new pages in a day. ChatGPT takes weeks.

Alex — Mon, 31 Aug 2026 15:46:53 +0000

Every guide to generative engine optimization explains what to do. Almost none of them say when it pays off, which is the only question anyone actually asks before committing budget to it.

That gap existed for a good reason. Answering it properly needs something nobody had bothered to build: a set of pages published at a known moment and then checked against several answer engines every single day afterwards. Without that, every claimed timeline was somebody's impression of their own client work, and impressions are exactly the wrong instrument for a system that regenerates its answers every couple of days.

The measurement now exists. Eighty-one newly published pages on a single domain, queried daily for thirty days across Google's AI Mode and ChatGPT search. Here is what it found.

Google moves in days. ChatGPT moves in weeks.

The gap at the start is larger than most people expect. Within 24 hours of publishing, 36% of the test pages were already being cited somewhere in Google AI Mode. ChatGPT search had picked up 10% in the same window, more than three times slower off the line. By day seven, Google was at 56% and ChatGPT at 17%.

Then the curves change character, and this is the part that matters. Google's coverage fluctuated week to week, rising toward a peak of 59% and falling back from it repeatedly. ChatGPT's climbed steadily and did not give ground: 35% at two weeks, 42% at thirty days. Google is fast and noisy. ChatGPT is slow and cumulative.

That has a direct consequence for how you report on this work, and it is where people get burned. A project reviewed at four weeks looks like a Google success and a ChatGPT failure, and both readings are artefacts of the review date rather than facts about the work. The correct checkpoint is somewhere past six weeks, and the correct framing is two separate curves rather than an average that describes neither.

Perplexity sits outside the comparison for a structural reason. A significant share of its crawling is triggered by a user's question rather than by a schedule, so on a narrow, specific prompt it can be the fastest of the three, and on a broad one it can lag both.

The clock starts at crawl, not at publish

The first week is not a content problem, and treating it as one wastes it. OpenAI runs three separate bots and the distinction matters: OAI-SearchBot surfaces sites in ChatGPT's search features, GPTBot crawls for model training, and both obey robots.txt, while ChatGPT-User fetches a page live because a person asked a question and is explicitly not bound by the same rules. Perplexity splits the same way, with PerplexityBot building the index under robots.txt and Perplexity-User fetching live during a question.

Both operators quote roughly the same latency on the control side, about 24 hours from a robots.txt update for their systems to adjust. That is a small number with a sharp edge, because an accidental block costs a day to undo after somebody notices. And the most common cause is not robots.txt at all. It is a bot-protection rule in a CDN that nobody remembers switching on.

Google is a different case entirely, and simpler. Its own documentation states there are no additional requirements to appear in AI Overviews or AI Mode and no special markup necessary, but that to be used as a supporting link a page must be indexed and eligible to be shown in Google Search with a snippet. The AI clock and the classic indexing clock are the same clock. There is no separate AI index to get into, and if the page is not indexed, no amount of GEO work reaches it.

So before blaming your content, check whether the crawlers are in your server logs at all, whether a firewall rule is turning them away, whether the page is indexed, and whether it renders anything useful without JavaScript. A crawler that receives an empty shell has technically fetched you and learned nothing. Those four cover most cases of "we are not being cited anywhere".

The ceiling nobody quotes

The number worth carrying out of this is not a speed, it is a limit. Over a full month, Google AI Mode peaked at 59% of the test pages and ChatGPT search reached 42%. That was on a domain with strong established authority publishing squarely inside its own subject area. Even under those conditions, roughly half of everything published was never cited anywhere.

This is the honest correction to how GEO is usually sold. The pitch implies that structuring content correctly makes it citable. The data says structuring content correctly makes it eligible, and selection is a separate, competitive step you do not control.

Plan on that basis and the economics stay sane. A page that fails to get cited is not a failed page, it is the expected outcome for about half of them, and the ones that do get cited tend to keep the citation. The practical consequence is that publishing a small number of genuinely specific answer pages beats publishing many broad ones, because the ceiling is per-page and broad pages lose to bigger entities every time.

A single check tells you almost nothing

This is the least intuitive finding in the area and it should change how you measure. Ahrefs tracked more than 43,000 keywords with at least sixteen recorded AI Overviews each and found an average persistence of 2.15 days, meaning an AI Overview has roughly a 70% chance of being different from one observation to the next. More usefully, only 54.5% of the cited URLs overlap between consecutive runs of the same query. Close to half the source list is replaced every time.

What does not change is the meaning. The same query re-run produces answers with an average cosine similarity of 0.95, and 54% of the named entities stay put. The system is confident about what it thinks and casual about who it credits, which means a citation appearing or disappearing on any given day carries far less signal than it feels like it does.

The rule follows immediately. Never measure AI visibility with a single check. Run a fixed prompt list at least ten times across several days and report the share of runs in which you appear, and freeze that list early, because changing it mid-measurement destroys the comparison and is how most in-house tracking quietly dies. A binary "are we cited" screenshot is roughly a coin flip, and I have watched more than one agency relationship turn on one taken at the wrong moment.

Ranking still helps, but a lot less than it did

For most of the AI-search era the reassuring answer to "how do we get cited" was "rank well, the rest follows". In July 2025 that held up, when Ahrefs analysed 1.9 million citations from a million AI Overviews and found 76.1% of cited pages ranked in Google's top 10.

By March 2026 the same analysis over 863,000 SERPs and four million AI Overview URLs put the top-10 share at 37.9%, with 31.0% coming from pages beyond the first hundred results entirely. Ahrefs attribute the shift to Google changing the model behind AI Overviews in January 2026 and expanding its query fan-out more aggressively, pulling sources from related searches rather than only the one the user typed.

Two things follow, and they pull in opposite directions on purpose. Ranking is no longer a reliable route to citation, so a strong SEO position is not the guarantee it was. But not ranking is no longer disqualifying either, and roughly a third of citations now coming from pages nobody would find in a normal search is the first genuinely new opportunity this field has produced for smaller sites.

What to do while you wait

The waiting period is not idle time, and the work that pays during it is unglamorous. Fix what the crawlers see: server-rendered content, clean headings, a direct answer near the top of each section rather than three paragraphs in, and structured data that states plainly who you are and what you sell. None of that is AI-specific. It is the same technical foundation classic search rewards, which is why the two disciplines are additive rather than alternatives.

Then work on the half you cannot edit. Answer engines cross-reference sources rather than trusting one, so the same facts about your business appearing consistently across directories, profiles and comparisons does more for attribution than another rewrite of your homepage. A business described three different ways in three places gets attributed less confidently than one described identically everywhere.

The caveats, stated plainly

The day-by-day figures come from one controlled test on one domain with strong existing authority. Treat them as a best case rather than an average, because a newer or weaker site should expect the same shape stretched over a longer axis and a lower ceiling.

There is also no submission form, no priority queue and no paid inclusion route anywhere across these engines. The only levers are being crawlable by the right agents, being indexed where indexing is the gate, and being a page worth selecting over the alternatives. Anyone offering to fast-track you is selling something that does not exist.

And the honest one: Google changed the model behind AI Overviews in January 2026 and the ranking-to-citation relationship roughly halved within months. Any timeline in this field, including this one, describes a moving system rather than a law. What has stayed stable across every change so far is only the shape. Google is fast and volatile, ChatGPT is slow and cumulative, and about half of what you publish is never cited at all.

Read the full version

This is the condensed version. The full article walks the first six months week by week, covers what each engine specifically needs before it can cite you, and answers the parts I skipped here, including whether you should be blocking AI crawlers at all.

How long until ChatGPT and Perplexity actually cite you?

Sources

The citation timings come from Semrush's controlled test of 81 newly published pages. The volatility figures are from Ahrefs on how often AI Overviews change, and the ranking comparison from their July 2025 analysis and its March 2026 re-run. Crawler behaviour is documented by OpenAI, Perplexity and Google directly. The measurement advice and the read on what all this means for a smaller site are mine.

If you have been tracking your own citation timings I would genuinely like to hear how they compare, particularly from anyone on a newer domain, since that is exactly the case the published study does not cover.

DEV Community: Alex