Tag: AI search visibility

For most of the last two decades, a search choice screen was a way of picking between conventional search engines. A proposal from the UK’s Competition and Markets Authority (CMA) would change that. It would put AI assistants such as ChatGPT and Perplexity on the screens Google shows UK users, and it would attach a condition: any provider listed must fairly attribute the publisher content it uses.

This is a proposal, not a rule, and it applies to the UK only. But it shows a regulator treating AI assistants as search alternatives, and source attribution as a formal requirement. This piece sets out what the CMA proposed, how it would work, what is undecided and what teams responsible for AI visibility should watch.

What the CMA proposed

On September 23, 2026, the CMA published strengthened proposals to give UK users of Google’s Android and Chrome products greater choice and more control over their search services. The CMA says it listened to publishers, search providers and consumer groups, and that the update responds to services such as ChatGPT and Perplexity.

The core elements are these. Google would show Android and Chrome users a choice of search services when they first use an Android phone or open Chrome. Users would be prompted again every year to choose their default. AI assistants could be included on the choice screens if they meet the relevant criteria, including technical and security requirements. And search providers on the screens would have to fairly attribute publisher content when it is used, so that people can easily reach the original sources.

Sarah Cardell, the CMA’s Chief Executive, said the CMA is “responding quickly to the rise of AI assistants”, which people increasingly use as search services but which do not appear on Google’s choice screens today.

The consultation closed at 5pm on October 9, 2026. The CMA expects to make its final decision on this requirement by the end of 2026.

Where this sits in the CMA’s wider approach

Google was designated as having strategic market status in general search and search advertising in October 2025, which lets the CMA set conduct requirements for it. According to the CMA, it imposed publisher controls on June 3, 2026, and fair ranking and data portability requirements on June 17. The user choice requirement is the one still being decided, an updated version of a January 2026 proposal.

How a choice screen would work

The press release gives the outline. The CMA’s consultation document, which runs to 71 pages according to PPC Land, holds the detail. We could not open that PDF, so the following comes from two independent analyses of it, by the trade publication PPC Land and by the law firm Bratby Law, which agree on the main points.

According to those analyses, the screen would list up to 12 providers. If more than 12 qualify, Google would be expected to show the five most popular plus seven chosen at random. Providers would appear in random order, and users would have to scroll the whole list before confirming. Users could trial a provider for at least a week. The screen would be unskippable on new Android devices and fresh Chrome installs, and a skippable reminder would bring it back at least once a year.

These details come from analysis of a proposal that may change, so treat them as the current draft.

How an AI assistant could qualify

The CMA says AI assistants “could in theory be able to be listed”, a phrase quoted by Bratby Law from the consultation document. Nothing guarantees a place. Per those analyses, a service would have to meet three criteria.

First, it must adequately address the full range of use cases of a general search service. The CMA’s notes describe four types of query: navigational, transactional, simple informational and more complex informational.

Second, it must provide a functioning general search experience. That covers technical operation, compatibility with Android, minimum security requirements and credible plans to operate sustainably.

Third, a significant proportion of its UK users must consider it a general search service. The notes say this means a meaningful proportion, not a majority.

A service does not need its own web index. A syndicated index is acceptable.

Who decides matters too. Under the proposal, Google, not the CMA, would run the eligibility process every six months, deciding within two months and sending written reasons for any rejection to the applicant and the CMA. Bratby Law notes that the draft contains no appeal route to the CMA against those decisions, and that the written reasons are the main check. That is one lawyer’s reading of a draft.

The condition for publishers

The attribution condition is the part most relevant to anyone who creates content. Per the CMA, search providers on the choice screens would have to fairly attribute publisher content when it is used, so people can easily access the source. In the analyses of the consultation document, any generative AI summary would have to attribute web content clearly and accurately, with a clear route to the source, and each provider could choose how to do it.

Three points deserve care.

The condition is a requirement of being listed on the screen, not a general rule for all AI products. Because the CMA can impose requirements only on Google, it works as a condition of access to Google’s distribution. Bratby Law argues that it therefore becomes a de facto standard for any rival that wants a place, though that is the author’s view.

The condition does not, in anything we reviewed, require payment to publishers or guarantee any amount of traffic. It concerns attribution and a way to reach the source.

How it would be enforced, and how fairness or accuracy would be judged, are not set out in the press release. Watch the final text.

What is not decided

A lot remains open. The requirement has not been imposed, and the criteria, slots, attribution wording and timing could all change after consultation responses. Google has not commented, according to press reports.

Timing is also uncertain. The final decision is expected by the end of 2026. According to PPC Land’s reading, some obligations would start about a month after the requirement is imposed, and the rest within six months, so screens showing AI assistants may not reach UK users until some time after that. Do not plan around a date.

Geography matters too. This is UK law, covering UK users of Android and Chrome. It changes nothing for users in the United States, so for mainly American clients it is a signal to watch, not a task for this quarter.

Why teams working on AI visibility should watch it

There are three reasons.

AI assistants are being treated as search. The CMA describes them as increasingly used as search services and wants them offered as alternatives. For anyone working on generative engine optimization, that confirms what many teams assume: visibility in AI answers is becoming part of search visibility, in the eyes of regulators as well as users.

Attribution is becoming a formal concept. If a regulator makes fair attribution a condition for AI assistants to reach users, it normalizes the idea that answers should point back to sources. That could affect how assistants display citations over time, but it is a possibility, and we found no evidence yet of any change.

Distribution could shift. If more UK users chose an assistant, more searches might begin there, which would raise the importance of answer engine optimization, being cited and recommended in AI answers. That is a scenario. The proposal does not say how many people would pick an assistant.

None of this reduces the value of traditional search engine optimization. The sensible view is that search is widening, not being replaced.

What to do now

You do not need to change strategy because of a consultation. A few low-cost steps keep you ready.

Measure AI referrals now. Check your analytics for traffic from ChatGPT, Perplexity, Gemini and other assistants, and note how it changes. A baseline lets you see any shift later, whatever the cause.

Make attribution easy. Use clear titles, visible authorship and dates, stable canonical URLs and descriptive headings, so any assistant that wants to credit you can do so accurately.

Check how you are cited. Run real customer questions in several assistants and record whether your brand and pages appear and are attributed correctly. A free AI Visibility Audit does this across platforms.

Keep your source pages strong. Pages that answer a specific question clearly and carry original information are the likeliest to be cited, which is the same work that supports search engine optimization and generative engine optimization.

For UK clients, set a review date. Look again when the CMA publishes its decision, and read the final attribution wording before advising anyone.

Where this leaves you

The confirmed facts are these. On September 23, 2026, the CMA proposed that Google show Android and Chrome users in the UK a choice of search services at first use and prompt them annually. AI assistants could be listed if they meet the criteria, and providers listed would have to fairly attribute publisher content. The consultation closed on October 9, and a final decision is expected by the end of 2026. It is a proposal for the UK only.

The lasting lesson is about direction. Regulators are beginning to treat AI assistants as search services, and attribution as something that can be required, which matters for answer engine optimization planning. For teams reviewing how a brand appears in AI answers, that is a reason to measure now and watch the final wording.

Frequently Asked Questions

1. What did the CMA propose on September 23, 2026?
It proposed that Google show UK users of Android and Chrome a choice of search services at first use and prompt them again every year. AI assistants could be listed if they meet the criteria, and listed providers would have to fairly attribute publisher content.

2. Is this a final rule?
No. It is a proposed conduct requirement. The consultation closed on October 9, 2026, and the CMA expects to make its final decision by the end of 2026.

3. Will ChatGPT and Perplexity definitely appear on the screens?
No. The CMA says assistants could be listed if they meet the criteria, and analyses of the proposal describe three: covering the full range of search queries, providing a functioning search experience and being considered a general search service by a significant proportion of UK users.

4. Who decides which services are listed?
Under the proposal, Google decides, through an eligibility process every six months, with written reasons for rejections sent to the applicant and the CMA. Bratby Law notes that the draft contains no appeal route to the CMA.

5. What does the attribution condition require?
Providers on the screen would have to fairly attribute publisher content when it is used, so users can reach the original source. Analyses of the consultation document say generative AI summaries must attribute web content clearly and accurately. We found no requirement for payment or traffic.

6. Does this affect businesses outside the UK?
Not directly. It applies to UK users of Android and Chrome, but it signals how regulators are treating AI assistants and attribution.

7. What should I do about it now?
Measure traffic from AI assistants, make your pages easy to attribute and check how assistants cite you. Review again when the CMA publishes its final decision, rather than changing strategy on a proposal.

If you want to know how your brand appears across Google and AI answer engines today, that is exactly what we look at in a free AI Visibility Audit.

When someone searches for your business by name on their phone, the panel that appears is often the first and only thing they read. For years, that panel has been built from your Google Business Profile: hours, phone number, photos, reviews and a map pin. In September 2026, marketers began reporting something new at the top of it, an “AI Overview” label above a short, AI-written description of the business.

It is a test, not a launch, and Google has not announced it. But it points in a direction every local business should take seriously, because the description is no longer only what you wrote. This piece covers what was reported, what is and is not known, and a practical checklist of what to check now.

What was reported

On September 17, 2026, Search Engine Roundtable reported that some local knowledge panels, which are Google Business Profiles, now show an “AI Overview” label. A Show more button below the description expands the panel into a chat-style follow-up experience that resembles AI Mode. Ben Fisher, co-founder of the local search agency Steady Demand, posted about it first, and Barry Schwartz of Search Engine Roundtable said he was able to reproduce it himself.

On September 23, the UK agency Marwick Marketing described the same behavior. Its write-up says the change has so far been spotted only on mobile and that UK marketers have replicated it. It also says Google has not announced anything, and it describes the change as “not confirmed as a permanent change yet”.

That is the full set of established facts. We found no official Google statement or documentation describing the feature.

A test, not a rollout

The distinction matters because it should shape your response. A rollout is a settled change that you plan around. A test is something Google may expand, modify or withdraw, and may be showing to only some users, in some places, on some devices.

There is also precedent for this kind of experiment. Search Engine Roundtable reported in June 2024 that Google had shown AI-generated text as the description in some local panels, text that business owners had not written themselves. Schwartz’s September report says Google has been testing variations of this for a while. So the sensible reading is not that local search changed overnight. It is that Google keeps experimenting with letting AI describe a business, and the current version is the most visible so far.

The practical consequence is that some customers will see the classic panel and some may see the AI version. Both need to be right.

What the one detailed example shows

The most detailed public example is Schwartz’s own. He found that most of the AI-written description of his business was correct. One part, a section on what he calls niche solutions, was somewhat out of date. Importantly, he did not blame the AI for that. He noted that the content on his own company site was old, which suggests where the description drew its detail.

That small example carries a useful lesson. An error in an AI description may not be an AI invention. It may be an accurate summary of outdated information you published, or left in place, somewhere. Schwartz’s own reaction was mixed. He wrote that he was unsure he wanted AI describing his business “when I can” do it himself.

For a local business, the lesson is that every public statement about you, on your site, your profile and elsewhere, is a potential input. If a service you stopped offering is still listed, a summary can repeat it.

What a larger snapshot adds, with caution

On September 26, Fisher’s company published a study of how AI Overviews merge with business panels on mobile. It should be read with that context in mind: the author sells local search services, and the study is a single-day snapshot, which the author acknowledges.

The sample was 100 local brands across ten service industries, searched on mobile from a New York anchor point, with four query variations per brand. The study reported that 26% of brands showed an AI Overview on a plain brand-name search on mobile, against 6% on desktop. Of the overviews that appeared, about 80% were merged into the business panel. In four of 68 phone searches, the result surfaced a different, similarly named business. And in a three-day repeat test, one of 20 brands changed layout from one day to the next.

We would not treat those figures as a benchmark. One day, one city and an agency author make the findings suggestive, not conclusive. They support three cautious observations. The experience is not uniform. It appears more common on mobile. And a business can be confused with another of similar name, which is a reason to check your own results rather than assume.

What may feed the description, and what is unproven

The obvious question is which of your information the AI uses. Marwick Marketing says local panels draw on Google Business Profile data and on Google’s Knowledge Graph. Beyond that, advice circulating in the industry gets more speculative.

Some commentators say the services list matters more than website copy, or that review wording shapes the description, or that posts and photos act as freshness signals. Those are interpretations. We found no published test showing which fields drive the text, and one widely cited guide rests on a single reported example with no sample size or method.

So the honest position is this: nobody outside Google can say exactly how the description is built. What you can do is make every plausible source accurate, current and consistent. That costs little, and it is the same work that supports good local search engine optimization whether or not the test continues.

What to check now

Start with what customers see.

Search for your business by name on a phone while logged out of your Google account. Note whether an AI Overview label appears, read the description line by line, and take a dated screenshot. Do the same for a search like your name plus reviews. Results vary, so repeat the check on different days.

Look for errors. List anything wrong, outdated or missing, such as retired services, old locations or wrong hours, then trace each one to its source: your website, your profile, a directory or a review.

Then fix the sources.

Review your Business Profile against Google’s own guidelines. Choose categories carefully: Google tells businesses to use “the fewest number of categories” that describe the core business, and to pick the most specific ones. Make sure the services and products you list are real and current. Keep the business description honest and relevant, and remember that the guidelines do not allow special promotions, prices or links in it.

Check hours, including holiday and seasonal hours, and make sure your address and phone number match exactly across your website, your profile and your main directories. A mismatch gives any summarizer two versions to choose from.

Update your website. If the AI reads your site, an outdated services page or an old about page is a liability. Remove or revise anything that no longer applies.

Look at your reviews. Respond to recent ones, and read how customers describe you. Their language will appear in the public record about your business. Do not ask for scripted reviews or write fake ones.

Test for confusion. If your name is similar to another business, search from a different location or on a different device and see whether the wrong business appears. If it does, keep your name, address and categories distinct and accurate.

Finally, set a routine. Pick a day each month, repeat the checks and keep the dated screenshots in one place.

What not to do

Do not stuff keywords into your business name or description to influence the AI. Google’s guidelines say the name should reflect how customers know the business, and violations can lead to changes in your information or removal of your listing.

Do not assume you can opt out. Marwick Marketing reports that the only option it found was filtering AI results with the Web results toggle, which is a viewer’s choice, not a business’s.

Why this matters beyond Google

This is part of a broader shift in how people find local providers. Generative engine optimization and answer engine optimization are about being described correctly and recommended in AI-generated answers, and the AI Overview in a business panel is one of those answers, attached to your name. The same discipline applies on every platform: clear, accurate, consistent information published in places machines can read.

The Steady Demand snapshot also found that most AI citations in its sample pointed to third-party platforms, such as review sites and forums, rather than official brand sites. As a single-day sample, that is only an indication. But it is a reminder that what other people say about you may shape the answer as much as what you say yourself. Strong local search engine optimization, generative engine optimization and answer engine optimization now overlap, and a profile that is accurate everywhere is the common denominator.

Where this leaves you

The confirmed facts are these. Search Engine Roundtable reported on September 17, 2026 that some local knowledge panels show an AI Overview label, with a Show more button that opens a follow-up chat. The change was reproduced by Schwartz and by UK marketers, appears so far to be mobile only, and has not been announced by Google. It is a test.

The response is a routine, not a rebuild. An AI Visibility Audit can show where your information is wrong across AI platforms. Search for yourself, read what the panel says, trace any error to its source, and correct your profile, your website and your listings. Treat your public information as the raw material for every summary of your business, human or machine.

Frequently Asked Questions

1. What is the AI Overview label in local knowledge panels?
It is a label that some searchers see at the top of a business’s panel, above a short AI-written description. A Show more button opens a chat-style follow-up, similar to AI Mode. Search Engine Roundtable reported it on September 17, 2026.

2. Has Google confirmed this feature?
No. Google has not announced it, and we found no official documentation. It should be treated as a test that may change or disappear.

3. Is it only on mobile?
So far, reports describe mobile. Marwick Marketing says it has only been spotted there, and a September snapshot study by Steady Demand found AI Overviews far more often on mobile than on desktop. That study was a single-day, agency-run sample, so treat it as an indication.

4. Can I opt out of AI descriptions of my business?
We found no business-side opt-out. Marwick Marketing reports that the only option it found is a viewer filtering AI results with the Web results toggle.

5. What should I check first?
Search for your business by name on a phone while logged out, read the description, and screenshot it. Then check your profile categories, services, description, hours and contact details, and your website for outdated statements.

6. Does review wording affect the AI description?
Some commentators say so, but we found no published test that proves it. What you can do is respond to reviews, keep your service information accurate and never write or script fake reviews.

7. How often should I check?
Monthly is a reasonable routine for most local businesses. Keep dated screenshots so you can see what changed and when, and check more often if you make major changes to your services or hours.

If you want to know how your business appears across Google and AI answer engines, and where the information is wrong or missing, that is exactly what we look at in a free AI Visibility Audit.

Several agencies and software vendors now offer a free AI visibility audit, and they are not the same product. Some are self-serve tools that return a preview in seconds. Some are manual reviews run by a strategist. Some are checklists built around a local business. Foreignerds offers one too, and we describe it below. Because the offers differ so much in scope, speed, and who they suit, the most useful thing we can do is explain what a good audit should measure and how to compare the ones you find.

An AI visibility audit tests whether AI answer engines such as ChatGPT, Claude, Perplexity, Google AI Overviews, and Microsoft Copilot mention, cite, and describe your business accurately when someone asks a question you would want to answer. It is different from a traditional search engine optimization audit, which checks how you rank in a list of links. A site can rank well on Google and still be absent from the answers people read in an AI assistant.

What a free AI visibility audit is, and what it is not

An audit is a snapshot. It records how AI systems treat your business for a defined set of questions at a defined moment. It is useful for finding gaps, prioritizing work, and setting a baseline. It is not a ranking guarantee, and it is not a substitute for ongoing measurement.

Free audits are also a way for a provider to start a conversation. That is a fair exchange, provided you know what you are receiving. Before you request one, it helps to know what the findings should contain and whether you keep them regardless of whether you hire the provider.

One caution applies to any audit, free or paid. AI answers can differ from one run to the next and from one platform to the next. A single screenshot of one prompt is weak evidence. A credible audit uses a set of prompts, tests each platform separately, and reports patterns.

What a useful audit should measure

Whatever the provider, a useful audit covers a handful of things.

Citations and mentions. A citation is a link to your site inside an AI answer. A mention is your business name appearing without a link. They are different signals, and a business can have one without the other.

Share of voice against competitors. Knowing that you appear is less useful than knowing who appears instead of you for the same question.

Accuracy. AI systems sometimes state wrong facts about a business, such as outdated services, incorrect locations, or invented details. An audit should check what is being said, not only whether you are named.

Content structure. Pages that answer questions clearly, with direct answers, headings, and lists, are easier for AI systems to extract from. An audit should review the pages that matter most to revenue.

Structured data. Schema markup helps machines understand who you are and what you offer, so gaps here are worth listing.

Third-party footprint. Reviews, directories, press coverage, and community discussion shape what AI systems say about you. This is often the most overlooked part, and the research below explains why it matters.

Why each platform has to be tested separately

If one provider tells you that you are visible in AI search, ask which platforms they tested. The evidence suggests platforms behave differently enough that a single result does not transfer.

Averi’s benchmark report, citing a Profound analysis of 680 million citations collected between August 2024 and June 2025, states that only 11% of domains are cited by both ChatGPT and Perplexity. That is a domain-level figure across the whole citation landscape, not a statement about your own brand, and different studies define overlap differently. The direction is consistent, though: the two platforms draw on different sources.

Ahrefs found a similar divergence from another angle. In an analysis of 15,000 long-tail queries published on August 11, 2025, only 12% of URLs cited by ChatGPT, Gemini, and Copilot also ranked in Google’s top 10 for the same prompt. Perplexity was the exception, with 28.6% overlap. Ranking on Google is not the same as being cited in an AI answer.

Slate HQ’s 90-day study of six B2B software brands shows the same spread at brand level. For one email security client, brand-owned citation share ranged from 4.5% on ChatGPT to 10.2% on Google AI Mode. That is a single client’s result and not an average, but the gap between platforms for the same brand is the point.

The practical consequence is simple. An audit that tests only one platform, or blends all platforms into a single score, hides the variation you most need to see.

Why third-party sources belong in the audit

A research paper by Chen, Wang, Chen, and Koudas, submitted in September 2025, classified the sources AI systems cite as earned media, brand-owned, or social. In its consumer electronics tests, about 93.6% of ChatGPT’s citations and 93.7% of Claude’s came from earned media, meaning third-party sources. Perplexity and Gemini drew a smaller share from earned media and more from brand-owned and social sources. The paper is a preprint and covers consumer product queries, so it does not guarantee the same mix for a services business.

Slate HQ’s study adds context: it found that 76.3% of citations across its tracked brands went to domains that were neither the brand nor its direct competitors. Together, these findings mean an audit that reviews only your own website misses a large part of the picture. What others publish about you matters, which is why generative engine optimization work reaches beyond your own site, and an audit should say where you appear, where you do not, and which third-party sources AI systems already trust in your category.

Three kinds of free audit you will find

Descriptions below come from each provider’s own website at the time of writing. Offers change, so confirm the details before you request one.

Self-serve tools. Ahrefs offers a free AI visibility checker that needs no signup. It queries ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews and returns total mentions, a platform breakdown, top associated topics, and top cited domains. The free version is a limited preview, with results capped at five items per section, and historical tracking and competitor benchmarking sit in a paid product. It is a quick way to see whether you register at all.

Manual agency audits. DerivateX runs a free audit by hand through a senior strategist, covering ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, and AI Mode. It says results arrive within 48 business hours of confirming fit, and that findings remain with you whether or not you hire the agency. It states the audit suits B2B software companies with roughly $5 million to $50 million in annual recurring revenue and is not suited to e-commerce, local, or consumer businesses.

Business-type checklists. LocalFi offers a free audit built around local search signals. It reviews website structure, service pages, location pages, Google Business Profile, content clarity, entity signals, reputation markers, and search presence across ChatGPT, Gemini, Perplexity, and Google AI Overviews. It describes timing as dependent on the website and scope.

These are different tools for different jobs. A quick self-serve check tells you whether to worry. A manual audit tells you what is being said and who is being recommended instead. A local checklist focuses on the signals local searchers rely on.

What the Foreignerds free AI Visibility Audit covers

Our free AI Visibility Audit tests five platforms individually: ChatGPT, Claude, Google AI Overviews and AI Mode, Perplexity, and Microsoft Copilot. It measures seven things: citation rate, brand mention rate, AI share of voice against named competitors, sentiment and accuracy of what AI says about you, content extractability, structured data coverage, and third-party brand footprint.

The deliverables are an overall visibility score benchmarked against three to five competitors, a custom test set of 20 to 30 buyer prompts, a platform-by-platform breakdown, a check for inaccurate statements, a schema and structured data gap report, a prioritized 90-day action plan ranked by revenue impact, and a live dashboard for tracking citation rate and share of voice over time.

It runs over four weeks. In week one we set the baseline and map the prompts. In week two we audit five to ten of your highest-priority revenue pages. In week three we review schema, entity consistency, and third-party footprint. In week four we deliver the report and a live walkthrough. It is free with no obligation.

The trade-off is time against depth. Four weeks is slower than a tool that answers in seconds or a review that returns in two days. It covers more dimensions than a quick preview, and it is built around your own buyer questions and competitors. Which fits depends on how much detail you need and how soon.

How to compare offers

Whichever provider you consider, these questions separate a useful audit from a sales form.

Who chooses the prompts? Strong prompts come from real buyer questions in your market, not generic ones.

How many platforms are tested, and are they reported separately? Blended scores hide the differences described above.

Are competitors benchmarked? Visibility is relative.

Does the audit check accuracy? Being mentioned incorrectly can be worse than not being mentioned.

Does it look beyond your website? Third-party footprint should be part of the review.

What do you keep? Confirm that you receive the findings without a commitment.

Who runs it, and how long does it take? Automated, manual, and hybrid audits differ in speed and judgment.

Is there a sample or a clear description of the report? If a provider cannot describe what you will receive, treat that as a warning.

Where this leaves you

A free AI visibility audit is a reasonable first step if you treat it as a baseline. Pick one that tests platforms separately, uses your own buyer questions, benchmarks competitors, checks accuracy, and looks past your own site. Then use the results to decide where to invest, whether that is content structure, third-party coverage, or wider generative engine optimization and answer engine optimization work. Repeat the check on a schedule, because the answers change.

Frequently Asked Questions

1. Which agencies offer a free AI visibility audit?
Answer: Several do, along with software vendors. They differ in scope: some are self-serve tools, some are manual reviews, and some are aimed at specific business types. Foreignerds offers a free AI Visibility Audit covering five platforms and seven measures over four weeks.

2. What does an AI visibility audit measure?
Answer: A useful audit measures citations, brand mentions, share of voice against competitors, accuracy of what AI says about you, content structure, structured data, and your third-party footprint.

3. How is it different from an SEO audit?
Answer: A traditional search engine optimization audit checks how you rank and perform in search results. An AI visibility audit checks whether AI systems mention, cite, and accurately describe you in their answers, which makes it a core input to answer engine optimization.

4. Why test each AI platform separately?
Answer: Platforms cite different sources. One study found only 11% of domains were cited by both ChatGPT and Perplexity, so a result on one platform does not predict another.

5. Is a free audit really free?
Answer: Offers vary, so confirm what you keep and whether there is any obligation. Foreignerds’ audit is free with no obligation.

6. How often should I repeat an audit?
Answer: AI answers change, so a periodic check against your original baseline is sensible. A quarterly review is a reasonable starting cadence, and a tracking dashboard can help between audits.

If you want to see how ChatGPT, Claude, Perplexity, and Google describe your business today, request your free AI Visibility Audit from Foreignerds.

If your entire AI visibility plan is to publish better pages on your own site, the research says you are working on the smaller half of the problem. Two academic studies and one large industry analysis, each looking at a different slice of AI search, land on the same practical conclusion: what other people publish about you carries far more weight in AI answers than what you publish about yourself.

That is a strong claim, and it is also one that gets stretched in the retelling. The headline figure of 93% applies to specific engines in a specific kind of query, not to every AI platform in every situation. Here is what each source actually measured, where the numbers hold, where they do not, and what it means for how you spend a marketing budget.

Study one: a small group of outlets dominates news citations

The first paper is “News Source Citing Patterns in AI Search Systems” by Kai-Cheng Yang, posted to arXiv on July 7, 2025. It analyzed 366,087 citations across 12 AI search models from three providers: OpenAI, Perplexity, and Google. The data was collected over seven weeks between March and May 2025 from the AI Search Arena dataset.

A little over 9% of those citations, 32,865 in total, pointed to news outlets. That is worth stating up front, because it means the paper is not claiming that AI engines mostly cite journalism. It is examining the news slice of citations and finding that the slice is extremely concentrated.

The concentration differs sharply by provider. The paper reports that the 20 most frequently cited news sources account for 67.3% of citations for OpenAI models, compared with 31.9% for Google and 28.5% for Perplexity. Reuters and the Associated Press alone were cited at rates of 22.8% and 12.2% for OpenAI models. In other words, when ChatGPT-style models reach for a news source, they reach for a very short list.

One caution applies. The paper is a preprint posted on arXiv, so read it as a well-documented research finding rather than settled consensus, and note that it covers models tested in 2025.

The takeaway for marketers is narrower than the headline suggests. If you want news-based visibility in OpenAI-powered answers, the door is small, and the outlets standing in it are wire services and major national publishers. That does not make earned media pointless. It tells you that not all earned media is equal.

Study two: earned media versus your own site

The second paper is “Generative Engine Optimization: How to Dominate AI Search” by Mahe Chen, Xiaoxuan Wang, Kaiwen Chen, and Nick Koudas, submitted on September 10, 2025. The authors ran controlled experiments across ten consumer product verticals, with 100 queries each, and classified every cited source as earned media, brand-owned, or social content. Their abstract describes a systematic and overwhelming bias toward earned media, meaning third-party authoritative sources, over brand-owned and social content.

The numbers vary by engine, and the variation matters. In the consumer electronics vertical, Claude drew about 93.7% of its citations from earned media and ChatGPT about 93.6%. Perplexity was far lower at 53.3% earned, with 31.6% from brand-owned sites and 15.1% from social platforms.

Across the paper’s broader brand tests, the pattern held. For well-known brands, ChatGPT cited earned media 93.5% of the time and brand-owned pages 6.5%. Claude sat at 87.3% earned and 6.8% brand-owned. Perplexity came in at 67.4% earned and 8.8% brand-owned, with 23.8% from social. Gemini showed 63.4% earned and 25.1% brand-owned. For niche brands, ChatGPT reached 95.1% earned and 4.9% brand-owned, and Claude 86.3% earned and 10.6% brand-owned.

So the tidy line that AI engines cite earned media 93% of the time is accurate for ChatGPT and, in one vertical, for Claude. It is not accurate for Perplexity, and it clearly overstates the case for Gemini, which gave brand-owned pages a quarter of its citations in the well-known brand test. Your own website is close to invisible for ChatGPT on product queries. It is a real contributor to Gemini.

Note also that this is a preprint, and the queries are consumer product comparisons. A B2B services buyer asking about agencies or software may see different source mixes. Treat the paper as strong evidence about direction, not a guarantee for your category.

The third source: most citations do not come from Google’s first page

Ahrefs added two findings that support the same conclusion from a different angle.

In an analysis of 15,000 long-tail queries published on August 11, 2025, Ahrefs found that only 12% of URLs cited by ChatGPT, Gemini, and Copilot also appeared in Google’s top 10 for the same prompt. Put the other way, roughly 88% did not. Perplexity was the exception, with 28.6% overlap. This is why ranking well on Google, while still valuable, does not guarantee a citation. The AI engines are retrieved from a different set of pages.

The second finding is more useful for anyone planning outreach. Ahrefs examined the 1,000 pages ChatGPT cited most in September 2025 and concluded that roughly two-thirds of them are effectively off-limits to traditional outreach. The breakdown was Wikipedia at 29.7%, homepages and landing pages at 23.8%, educational pages at 19.4%, app stores at 6.6%, reviews at 5.8%, news and media at 5.2%, language and grammar sites at 4.0%, dictionary and reference at 2.2%, blog articles at 1.9%, community forums at 0.9%, and corporate pages at 0.5%.

Ahrefs counted only four categories as influenceable: educational pages, reviews, news and media, and blog articles. Together they make up 32.3% of the top 1,000.

This corrects a common retelling. Several summaries describe the off-limits share as Wikipedia, government sites, education, and major news. That is not what Ahrefs found. In the Ahrefs breakdown, educational pages and news were on the influenceable side, and the off-limits side was mostly Wikipedia, other organizations’ homepages, and reference-style sites. The opportunity is in educational content, reviews, news mentions, and articles.

What this does and does not prove

It proves that publishing on your own domain is not enough to earn citations from ChatGPT or Claude on product research queries. It supports the case that third-party mentions matter, and that they matter more on some engines than others.

It does not prove that your own website is irrelevant. Brand-owned content still accounts for 5% to 25% of citations in the Chen study depending on the engine, and pages that AI systems retrieve from your site are part of what makes third-party coverage credible. Your site is where a cited claim gets verified.

It also does not prove that any earned mention helps. The Yang paper shows that the citation pool for news is narrow, and the Ahrefs data shows that news is only 5.2% of ChatGPT’s most cited pages. A mention in a low-authority outlet is unlikely to move the needle.

What this means for link building and digital PR

The strongest business case in this research is for redefining what link building and digital PR are for. For years, the goal was a link that passed ranking signals. The goal now includes being named, described, and reviewed in the places AI engines already trust.

That changes priorities in four ways.

Prioritize the sources engines already cite. Before pitching anyone, check which publications, review platforms, and educational sites appear in AI answers for your category. Build the target list from that, not from domain authority alone.

Pursue reviews and comparisons, not just news. Reviews and educational pages are a much larger share of ChatGPT’s influenceable citations than news. Accurate, current listings on review platforms and inclusion in independent comparison content deserve more of the budget than they usually get.

Create material that other people want to cite. Original data, clear definitions, and expert commentary give journalists and educators a reason to reference you. This is the foundation of effective digital PR, and it also gives educational and blog sources something to link to.

Keep your own site accurate and consistent. When an outlet mentions you, engines and users will check your site. Consistent naming, clear service descriptions, and current facts make the third-party mention easier to trust.

Treat Wikipedia as a consequence, not a target. At 29.7% of ChatGPT’s top 1,000 cited pages, it is the single largest source, and Ahrefs places it outside the reach of outreach. Trying to force an entry is the wrong response. The sustainable route is to earn the independent coverage that makes a company notable in the first place, which is exactly what a disciplined digital PR program produces over time.

Expect a slow payoff. Third-party coverage takes weeks to publish and longer to be picked up by AI engines, so judge a campaign over quarters, not days. Pair the outreach with a monthly citation check so you can see which placements actually change what the engines say.

Set a baseline before you begin. Record which prompts cite your brand today, on which platforms, and from which sources. Repeat monthly. Without that, you cannot separate the effect of an outreach campaign from ordinary platform variability.

Where this leaves you

Optimizing your own site remains necessary. Sound search engine optimization fundamentals still determine whether you are found and understood. But the evidence now points to a second requirement that sits outside your domain. On ChatGPT and Claude, earned media supplies roughly nine in ten citations for product research. On Perplexity and Gemini the share is lower but still a majority. That is the reason generative engine optimization and answer engine optimization now include off-site work, and why link building teams are being asked to think about mentions, reviews, and reference sources, not just links.

Frequently Asked Questions

1. Do AI engines really cite earned media 93% of the time?
Answer: For ChatGPT and Claude in the consumer electronics vertical of the Chen study, yes: about 93.6% and 93.7%. Perplexity was near 53% and Gemini near 63% to 66% in other tests, so the figure does not apply to every engine.

2. What counts as earned media in these studies?
Answer: Third-party authoritative sources such as independent reviews, journalism, and expert publications. Brand-owned pages and social content are classified separately.

3. Does this mean my website does not matter for AI visibility?
Answer: No. Brand-owned pages still receive about 5% to 25% of citations depending on the engine, and your site is where third-party claims get checked. It is necessary but not sufficient, which is why answer engine optimization now includes off-site work.

4. Is it true that two-thirds of ChatGPT’s top citations cannot be influenced?
Answer: Ahrefs reported that roughly 67% of ChatGPT’s top 1,000 cited pages fell outside traditional outreach. Educational pages, reviews, news, and blog articles, about 32.3%, were the influenceable share.

5. Which earned media should I prioritize?
Answer: Start with sources AI engines already cite in your category: review platforms, educational content, and a short list of authoritative publications. Wire services and major national outlets dominate news citations for OpenAI models.

6. How do I measure whether digital PR is improving my AI citations?
Answer: Track a fixed set of buyer prompts on ChatGPT, Claude, Perplexity, and Gemini each month, and record whether your brand is cited and from which source.

If you want to see which third-party sources are shaping AI answers about your brand today, that is exactly what we cover in a free AI Visibility Audit.

This is the third piece in our series on how AI platforms decide who gets cited. The first looked at how much AI referral traffic ChatGPT controls. The second looked at how selective it is about which sources it trusts. This one asks the practical follow-up: if two of the biggest AI platforms disagree this much about sources, what does that do to the way you measure visibility?

The headline number is that only about 11% of domains are cited by both ChatGPT and Perplexity. It has been repeated in dozens of articles this year, usually with a sentence claiming that single-platform tracking leaves 89% of your visibility invisible. That last part goes further than the data supports. The 11% figure is real and worth acting on, but it is also easy to misread, and several of the studies stacked underneath it measure different things. Here is what each one actually found, and what it means for how you track your brand.

Where the 11% comes from

The figure appears in three related but distinct places, and they are not the same study repeated three times.

The first is a dataset of 680 million citations covering August 2024 to June 2025, analyzed by Profound and cited in Averi’s B2B SaaS benchmark report. Averi’s report states that only 11% of domains are cited by both ChatGPT and Perplexity. Worth noting: the 680 million analysis is Profound’s work, and Averi uses it as a supporting reference rather than presenting original research of its own. Several articles online attribute the analysis to Averi directly, which is a small but real attribution error.

The second is a Qwairy analysis of 118,000 AI responses collected between January and March 2026 across ChatGPT, Perplexity, Google AI Mode, and Claude, which Whitehat SEO cites in its own comparison. It found that only 11% of cited domains appeared across multiple platforms. That is a slightly different measurement from the first one. It looks across four platforms at once instead of comparing ChatGPT and Perplexity as a pair, so the matching 11% is a coincidence of two different methods, not one result confirmed twice.

The third is a Wellows study of 22.7 million citations across 1.15 million questions, covering January to June 2026. It approaches the same question from the opposite direction and reports that 89% of websites ChatGPT cited on a given question were never cited by Perplexity on that same question. That is essentially the mirror image of the 11% figure, measured at the website level, on identical questions.

So three datasets, three time windows, and two different definitions of overlap all point the same way: the two platforms draw on substantially different sets of sources. That agreement is the meaningful finding. The exact percentage matters less than the direction.

What the 11% does and does not mean

Now for the overreach. A low overlap between two platforms’ cited domains does not mean 89% of your potential visibility is hidden from you. Authority Tech, which curates and compares these studies, states explicitly that the figure does not imply that. There are three reasons.

First, the overlap is measured across all domains cited, not across your domain. Your brand could appear consistently on both platforms while the rest of the web fragments around you. The 11% describes the whole citation landscape, and your own footprint inside it is a separate question.

Second, Wellows itself is careful about this. Its authors describe the gap as a different candidate pool rather than a more unpredictable engine, and they point out that the number measures which sources answer a particular question, not which websites are permanently available to each platform.

Third, overlap depends heavily on how strictly you match. Wellows reports different results at website level, page level, and with different levels of address cleanup. A percentage that moves that much with the matching rule should be read as a range, not a fixed law.

The accurate version of the claim is narrower and still useful: if you only measure one platform, you are measuring a different slice of the web than the one your buyers see on the other. That alone is reason enough to change how you track.

A different 12% that gets mixed in

Ahrefs published a widely quoted 12% figure that often gets bundled into this conversation, and it deserves separating out. Ahrefs analyzed 15,000 long-tail queries in a study published on August 11, 2025, and found that only 12% of URLs cited by ChatGPT, Gemini, and Copilot also ranked in Google’s top 10 for the same prompt. The measurement compares AI citations with Google rankings. It does not compare one AI platform with another.

The Ahrefs data does contain a useful platform-level detail, though. Perplexity’s overlap with Google’s top 10 was 28.6%, far higher than ChatGPT at around 8%. So Perplexity leans on pages that also rank well in traditional search, while ChatGPT draws more heavily from pages that do not. Placed next to the 11% cross-platform figure, that goes a long way toward explaining why the two platforms disagree. They are retrieving from different places.

For anyone doing search engine optimization alongside AI visibility work, this is the important takeaway. Ranking well in Google helps you with Perplexity noticeably more than it helps you with ChatGPT. Treating them as one channel with one strategy will over-serve one and under-serve the other.

The same brand, very different results

Two more datasets show what this looks like at the brand level, and both are better evidence for multi-platform tracking than the 11% headline.

Superlines analyzed 34,234 AI responses across 10 platforms between January 14 and February 13, 2026, and reported a 615x gap in citation volume between the highest-citing platform, Grok at a 27.01% citation rate, and the lowest, Claude, at close to zero. The number is striking, but it needs context. It reflects how differently each platform handles linking out to commercial brand sites, which is partly a product design difference and not purely a measure of how much each platform likes any given brand.

Slate HQ published a 90-day study on March 30, 2026, tracking six B2B SaaS brands across ChatGPT, Perplexity, Gemini, Claude, Google AI Overview, and Google AI Mode, with more than 300,000 citation data points. Its central finding is that 76.3% of all citations went to domains that were neither the brand nor its direct competitors. Most of the visibility lives on third-party sources.

The per-platform breakdown Slate published for one email security client illustrates the spread. Brand-owned citation share was 10.2% on Google AI Mode, 10.0% on Google AI Overview, 9.1% on Claude, 6.9% on Gemini, 6.8% on Perplexity, and 4.5% on ChatGPT. Those are the numbers for one client, not an average across all six brands, and it would be wrong to say ChatGPT was the weakest platform for every company studied. But the spread within that single brand, more than double between the highest and lowest platform, makes the point clear. The same company can look strong on one platform and weak on another during the same 90 days.

Slate also found that Claude had the narrowest external source pool at 54% unidentified sources, while Perplexity cast the widest net at 67.7%. Different platforms are not just citing different pages. They are behaving differently in how broadly they search.

What to do with this

The practical response is not to panic about an invisible 89%. It is to stop treating AI visibility as a single number.

Track each platform separately. A blended score hides exactly the variation the studies above describe. At a minimum, report ChatGPT, Perplexity, Google AI Overviews, and Claude as separate lines, and watch each for movement independently. This is the foundation of any serious generative engine optimization program.

Identify which sources each platform trusts in your category. Since most citations go to third-party domains, your visibility depends on where you are mentioned as much as on what you publish. Check which review sites, directories, and community platforms each engine leans on for your topics, then look at whether you are present there.

Match your optimization to the platform. Because Perplexity correlates more strongly with Google rankings, traditional ranking work supports it. ChatGPT draws from a broader mix, so entity clarity, third-party mentions, and presence on the sources it favors carry more weight. This is where generative engine optimization and answer engine optimization diverge in practice, even though they are often discussed as one thing.

Set a baseline before changing anything. Record your citation presence per platform for a fixed set of prompts this week, then repeat monthly. Without a starting point, you cannot tell whether a change came from your work or from platform variability.

Treat every overlap statistic as a range. Different matching rules produce different percentages, and the sample sizes, time windows, and definitions vary. Use the studies to understand direction, then validate against your own prompts.

Where this leaves you

Only 11% of domains being cited by both platforms is a real signal, backed by three independent datasets using different methods. It does not mean 89% of your visibility is lost, and it is not the same as the Ahrefs 12% figure, which compares AI citations with Google rankings. What it does show is that ChatGPT and Perplexity behave like separate channels with separate source preferences, and that measurement built around one will miss what the other is doing. Track them separately, benchmark against a baseline, and treat the percentages as direction, not precision. That is the discipline answer engine optimization now demands.

Frequently Asked Questions

1. Is it true that only 11% of domains are cited by both ChatGPT and Perplexity?
Answer: Yes, that figure appears in several studies, including a 680 million citation dataset analyzed by Profound and a Qwairy analysis of 118,000 responses. Definitions differ slightly between studies, so treat 11% as an approximate indicator of low overlap, not an exact constant.

2. Does this mean 89% of my AI visibility is hidden if I only track one platform?
Answer: Not exactly. The 11% describes overlap across all cited domains, not your own brand. It does show that each platform draws on a different pool of sources, so single-platform tracking gives you an incomplete picture.

3. Is the Ahrefs 12% the same statistic?
Answer: No. Ahrefs found that only 12% of URLs cited by ChatGPT, Gemini, and Copilot also rank in Google’s top 10 for the same prompt. That compares AI citations with Google rankings, not one AI platform with another.

4. Which platform gives brands the highest citation share?
Answer: It depends on the brand and category. In Slate HQ’s example for one email security client, Google AI Mode and AI Overview were highest and ChatGPT was lowest. That is a single-client result and should not be generalized to every company.

5. What does the 615x citation gap mean?
Answer: Superlines found a 615x difference in citation volume between the highest platform, Grok, and the lowest, Claude, across 34,234 responses. It reflects how differently platforms link to commercial sites, so it shows platform behavior more than brand preference.

6. What should I track first?
Answer: Start with a fixed list of prompts your buyers actually ask, then record whether your brand is cited on ChatGPT, Perplexity, Google AI Overviews, and Claude separately. Repeat monthly and compare against that baseline.

If you want to see where your brand stands on each platform, not just one, that is exactly what we cover in a free AI Visibility Audit.

On August 27, SEO consultant Chris Long spotted something new in live search results and posted it immediately: “Holy moly SEOs: Google is now revealing the AI Mode prompt box BY DEFAULT for some queries.” His reading was blunt. “This is how Google continues to push AI Mode as the default search experience.”

Google confirmed it the next day. In a statement to Search Engine Roundtable, the company said: “For some queries, AI Overviews may dynamically expand for topics where our systems determine it’s most useful for people. Our research has shown that with this dynamic experience, users find Search more helpful and engage deeper in follow-up exploration.” Robby Stein, VP of Product for Google Search, added a detail that matters if you’re mid-read: “If you’ve already started scrolling, the expansion stops to keep your place.”

What actually changed is this. An AI Overview used to load as a short snippet, with a manual “Show More” click needed to see the full answer. For some queries now, it loads fully expanded from the start, with an open “Ask anything” box sitting underneath it, the same interface AI Mode uses. Google has not said which share of queries trigger this, whether that share will grow, or when the behaviour might change again. Stein was explicit that it’s still being tuned: “We’re always testing and tuning these experiences, so we’ll continue to refine based on user feedback.”

A related change, one week earlier

A week before dynamic expansion, on August 19, Google rolled out a related but separate change: generative UI moving from AI Mode into AI Overviews. This is the feature that builds a custom interactive tool for a query instead of describing an answer in text. Google’s own example is searching “ph scale” and getting an interactive visual built on the spot inside the overview, not a paragraph explaining one. Practice quizzes for exam prep, custom document generation, and a homework-helper feature using Lens all shipped alongside it.

These two changes are easy to conflate because they’re pushing the same direction, but they were confirmed on different dates for different reasons, and it’s worth keeping them separate when you’re explaining this to a client. Generative UI changes what the answer looks like. Dynamic expansion changes how much of it you see before doing anything at all.

Neither is really a standalone event either. Both sit inside a longer arc that started at Google’s I/O in May 2026, when AI Overviews and AI Mode formally merged into one integrated search experience running on Gemini 3.5 Flash. Everything since then, including this, has been Google steadily erasing the line between a summary above the results and a conversation instead of the results.

The 48% and the 58% are not the same statistic

Coverage of this topic has been sloppy about one thing, and it’s worth being precise here because it’s an easy stat to misquote. BrightEdge found, in March 2026, that AI Overviews appear on roughly 48% of tracked queries, with the rate far higher in specific verticals: 88% for healthcare, 83% for education, 82% for B2B technology. Separately, Ahrefs found in February 2026 that when an AI Overview appears, the top organic result’s click-through rate falls by 58%. One is a prevalence number. The other is a click-loss number. They come from different studies, measuring different things, and citing them together as one connected trend, the way they often get repeated, overstates what either study actually claims on its own.

What both numbers agree on, independently, is the direction. AI Overviews show up on a large and growing share of searches, and when they do, fewer people click through afterward. Two separate, peer-reviewed studies this year support the second half of that. Pew Research found clicks on 8% of visits where an AI Overview appeared, against 15% where it didn’t, a gap that held even after controlling for query length and individual searcher habits. A randomized field experiment in July found that hiding AI Overviews entirely lifted outbound clicks by roughly 40% per search. A second randomized experiment in August found a smaller but still real 8.8 percentage point lift, and a genuinely interesting null result alongside it: removing the AI Overview didn’t measurably change how satisfied or trusting people felt about their search. The benefit Google says it delivers isn’t showing up in what users report either way.

Why “position” barely means what it used to

Here’s the detail most content advice skips, and it’s the one that should change how you think about your own reporting. Google’s own Search Console documentation states that an AI Overview occupies a single position in search results, and every link inside that overview is assigned that same shared position. There is no internal ranking. You cannot see whether your link sat first or fourth inside the answer, only that the whole block sat wherever it sat. This is exactly why treating an AI Overview citation as a traditional search engine optimization ranking problem misses the point, there is no position left to optimize toward once you’re inside the block.

The impression rule is stricter too. A normal search result counts as an impression once it’s served on the page. A link inside an AI Overview only counts once it’s actually scrolled or expanded into view. Two links from the same site inside one overview count as a single impression on the chart-level view, though they’re counted separately on the page-level table, which means the two numbers in your own Search Console won’t always add up to each other, and that’s not a bug, it’s two different ways of aggregating the same event.

There’s a real attribution problem sitting underneath all of this too. One publisher who tracked this properly, using a custom GA4 dimension that catches the text-fragment links Google sometimes appends to AI Overview citations, found that 22.4% of that traffic was landing in their analytics as Direct, not Organic Search. Nothing in a standard analytics setup tells you this is happening. If you get meaningful AI Overview citations, some real share of that traffic is currently invisible to whoever reports on your channels.

The surface nobody’s talking about

There’s one more wrinkle worth knowing if you’re trying to explain the scale of this to a client. A separate but related surface, People Also Ask, has effectively been taken over by the same shift. Independent tracking in early September found 97% of People Also Ask answers are now AI Overviews, up from just 12.6% a year earlier. A second, independently run dataset checking the old snippet endpoint directly found it had stopped returning results at all from August onward. Whatever you think dynamic expansion means for search results generally, the box that used to sit quietly beneath a query with a handful of related questions has already, almost entirely, become the same AI-generated surface this whole piece is about.

What to actually check on your own site

Pull Search Console and look at click-through rate by query, not just average position. A page holding position one or two with a falling CTR on an informational query is a strong sign an AI Overview is intercepting the click before your listing is ever really seen, whatever the position column says.

Separate your query types honestly. Local, transactional, and highly specific searches remain far harder for an AI Overview to fully resolve on its own, and that’s still where organic clicks convert. Simple factual and broad comparison queries are exactly the ones dynamic expansion is built for, and they’re the ones where you should expect this to get worse before it gets better.

Check whether your content is actually being cited inside the overview for your target terms, separate from whether you rank underneath it. Under dynamic expansion, being the source Google pulls from now matters more than the position of the blue link sitting below a fully expanded answer nobody has to click to read. This is the whole premise behind generative engine optimization and answer engine optimization as disciplines distinct from traditional ranking work, earning the citation itself rather than the position beneath it.

And don’t chase this as if it were a ranking algorithm problem, because it isn’t one. This is an interface change, not a core update. Rewriting content to win back a click that an interface decision is now suppressing by design treats the wrong layer of the problem.

What this means going forward

Google’s Search Console rolled its generative AI performance reports out to every property worldwide on August 31, closing the gap for site owners who previously had no visibility into any of this at all. It’s worth using, even with its limits: impressions only, no clicks, no queries, and the counting quirks above. It’s still the closest thing to a first-party signal you’ll get for how often your content actually surfaces inside these expanding answers.

The broader shift is straightforward to state and harder to act on. A “Show More” click used to be a small piece of friction that gave your listing a chance to be seen before someone gave up and left. That friction is being removed, query by query, and Google has told you plainly that it intends to keep tuning the removal rather than reverse it. Structuring content so it earns the citation inside the answer, not just a ranking beneath it, through deliberate answer engine optimization and generative engine optimization work rather than traditional keyword targeting alone, is no longer a forward-looking recommendation. It’s the baseline for anything you publish from here.

Frequently asked questions

1. Is “dynamic expansion” a new Google algorithm update?
Answer: No. It’s an interface change to how AI Overviews display, confirmed by Google on August 28, not a change to the ranking algorithm. It affects how much of an AI-generated answer a searcher sees automatically, not which pages rank where beneath it.

2. Are the 48% and 58% AI Overview statistics from the same study?
Answer: No, and they measure different things. BrightEdge’s 48% (March 2026) is how often AI Overviews appear across tracked queries. Ahrefs’ 58% (February 2026) is how much the top organic result’s click-through rate falls when an AI Overview appears. They’re often quoted together as if connected, but they come from separate research.

3. Does average position still mean anything if I’m cited inside an AI Overview?
Answer: Less than it used to. Google’s documentation confirms every link inside an AI Overview shares one single position value, with no visibility into internal ordering. Whether you’re cited at all now matters more than where within the overview that citation sits.

4. Why is my AI Overview traffic showing up wrong in my analytics?
Answer: A documented case found 22.4% of one site’s AI Overview traffic was misattributed as Direct rather than Organic Search, due to how citation links are sometimes formatted. Standard analytics setups don’t catch this automatically.

5. What should I actually change first?
Answer: Start with Search Console’s generative AI performance report, live worldwide since August 31, and cross-check it against click-through rate by query in your normal performance report. Prioritise fixing content for the query types AI Overviews answer most fully rather than reacting site-wide.

If you want to know whether your content is actually earning citations inside AI Overviews and AI Mode, or just ranking beneath answers nobody clicks through, that is exactly what we check in a free AI Visibility Audit.

On September 12, Glenn Gabe posted something he’d sat on for a full day first, waiting for the data to firm up before going public. His read: an unconfirmed update rolled out in early September, sometime around the 3rd into the 4th. Marie Haynes reached the same conclusion independently, placing the shift closer to September 2. Two of the more measured, data-first voices in the SEO industry, arriving at the same window without coordinating, is not nothing.

Google has said nothing at all.

Here is the part that makes this genuinely confusing rather than simply another unconfirmed update: two days before Gabe and Haynes went public, a separate outlet checked the two most common volatility trackers and found almost nothing unusual. Semrush’s Sensor read 0.6 out of 10 on September 9, labelled Low. DataForSEO’s volatility index read 5.82 on September 8, a middling number with no sharp spike against its own recent baseline. Neither tool was flashing a warning.

So which is it. Did something real happen in early September, or did two respected analysts read noise into a quiet week that the aggregate trackers correctly saw as unremarkable.

Why the tools and the analysts can both be right

This isn’t actually a contradiction once you understand what each source is measuring.

Volatility trackers like Semrush Sensor and DataForSEO work by fingerprinting a large basket of tracked keywords and measuring how much the top results shuffle across that basket, in aggregate, across the whole web. That’s a genuinely useful signal for catching a broad core update. It is a much weaker signal for catching something narrower, a change that hits a specific content type, a specific vertical, or a specific query pattern hard while leaving most of the basket untouched.

Gabe and Haynes work differently. Their read comes substantially from looking at real client Search Console data directly, property by property. A shift that’s too narrow or too unevenly distributed to move an aggregate sensor can still show up clearly in the actual accounts they’re watching. This kind of week-to-week vigilance, checking real account data rather than waiting for a tool to flash red, is exactly what ongoing search engine optimization work is supposed to catch before a quiet shift turns into a real problem.

Neither approach is wrong. They’re measuring different things at different resolutions. Which means the honest starting position here is not “an update happened” or “an update didn’t happen.” It’s that something may have shifted for a meaningful subset of sites, without being broad enough to register clearly on tools built to catch a wide core update.

That distinction should change how you read your own data, not just whether you believe an update occurred. It also tells you something about which kind of site is more likely to feel this. A large publisher with thousands of tracked queries feeding into an aggregate sensor is exactly the profile a broad tool is built to catch. A smaller, more specialised site sitting in one content vertical is exactly the profile that can move meaningfully in its own Search Console data while barely registering on a basket built from the whole web. If you run a niche or narrowly scoped site, a quiet volatility sensor tells you less than it would for a large, broad publisher.

September has history, and that cuts both ways

Before assuming this fits a pattern, it’s worth knowing what the pattern actually is. Google’s confirmed rollout record shows real updates landing in or reaching into September repeatedly: an August 2025 spam update that ran all the way from August 26 through September 22, an August 2024 core update that carried into early September, a September 2023 helpful content update, and a September 2022 core update named for the month itself.

September is a genuinely plausible window for a rollout, based on that history. It is not, on its own, evidence that this specific unconfirmed shift is one. Google’s own recent cadence has been uneven, updates roughly 8 weeks apart at one point in 2026 and over 5 months apart at another. History tells you a September update wouldn’t be surprising. It doesn’t tell you this is one, and treating “September has a history” as confirmation is exactly the kind of shortcut that leads to premature action.

Rule out the boring explanations first

Before you treat a September dip as an algorithm story, rule out the things that look identical to one but aren’t.

Google Analytics reportedly showed missing data for September 1, including accounts reading zero users for part of that day. Separately, Google confirmed a brief Search Console indexing-reporting issue that existed and was resolved around September 9, with no exact start time given. Neither of these is a ranking change. Both can produce a chart that looks exactly like one if you’re only glancing at a dashboard rather than checking what’s actually being measured.

There’s a third confound worth naming: Google has also clarified how AI Overview impressions get counted inside Search Console, and average position no longer maps cleanly onto the old idea of ten blue links. This is precisely the shift that makes generative engine optimization and answer engine optimization work alongside traditional ranking now, not instead of it. A position metric that shifts because the reporting logic changed underneath it looks identical, on a graph, to a position metric that shifted because your rankings actually moved. And if a meaningful share of your queries now triggers an AI Overview that didn’t before, your click and position numbers can move for reasons that have nothing to do with a ranking algorithm changing at all.

There’s a fourth, narrower confound if a meaningful share of your traffic is European: separate reporting this same week covered changes to how Google’s results behave in the EU specifically. If your traffic is heavily skewed toward European markets, a regional change could easily be mistaken for a global algorithm shift when it’s actually confined to one geography.

Check these before anything else: was your reporting tool itself affected on the specific dates in question, is the change specific to position or AI Overview counted queries, does a large share of the affected traffic sit in Europe, and does the timing match a known reporting hiccup rather than the September 2-4 window Gabe and Haynes described.

Reddit is not a reliable second opinion here

If you go looking for corroboration in SEO forums, be careful what you count as confirmation. A review of early September threads across the main SEO communities found accounts that don’t cleanly agree with each other or with the Gabe/Haynes window. One report placed an initial drop on August 2-3, with a second decline on August 13, well before the window in question. Another, posted September 10, described clicks rising while impressions and position fell, a different pattern entirely, with no clear onset date attached. Cross-posted complaints without dated, corroborated specifics aren’t a second data point. They’re the same uncertainty wearing crowd.

How to actually read your own data

Once you’ve ruled out reporting glitches, the diagnosis comes down to three questions, in this order.

First, is the shift concentrated or spread out. Pull Search Console, filtered by page and by query, for September 2 through 8 against the week before. A decline that’s isolated to one content cluster or query type is a different problem than one that touches everything evenly. The former points toward something specific to that content. The latter points more toward something systemic, either on Google’s side or in a technical issue of yours.

Second, which metric actually moved. If clicks fell while impressions held steady, your visibility didn’t change, people just stopped clicking, which usually points to a SERP layout change or an AI Overview absorbing the click rather than a ranking loss. If impressions and clicks both fell while your average position held, Google is showing you for fewer queries than before, a relevance signal rather than a ranking signal. If position itself moved and the rest followed, that’s the pattern most people assume by default, and it’s worth confirming rather than presupposing.

Third, compare like for like. Use the same days of the week on both sides of the comparison, not a rolling seven days that happens to straddle a weekend dip. A lot of false alarms are just a Friday being compared against a Tuesday.

What not to touch until the picture is clear

The instinct to do something productive is exactly what causes the most damage here. Rewriting or gutting pages removes the version Google was already evaluating, so if this reverses on its own, you’ve destroyed the thing that would have recovered. Mass title tag edits add a new variable on top of an already unclear signal, and you won’t be able to tell which change caused which outcome. Filing a disavow request in response to a ranking dip is treating a possible relevance shift as a link penalty, which it almost certainly isn’t unless you have an actual manual action notice. And publishing new content quickly to “fill the gap” runs directly against whatever quality signal might be involved in the first place.

Every one of these actions makes sense emotionally and makes the diagnosis harder afterward.

When you actually have enough to act on

Give it a genuinely stable two-week window once volatility settles, not the first quiet Tuesday after a rocky week. Compare that window against the two weeks before September 2, same days, same metrics. If a clear, sustained pattern holds across that full window, you have something real to work from.

Then build the response around the specific pages and query clusters that actually moved, not the whole site. A shift this narrowly sourced, disagreed on even between two separate volatility tools, is unlikely to call for a site-wide response. It calls for a response scoped to wherever your own data actually shows movement.

Frequently asked questions

1. Is this a confirmed Google update?
Answer: No. Two independent, well-regarded analysts, Glenn Gabe and Marie Haynes, both reported evidence of an unannounced shift in early September, dated to roughly September 2 through 4. Google has not confirmed anything, and standard volatility trackers were reading low to moderate just before those reports went public.

2. Why did Semrush and DataForSEO not pick this up if something really happened?
Answer: Aggregate volatility tools measure shuffling across a large basket of tracked keywords site-wide across the web. A shift narrow enough to hit a specific content type or a specific set of properties hard, without moving the wider aggregate much, can be real and still not register clearly on those tools.

3. My traffic dropped around September 1. Is this the same thing?
Answer: Check your reporting tools before assuming so. Google Analytics reportedly had missing data for September 1, and a separate Search Console indexing-reporting issue existed and was resolved around September 9. Rule both out against your exact dates before treating a September 1 dip as part of this update.

4. How long should I wait before making changes?
Answer: At minimum, two full weeks of stable data after the volatility settles, compared against a matching two-week window before September 2. For an unconfirmed shift with no announced end date, waiting longer than you would for a named core update is the safer default.

5. What should I actually change if the pattern holds up after two weeks?
Answer: Only what your own segmented data points to. If the drop is concentrated in a specific content cluster or query type, address that cluster specifically. A site-wide response to a narrowly sourced shift usually does more harm than the shift itself.

If you want a second set of eyes on your own September numbers, whether the underlying issue is technical SEO, generative engine optimization, or plain reporting noise, that is exactly what we check in a free AI Visibility Audit.

In August 2025, Google’s own John Mueller warned the SEO industry about something specific. Speaking about the growing pile of new acronyms, GEO, AIO, AEO, he warned that pushing new terminology too aggressively was often a signal of low quality tactics rather than genuine innovation. A month later, in September 2025, when a fake screenshot claimed Google had built a dedicated AI Overview filter inside Search Console, Mueller shut that down too, calling the screenshot fake and confirming no such feature existed.

Eleven months later, on 31 August 2026, Google built exactly that feature, for real, and rolled it out to every website on earth.

I want to walk through what actually changed, why the timing and the backstory matter more than the feature itself, and exactly how to check this on your own site.

The rumour that turned out to be a preview of the future

The story is worth telling properly, because it says something about how much pent up demand existed for this.

On 15 September 2025, a user holding Platinum Product Expert status in Google’s own Search Console help forums, Adrian Coutin, posted that Google had integrated data for AI-generated search features directly into Search Console’s Performance reports. The post included a fabricated screenshot showing a new Search Appearance filter specifically for AI Overview data. It spread fast, because it was exactly what site owners had been asking for.

Mueller responded on LinkedIn within hours. “There’s no filter like this in Search Console, and the screenshot itself is fake,” he wrote. Coutin deleted his original post and apologised for the error.

For the following year, Google’s official position stayed consistent, AI Overview and AI Mode data was folded into the standard performance numbers, with no way to separate it out, and no committed timeline to change that. Multiple SEO guides published through late 2025 and into early 2026 describe workarounds involving third party tools precisely because Google offered nothing native.

Then, in June 2026, Google quietly began testing a real version of exactly the feature Coutin had faked, on a limited subset of properties. On 31 August, that testing phase ended, and the feature went live everywhere.

What actually shipped

Google confirmed the global rollout on its own Search Central blog, describing the new reports as dedicated views of impressions specifically within generative AI features on Search, covering AI Overviews and AI Mode, as well as the generative AI features that now appear inside Discover.

Search Engine Journal’s coverage of the global launch adds a detail worth knowing before you go looking for it. Not every property has access yet, and sites with a low volume of AI impressions may not show meaningful data in the report even where it is available. If you check and see very little, that is not necessarily a sign the report is broken. It may simply reflect where your content currently sits in AI systems.

The report breaks impressions down by page, by country, and by date. What it does not include, and this is the detail every source I checked agrees on without exception, is clicks, click through rate, or the specific queries that triggered an appearance. You can see that a page from your site showed up inside an AI generated answer. You cannot see what someone typed to get that answer, and you cannot see whether they clicked through to you afterward.

The report is best understood as a record of where you showed up, not a measure of what that appearance actually earned you in traffic or business.

Google also shipped an opt out control alongside the reporting, letting site owners remove themselves from these AI features entirely. Google has stated that using this toggle does not affect a site’s ranking anywhere else in standard Search results, which matters, because it means the choice to opt out is a genuine preference rather than a trade-off against traditional visibility.

Why the backstory matters more than the button

I keep returning to the Mueller timeline because it tells you something the feature announcement alone does not.

In August 2025, Google’s own search advocate was publicly wary of the entire AI visibility conversation, treating the proliferation of GEO and AEO terminology as a likely signal of low quality hype rather than a legitimate new discipline. A month later, Google was actively denying it had built any tooling to support that conversation.

Less than a year on, Google built the tooling anyway, at a global scale, unprompted by any specific competitive pressure that forced its hand. That sequence is a stronger signal than the launch blog post itself. It suggests the internal case for treating AI visibility as a distinct, measurable category became undeniable even to a company that was publicly skeptical of the framing not long before.

For anyone running SEO or GEO work for a client, that timeline is useful ammunition. The scepticism you may have encountered from more traditional stakeholders, the idea that AI visibility is a marketing buzzword rather than something worth the budget, gets harder to sustain once the platform itself has built a dedicated reporting surface for exactly that.

The data that explains why Google finally built this

Two independent data points, unrelated to this launch, explain the pressure Google was likely responding to.

Semrush’s AI Visibility Index, tracking brand mentions across Google’s AI Mode over several thousand prompts, found a 4 percent drop in mention share between August and October last year, while the diversity of sources being cited across the same period rose 13 percent. Visibility was getting harder to hold onto and more competitive at the same time, with no native way for an affected business to even confirm it was happening.

BrightEdge’s industry wide data adds the commercial pressure point. Search impressions are up 49 percent year over year, while click through rate on those impressions is down 30 percent over the same period. Businesses have been watching their visibility numbers climb while their actual traffic stalls or falls, with no first party data source able to explain why. That is precisely the gap this report is built to close, even if only partially.

How to actually find and use this on your own site

Coverage of exactly where this sits inside the Search Console interface is not fully consistent across sources I checked, some describe it as a dedicated left navigation item, others as a filter within the existing Performance report. Rather than send you hunting for one specific click path that may not match what you see, the reliable approach is this. Open your Performance report inside Search Console for the property in question, and look for a Generative AI or AI features option, either as a separate section or as a filter on your existing search appearance data. If it is not visible yet, that is consistent with Google’s own rollout note that access is expanding property by property rather than appearing identically for everyone on day one.

Once you find it, compare the AI impression volume against your total Web impressions for the same period. If AI generated impressions represent a meaningful share of your total visibility, and your click through rate has been falling without a ranking drop to explain it, that gap is very likely hiding inside exactly this data. This is the same pattern the BrightEdge numbers describe industry wide, positions can hold while clicks fall, because an increasing share of answers are being delivered before anyone reaches your listing.

If the number is small or close to zero, treat that as informative in a different way. It likely means your content is not currently being retrieved for the kinds of questions that trigger these AI features in your category, which is a different and in some ways more urgent problem than a visibility measurement gap.

What not to do with this data yet

Do not draw conclusions from a single week. This is newly global infrastructure, and Google’s own historical pattern with new reporting features is that data takes time to stabilise as more properties gain access and reporting pipelines mature.

The useful read is a trend over four to eight weeks, not a snapshot. Is your share of AI impressions climbing, flat, or falling relative to your total visibility over that window. That trajectory tells you far more than any single day’s number, particularly while the feature is still rolling out unevenly across different sites.

Frequently asked questions

1. Does this show me if ChatGPT or Perplexity cite my site?
Answer: No. This report is scoped entirely to Google’s own AI features, AI Overviews, AI Mode, and Discover’s generative surfaces. It has no visibility into any AI platform outside Google’s own products. Measuring citation inside ChatGPT, Claude, Gemini, or Perplexity still requires testing those platforms directly, since none of them currently offer a first party reporting tool of their own.

2. Does a high AI impression count mean I am getting more traffic?
Answer: Not necessarily, and this is the central limitation to keep in mind. The report measures impressions only. BrightEdge’s industry data shows click through rate falling even as impressions rise broadly, so a strong impression number does not guarantee a proportional lift in actual visits.

3. Can I see which search queries triggered my AI impressions?
Answer: No, and every source covering this launch agrees on that point. Query level detail is one of the more consistently requested features Google has not shipped, alongside click data.

4. Should I use the opt out toggle?
Answer: For most businesses, no. Google has confirmed opting out does not improve or protect your standing in standard Search results, so opting out only removes a visibility channel without gaining you anything in return. It exists mainly for edge cases such as publishers protecting content behind a specific business model, not for typical service or B2B businesses trying to be found.

5. Why did it take Google so long to build this, given the September 2025 rumour showed clear demand?
Answer: Google’s public position throughout that period was that AI Overview data belonged inside standard performance reporting rather than a separate view. The shift to building a dedicated report suggests the internal case changed, likely under the same pressure reflected in the Semrush and BrightEdge data, impressions climbing, clicks falling, and no way for site owners to see why.

If you want visibility that goes further than what Google’s own tools can show, including whether ChatGPT, Claude, Gemini, and Perplexity are actually citing you, that is exactly what we check in a free AI Visibility Audit.