Tag: GEO

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 AI systems sometimes state wrong facts about a business, such as outdated services, incorrect locations, or invented details.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 22 July 2026, Barry Schwartz of Search Engine Roundtable and algorithm analyst Glenn Gabe both spotted the same thing within hours of each other. Google has quietly rewritten its “Optimize your crawl budget” documentation, the guide that tells large sites how Googlebot decides what to crawl and how much.

Google’s stated reason was modest, improving clarity, terminology consistency, and flow. The actual content of the rewrite goes further than a wording cleanup. Three real changes sit inside it, and one of them affects every site running AI crawlers alongside Googlebot, whether that site is actively doing generative engine optimization work or not.

What crawl budget actually means, before the changes

The documentation splits the crawl budget into two separate ideas that are easy to blend together but behave differently. Crawl demand is Google’s own interest in fetching your URLs, driven by how valuable and how fresh Google judges your content to be. Crawl capacity is a separate ceiling, how much load your server can handle before Google backs off to avoid causing problems for your site. A site can publish millions of technically crawlable URLs and still see very little crawling, because demand, not the raw count of pages, is what is actually constraining it. Capacity, by contrast, is something your own server behaviour can move, largely independent of how interesting Google finds your content.

That distinction matters for reading everything that changed on 22 July, because the rewrite touches capacity specifically, not demand.

What Google says changed

The most significant addition is about how crawl capacity begins. Every site now starts with the same default, conservative crawl capacity limit. Google raises that limit over time only if two conditions hold, there is genuine crawl demand for your content, and your server stays fast and healthy while serving it. There is no earned baseline you carry in from your site’s age, authority, or size. Everyone starts in the same place.

Google has not published what that default number actually is. Multiple sites covering the update have said this explicitly, there is no new published crawl limit. Any specific figure you see attached to this story did not come from Google’s own documentation.

The second change matters more for anyone running AI crawlers alongside traditional search indexing. The rewrite confirms that crawl capacity is shared across all of Google’s own crawlers. Google does not run one crawler, it runs several, Googlebot for standard pages, Googlebot-Image, AdsBot for ad landing pages, and a separate crawler for Shopping and Merchant feeds, among others. All of them draw from the same pool of capacity on your server. Heavy demand from one reduces what is left for the others.

The third change is more tactical. The documentation now gives clearer emphasis to two efficiency levers that were always available but were not framed as prominently before. Faster server response times let Google fetch more within the same capacity window. Supporting HTTP 304 Not Modified responses, using ETag or Last-Modified headers, tells Googlebot it can reuse a cached version of a page instead of redownloading it, which frees that saved capacity for new or updated URLs instead.

One more housekeeping detail worth knowing if you have ever bookmarked or cited Google’s crawl budget guidance. The documentation moved. The older URL, under the large site managing crawl budget path, now issues a permanent redirect to a new address under Google’s crawling docs section. If you have internal documentation or a past search engine optimization audit referencing the old link, it still works through the redirect, but it is worth updating.

Why the shared capacity detail matters beyond Google’s own crawlers

The documentation itself talks about Google’s own crawlers competing for the same pool. It does not need to say the obvious follow-on, because every technical SEO reader already knows it. Your server does not distinguish between Googlebot and every other bot hitting it. If your site is being crawled heavily by AI crawlers, whether for model training or for live retrieval when someone asks ChatGPT or Perplexity a question, that traffic is competing for the same server response headroom Googlebot needs to do its own job well.

This is the same underlying mechanic we wrote about when Cloudflare changed its default AI crawler settings. A slow or overloaded server reads as a signal to Googlebot to back off, which caps how much of a large site actually gets crawled and indexed in a given period. If AI crawler traffic is heavy enough to slow your server down, you are not just making a decision about AI training data. You are quietly affecting how much of your site Google itself chooses to crawl.

Separate research on crawl budget mechanics, not part of this specific July rewrite but relevant context, has found that crawlers like GPTBot can consume up to 40 percent of available server bandwidth during deep crawl cycles. That same research cited data suggesting sites that block GPTBot entirely in robots.txt were cited 73 percent less often in ChatGPT responses. I am flagging that stat with its actual source rather than presenting it as part of Google’s July update, because it comes from separate technical SEO research, not from Google’s documentation itself. But it illustrates exactly why the crawl capacity sharing point is not academic. Blocking AI crawlers protects server capacity for Googlebot. It also appears to reduce your citation rate inside AI answers, which is the entire premise behind doing generative engine optimization work in the first place. There is a real tension here, and Google’s rewrite does not resolve it, it just makes the underlying mechanics clearer.

Why this update is arriving now

Google did not explain its timing, and the documentation itself frames the changes as clarity improvements rather than a response to any specific pressure. But the context is hard to ignore. The volume of non-Googlebot traffic hitting servers, from AI training crawlers, live AI retrieval agents, and third party scraping tools, has grown substantially industry wide over the past two years. A documentation update that makes shared capacity explicit, when it was previously left implicit, reads naturally as Google catching its own guidance up to a server environment that looks different than it did when the original guidance was written.

What this means if you run a large site

If your site has thousands or millions of URLs, this update is worth reading in full rather than taking secondhand. Two practical questions follow directly from it.

First, is your server capacity being spent efficiently. The documentation’s own answer is to check your logs, not to assume. Which URL patterns is Googlebot actually requesting, and how often. Are other Google crawlers, or aggressive AI and third-party crawlers, consuming capacity that would otherwise go to your most important pages. A server log analysis, even a basic one, will show you the actual split between Googlebot requests, other identified bots, and everything else, which is a more reliable starting point than reading the documentation and guessing where your own site stands.

Second, is your crawl demand actually low, or is your capacity the constraint. These are different problems with different fixes. A site can expose millions of crawlable URLs and still see very little crawling, because Google’s interest in those URLs, the demand side, is the actual limit, not how many pages you have published. Improving server speed raises your capacity ceiling. It does nothing for a site full of URLs Google has no interest in crawling in the first place. Pruning thin or duplicate URL inventory raises effective demand per page that actually matters, without touching your server at all. Treating a demand problem as a capacity problem, or the reverse, means fixing the wrong thing while the actual constraint stays exactly where it was.

What this means if you run a smaller site

If your site is not operating at large scale, thousands of pages or more, the honest answer every source covering this update gives is the same one Google has given for years. Crawl budget is not something you need to actively manage. Google’s systems can typically crawl a smaller, healthy site fully without the capacity constraints this documentation is written for. This update does not change that guidance. It exists for the specific case of very large sites where crawl demand genuinely exceeds what Google is willing to spend crawling everything, which is simply not the situation most smaller sites are in.

Where to actually check this on your own site

Search Console’s Crawl Stats report is the relevant measurement surface here, not the crawl budget documentation itself. It shows request volume by response type, by file type, and by Googlebot type, which lets you see whether crawling is trending down, flat, or being spent disproportionately on low value URL patterns. Look specifically at the trend over recent months rather than a single day, since crawl volume naturally fluctuates and a single low reading is not necessarily meaningful on its own.

If you also run AI crawlers against your site, whether deliberately allowed for generative engine optimization purposes or simply not blocked, cross-referencing your server logs against both Googlebot and known AI crawler user agents will tell you whether the two are genuinely competing for capacity on your specific server, rather than guessing based on what the documentation describes in general terms. This is a few hours of log analysis for most sites, and it replaces speculation with an actual answer specific to your own infrastructure.

Frequently asked questions

1. Does this update mean my site has a new, lower crawl limit than before?
Answer: No specific new limit has been published anywhere in Google’s documentation. What changed is the description of how the limit begins, at a conservative default for every site, rising over time based on demand and server health, rather than any published number changing for existing sites.

2. Does blocking AI crawlers protect my Googlebot crawl capacity?
Answer: In principle, yes, since capacity is shared across whatever is hitting your server. In practice, separate research suggests sites blocking crawlers like GPTBot see meaningfully reduced citation rates in AI answers. The trade-off is real, and this update does not remove it, it just confirms the shared capacity mechanic that makes the trade-off exist.

3. Do I need to worry about the crawl budget if I run a small business website?
Answer: No. Every source covering this update agrees on that point, including implicitly Google’s own documentation, which is written for large sites managing crawl demand at scale. A small, healthy site is very unlikely to hit a capacity constraint Google’s systems cannot handle by default.

4. What is the difference between crawl demand and crawl capacity in practical terms?
Answer: Demand is Google’s interest in your content, driven by perceived value and freshness. Capacity is a ceiling for your own server performance controls. A slow server lowers your capacity regardless of how much Google wants to crawl you. Thin or low value content lowers demand regardless of how fast your server responds. They need different fixes, and confusing the two leads to solving the wrong problem.

5. What is the single most useful thing to do after reading this?
Answer: Check your Search Console Crawl Stats report before assuming anything about your own site based on this article. The documentation change is general guidance. Your logs are the only source that tells you whether shared crawl capacity is actually a live constraint for your specific site.

6. How does this connect to AI visibility work specifically?
Answer: Crawl access is the prerequisite underneath everything else. An AI system, or Google’s own AI features, cannot cite content it cannot successfully retrieve. If crawl capacity constraints or AI crawler blocking are limiting what gets fetched from your site, that is a foundational issue sitting underneath any search engine optimization or SEEO work layered on top of it.

If you want to know whether crawl access, AI crawler blocking, or something else entirely is limiting your visibility in AI answers, 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.