Tag: generative engine optimization

Google has added a paragraph to its “Creating helpful, reliable, people-first content” page warning site owners against fabricated author identities. It sits in the section on who created the content. Google’s page now shows a last-updated date of October 5, 2026 (UTC), and trade reports place the first appearance of the paragraph around October 1 to 2. Google announced no ranking update alongside it.

This piece sets out what the page says, what is new and what is not, and what the evidence does and does not show about bylines. It is written for teams responsible for search engine optimization and content marketing, and it ends with a short audit you can run on your own author pages.

What Google’s page says now

Read from Google’s documentation, the section on who created the content does three things in sequence. It encourages adding accurate authorship information, such as bylines, where readers might expect it. It tells site owners to avoid deceptive authorship information. And it says that fabricating creator profiles, for example with AI-generated headshots, made-up names or false credentials meant to make content look as if human experts wrote it, is a form of deception.

The consequence is stated plainly. Deception, Google says, “makes a page untrustworthy to both users and our automated quality systems.” The page adds that it is a signal of a low-quality page.

Two things about that wording matter. The target is fabrication: invented people, fake faces and false credentials. And the stated purpose is to make content appear human-expert written, which ties the problem to misleading readers about who is behind the content.

What is new, and what is not

The paragraph itself is new. Search Engine Journal’s Matt G. Southern reported on October 2 that an archived copy of the page from June 8 lacked it. The same revision added a definition of main content and four attributes that Google says its raters assess: effort, originality, talent or skill, and accuracy, with a higher bar for accuracy on topics that affect health, finances, safety or well-being.

The principle is not new. Google’s Search Quality Rater Guidelines already treat deception as grounds for the lowest rating. Section 4.5.3 of the September 11, 2025 edition covers deceptive information about a website or its content creators, and says pages using deception should be rated Lowest. Search Engine Journal adds that those guidelines already named fake creator profiles. Relevant Audience makes the useful point that what changed is where the statement appears: in public documentation aimed at site owners, not only in guidance for raters.

The date, checked at Google’s page

This is where care is needed. The date on Google’s page is the date of the latest revision, not necessarily the date the paragraph was added. Search Engine Journal described a version dated October 1 on October 2, and the page now says October 5, so it has been revised since. SEO consultant Marie Haynes said the change was made on October 2. Search Engine Journal also reported that Google’s documentation updates page did not list the edit, and we did not find an entry for it either.

The safest wording is that the paragraph appeared in early October 2026 and that Google has not published a dated announcement for it.

What it does not say

The guidance leaves several things open, and some readings going around go further than the page does.

It announces no ranking update, no system name and no penalty. It does not explain how Google would detect a fabricated profile. Google’s page also notes that its raters do not control how pages rank, so the rater attributes describe what quality evaluation looks for, not a published ranking formula.

It does not say bylines help you rank. Google’s Search Liaison said in January 2024 that “Just adding a byline doesn’t give a ranking boost.” The new paragraph does not contradict that. It says deception is a risk, not that honest authorship is a lever.

And it is not a ban on AI. The sentence concerns fabricated people presented as human experts. Our reading is that a clearly labeled editorial team, or AI assistance disclosed where readers would wonder, is a different situation from an invented doctor with a generated photo. Google’s page does suggest disclosing automation where readers would reasonably ask how content was made.

Bylines and AI citation: what the evidence shows

It is tempting to treat real authors as a way to win citations in AI answers. The evidence does not support that yet, so we would not frame it that way.

The most direct data we found comes from Discovered Labs, a vendor that sells answer engine services, so read it with that in mind. It tested Perplexity only, using 400 questions and 5,770 pages from Google’s first results page. Pages published by companies were cited more often than pages by individual authors: 39.5% against 27.1%. The study calls the gap an association, not proof of cause. It also classifies a staff byline on a company blog as company content, so it does not test bylines directly. It cites a separate study of 497 Google results across 30 queries that found no measurable effect from bylines and credentials on citation.

That is thin evidence in both directions. What it supports is modest: treat authorship as a trust and risk matter, not as a citation tactic. Real people behind your content protect you from the downside Google has now named, and they help readers judge whether to trust you. They are not a proven way to get cited.

What counts as a fabricated profile

Google’s sentence names three examples. In practice, a team should check for these:

An author who does not exist. A name invented to give content a face, with no real person who wrote, reviewed or answers for it.

A photo that is not the person. An AI-generated headshot or stock image presented as the author.

Credentials that cannot be verified. Titles, years of experience, degrees or licenses that the person does not hold.

A fourth case sits close to the line, and it is our reading, not Google’s. A real expert’s name placed on content they did not write or review. Google’s separate instruction to avoid deceptive authorship information is broader than the fabricated-profile sentence, so this is worth treating as a risk.

A byline audit you can run this week

Start with a list of every author name that appears on your site, including old posts and contributors who have left.

Check that each name belongs to a real person who can confirm they wrote or reviewed the content. If a byline is a pen name, replace it with a transparent label such as your editorial team, and say so.

Check the photo. Use a real photograph of that person, or no photo.

Check the credentials. Every claim should be something the person can document: a license, a publication, a role at a named employer.

Check the author page. Google’s page asks whether bylines lead to further information about the author and the topics they write about. Each byline should link to a page with a real bio, links to their professional profiles and a list of their work. A bio at the end of an article with no link to a profile page leaves that question half answered.

Check sensitive topics. Where content covers health, legal or financial decisions, the accuracy bar is higher. Name the expert reviewer and the review date. Teams in healthcare and legal fields should be the strictest.

Check AI disclosure. Where AI assisted the draft, decide what readers should be told, and make sure a named person is accountable for the final text.

Fix, do not delete by reflex. If an author profile cannot be verified, correct the byline, label it accurately, or have a qualified person review and sign off on the content. Removing posts without a plan can cost you pages that were working.

Why this matters for AI search visibility

AI assistants and answer engines draw on pages that readers and search systems already treat as credible. Our guide to how AI search works explains how a page can be cited without ranking at the top, and why clear answers, specific claims and self-contained sections matter more than decoration. For generative engine optimization, the practical lesson from Google’s change is to build content on accurate, attributable information so that no part of it depends on a fake face. Our full guide to generative engine optimization covers that groundwork.

The same logic applies to answer engine optimization. Answers drawn from your pages carry your name, so the people and claims behind them need to hold up. Our look at why third-party mentions matter in AI search makes a related point: independent credibility is earned, and it is hard to fake.

To see where your brand and its experts are actually mentioned, a free AI Visibility Audit checks real questions across platforms. Our complete guide to the AI Visibility Audit explains the method.

Where this leaves you

The confirmed facts are these. Google’s people-first content page now warns that fabricating creator profiles, such as with AI-generated headshots, made-up names or false credentials, is a form of deception that makes a page untrustworthy to users and automated quality systems. The page shows a last-updated date of October 5, 2026, and trade reports place the paragraph’s first appearance at October 1 to 2. Google announced no ranking update, penalty or detection method, and the page does not say bylines improve rankings.

The practical response is an audit, not a rewrite. Make sure every byline is a real person, every photo is that person, every credential can be verified and every author has a page that proves it. Whether the aim is generative engine optimization, answer engine optimization or plain search engine optimization, treat that as a baseline for trust, not as a trick.

Frequently Asked Questions

1. What did Google add to its people-first content guidance?
A paragraph in the section on who created the content saying that fabricating creator profiles, such as with AI-generated headshots, made-up names or false credentials to appear expert, is a form of deception.

2. When did Google add it?
Google’s page shows a last-updated date of October 5, 2026, which is the latest revision. Trade reports place the first appearance of the paragraph around October 1 to 2. Google has not published a dated announcement for it.

3. Is this a ranking update?
Nothing announced says so. Google named no update, system, penalty or detection method. The wording describes what quality evaluation looks for.

4. Do author bylines improve rankings?
Google’s Search Liaison said in January 2024 that adding a byline does not give a ranking boost. Google’s page encourages accurate bylines for readers, but does not call them a ranking factor.

5. Does this ban AI-generated content or AI headshots?
No. The sentence concerns fabricated profiles presented to make content seem expert-written. Google’s page does suggest disclosing automation where readers would wonder how content was made.

6. Will real authors help me get cited in AI answers?
There is no solid evidence that they do. One vendor study of Perplexity found company pages cited more often than individual-author pages, and a second study it cites reported no measurable byline effect. Treat authorship as trust, not a citation tactic.

7. What should I do first?
List every author on your site, confirm each is a real person with a real photo and verifiable credentials, and make sure each byline links to a genuine author page.

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

Every marketer working on AI visibility has the same suspicion: if AI assistants lean on third-party sources, the shortest route to a mention is to create those sources. A policy update from Anthropic, the company behind Claude, puts a line through part of that idea. Announced on October 8, 2026 and effective November 12, it prohibits using Anthropic’s products to seed the sources AI systems draw answers from with content that misrepresents who is behind it.

The wording is narrower than some headlines suggest, and the policy binds users of Anthropic’s products, not the web at large. This piece sets out what the text says, what it does not say, and the practical test it offers brands deciding between earned mentions and planted ones. It is written for teams planning generative engine optimization and digital PR programs.

What the policy says

The change sits in a new section of Anthropic’s usage policy on deceptive campaigns and artificial activity. Read from the policy page, it prohibits using Anthropic’s products to create fake personas, accounts, media outlets or organizations, or to impersonate real ones. It prohibits hiding the sponsorship of content meant to influence opinion or decision-makers, and spreading content through networks of fake or ostensibly independent sites that share a common source. It also covers building tools for deceptive campaigns and marketing any of these activities.

The clause that matters here prohibits manipulating the sources from which search engines or AI systems draw answers, by “seeding them with content that misrepresents its origin, authorship, or independence.” The policy’s own example is a network of sites posing as unaffiliated sources that corroborate the same claims.

Notice what triggers the rule. It is not seeding, volume or the use of AI. It is misrepresentation: content that hides its origin, its author or its lack of independence.

Who it binds, and who it does not

The policy applies to anyone who submits inputs to Anthropic’s products. That covers individuals using Claude.ai or Claude Code, businesses using the API, customers who reach Claude through cloud providers or resellers, and end users of products built on Claude. A marketing tool built on Claude puts its own users under the same rules, as Relevant Audience points out.

It does not bind search engines, other AI companies or the open web, and it does not govern how Claude ranks or cites content. Mike King of the GEO consultancy iPullRank makes the same point in his analysis: the policy is about how people use Claude.

Enforcement works against the account, not the website. Anthropic’s Safeguards Team monitors for violations, and if one is suspected Anthropic may warn, throttle, limit, suspend or terminate access to its products. That differs from Google’s spam policies, which act on sites.

A new rule, or a clearer one?

Search Engine Journal reads the update as a clarification, not a brand-new ban. The 2025 policy did not mention search engines or AI answers, but Anthropic says it already prohibited fake-account networks and fake news sites, spread across other sections. The new text states the principle directly and applies it to the sources AI draws on.

Search Engine Journal also describes the case Anthropic linked to the section. According to that report, Anthropic found a network of around 70 sites posing as independent local news outlets, tied to a France-based advertising agency. The network used Claude to produce articles designed to “boost their site’s authority rankings on search engines.” It published at least 8,913 articles in about 20 languages, though most got little engagement. Anthropic removed the account and banned the organization.

Two cautions apply. The coverage does not say whether those sites improved their rankings or appeared in AI answers, so the case shows the tactic being attempted, not working. And Search Engine Journal does not link the full text of Anthropic’s report, so its details rest on that outlet’s summary.

Where the line sits

Relevant Audience reads the clause as aimed at deception about who is behind content, not at producing content for AI visibility. In its reading, the policy does not ban using Claude to write content, structure pages for AI answers or publish at volume. That is the publication’s interpretation of the text, which Anthropic has not spelled out for edge cases. For example, Anthropic has not said how it would treat several openly branded sites run by one company, and the policy text points to misrepresented independence as the trigger.

Read together, the sources suggest a useful test, which Relevant Audience frames as its own and not Anthropic’s: would the tactic still work if every reader knew who wrote the content and who paid for it? King puts a similar idea in terms of durability, arguing that manufactured independence has “an expiration date.”

Earned versus planted mentions

That test separates two things that can look alike from outside.

An earned mention exists because someone independent chose to write it. A journalist cites your original research. An expert quotes you. A customer reviews your product. A publisher runs a contributed article under a real byline. A sponsored placement is labeled as sponsored.

A planted mention only looks independent. A network of sites that recommend the same brand while hiding common ownership. Reviews from fake personas. Undisclosed paid listicles. Forum answers posted by accounts that conceal their affiliation. iPullRank, which sells GEO services and so has an interest in the framing, lists these among the tactics to avoid. The policy text itself names fake personas, hidden sponsorship and networks of ostensibly independent sites.

This is the distinction our earlier piece on why third-party mentions matter for AI search turns on. The reason independent sources carry weight in AI answers is that they are independent. Faking independence takes away the thing that made the mention valuable, and it now carries account-level risk for anyone doing it with Claude.

What to do before November 12

None of this requires a strategy rewrite. It asks for a short audit.

Check your own content network. If you or an agency have used Claude to produce content for several sites that appear independent but share an owner or a sponsor, review it now. Search Engine Journal suggests this audit.

Ask vendors direct questions. Relevant Audience recommends asking agencies and tools how they build AI visibility and whether any sites in their plans are owned by them without saying so. A vague answer is information.

Label sponsorship. Where a placement is paid, say so on the page. Where a comparison page is brand-owned, make that obvious.

Use real bylines and real experts. Contributed content should carry the name of the person who wrote it and a verifiable role.

Fund the earned route. Original data, expert commentary and useful tools give journalists and publishers a reason to cite you. That is the work behind digital PR and ethical link building, and the slower route is the durable one.

Measure what you earn. Track whether your brand is mentioned and cited across AI platforms, and which sources drive the mentions. A free AI Visibility Audit does this across platforms and shows where earned coverage is missing.

What this does not tell you

The policy is not evidence that seeding works, or that it fails. The reporting does not show whether the network Anthropic banned gained rankings or appeared in AI answers.

It does not describe how detection works beyond general monitoring, and it does not cover other AI companies. ChatGPT, Gemini and Perplexity have their own terms, and Google has its own spam policies. Do not read this one document as a rule for all of GEO.

It also does not tell you that AI-written content is off limits. The text turns on misrepresentation, though edge cases remain untested. If you use AI tools to draft, the safe course is the same as it has always been: a real author, accurate claims and no pretense about who is speaking.

Why this matters for AI search visibility

AI assistants increasingly shape which brands get recommended, so the temptation to manufacture sources will not disappear. What this update does is move one major provider’s rules in the opposite direction, and mark provenance as the thing platforms are watching. For answer engine optimization, the safest assets are the ones that can survive scrutiny: clear answers, original information, real authors and accurate facts. Our full guide to optimizing for AI search covers that groundwork, and sound search engine optimization supports all of it.

Where this leaves you

The confirmed facts are these. Anthropic announced an updated usage policy on October 8, 2026, effective November 12. A new section prohibits using its products to seed the sources search engines and AI systems draw answers from with content that misrepresents its origin, authorship or independence. Enforcement is against accounts. The policy binds users of Anthropic’s products, not other companies or the open web.

The practical response is to build mentions that would still work if everyone knew who was behind them. That favors original research, real experts, labeled sponsorship and honest digital PR and link building, backed by measurement of what AI platforms actually say about you. Whether the aim is generative engine optimization, answer engine optimization or plain search engine optimization, the same provenance rules apply.

Frequently Asked Questions

1. What did Anthropic’s updated usage policy change?
It added a section on deceptive campaigns and artificial activity, which prohibits using Anthropic’s products to seed the sources search engines and AI systems draw answers from with content that misrepresents its origin, authorship or independence. It takes effect November 12, 2026.

2. Does it ban GEO?
No. The trigger is misrepresentation, not optimization. Writing clear content, structuring pages for AI answers and earning mentions are not what the clause describes, though Anthropic has not addressed every edge case.

3. Who does the policy apply to?
Anyone who submits inputs to Anthropic’s products, including individuals, API developers, customers using Claude through cloud providers and end users of products built on Claude. It does not bind search engines or other AI companies.

4. What happens if someone violates it?
Anthropic may warn, throttle, limit, suspend or terminate access to its products. In the case Search Engine Journal describes, Anthropic removed the account and banned the organization.

5. Is it a new rule?
Search Engine Journal reads it as a clarification. Anthropic says fake-account networks and fake news sites were already prohibited under other sections, and the update names the search and AI answer context directly.

6. How do I tell an earned mention from a planted one?
Ask whether the tactic would still work if readers knew who wrote and paid for the content. Independent coverage, real reviews, labeled sponsorships and bylined expert content pass. Fake personas, hidden-owner site networks and undisclosed paid listicles do not.

7. What should I do before November 12?
Audit any Claude-made content running across sites that appear independent, ask agencies and tools how they build AI visibility, label sponsored content and use real bylines. Then measure your actual mentions across AI platforms.

If you want to know where your brand is mentioned and cited across Google and AI answer engines today, and which sources are driving it, that is exactly what we look at in a free AI Visibility Audit

Picture a Monday morning report. Your rank tracker says a key page has fallen out of the top 10, and the pages that replaced it look unrelated. The usual reaction is to start rewriting. A new question belongs first: did the ranking change, or did the tool receive the wrong results page?

On October 10, 2026, the rank-tracking company Nozzle published research claiming that since September, Google has been returning complete, normal-looking results pages to its rank trackers with the real results swapped out. Nozzle sells rank tracking, and Google has not commented. This piece sets out what was reported, what is and is not established, and how to check before you react. It is written for teams responsible for search engine optimization reporting.

What Nozzle reported

Nozzle’s page analyzes 27.5 million search results pages from its customer datasets, collected between August 20 and October 9, 2026, across 49 countries, desktop and mobile.

From August 20 to September 9, about 0.17% of pages were poisoned, which is Nozzle’s baseline. On September 13, requests to Google began failing in large numbers. The first poisoned pages appeared on September 20. They peaked at roughly a quarter of all pages between September 27 and 29, at 24.9%, 26.7% and 27.0%. Since October 1, daily rates have run at about 4% to 11%. Across the period since September 13, Nozzle puts it at 8.0%.

The word poisoned matters. Google does not refuse these requests, according to Nozzle. It answers with a full page of wrong results. Nozzle puts it this way: “A poisoned request succeeds.” There is no error, no captcha and no change in the page template, so the only way to spot it is to compare a keyword’s results over time.

How it was measured, and a caution about the 8.0%

A keyword’s usual sites are those in the top 10 on at least 60% of its baseline pages. A page counts as poisoned when fewer than half of them appear on any page of the results, and they return later. Those thresholds are the study’s own choices.

Several limits apply. Detection is inferred from each keyword’s history, not from a signal Google has confirmed. The sample comes from Nozzle’s own customers, and the last few days are still filling in. And the headline 8.0% does not state its denominator: the daily table is labeled “All SERPs,” while the country and industry tables calculate against judged pages. Cite it as “Nozzle reports about 8%,” not as a measured rate of Google behavior.

What a poisoned page looks like

Nozzle reports these patterns on poisoned pages, compared with its baseline.

Organic results kept only 20.7% of the previous page’s sites, against 88.5% at baseline. A tracked project’s own site fell out of the top 10 on 58.6% of poisoned pages, against 2.7% at baseline. Reddit rose from 20.8% to 28.8% of top-10 appearances, and Facebook from 28.3% to 33.8%. An AI Overview appeared on 34.9% of poisoned pages, against 54.2%. Ads and shopping results were largely stable.

One example shows how complete the swap can be. For the desktop, US search “white label local seo,” all nine page-one sites on October 4 were the usual vendors. On October 7, all nine had been replaced by unrelated results, including a WordPress tool, a steakhouse and government sites.

The effect is usually brief. About 80% of poisoned spells lasted a single page, and 77.9% of poisoned pages had their usual sites back within three days. A real ranking change persists, which is how the two can be told apart.

Where it appears to hit hardest

Nozzle’s tables show wide variation. Since September 13, the poisoned share was 8.5% in the United States, 10.5% in Canada and 11.5% in Australia, with the United Kingdom lowest at 3.0%. Treat country figures as indications, since they depend on Nozzle’s sample sizes.

“Near me” searches were poisoned more often (11.4%) than other queries (7.8%), and navigational brand searches least often (5.3%). If you manage local SEO reporting, that is worth knowing.

By industry in the US, legal was highest at 15.9%, peaking at 51.3% on September 27. Pets was lowest at 4.3%. Firms in the legal sector should be the most careful about reacting to a single day.

The tools matter too. On the same days, one collection route returned 1% to 7% poisoned pages while another returned 9% to 30%, so how your vendor collects data affects what it sees.

A second vendor, and a reporter’s concern

Nozzle is not alone. DemandSphere, another rank-tracking company, posted on October 7 that Google returns spurious results when it suspects bot traffic, affecting about 11% of its daily results pages. PPC Land reported the figure on October 13. DemandSphere’s post does not describe a method or sample size. Both figures are vendor-reported and unaudited, and they agree on the mechanism and rough scale, little more.

Barry Schwartz of Search Engine Roundtable reported on October 12 that third-party rank tracking is returning incomplete or inaccurate data. He relayed the three tactics that Nozzle’s Derek Perkins described: wide IP blocking aimed at large residential proxy networks, a big increase in captchas, and injecting bad data into suspect pages. Schwartz singled out the third: “The third approach, injecting bad data, concerns me the most.” His reasoning is that tools may not notice it, so they can appear to work while their data is wrong. Several vendors, including Sistrix, SE Ranking and DataForSEO, reportedly posted notices about data problems.

What is not established

Google has not publicly commented on Nozzle’s or DemandSphere’s figures or on the September changes. Everything about motive comes from vendors and the trade press.

Nozzle reads the pattern as Google’s response to bulk collection of its results for AI grounding, which rank tracking resembles. DemandSphere’s founder makes a similar argument. Schwartz’s view, as reported, is that Google is aiming at AI bots and scraping services, not SEO tools. Roger Montti of Search Engine Journal adds that altered pages could also confuse spammers reverse-engineering rankings, a point Nozzle did not raise. None of these readings is confirmed.

There is also a confound. Google’s September 2026 spam update ran from September 24 to October 8, according to PPC Land and Looploop, so real ranking volatility and altered data overlapped in the same weeks. Our earlier piece on the September spam update covers the real-volatility side. A single dashboard showing a drop in that window could not tell the two apart.

Even first-party data can lag. Looploop reports that the Search Console Performance report ran more than 64 hours behind on October 11, so check the date range first.

How to tell a real drop from a poisoned page

Start with Search Console. If it still shows impressions and clicks for the same queries and dates, the site very likely did not drop. Confirm that its data is current first.

Compare against history. Look at the keyword’s usual top 10 over several weeks, not just yesterday, because yesterday may also have been poisoned.

Wait one to three days. Most altered pages clear quickly, while a real change persists.

Read the shape of the page. Nozzle says fewer AI Overviews, fewer “People also search for” modules, more image packs and social or forum sites, and shorter pagination are suggestive, but none is reliable alone.

Cross-check tools. A drop that only one tool reports is a question, not a conclusion.

Ask your vendor how it detects swapped pages and what its error rates are. Relevant Audience, citing PPC Land, reports that most do not yet publish comparable figures.

Hold off on changes. Do not rewrite pages from one chart. Our guide to reading your own traffic before you change anything sets out a three-step method that works here too.

What to change in your reporting

Make first-party data the reference. Search Console, analytics and conversions should anchor the monthly report, with the rank tracker used for trends. Our look at Google’s AI visibility reports in Search Console explains what that data does and does not include.

Set a rule for alerts. A drop seen on one day, in one tool, should trigger a check, not a client email. Require it to persist, or to appear in Search Console, before it is reported as a ranking loss.

Annotate your dashboards. Mark September 13 to October 9 as a period of suspected data problems, so later readers do not mistake a poisoned dip for a penalty.

Why this matters for AI search visibility

Nozzle’s data shows AI Overviews appearing less often on poisoned pages (34.9% against 54.2%). Any measurement that infers AI visibility from collected Google pages may pick up the same noise. That is our inference, not Nozzle’s finding, and tools that measure AI answers by other methods may behave differently.

The practical response is to avoid relying on one source. Generative engine optimization and answer engine optimization are about being cited and recommended in AI answers, and measuring that well means testing real buyer questions across several platforms, not reading a single scraped page. Keep the basics of generative engine optimization and answer engine optimization strong: clear answers, accurate information and consistent entities. Our complete guide to the AI Visibility Audit explains how that cross-platform measurement works. Strong search engine optimization remains the base for all of it.

Where this leaves you

The confirmed facts are these. Nozzle published research on October 10, 2026, covering 27.5 million results pages, and reports that since September 13 about 8.0% were poisoned, against 0.17% at baseline, with a peak near 27% in late September. DemandSphere reports about 11% on its own data. Google has not commented, the mechanism is Nozzle’s account, the motive is interpretation, and both figures come from vendors who sell rank tracking.

The lasting lesson is about method. A rank tracker is a sample of what Google returned to a tool, not a record of what your customers saw. Before reacting to a drop, check Search Console, compare with history, wait a few days and look at more than one source.

Frequently Asked Questions

1. What did Nozzle report about Google and rank trackers?
On October 10, 2026, Nozzle reported that since September, Google has returned complete, normal-looking results pages to rank trackers with the real results swapped out. It measured 8.0% of 27.5 million pages as poisoned since September 13, against 0.17% at baseline.

2. Has Google confirmed this?
No. Google has not publicly commented on Nozzle’s or DemandSphere’s figures or on the September changes. The explanations come from vendors and trade press.

3. Can I trust the 8.0% figure?
Treat it as Nozzle’s reported estimate. It is from a rank-tracking vendor and inferred from keyword history.

4. How do I know whether my ranking drop is real?
Check Search Console for the same queries and dates, compare with several weeks of history, and wait one to three days. Altered pages usually clear within days, while a real ranking change persists.

5. Does this affect Search Console data?
Nozzle’s study concerns data collected from Google results pages, not Search Console. Search Console can lag, though, so confirm the date range first.

6. Does this change how I should track AI visibility?
Tools that infer AI visibility from collected Google pages may pick up the same noise, which is our inference. Testing real buyer questions across several AI platforms does not depend on one scraped page.

7. Should I stop using my rank tracker?
No. Use it for trends, and confirm important changes in Search Console and analytics. Ask your vendor how it detects swapped pages and what its error rates are.

If you want to know how your brand appears across Google and AI answer engines, measured from real answers and not a single dashboard, that is exactly what we look at in a free AI Visibility Audit.

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.

For years, shopping on Google followed a familiar path: a search, a click, a visit to your store, a purchase. Your analytics were built around it. In late September 2026, part of that path moved. Eligible US Shopify merchants can now sell directly inside Google’s AI Mode and the Gemini app, where a shopper taps Buy and pays without ever opening the merchant’s website.

For merchants, the change is both an opportunity and a measurement problem. Sales can arrive from a surface where your tracking does not run. This piece sets out what was announced, who is affected, how to opt out, what the checkout supports and what it does not, and a decision framework for whether to leave it switched on. We have kept to what documentation and reputable reporting confirm, and flagged what is unclear.

What changed

On September 22, 2026, Search Engine Land reported that Google is notifying eligible Shopify merchants that their products can be bought directly in AI Mode in Google Search and in the Gemini app. The checkout runs on the Universal Commerce Protocol, an open standard Google developed with other companies, including Shopify, that lets AI agents interact with merchants and payment systems during shopping.

Two details deserve attention. First, merchants did not have to apply. Search Engine Land reports that eligible products are included automatically once a Shopify store is matched with its Google Merchant Center account, and Shopify’s help center confirms that direct checkout is on by default for eligible stores. Second, the rollout is gradual. Shopify says direct checkout is still rolling out to Google users and may not yet be available in every store, and Google’s own documentation, as quoted by ContentGrip, describes the program as “a phased program for select participating merchants”.

Who is eligible

Shopify’s help center lists the requirements. Your store must be based in the United States and sell to US customers. You need a valid Google Merchant Center account and must meet Google’s requirements, including its free listing eligibility and return policy rules. Your products must be available in Merchant Center, either through Shopify’s Google and YouTube sales channel with product and shipping sync turned on, or through a third-party feed provider or manual upload. Products must also be eligible for Shopify Catalog, you must agree to the Shopify Agentic Storefronts Supplemental Terms of Service, and your terms of service, privacy policy and refund policy must be completed in your Shopify settings.

Direct checkout is shown only to US-based shoppers.

There is no extra fee. Shopify states that you pay only your standard payment processing fees. Merchants also keep the underlying customer and order relationship, according to Search Engine Land, and the retailer remains the seller of record, according to Google’s documentation as cited by ContentGrip.

How to opt out

If you want shoppers to complete purchases on your own site, Shopify’s help center gives these steps. In the Shopify admin, go to Sales channels, then Agentic. Deactivate the setting called Allow Shopify to manage for me. Click Google AI Mode and Gemini. Switch Direct checkout off, then click Save.

Opting out does not remove you from AI Mode. Your products can still appear in results, but shoppers are redirected to your online store to complete the purchase.

What direct checkout supports, and what it does not

The in-AI checkout is not an exact copy of your store’s checkout. Shopify’s documentation sets out the gaps.

Supported: automatic discounts and discount codes, and Shopify Functions for shipping, discounts and cart and checkout validation.

Not supported: subscriptions, product bundles, customizable products and B2B-only products. Local delivery, in-store pickup and pickup points. Requiring customers to sign in before checkout. Only a subset of accelerated checkout options. Many checkout blocks, including upsells, loyalty and rewards blocks, consent collection blocks, chat blocks and trust badges, may not display, so the checkout can look different from your own. Shopify also asks that required legal disclosures appear within the first 6,000 characters of a product description.

Why this is a measurement problem

Shopify’s help page states plainly that “Google Analytics and custom pixels won’t fire” in direct checkout. Only server-to-server pixel events fire, and only two of them: checkout started and checkout completed.

Your analytics tag, ad platform pixels and attribution tools all depend on code running in the shopper’s browser on your site. When the purchase happens inside Google’s surface, that code never loads. The sale is real, but the session your tools normally record does not exist on your side.

Three practical consequences follow.

Revenue can be undercounted in tools that rely on client-side tracking. If you compare Google Analytics revenue with Shopify order totals, a widening gap may be this checkout, not a tracking bug.

Channel reporting can shift. Because the journey no longer includes a visit to your site, orders may not be credited the way you expect. Shopify’s page does not describe how these orders are labeled in your admin or reports, and we found no authoritative source that does, so treat any confident claim about it with caution.

Funnel analysis loses steps. These buyers generate no product page views, add-to-cart events or checkout steps on your site, so a shift in conversion rate could reflect where people buy, not how well your pages convert.

The practical answer is to reconcile against Shopify’s own order data, which records the purchase wherever it happens, and to stop treating the browser-side analytics tag as the only source of truth for revenue.

What is still unclear

Some claims in circulation should stay out of your planning until they are confirmed. Some coverage repeats specific review thresholds, such as a minimum number of reviews and a minimum star rating. Those figures relate to Merchant Center’s store ratings rules for showing ratings with ads, and we could not confirm that they are conditions for this checkout. Shopify’s eligibility list above does not include them.

Reports of a separate, wider default-on rollout of agent checkout across Shopify, including a figure of about one million merchants, rest on thin sourcing, so we have not relied on them.

Finally, there are open questions on reporting: how these orders are labeled, what Google will offer in Merchant Center for AI surface performance, and how consent settings apply. Recheck the documentation before committing.

Opt in or opt out: a decision framework

There is no single right answer. It depends on what you sell and how you measure it. Use these questions.

Do you sell subscriptions, bundles, customizable items or B2B-only products? If these make up a large share of revenue, the AI checkout cannot handle them, and keeping the feature on mainly matters for your simpler products.

How much of your profit comes from checkout upsells or loyalty features? If the answer is a lot, redirecting shoppers to your own checkout protects that revenue.

How dependent is your reporting on client-side tracking? If your team, or your agency, cannot trust revenue numbers without it, you may prefer to opt out until your reporting is ready.

Do you want to learn? Some merchants will reasonably leave it on for a few weeks and compare Shopify order data with previous periods, as long as they set a review date.

For most stores, a good default is to keep the feature on for a defined trial period if your product mix is mostly simple, one-time purchases, and to switch it off if subscriptions, bundles or upsells drive revenue.

What to do this week

Open the Agentic section in Shopify and see whether direct checkout is on for your store.

Review your Merchant Center feed and your Shopify policies. Product discovery in AI Mode is powered by Merchant Center, so missing attributes, mismatched prices or incomplete return policies can limit what shows up.

Check product descriptions for legal disclosures in the first 6,000 characters.

Place a test order if possible and see how it appears in Shopify and in your analytics.

Reconcile weekly. Compare Shopify order totals with analytics revenue.

Why this matters for visibility

This checkout sits at the end of a discovery path, and the path begins with product data. Shopify’s documentation says product discovery in AI Mode is powered by Google Merchant Center, so a store whose data is complete and consistent has a better chance of being shown. That is where search engine optimization, generative engine optimization and answer engine optimization meet ecommerce: accurate titles, complete attributes, clear policies, authentic reviews and consistent information across your site and your feed.

The checkout does not change the basics. It raises the stakes on them, because a recommendation in an AI answer can now turn into a sale without a visit to your site. For brands that want to understand how they appear in these answers before deciding how to respond, an AI Visibility Audit is a sensible starting point, because you cannot manage what you have not measured.

Where this leaves you

The confirmed facts are these. Google and Shopify have enabled a native checkout in AI Mode and the Gemini app for eligible US Shopify merchants, powered by the Universal Commerce Protocol. It is on by default for eligible stores and can be turned off in the Agentic settings. Merchants keep the customer and order relationship. Google Analytics and custom pixels do not fire, and only server-to-server started and completed events are sent. Several features, including subscriptions and bundles, are unsupported.

The response is a decision, not a panic. Check your setting, choose deliberately, protect what depends on your own checkout, and measure through Shopify’s order data while your reporting catches up. Strong search engine optimization, generative engine optimization and answer engine optimization all start with clean product data, either way.

Frequently Asked Questions

1. Is Google’s AI Mode checkout turned on for my Shopify store?
If your store is based in the US, sells to US customers and is matched with a valid Merchant Center account, it is likely eligible, and Shopify says direct checkout is on by default for eligible stores. The rollout is gradual, so check Sales channels, then Agentic, in your Shopify admin.

2. How do I opt out of direct checkout in AI Mode?
In Shopify admin, go to Sales channels, then Agentic. Deactivate Allow Shopify to manage for me, click Google AI Mode and Gemini, switch Direct checkout off and save. Your products can still appear, but shoppers are sent to your online store to buy.

3. Do I still own the customer and the order?
Search Engine Land reports that merchants keep the underlying customer and order relationship, and Google’s documentation, as cited by ContentGrip, says the retailer remains the seller of record. Shopify charges no extra fee beyond standard payment processing.

4. Will Google Analytics track these purchases?
No. Shopify’s help page says Google Analytics and custom pixels do not fire in direct checkout. Only server-to-server started and completed events are sent. Use Shopify’s order data to reconcile revenue.

5. Which products and features does the AI checkout not support?
Subscriptions, bundles, customizable products and B2B-only products are not supported, nor are local delivery, pickup or required sign-in. Many checkout blocks, such as upsells, loyalty and consent blocks, may not display.

6. Do I need 100 reviews and a 3.5 star rating?
We could not confirm that. Those figures relate to Merchant Center store ratings for ads and are not on Shopify’s eligibility list.

7. Should I leave it on or switch it off?
It depends on your product mix and your reporting. Stores with mostly simple, one-time purchases may reasonably test it for a defined period. Stores that rely on subscriptions, bundles, upsells or client-side tracking may prefer to opt out.

If you want to know how your brand and products show up across Google and AI answer engines, 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 26, a Google spokesperson confirmed what SEOs had already been spotting in the wild since late June. Search result links are being rewritten to route through google.com/goto before landing on the actual destination page. Google’s stated reason was procedural: it said it regularly deploys technical measures to protect its services and users from evolving abuse. This sits alongside a separate legal fight, Google’s suit against SerpAPI, first filed in July and amended in August, over how that company accesses Google’s search results.

That much is confirmed. What happens after that point, in coverage of this story, splits into two very different camps, and it is worth being precise about which claims are actually settled and which ones are not before you act on either.

What actually changed, mechanically

Instead of a result linking straight to a destination page, the link now points to google.com/goto, with the destination URL encoded using Protocol Buffers, Google’s own binary data format. A server-side redirect on Google’s end then forwards the visitor to the real page. For a person clicking a result, nothing looks different. They click, they land where they expected. What changed sits entirely underneath that click, in a URL structure that any tool reading destination links now has to parse differently than before.

Third-party tools that read destination URLs directly took the first hit, and here the evidence is solid because the vendors themselves reported it. SerpAPI confirmed impact across several of its products. Ahrefs confirmed what it called a temporary inconsistency in its data. One rank-tracking vendor documented a real six-day window, August 26 through September 2, where its Share of Voice reporting misattributed clicks because its parser was not reading the new format correctly. That has since been fixed, with the affected data being backfilled for customers.

Not every tool broke the same way, and this is worth sitting with for a moment. SE Ranking and Semrush both confirmed no impact on their own position tracking data at all. So the blanket claim that the /goto redirect broke SEO tools is not quite accurate. Some tools broke. Others, reading the exact same search results pages, did not, which suggests the difference has more to do with how each vendor’s parser was originally built than with the redirect itself being universally disruptive.

Where the coverage actually splits

Here is where a real divide appears. Several sources treat GA4 organic-to-direct misattribution as an established, active problem, describing what one piece called “massive organic attribution data loss” and sudden, unexplained spikes in Direct traffic. That framing has spread fast, because it is the scariest possible read of a genuinely technical, genuinely unresolved situation, and alarming framing tends to travel faster online than careful, hedged framing ever does.

Now look at what the more carefully sourced coverage actually says. As of late August, one detailed technical write-up stated plainly that nobody had reported confirmed organic undercounting inside GA4. A separate analysis went further and ran an actual test across ten device and browser combinations, checking whether referrer data survived the redirect chain. It found the referrer stayed intact in every case, and GA4 correctly classified those sessions as Organic Search each time. Multiple outlets, working independently of each other, land on the same conclusion. Google has not confirmed a GA4 attribution impact, and the direct evidence gathered so far does not show one either.

None of that means the concern is baseless. The mechanism Google’s own documentation describes makes an attribution gap plausible in principle. If a redirect chain ever strips referrer data before it reaches your site, GA4’s fallback logic would file that session as Direct rather than Organic Search, silently, with no alert generated anywhere in your dashboard. That is a real risk worth watching closely. It is not the same thing as a confirmed, ongoing problem, and treating it as something that has already happened is exactly the kind of overstatement that risks needing a correction once more data actually comes in, one way or the other.

Why the distinction actually matters

It would be easy to read the last two sections and conclude the difference between disputed and confirmed is mostly semantic. It is not, and the practical stakes are worth spelling out.

If a team reacts to this as a confirmed GA4 problem, the natural response is to start second-guessing every organic traffic number reported for the past month, which burns real time reconciling data against a theory that current testing does not support. Budget conversations get harder to have with confidence when the underlying numbers are being treated as suspect without evidence. Reporting to a client or a leadership team gets shakier too, since telling a room that your organic numbers might be wrong because of a redirect Google has not confirmed affects attribution is not a comfortable thing to say out loud in a review meeting.

If a team instead treats this as a plausible, unconfirmed risk worth monitoring, the response looks completely different. You set a baseline, you watch two specific ratios over the following weeks, and you keep operating normally in the meantime rather than freezing decisions around a risk that has not actually materialized in the testing done so far. That is the more defensible position given what is currently known, and it is also simply less exhausting to maintain.

A second, less-discussed layer

There is a second angle here that is easy to miss if you are only thinking about traditional search engine optimization reporting. Reports say /goto-wrapped URLs are starting to show up as cited sources inside ChatGPT, Perplexity, and Claude, wherever those systems pull from Google’s search results as part of answering a query. If a citation-tracking tool has the same blind spot rank trackers had in late August, before their parsers caught up, the same kind of silent misattribution could in theory apply to AI citation counts, not only to GA4 sessions.

Nobody has published confirmed data on this specifically yet either. It follows the same underlying mechanism, and the same caution about calling something confirmed before the evidence is actually in applies here just as much. This is precisely the kind of gap that generative engine optimization and answer engine optimization work now has to account for. Visibility inside an AI answer is only useful if you can actually measure it, and a measurement tool with an unpatched blind spot will quietly undercount exposure it should be reporting, in exactly the way several rank trackers briefly did in late August.

Setting a baseline instead of guessing

The sensible response to a disputed, plausible-but-unconfirmed risk is not to panic and it is not to ignore it. It is to set a baseline now, so that if this ever does show up in your own data, you catch it immediately instead of noticing it months later as an unexplained trend with no starting point to compare against.

Record this week’s ratio of Search Console clicks to GA4 organic sessions for your key properties, and write the number down somewhere you will actually revisit. Do the same for your current organic-to-direct traffic split. These two figures are your tripwire. If either one moves meaningfully in the weeks ahead with nothing else in your setup changing, that is your signal to look closer, rather than reacting to someone else’s alarmed blog post as though it were your own data.

Reconcile Search Console against GA4 weekly for the next month, instead of your usual monthly cadence. A real attribution gap, if one exists, would show up as a widening divergence between the two figures over time, not as a single dramatic jump you would notice immediately anyway.

Check your GA4 referral exclusion list for any new entries appearing as google.com/goto. If Google’s own redirect domain starts showing up there as a distinct referral source, rather than resolving cleanly through to Organic Search, add it to your exclusions so it does not fragment your organic channel into two separate, harder-to-reconcile pieces.

Hold off on reacting to a rank-tracker alert this week unless you have confirmed that specific tool already adjusted its parser to read the new /goto format correctly. Given that SE Ranking and Semrush reported no impact while SerpAPI and Ahrefs did, an alert from a tool that has not caught up yet is more likely a parsing problem on their end than an actual ranking change on your site.

And if you are already doing any AI visibility tracking, apply the same caution to what you see there. A cited source appearing as a raw /goto link rather than a resolved domain in your reporting is a sign your tool needs to catch up, not evidence that your underlying citation count has actually changed.

Where this leaves you

Google confirmed the redirect itself on August 26. It has not been confirmed, and the direct testing done so far does not support a GA4 attribution problem. Some SEO tools broke, others did not, and the ones that did have mostly already fixed their parsers by now. The plausible risk to your own analytics is real enough to baseline and monitor closely. It is not, as of this writing, a confirmed, active problem serious enough to justify the level of alarm some coverage is giving it, and treating an unconfirmed risk as settled fact is its own kind of mistake, one that tends to send teams chasing a problem that is not actually there yet while a genuinely useful baselining exercise gets skipped entirely.

Frequently Asked Questions

1. Is Google’s /goto redirect confirmed?
Answer: Yes. A Google spokesperson confirmed the rollout on August 26 and described it as a technical measure to protect Google’s services and users from evolving abuse. The rollout itself is not in dispute anywhere in the coverage. What remains unsettled is how far its effects reach.

2. Is my GA4 organic traffic being misattributed because of it?
Answer: Not as far as anyone has confirmed. Google has not acknowledged a GA4 attribution impact, and one technical test across ten device and browser combinations found referrer data stayed intact, with sessions classified as Organic Search. The risk is plausible in principle, so it is worth monitoring, but it is not a confirmed problem.

3. Why is my rank tracker showing odd data?
Answer: It depends on the tool. SerpAPI and Ahrefs confirmed some impact, with Ahrefs calling it a temporary inconsistency, while SE Ranking and Semrush confirmed none. Check whether your vendor has published a statement or a fix before assuming your rankings actually moved.

4. Does this affect AI citation tracking?
Answer: Possibly, but nothing is confirmed. /goto-wrapped links are reportedly appearing as cited sources in ChatGPT, Perplexity, and Claude. A tool that cannot parse them could undercount your citations. Treat any sudden citation drop as a possible tracking issue first, and verify it manually.

5. What should I check first?
Answer: Record this week’s ratio of Search Console clicks to GA4 organic sessions, plus your organic-to-direct traffic split. These two numbers are your baseline. If either shifts meaningfully with nothing else changing, investigate then.

6. How often should I reconcile Search Console and GA4?
Answer: Weekly for the next month, instead of monthly. A genuine attribution gap would show up as a widening divergence between the two over several weeks, not as a single sudden jump.

7. Should I change my SEO strategy because of this?
Answer: No. Nothing about the redirect changes how rankings are earned. Keep your strategy as it is, baseline your data, and update only if a confirmed impact emerges.

If you want a second set of eyes on whether your own reporting, or your AI visibility specifically, is being affected, that is exactly what we check in a free AI Visibility Audit.