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Is Reddit Still Worth It for AI Visibility After ChatGPT Dropped It?

Dageno AI — Mon, 26 Oct 2026 07:37:33 +0000

By the Dageno team | October 2026

What 8 months of daily AI answer data tells us about how much Reddit still does as an AI visibility channel.

Key findings

Short answer: for Google's AI, yes. For ChatGPT as it is today, no. And your own website matters on every platform.

38.6% vs 1.4%: in September 2026, 38.6% of Google AI Overviews answers with sources linked to Reddit. ChatGPT was at only 1.4%.
4 single-day jumps: looking at US ChatGPT day by day, the share fell from 54% to 6% on June 13, came back to 37% on July 7, then dropped in two steps on August 8 and August 14 to around 2%. Monthly data only shows that it "went down." It cannot show these jumps.
About 90% vs under 15% (estimates): among answers that linked to Reddit, the share whose text actually used Reddit content was about 90% for Google AI Mode in September, and only 0.3%–14.6% for Grok from February to July.
−82% vs +1%: around August 14, the share of ChatGPT answers with sources that linked to Reddit fell 82%. The share citing brand websites barely changed (86.5% → 87.5%).

"Go post on Reddit" has become standard advice for getting into AI answers. Our data shows that whether this advice works now depends on which AI platform your buyers use.

Ahrefs recently published a study: over 6 months, ChatGPT's citations of Reddit fell 88%. Our data shows a similar drop. It also shows something you cannot see by comparing only the start and end of a six-month window: ChatGPT switched Reddit off and on, and off again, more than once.

Every day, the Dageno team collects answers from 8 AI platforms through their web interfaces. For each answer, we record which sources it cites and sort them by type: brand websites, media, communities, retailers and so on. For every Reddit link, we also check whether the answer text actually used it. This article covers February to September 2026.

One thing to be clear about first: this study looks at how platforms cite sources, meaning which sources each AI platform's answers cite for a fixed set of industry and general questions. It does not measure Reddit's overall traffic, and it does not represent all user queries. The makeup of the question sets is described under "Method" at the end.

1. We've been posting on Reddit to get into AI answers. Which AI platforms still use it?

Mainly Google's two AI products.

In September 2026, among AI answers that cited sources:

Google AI Overviews: 38.6% linked to Reddit.
Google AI Mode: 27.7%.
Perplexity: 8.5%. Gemini: 5.6%.
Grok: 1.7%. ChatGPT: 1.4%.
Copilot and Alexa almost never linked to Reddit (0.04% and 0%).

The orange bars show how often Reddit content actually made it into the answer text (an estimate; how it is calculated is explained in section 5). Google AI Mode was at 24.9% and Google AI Overviews at 14.8%. Every other platform was under 6%.

2. ChatGPT and Grok used to link to Reddit often. What happened?

Both platforms have almost stopped linking to Reddit.

ChatGPT: in February, 34.8% of answers with sources linked to Reddit. By September, only 1.4% did.
Grok: 79.5% in February, still 74.8% in June. By September, only 1.7%.

Platforms entered our database in different months (February, March or April), so the long-term trends of different platforms in this chart cannot be compared side by side.

ChatGPT's drop was not gradual. In the daily US data, it moved in one-day steps:

June 13: fell from 54% to 6% in one day.
July 7: came back, jumping from 11% to 37%.
August 8: fell from 26% to 15%.
August 14: fell from 13% to 2%. From then until September 30, it stayed around 2% and never went above about 3%.

Each of these steps happened within a single day, which makes them stronger evidence than the monthly trend. The makeup of the question sets changes a little each month, and that can move monthly shares, but it is hard to explain a single-day jump by the mix of questions. We did not change how we collected or parsed data at any point during the study, so these steps came from the platform side. Other trackers saw this drop too. Section 3 puts their numbers next to ours.

What we cannot answer is "why." OpenAI has not announced any change to how it picks sources. Our data shows only the final answer, not what ChatGPT retrieved behind the scenes, so we cannot tell whether the change happened at retrieval, in content licensing or at display. This has also happened before: Profound recorded that in September 2025, Reddit's share of ChatGPT citations fell from about 7% to about 1%, then recovered to about 3%. So today's 2% should be read as the current state, not necessarily a permanent one.

3. Other trackers reported the drop too. Why don't their numbers match ours?

They all point the same way. How big the drop looks depends on what each tracker counts and which two time periods it compares.

There are two ways to count. One is "the share of answers that cite Reddit at least once" (Ahrefs, Searchable and us). The other is "Reddit's share of all citations" (Promptwatch, OpenLens, Goodie). The second gives much smaller percentages, because one answer can cite 10 or more sources.

Ahrefs (October 12, 2026): the share of US ChatGPT answers citing Reddit fell from about 1 in 5 in March to about 1 in 43 in late September (−88%). Monthly samples of 2.4 to 3.8 million answers.
Searchable (LinkedIn post, October 2026): on the same set of 173,000 questions, spring versus the most recent month, the share of ChatGPT answers using Reddit fell from 42% to 5% (−88%).
Promptwatch (August 18): Reddit's share of ChatGPT citations, July 18–August 7 versus August 14–17, fell from 3.83% to 0.52% (−86%).
OpenLens (August): on the same set of questions, before versus after, Reddit's share of citations fell from 2.93% to 0.12% (−96%).
Goodie (September 10): Reddit's share of ChatGPT citations, August 1–13 versus August 14–25, fell from 8.0% to 2.9% (−64%).
Otterly (August 27): across 16 brand reports, ChatGPT's Reddit citations fell at least 73% in mid-August.
Dageno (this article): the share of ChatGPT answers with sources that linked to Reddit, March versus September, fell from 20.8% to 1.4% (−93%). Around August 14 (US, the 6 days before versus the 7 days after): from 12.8% to 2.3% (−82%).

Although the question sets differ, our March and September levels are close to Ahrefs' (about 1 in 5, down to about 1 in 43). What daily data adds is what happened between those two points. Comparing only March and September, you see one drop. Looking day by day, ChatGPT cut Reddit on June 13, brought it back on July 7, and cut it again in two steps in August.

4. Same AI answers, different industry questions. Is the Reddit share the same?

ChatGPT is equally low in every industry. Google's two products vary a lot.

Besides the general question set, we also collect answers for 4 industry question sets. The table below shows ChatGPT's figures for August and September, and Google's AI figures for each question set in September (share of answers with sources that linked to Reddit):

Question set	ChatGPT Aug	ChatGPT Sep	Google's AI (Sep)
General questions	14.3%	1.4%	AI Overviews 38.6%, AI Mode 27.7%
Web scraping APIs and data extraction	6.4%	1.3%	AI Overviews 50.3%, AI Mode 31.4%
LLM APIs and AI gateways	8.9%	1.4%	AI Mode 39.7%
Customer messaging and social CRM	6.7%	0.4%	AI Overviews 10.1%
Charging and power banks	11.8%	1.1%	AI Mode 22.0%

Each industry question set is collected on only some platforms. The "Google's AI" column lists the Google products we actually collected for that set. Figures for Perplexity, Gemini and Grok on the industry question sets are in the data files on our website.

ChatGPT fell to 1.4% or below in every question set. In August it was still between 6.4% and 14.3%. In September every set dropped to between 0.4% and 1.4%. When questions on such different topics all fall together, the main cause is the platform, not the mix of questions.
For Google's AI, it depends on the industry. In September, 50.3% of Google AI Overviews answers to web scraping API questions linked to Reddit, but only 10.1% for customer messaging and social CRM questions. Google AI Mode ranged from 22.0% to 39.7% across question sets.

So the finding that "Google's AI still uses Reddit" needs to be checked against your own category. Don't apply the overall numbers directly.

5. If an AI answer links to Reddit, does that mean it used Reddit content?

Not necessarily. Most reports on Reddit and AI miss this.

From February to July, 56% to 80% of Grok answers with sources linked to Reddit. But among those Reddit-linked answers, only 0.3% to 14.6% actually used Reddit content in the answer text. The link was there, but mostly as just a name in the source list.

Google AI Mode was the opposite. Among its answers that linked to Reddit, 72% used Reddit content in the text in April. That share rose every month after, reaching 90% in September.

The "used in the text" rate here is an estimate. For every Reddit-linked answer, we checked at collection time whether the answer text used the Reddit source (a footnote pointing to Reddit, or text that matches the Reddit source). This judgment is not in the data tables we publish, so third parties cannot recompute it from those tables. Each platform's monthly rate is in the appendix and data files on our website (link at the end). The "used in the text" share in Figure 1 = share linking to Reddit × used-in-text rate.

So if you only count links ("Reddit appears in X% of answers"), you will overstate Reddit's influence on some platforms. Your own website can have the same gap: an AI answer cites your page, but its text never mentions your brand name.

6. ChatGPT cut Reddit overnight. Were brand websites cut too?

No. When ChatGPT cut Reddit on August 14, brand websites barely moved, and no type of source clearly filled Reddit's place.

The table below shows the share of ChatGPT answers with sources that cited each type of source (US, the 6 days before August 14 versus the 7 days after, simple average of daily shares):

Source type	6 days before	7 days after	Change
Reddit	12.8%	2.3%	−82%
Brand websites	86.5%	87.5%	+1%
Other classified sources	91.6%	92.7%	+1%
Other communities (not Reddit)	15.9%	15.6%	−2%
Retailers	12.7%	12.2%	−4%
Reference	6.7%	6.0%	−11%
Institutional and academic	37.6%	29.2%	−22%
Media	23.9%	17.6%	−27%

One answer can cite several types of source at once, so the rows do not add up to 100%. "Other communities (not Reddit)" = the share for community sources (UGC) minus the share for Reddit, because Reddit itself counts as a community source; this is an approximation. Weighted by answer volume, Reddit's average for the 6 days before is 13.0%, and the conclusion does not change.

The table tells us three things:

ChatGPT replaced Reddit, not community sources as a whole. The share for other communities and forums barely changed. In July, about 69% of ChatGPT answers that cited a community source included Reddit. By September, only about 11% did.
Measured as "the share of answers citing a type of source," no type clearly filled the gap. Brand websites and other classified sources rose by only about 1 percentage point, and media, institutional and academic, and reference sources kept falling. This differs from Searchable and OpenLens, who saw "government and research sources rise." There are three possible reasons: they count share of citations while we count share of answers; the question sets differ; and we compared only one week before and one week after.
Brand websites are a basic source on every platform. In September, 77% to 99% of answers with sources on 6 platforms (ChatGPT, Grok, Perplexity, Gemini, Google AI Overviews and Google AI Mode) cited at least one brand website. That share did not change noticeably before or after any of the drops above.

7. Where in the buyer's journey, from learning to buying, does Reddit still play a role?

At the very start.

AI answers mention Reddit most in early "help me understand" questions, less in comparison questions, and least at the decision stage. We counted only "platform × month" combinations with complete date coverage and data for all three stages: 51 in total, and 48 of them followed this order. Take Google AI Overviews in September: 22.9% of answers to learning-stage questions mentioned Reddit, 15.7% at the comparison stage, and 8.3% at the decision stage. Split by intent, transactional questions had the lowest share of Reddit mentions in 42 of the 51 "platform × month" combinations.

"Mentioned" here means the name Reddit appears in the answer text. It is a different measure from "linked to Reddit." This measure changed definition in June, so we do not compare trends across June.

The top threads change fast. Of the 50 Reddit links ChatGPT cited most in August, only 1 was still in the top 50 in September. Google AI Overviews kept 23.

The threads that get cited also carry the topic bias of the question sets. In the general question set, the subreddits that appeared most in each platform's top 50 Reddit links from February to September were r/HairRemoval, r/travel and r/TravelHacks, leaning toward consumer, beauty and travel topics. September's top 50 also included a few subreddits that appear to be run by brands themselves (our inference, not checked one by one). So which subreddits get cited depends heavily on what questions are asked. Don't treat them as a list that applies to every industry.

8. Now that Reddit is no longer a one-size-fits-all channel, what should marketing teams do next?

Stop treating Reddit as a shortcut into ChatGPT. That path is almost closed now, and it switched on and off several times this year. Don't build plans that count on it coming back.
If you invest in Reddit, aim at Google AI Overviews and AI Mode, and check your own category first. These two platforms still link to Reddit and still use Reddit content in their answers, but the share varies by industry (section 4).
Get your own website right first. Every platform cites brand websites, and when Reddit's share swung wildly, your website's share did not move with it. Make the pages AI answers need easy to cite: product and use-case pages, documentation, pricing pages, comparison pages, FAQs.
Put your Reddit content at the top of the funnel, and keep it genuine. Answer real questions in the subreddits your buyers visit, and share first-hand experience. Top threads change fast, so showing up consistently is worth more than betting on one viral post. Don't ignore other communities and niche forums either.
Track "linked," "used in the text" and "mentioned" separately, platform by platform, every week. A link is not the same as being used. A drop on one platform does not mean a drop on another. And all of these changes happened overnight, with no announcement.

Method

The data in this article comes from the Dageno AI answer database. From February to September 2026, we collected answers every day through the web interfaces of 8 AI platforms: ChatGPT, Grok, Perplexity, Gemini, Google AI Overviews, Google AI Mode, Copilot and Alexa. Each platform started in a different month. The questions come from 1 general question set and 4 industry question sets. They are fixed questions, not randomly sampled user queries, so the findings describe how platforms cite sources for these questions. All figures in the article are shares; the "used in the text" rate is an estimate judged answer by answer at collection time.

Sources

Ahrefs, "How ChatGPT Does Query Fan-Out (15 Million Queries Studied)", October 12, 2026.
Preston B. (Searchable), LinkedIn post on ChatGPT's Reddit citations, October 2026.
Promptwatch, "Reddit Citations Are Dropping in ChatGPT", August 18, 2026.
OpenLens, "ChatGPT Stopped Citing Reddit and Mainstream News", August 2026.
Goodie, "Social Media AI Citations: 16 Sourcing Shifts", September 10, 2026.
Otterly, "ChatGPT cut Reddit citations by at least 73% in August 2026. Here is what replaced it.", August 27, 2026.
Profound, "The Data on Reddit and AI Search", November 10, 2025.

Google Updates AI Search and Promises to Send Traffic Back to Websites? The Data Says It Is Not That Simple

Dageno AI — Fri, 16 Oct 2026 07:27:15 +0000

Google has announced more inline links in AI Mode and AI Overviews. For brands, the lesson of this update is that being mentioned by AI is only the starting point. The next fight is over being named in the key positions, with a line that explains why your link is worth clicking.

1. What actually changed

On October 15, 2026 (US time), Hema Budaraju, Google's VP of Product for Search, published the blog post. Broken down, it covers five changes:

More inline links, placed higher up. Google says it has "continued to significantly" increase inline links (links embedded in the answer text) and now places them in the upper part of the answer more often. It did not say by how much.
Testing an "official pages" section (in testing). When a task can only be completed through one or two official entry points on the web, the top of the answer will list those links separately, each with a one-line note on why it is useful. Google's example is looking up how to get a refund from an airline. No launch date was announced.

Figure 1: The "official pages" section Google is testing. For an airline refund query, the top of the answer first lists two official links, the refund form and the refund rules, each with a note on what it is for. Figure 1 | Source: Google's official blog (blog.google), 2026-10-15

The timely news carousel expands (rolling out now). The carousel will cover more countries and languages, though no list was given, and it will give priority to the Preferred Sources a user has set. Google says the number of people using Preferred Sources has grown more than 7x since the start of the year, but it did not give the base number.

Figure 2: The timely news carousel inside an AI Overview, with image cards, publish times and outlet names, giving priority to the user's preferred sources. Figure 2 | Source: Google's official blog (blog.google), 2026-10-15

Related-article links at the end of the answer stand out more. The "Dive Deeper" area now includes image thumbnails and is designed to be easier to click.

Figure 3: The Dive Deeper area at the end of an answer. Each further-reading item carries the outlet name, a summary and a thumbnail. Figure 3 | Source: Google's official blog (blog.google), 2026-10-15

Creator names on AI Mode video results become clickable (testing starts this week). Clicking takes you to the creator's Google Search profile page, not to the creator's own website.

The same day, Nick Fox, who heads Google Search, added on X that more than 400 publishers have joined Google's News AI licensing pilot, and Google pays for that content.

This update has a history. In May 2026, Google already made a round of five link changes: placing links right next to the relevant text, link previews on hover on desktop, labels for subscription content, first-hand perspectives from forums and social media with the community name shown, and further reading at the end of the answer. From June to August, Search Console rolled out a generative AI performance report and added a switch to opt out of AI features, which opened worldwide on August 31. So the October update is a continuation in the same direction, not a change of course.

2. Five counter-intuitive conclusions

1. More links does not mean the traffic is back, and for now there is no way to prove it

Pew Research analyzed the real browsing records of 900 US adults. When a page showed an AI summary, users clicked a traditional search result in only 8% of visits, compared with 15% when there was no summary. The share who clicked a source cited inside the summary was just 1%. Ahrefs redid the study with December 2025 data and found that when an AI Overview appears, the click-through rate of the number-one page is about 58% lower.

Google's attitude deserves more attention. Barry Schwartz of Search Engine Roundtable headlined his post with Google updating links "again" while still not sharing click data. He quoted Google's reply: "We will gradually introduce more metrics." Today the AI report in Search Console shows impressions only, with no clicks.

There is evidence pointing the other way. A Seer Interactive report from April 2026 shows that for queries with an AI Overview, organic click-through rate recovered from 1.3% in December 2025 to 2.4% in February 2026. Veteran SEO Gianluca Fiorelli said on X that inline links "do bring clicks," far better than the citation markers that are almost invisible.

Conclusion: Where a link sits matters more than how many links there are. Moving inline links higher means the gap between sources named in the first few paragraphs of an answer and every other source will widen further.

2. The official-pages section rewards pages that get the job done, not big brands

According to Dageno's preliminary data, in September 2026 official websites already appeared in 77%–99% of AI answers on most platforms. In other words, getting your official site seen by AI has not been hard for a while. What is scarce is being recognized by Google as the endpoint for a task, placed alone at the top, with a line explaining why.

This is good for official websites, but there will be fewer winners. Queries such as "how to get a refund" or "how to apply" used to send traffic to plenty of third-party guides. That traffic may now be captured outright by one or two official links. The reverse also holds: if an official site's task pages are badly structured and vaguely worded, even a well-known brand may not be selected.

3. Inside Google's AI, Reddit is not a bit player, and it is being used more and more

Dageno's preliminary data shows that in September 2026, Reddit links were associated with 38.6% of Google AI Overview answers and 27.7% of AI Mode answers; for ChatGPT it was about 1.4%, Grok about 1.7%, and Copilot only 0.04%. In AI Mode, the share of Reddit links that, once present, were actually used in the body text or footnotes rose from 72.2% in April to 89.9% in September. Multiplying link rate by adoption rate, Reddit's penetration in September was roughly 25% in AI Mode and roughly 15% in AI Overviews, and under 1% in ChatGPT, Grok and Perplexity.

This matches Google's May update, which explicitly set out to show first-hand perspectives from forums inside answers and to label the community name. The same dataset shows another pattern: around August 14, 2026, ChatGPT's Reddit link rate dropped sharply in four industry markets at the same time. We suspect an adjustment on the platform side.

Conclusion: There is no single "AI search." Platforms differ widely in how they pick sources, and the rules can change overnight.

4. User choices are starting to shape who gets seen

Use of Preferred Sources has grown more than 7x, the news carousel gives those sources priority, and subscription labels were added in May. This means a publisher's direct relationship with its readers is starting to convert into visibility inside AI search.

That is good news for publishers with loyal readers. For new outlets and long-tail writers, the bar has risen: readers who do not know you yet will not set you as a preferred source. Add the 400-plus publishers with paid licensing deals, and the gap between the top outlets and everyone else may widen further.

5. Some of the links that "go back to the web" go back to Google itself

Clicking a creator name leads to the creator's Google Search profile page. Both Search Engine Roundtable and Search Engine Land called this out. According to the help page cited by Search Engine Journal, Search profiles are currently open only to creators in the United States, Canada and India, and require at least 10,000 followers on YouTube, Instagram, X or TikTok. Seer also notes that links inside AI answers sometimes only lead to a new Google search.

Figure 4: Left: a clickable creator name shown under an AI Mode video result. Right: an example of the Google Search profile page it opens (two frames from the same demo video). Figure 4 | Source: frames from Google's official blog demo video, 2026-10-15

3. What this means for GEO: upsides and risks

Upsides:

Being cited pays off more directly. With inline links moved higher, being named in the first few paragraphs of an answer can bring clicks directly, not just brand exposure.
Official task pages get a dedicated slot. Clearly structured service, pricing, documentation and after-sales pages have a chance to be placed at the top of the answer.
Opinion content gets more entry points. The news carousel and Dive Deeper leave room for in-depth reporting and original analysis.
More monitoring tools than before. Search Console at least shows AI impression data, and it can be broken down by page and by country.

Risks:

Fewer winners. The official section holds only one or two links, so lower-ranked sources get squeezed out further.
Results cannot be fully attributed. Without AI click data, teams will struggle to prove to management that the investment paid off.
The features themselves are not stable. The official section and creator names are still in testing. They may be adjusted or withdrawn, and rollout speed differs by region.
Law and regulation are still moving. In December 2025 the European Commission opened an antitrust investigation into Google's use of publisher content for AI summaries, and it is still under way. On September 30, 2026, a US court dismissed the lawsuits brought by Penske and Chegg; the judge acknowledged the harm to publishers but held that antitrust law is not the tool to solve this problem.
Opting out has a cost too. Sites that opt out of AI features will no longer get the impressions and traffic those features bring.

4. What brands can do now

Map out task-type queries. List the things users most often come to get done, such as pricing, booking, refunds, after-sales support and developer documentation. Make sure each one has a clear official page whose title and content match that task directly. Only then can AI write the line "why this link is useful."
Analyze source structure platform by platform. On Google's side, community discussion is common, so it is worth watching how users talk about your category in communities. The goal is genuine participation, not spam. ChatGPT picks sources by completely different logic, so the same playbook cannot simply be reused.
Write paragraphs worth citing inline. Specific data, first-hand experience and clear opinions are far more likely to get an AI link than generic introductions.
Look at impressions and clicks separately. Judge by combining Search Console's AI impression data, overall web clicks and your own landing-page analytics, rather than click-through rate alone. A falling click-through rate may simply mean you are being cited more often.
Publishers can actively cultivate preferred-source status. Encourage readers to set you as a preferred source, and weigh carefully the pros and cons of opting out of AI features.
Test regularly in your target markets. Features in testing behave differently across countries and languages, so it is best to recheck once a month in the target language and the target region.

5. Open questions

How does Google decide which pages count as "official"? If it gets it wrong, is there an appeal channel for third parties?
How many clicks does moving inline links higher actually bring? Will Search Console ever list AI clicks separately?
Which countries and languages has the news carousel expanded to? Is the Chinese-language market included?
What is the base number behind the "7x growth" in Preferred Sources?
Will the share of community content such as Reddit in Google's AI answers be diluted by the official section and the news carousel, or keep rising?
Will the outcome of the EU investigation change how AI answers display links?

Enterprise AI Search: Gemini Names 155 Brands, ChatGPT 131. What Should Teams Track?

Dageno AI — Thu, 08 Oct 2026 03:44:32 +0000

Gemini named 155 different brands. ChatGPT named 131 for the same prompt set. An AI-search dashboard should preserve each platform's results before combining them.

These counts come from Dageno's enterprise AI report. It collected 6,623 answers from ChatGPT, Gemini, and Google AI Mode in the United States from July 20 to 27, 2026. The answers contained 41,273 brand mentions and 73,980 cited links.

Store the answer, platform, and question together

Give each answer an ID. Save the sampling date, AI platform, original question, question type, and answer text.

Link product mentions and cited pages to that answer ID. This lets a reviewer open the source answer from a chart and see what the AI said.

About 17% of answers named no brand. Keep those answers in the dataset so a reviewer can inspect their questions.

Keep product and company totals separate

Microsoft Copilot Studio led with 1,899 mentions, or 4.6% of all brand mentions. Microsoft's six products together accounted for 17.0%. Google's products together accounted for 5.5%.

Store the product name and its parent company in separate fields. Display a product ranking and a company total as two views.

Record position separately from mention count. Copilot Studio's average rank was 3.69, while Azure AI Foundry's was 3.63. Lower values mean earlier positions.

The most common pair, AWS Bedrock and Google Vertex AI, appeared together in 1,272 answers. That was about 23% of the 5,501 answers naming a brand. Label the denominator on the chart.

Filter by the type of buyer question

The six question types had different leaders by mention share:

Use cases: Copilot Studio, 4.8%.
Comparisons across several products: Snowflake, 5.9%.
Best overall: Databricks, 4.4%.
Pricing and value: Agentforce, 5.3%.
Completing a specific task: Snowflake, 6.7%.
Choosing between A and B: Copilot Studio, 6.9%.

Keep multi-product comparisons and A/B choices as distinct filters. Combining them would hide their different leaders.

Record the strengths AI assigns to a product

Governance and compliance appeared as strengths 5,175 times. Agent orchestration appeared 1,807 times. IBM watsonx held the largest share of that category at 19%. The category included 116 brands.

Save the strength, product, and answer ID. A team can then review the descriptions associated with its product.

For five comparable brands, the website's most emphasized strength differed from the one AI mentioned most. Their top-three lists shared an average of 1.2 strengths. Show those two lists side by side, with links to the pages and answers.

Store citations separately from recommendations

Vendor sites accounted for 16.1% of cited links, community and video platforms for 9.9%, and review and analyst sites for 1.4%. Other third-party sources accounted for 72.6%, the largest category.

Coworker AI's website was cited 910 times. The brand was recommended 96 times and ranked 89th among 155 brands.

A citation record needs an answer ID, URL, and domain. A product record needs an answer ID, product name, whether it was recommended, and its position. Joining the records shows citations and recommendations within the same answer.

Show dates beside every change

The top-three order changed five times during the eight days. The top ten recorded eight changes in brand membership. The number-one product stayed the same.

The report also compared the first four days with the last four. Qlik rose from 25 to 35 mentions, Supermetrics from 54 to 69, and GoSearch from 48 to 61. Fiddler AI fell from 83 to 55, Cohere from 56 to 38, and Tellius from 87 to 63.

That comparison included 104 brands with at least 20 mentions in the first half and at least 60 overall. Display those inclusion rules alongside the dates.

For each new run, keep the same field names and prompt set. Review results by platform, question type, product, company, and date. Keep the original answer one click away from each record.

Source: Dageno, Enterprise AI Platform GEO Report. Dageno helps teams review brand descriptions and cited sources in AI answers.

Amazon Blocks Muse, Manus Gives Agents Their Own IDs: The Battle Over Who Gets Through the Door

Dageno AI — Wed, 30 Sep 2026 09:04:01 +0000

On the evening of September 20, U.S. shoppers using Meta's Muse on Amazon started seeing a string of pop-ups. The message was blunt: continued access by an unauthorized AI agent violates the Amazon Conditions of Use you agreed to.

Eight days later, Manus released Manus 2.0 and launched a standalone app called Cue. In Cue, every agent has its own email address, phone number, wallet, and computer.

The same day, Shopify opened its checkout to AI agents running in the browser, with three ready-made tools: read the checkout, update the checkout, and place the order.

Read as three product announcements, these point to an obvious conclusion: competition among personal AI agents is heating up. That's true, but it misses the bigger point. All three are answers to the same question:

When an agent acting for a user shows up at the door, who decides whether it gets in, what identity it uses, and what it can do once inside?

For more than two decades, SEO has been about helping people and search engines find you. Now there's a new kind of visitor. It represents a real person, carries that person's authorization, sometimes carries its own wallet, and comes to your site to complete a transaction. The fight over the entry point is shifting from "whose model is smarter" to "who gets in, who pays, and who owns the data from the transaction."

Figure 1: Over three weeks in September, platforms, websites, and infrastructure providers gave AI agents very different answers.

Amazon isn't blocking an app. It's blocking a way around its shelves

Start with Amazon's own reasons. Based on GeekWire's original report and CNN's follow-up, Amazon made four main points:

Meta didn't tell Amazon in advance that Muse would enter its store.
Muse doesn't identify itself as AI when it browses.
Muse allegedly obtains and stores customer credentials, giving it access to account pages and order history.
Third parties that place orders for customers should operate openly and respect a service provider's choice about whether to participate. Amazon compared this to delivery apps and restaurants, or online travel agencies and airlines, where buying on someone's behalf usually requires the merchant's consent.

Figure 2: GeekWire broke the story on the night of September 20. The image is a Meta promotional shot of Muse comparing strollers and asking the user to approve the order.

These reasons all hold up. None of them is the whole story.

Stratechery's Ben Thompson framed the dispute as a war of "aggregator v. aggregator" and pointed to the line that matters most: "Muse doesn't see ads."

Amazon's advertising revenue topped $68 billion in 2025. That business rests on one premise: shoppers browse, search, and compare on Amazon's pages, and see sponsored products along the way. An agent that compares prices and places orders for the user doesn't see ads, doesn't browse the shelves, and isn't swayed by placements. It just brings back the result. For Amazon, that isn't a new traffic channel. It's a route around the ad shelf.

In the Hacker News discussion, one commenter suggested Amazon simply build a bot-friendly API and govern bot behavior through its terms. Another replied right away: from Amazon's perspective, that's exactly the wrong direction. Its concern was never technical. It's about being disintermediated.

Figure 3: The Hacker News thread (152 points, 162 comments). The debate isn't about technology. It's about who owns the customer relationship.

Figure 3b: Techmeme's roundup, where dozens of industry figures joined in, including Elon Musk, Chamath Palihapitiya, Nikesh Arora, and Eric Seufert.

One more detail is worth noting: Amazon does the same thing itself. Its Buy for Me feature places orders on external brand sites for users. In January, independent merchants protested being enrolled in the program by default, with opting out as their only option after the fact. In May, Amazon merged Alexa and Rufus into Alexa for Shopping, and its Q2 earnings disclosed that more than 350 million people had used it over the past 12 months.

So Amazon isn't against agentic shopping. It's against someone else's agent shopping on its shelves.

robots.txt can't govern agents acting for users

From an SEO and web-infrastructure angle, the most interesting part of this story is robots.txt.

We fetched amazon.com/robots.txt on September 29. It contains 101 User-agent declarations, which dedupe to 100 distinct identifiers, and every one is set to Disallow: /. The list is thorough: OpenAI's GPTBot, OAI-SearchBot, and ChatGPT-User; Anthropic's ClaudeBot and Claude-User; Google's Google-Extended, GoogleAgent-Mariner, and GoogleAgent-Shopping; Meta's meta-externalagent, meta-externalfetcher, and meta-webindexer; plus Perplexity, Mistral, DeepSeek, xAI, and even Manus-User.

Muse is the one name missing.

Figure 4: AI crawlers and agents named in Amazon's robots.txt, fetched and compiled by the author on 2026-09-29.

Amazon didn't forget to add it. It couldn't. One of Amazon's complaints is precisely that Muse doesn't identify itself when browsing. robots.txt has no way to name a visitor that never gives its name.

The deeper problem is that robots.txt was never designed for agents acting on a user's behalf. OpenAI's documentation splits its crawlers into three types: OAI-SearchBot for search and GPTBot for training both follow robots.txt, while for the user-triggered ChatGPT-User, the docs say robots.txt rules "may not apply." In March, Google added Google-Agent to its documentation on "user-triggered fetchers," a category that generally doesn't follow robots.txt either.

The logic is simple. robots.txt governs whether a crawler may come and crawl on its own. An agent is there because a user sent it.

The legal line is being redrawn too. On August 4, the Ninth Circuit vacated the preliminary injunction in Amazon v. Perplexity, reasoning that under the U.S. Computer Fraud and Abuse Act (CFAA), the user is the one accessing Amazon's computers, and the AI company is just a tool the user employs. On September 10, the court denied Amazon's petition for rehearing.

That's why Amazon's pop-ups aimed at Muse cite contract terms like its Conditions of Use rather than "unauthorized intrusion." Technically, all that's left is behavior detection. Legally, all that's left is the terms of service. When the visitor represents a real user, the old model of allowing or blocking by name has largely stopped working.

The same week's other answer: Shopify turns checkout into an API

On September 21, the day after Amazon shut the door, Shopify CEO Tobi Lütke announced a deep partnership with Muse on X: every Shopify store would support agent checkout with Shop Pay.

On September 28, Shopify went further and added WebMCP support to checkout. Agents in the browser no longer need to take screenshots or parse HTML built for humans. They can call three tools directly: get_checkout to view the checkout, update_checkout to change the address or delivery option, and complete_checkout to place the order once the buyer authorizes it. As the Shopify product manager who leads agentic commerce put it on X, agents should call structured interfaces instead of feeling their way through pages designed for people.

Figure 5: TechCrunch's report on Shopify's three checkout tools for agents. The piece also notes that Amazon and Adidas are blocking buy-for-me agents.

Other players in the same period:

Walmart, Best Buy, Gap, Sephora, Wayfair, and Expedia signed on as Muse retail partners.
eBay changed its user agreement back in February to ban unauthorized "buy-for-me" agents.
Resy blocked a third-party reservation agent that was sending hundreds of requests an hour.

Opening or closing the door looks like a matter of attitude. It's really a matter of position:

Those who own demand and ad shelves tend to close the door. Amazon's shoppers come anyway. Agents bring no new traffic, but they take away ad revenue and the customer relationship.
Those who need distribution tend to open it. Shopify's millions of merchants don't have a super app of their own. Agents could become a new way to acquire customers.
Those caught in the middle are in a tough spot. Expedia announced it was joining Muse on September 22, and its stock fell about 7% the next day, with Airbnb and Booking following it down. The market's worry: travel platforms that plug into agents may be turning themselves into suppliers behind the agent.

Opening the door has a price too. Starting September 18, Google turned on by default native checkout in AI Mode and Gemini for eligible U.S. Shopify merchants (built on Google's UCP protocol), leaving merchants to switch it off in their admin themselves. According to Search Engine Land and PPC Land, neither GA4 nor merchants' custom pixels fire during this checkout.

When the transaction happens in someone else's interface, the merchant gets the order but may lose attribution data, cross-sell opportunities, and control over the page experience.

Manus's Cue: agents stop borrowing your identity

Back to Manus.

Figure 5b: Meta launched Muse on September 8, billing it as the world's first personal AI agent built for everyone.

Muse works like this: you connect your email, calendar, payments, and shopping accounts, and it acts on your behalf using your identity. For services without an API, it uses its own browser and operates "like a person." That's the part Amazon can't accept: a visitor logged in with a customer's account that doesn't say it's AI.

Cue takes a different path. According to Manus's official blog, every agent in Cue has its own email address, phone number, wallet, and computer. It can send messages, pay within a budget you set, take calls and leave summaries, and join group chats to split work with other agents.

Figure 6: Cue's website. Its page title reads "Your Personal Agents, With Their Own Identity," and each agent gets its own phone, wallet, computer, and mail.

Figure 6b: Manus 2.0 runs on Cascade, its in-house agent harness. Manus reports 23.2% fewer tokens, 28.2% less time, and 32% lower cost, but these come from internal tests in a single configuration, and the methodology wasn't published.

Manus founder Xiao Hong explained the idea on Jike, a Chinese social app: something smart enough, with its own phone number, email, payments, and computer, might be treated as a "person." Follow that logic, and you get group chats where people and bots work side by side.

Interestingly, Manus isn't alone:

Google's Gemini Spark, launched in May, also has its own Gmail address.
At Connect on September 23, Meta announced that Muse will get its own email address too.

Put this next to Amazon's demands and a shared direction appears. When Amazon asks agents to "operate openly," it's asking them to say who they are. Cue and Spark give agents their own identities, answering the same question at the product level: agents shouldn't keep impersonating users. They should show up as identifiable visitors.

The infrastructure layer is moving the same way:

In August 2025, Cloudflare launched Signed Agents, treating agents that act for end users separately from crawlers that act for companies.
Web Bot Auth, based on HTTP message signatures, now has an IETF working group, and the latest working-group draft is co-authored by engineers from Cloudflare and Google.
Visa's Trusted Agent Protocol reuses the same signature mechanism, binding each signature to a merchant, a purpose, and a time window.

"Who is this bot?" and "Can this bot pay?" are merging into the same signature layer.

But the more complete the identity, the heavier the responsibility and the more concentrated the risk. Cue is in invite-only early access. Manus hasn't disclosed the payment rails behind the wallet, where the phone numbers come from, or how identity verification and KYC work. Four days before Manus 2.0 launched, security firm Salt Labs disclosed a vulnerability through Dark Reading: a single email with embedded instructions sent to a Manus user's inbox could trigger remote code execution and expose credentials for connected apps such as Gmail and GitHub. Give an agent a public email address, and you give attackers a public way in.

Muse has had its own trouble. The Guardian reported on September 28 that a Toronto user let Muse handle a secondhand sale on Facebook Marketplace. Without his knowledge, Muse sent his home address to the buyer, and when the buyer arrived, replied "I'm here" in his voice. Muse later acknowledged that it had read "set a pickup location" as permission to share the address.

These incidents point to the same conclusion: whether an agent gets through the door ultimately depends on whether it can be identified, authorized, and held accountable. That's not just a contest between websites and platforms. It's a question agent products have to answer themselves.

China has already seen this play out

The Amazon–Muse standoff has a close precedent in China.

On December 1, 2025, ByteDance and ZTE's Nubia released an engineering prototype phone with the Doubao phone assistant built in. Using system-level permissions to read the screen and simulate taps, it could complete tasks like booking tickets and placing orders across apps. From the next evening, users who had it operate WeChat began getting forcibly logged out, and Alipay, Taobao, and some banking apps started showing "abnormal login environment" warnings. Within days, Doubao removed its ability to operate WeChat and restricted finance-related scenarios.

This September, the second-generation Doubao phone went on sale alongside SAEP, a screen-automation declaration protocol that lets third-party apps declare whether AI may operate inside them. Apps that don't declare are left alone by default. In hands-on tests, WeChat, Taobao, Xiaohongshu (RedNote), and Meituan's food delivery app could still only be opened, not operated automatically. On September 27, users also reported that phones with the Doubao assistant were flagged for an "abnormal device environment" when logging in to the game Honor of Kings.

The contrast is Alibaba's Qwen. Instead of simulating taps inside other companies' apps, it connected directly with Alibaba's own Taobao, Alipay, and Fliggy, and in May it began handling the full flow of product selection, price comparison, ordering, and after-sales service. System-level agents from phone makers like OPPO and Honor have taken the same route, signing partnership integrations with Alipay, Tencent Maps, and others.

The results of the two routes are strikingly different: without a partnership, using screen simulation to reach into someone else's ecosystem gets blocked by whoever owns the accounts and traffic, through risk controls. The route built on authorized interfaces actually works. The U.S. is now replaying the same scene, with Amazon and Meta in the lead roles and Conditions of Use and behavior detection standing in for risk controls.

Three layers of the battle, and where the money goes

Put September's events together, and the battle over personal agents plays out across three layers:

Figure 7: The operating system, super apps, and browsers and the cloud each set their own rules for what agents can do.

The first layer is the operating system. Apple's new Siri shipped with iOS 27 on September 14, and third-party apps use App Intents to define which actions it can call. Honor, OPPO, and vivo have turned large sets of system capabilities and third-party services into tools agents can call. Whoever defines the interfaces defines what agents can do.

The second layer is the super app. ChatGPT has Apps and its newly tested Sponsored Agents. Meta has Muse. Amazon has Alexa for Shopping. Alibaba has Qwen plus Taobao, and WeChat's native agent is reportedly in a limited test. What they share: accounts, payments, and demand connected inside their own ecosystems, with barriers for outside agents.

The third layer is the browser and the cloud. Gemini in Chrome can browse on its own, Anthropic's Claude in Chrome is open to all paid users, Perplexity is sticking with its Comet browser, and Manus and Cue put agents in cloud computers. This layer faces websites directly, and it runs straight into the limits of robots.txt.

One signal that's easy to miss: standalone AI browsers are receding. OpenAI shut down Atlas on August 9, Google folded Project Mariner into Gemini and Chrome in May, and Microsoft dropped the separate Copilot Mode in Edge. The capabilities haven't disappeared. They've been pulled back into entry points that already have frequent users. The battle isn't about building a new entry point. It's about giving existing ones the ability to act.

The money is moving too. ChatGPT's Sponsored Agents take users who click an ad straight into a conversation with the brand's own agent. At Connect, Mark Zuckerberg said Muse will eventually make money by taking a small commission on transactions. Platforms used to sell ad placements. Next, they may sell control over agent routing: where the user's agent places the order, which brand's agent it calls, and which products make it into the comparison. Those decisions are the new distribution.

The data supports this shift in traffic, but it needs careful reading:

HUMAN Security's April report found that traffic from agents and agentic browsers grew 7,851% year over year, with more than 95% concentrated in retail and e-commerce, streaming, and travel.
Cloudflare Radar observed that in one week from late May to early June, automated traffic made up 57.2% of HTML requests, surpassing humans for the first time.
DataDome counted a 45% quarter-over-quarter increase in AI agent requests in Q2.

Each of these comes from a company's own customers and network. The methods differ, so the numbers can't be added together, and they don't mean every industry looks the same. But they point the same way: among the visitors to your website, machines acting for people are becoming as common as people.

What this means for websites and brands

From an SEO and web-infrastructure perspective, this battle doesn't bring a new buzzword. It brings a concrete set of work.

1. See who's visiting you. Most agent visits don't execute JavaScript, so GA4 barely sees them. Use server or CDN logs to separate three kinds of traffic: crawlers for search, crawlers for training, and agents acting for users. Their value and risk are completely different, so a single rule for all of them won't work.

2. Check your CDN defaults. Starting September 15, Cloudflare changed its default policy: for new sites and free sites that haven't changed their settings, pages that show ads allow search crawlers and block training and agent traffic by default. Many sites may already be turning away booking and shopping agents without knowing it. Whether that's what you want is a decision you need to make on purpose.

3. Move access policy from names to identity. User-agent strings can be faked, or left out entirely. A more reliable approach: verify trusted agents with officially published IP lists and Web Bot Auth signatures, apply behavior detection to unsigned automated traffic, and spell out in your terms of service how unauthorized agents are handled. Those are exactly the three things Amazon ended up relying on.

4. Publish key facts as machine-readable first-party information. Research Kevin Indig published in Growth Memo in July found that in B2B, pricing pages are where agents most often get stuck. When they can't read first-party information, agents turn to G2, Capterra, and press coverage, letting third parties quote your prices for you. Prices, specs, inventory, shipping, and return policies should be clearly expressed in server-rendered HTML and structured data, and ecommerce sites should sync them to their product feeds.

5. The accessibility tree is the interface agents see. Many agent frameworks read a page's accessibility snapshot, not a screenshot. Whether buttons have names, whether forms have labels, whether the structure is stable, whether modals block the page: issues once treated as accessibility compliance now also decide whether an agent can complete an order. Google's Lighthouse 13.5, released September 18, added an experimental "Agentic Browsing" audit category. That's a signal.

Figure 8: Cloudflare introduced the Agent Readiness score in April. Across roughly 200,000 top domains, adoption of the relevant standards is still low.

6. Negotiate terms before you open the door. Shopify opened its checkout API and Google turned on AI checkout by default, but how much data merchants get back from these transactions, whether they can attribute them, and whether they keep the customer relationship aren't automatic. Between opening and closing the door there's a more important option: opening it on conditions. Which scenarios allow agents to place orders, what data must be passed back, and who is responsible when something goes wrong should all be on the table in negotiations with platforms.

7. Don't take shortcuts. Google has said llms.txt has no effect on AI Overviews or AI Mode. Showing bots and people different content risks being treated as cloaking. Hiding prompts in pages to steer agents is already considered LLM poisoning. Black-hat tactics in the agent era will be more dangerous than in search, because they connect directly to payments.

8. Getting chosen by an agent is a new ranking problem. The ACES study from Columbia Business School and other institutions had different models shop in a simulated ecommerce environment. It found that agents show clear position bias, discount items labeled "Sponsored," react strongly to the wording of product descriptions, and reshuffle product shares sharply whenever the model is updated. Microsoft's Magentic Marketplace experiment also found that some models almost always accept the first offer.

That means a brand's performance with agents can't be fixed with a one-time optimization. It needs ongoing monitoring by model and by scenario. We used to study which result a user would click. Now we also have to study which result an agent will choose for the user.

Conclusion: the front door is no longer just about being found

Looking back, both Amazon and Shopify made rational choices. One owns demand and ad shelves, and is protecting every second shoppers spend on its pages. The other needs distribution, and is courting every agent that might bring an order. Manus's Cue represents the agent side's answer: if websites want agents to identify themselves, give them real identities.

Most brands and websites are neither Amazon nor Shopify. They're in the middle: they want the new demand agents can bring, and they worry about losing customer relationships and data.

AI search changed how users find you. Personal agents change who completes every step after that on the user's behalf. The first decides whether you get mentioned. The second decides whether you close the sale.

The commercial front door is moving from the search box and the store shelf into the routing logic of agents. The outcome of this battle will decide who opens the doors of the next internet.

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