In September 2026, the month before Paramount closed its acquisition of Warner Bros. Discovery, Paramount+, HBO, Max, HBO Max, Pluto TV and Discovery+ together held 21.35% of brand mentions on ChatGPT and 21.11% on Google AI Overviews, measured across thirteen streaming brands. Second to Netflix on both. Three of those six are HBO-related brand names, and the two engines rank them differently: ChatGPT mentions HBO most often, Google AI Overviews mentions Max most often.

What this measures: BrightEdge AI Hyper Cube data with share of brand mentions inside AI answers, calculated as each brand's mentions divided by total mentions across the top thirteen streaming brands measured, in the United States, on ChatGPT and Google AI Overviews. It does not measure clicks, traffic, subscribers or revenue. See Technical Methodology.

The AI visibility position Skydance inherited

Skydance has a potential brand problem through its acquisition of Warner Bros. Discovery, which closed on 6 October 2026. The deal brings together a streaming portfolio with some naming consistency issues.

Table 1. Share of brand mentions across thirteen streaming brands, September 2026, United States. The six are Paramount+, HBO, Max, HBO Max, Pluto TV and Discovery+. Source: BrightEdge AI Hyper Cube.

Netflix leads the six combined by 6.58 points on ChatGPT and 7.12 points on Google AI Overviews. Skydance, read as one portfolio, is a clear second. But read as the engines actually report it, it is six separate brand names, none of which reaches 7%.

Three HBO-related names, two engines that rank them differently

Three of the six are HBO-related brand names: HBO, Max and HBO Max. HBO is a channel and a programming brand as well as a streaming label, so the engines may be answering about different things under each name. What the data shows is which name each engine uses.

Table 2. Share of brand mentions for the three HBO-related names, September 2026, United States. Source: BrightEdge AI Hyper Cube.

ChatGPT mentions HBO most often, at 1.59 times the next of the three. Google AI Overviews mentions Max most often, at 1.07 times. Neither engine collapses the three into one, and the two do not agree on which name they reach for.

Older brand names can remain prominent well after a rebrand. HBO Max launched in 2020, was renamed Max in 2023 to signal a broader mix of programming, and went back to HBO Max in July 2025. For HBO Max, our September data shows that the former Max name continues to appear frequently in AI answers: more than a year after the name returned, Google AI Overviews still mentions Max more than HBO Max, and ChatGPT has the two at near parity.

Skydance should consider this learning as it looks at consolidating further. Tracking the same three names through a branding change is what would show how visibility moves. This research does not establish what any one name would be worth after such a change.

Much like TV ratings, live events dominate AI conversations

Table 3. Share of top-prompt volume by category, September 2026. Prompt-mix analysis by BrightEdge Professional Services on the top 60 to 100 prompts by estimated volume per engine, categorized by keyword rules. Read as indicative.

Among the top prompts analyzed by estimated volume, live events (prompts like "What channel is the F1 race on today?") lead on both engines, followed by where to watch (prompts like "Where to watch Euphoria"). Despite the big news surrounding the acquisition, ownership questions were rarely seen, accounting for under 1%. These shares describe that sample rather than every question put to either engine.

What We Analyzed

BrightEdge measured brand mentions across thirteen streaming brands in AI answers on ChatGPT and Google AI Overviews, in the United States, for September 2026. No prompt filter was applied. The six Paramount and Warner Bros. Discovery streaming brands were then compared against each other and against the rest of the field, and the three HBO-family labels were compared separately on each engine.

Data Collected

Key Finding

Measured. The six brands together held 21.35% of brand mentions on ChatGPT and 21.11% on Google AI Overviews, second to Netflix on both. Within the HBO family, ChatGPT ranked HBO first at 5.52%, HBO Max second at 3.47% and Max third at 3.11%. Google AI Overviews ranked Max first at 5.05%, HBO second at 4.70% and HBO Max third at 2.33%.

Inferred. Brands are complicated and persistent. A company answering to several brand names is treated as several brands by both engines, and an older name can stay prominent well after a rebrand. More than a year after HBO Max returned to its original name, Google AI Overviews still mentions Max more frequently. That shows why marketers need to track both current and former brand names. It is not a finding about HBO specifically, and we were unable to identify a clear reason why one would be captured vs. the other, nor how persistent these kinds of discussions are.

How that affects your measurement

Share of mentions is calculated per brand name. An engine that answers with "Max" records a mention for Max, and an engine that answers with "HBO" records one for HBO. Nothing joins them without coordinating brands and sub-brands.

That has two consequences for any company with more than one name.

Each name is measured separately, so a dashboard filtered to one of them shows part of the position rather than all of it. The three HBO-related names individually range from 2.33% to 5.52%; together they are 12.10% on ChatGPT and 12.08% on Google AI Overviews.

The sum is a valid figure, and it answers a specific question. Because the metric counts mention instances, adding the shares gives the group's share of all mention instances across the thirteen brands. It is not the share of prompts in which at least one of those names appears.

What Marketers Need to Know

Use brand grouping to create a consistent definition of who your brand is. A short name, a legal name and a product name are three brands to an AI engine. Create a standard definition of a Grouped Brand to represent all your brand names.

Grouped Brands Settings in BrightEdge AI Catalyst. Brand matching groups any sub-brand that starts with the parent name, and additional sub-brands are selected per parent brand so that every name a company answers to is measured as one.

Compare the engines against each other. ChatGPT and Google AI Overviews ranked the same three names differently here. A reading taken from one engine is half the picture.

Record the baseline before any branding change. A rename, a merge or a retirement can only be assessed against a measured starting position. Put the current names on record first, then track the same set through the change.

Work on the questions that carry the volume. Among the top prompts analyzed by estimated volume, live events lead on both engines. An evergreen how-to-watch page answering time, channel and stream covers the largest category in that sample.

Technical Methodology

What Is Still Open

How Skydance handles the merger remains to be seen. The earlier Warner Bros.-Discovery merger left many brand names in place. With three major streaming platforms (HBO Max, Paramount+ and Discovery+) and new leadership, branding decisions, as we have shown, can have long-running implications for how you show up in AI.

• Whether the position moves after the close, which only post-merger data can show.
• Whether the engine disagreement holds. One month on two engines in one market is a snapshot. A second month would show whether ChatGPT's preference for HBO and Google AI Overviews' preference for Max are stable.
• Whether the conversation mix holds. The prompt-mix categories come from a single month's top prompts and have not been reproduced independently.

Key Takeaways

Frequently Asked Questions

Which streaming brands are the biggest in AI?

Across thirteen streaming brands in the United States, Netflix led on both engines, at 27.93% of brand mentions on ChatGPT and 28.23% on Google AI Overviews. The six Paramount and Warner Bros. Discovery names together held 21.35% and 21.11%, ahead of Apple TV, Hulu and Disney+.

Do ChatGPT and Google AI Overviews treat HBO, Max and HBO Max as the same brand?

Neither does. Both engines record a mention against the specific name used, so the three are measured as three brands. The engines also reach for different ones: ChatGPT mentions HBO most often, at 1.59 times the next of the three, while Google AI Overviews mentions Max most often, at 1.07 times. HBO is also a channel and a programming brand, not only a streaming label, so the three are related names rather than interchangeable ones.

How often does Google AI Overviews mention Max versus HBO Max after the rebrand?

In September 2026, Google AI Overviews mentioned Max in 5.05% of brand mentions across thirteen streaming brands, against 2.33% for HBO Max. The service had returned to the HBO Max name in July 2025, more than a year earlier. ChatGPT had the two closer together, at 3.11% for Max and 3.47% for HBO Max. This research records that the older name is still in use on both engines. It does not establish why, and a single month cannot show how long a name change generally takes to register.

How should a merged company measure AI visibility through a brand name change?

Record the position of every name involved before the change, on each engine, then measure the same set afterwards. This research is an example of the first half: September 2026 positions for all six names, on ChatGPT and Google AI Overviews, taken before the deal closed. It does not forecast what any name would be worth after a change, and no measurement taken before one can.

Does this research measure the effect of the Paramount and Warner Bros. Discovery merger?

No. Every figure is September 2026 and the deal closed on 6 October 2026, so this describes the position Skydance inherited. October data is not yet available. The first post-merger read is the one that can show movement.

What do people ask AI about streaming services, among the highest-volume prompts?

Among the top 60 to 100 prompts by estimated volume on each engine, live events lead at 65% on ChatGPT and 73% on Google AI Overviews. Where to watch a specific title follows at 15% and 22%. Questions about who owns which service account for under 1% on both. These shares describe that sample rather than every question put to either engine.

What does share of brand mentions measure, and what does it not?

It is a brand's mentions divided by total mentions across the brands measured, inside AI answers. It does not measure clicks, traffic, subscribers or revenue, it does not establish why a brand was mentioned, and it does not predict what a name would be worth after a branding change. It depends on the brand set chosen, so a different set gives different shares.

Source: BrightEdge AI Hyper Cube. Share of brand mentions across thirteen streaming brands, United States, September 2026, on ChatGPT and Google AI Overviews. Deal facts from Axios, 6 October 2026. Brand name history from Variety and The Hollywood Reporter.

More from this series

See the rest of the Weekly AI & Search Insights series.

On 16 September 2026, Product Grid listings stopped appearing in all 14 EEA markets BrightEdge tracks. A Comparison Sites section began appearing in the same markets on the same day. In the EEA a retailer's free product listings run through one comparison shopping service. Google Shopping appeared in the new section 16 to 35 times as often as the next-largest service. That is across Germany, France, Spain and Italy, on tracked queries from 9 to 29 September.

Which service represents your domain is quick to establish. It sits in the Merchant Center CSS dashboard, it costs nothing to check, and it decides which service places your free listings. Feed quality and your relationships with third-party catalogs sit alongside it.

What this measures: This counts how many tracked keywords display each section. It does not show whether a given merchant's products appear inside it, and it does not measure clicks. The markets are the 14 EEA markets BrightEdge tracks, which are 13 EU countries plus Norway. Other EEA markets exist and were not measured.

The switch happened on one day, in every tracked market

Product Grid listings stopped appearing in all 14 tracked EEA markets after 15 September. The Comparison Sites section began on 16 September, the same day in every one of them. Product Grid stayed flat throughout in the UK, US, Canada and Australia, which sit outside the change.

Google removed the grid to comply with a European Commission ruling of 23 July 2026 under the Digital Markets Act. The ruling found that Google favored its own shopping, hotel, transport and sports results, and fined it EUR 460 million. Reuters reported a 60-day compliance window, and the September change landed inside it.

Google disagrees with the ruling. The change is a compliance step. Compliance does not establish that this particular layout has to remain, so treat the current arrangement as the position in the measured window.

One setting decides which service places your free listings

In the EEA, one comparison shopping service represents a merchant domain for free product listings. Google documents this per domain. Several services can be used for paid campaigns, and one carries the free listings. This rule predates the September change. What changed on 16 September is the place those listings appear.

Google Shopping appeared in the Comparison Sites section 16 to 35 times as often as the next-largest service. That is across Germany, France, Spain and Italy, on tracked queries from 9 to 29 September 2026. 71% of Comparison Sites appearances landed in result positions one to four, from 61% in France to 76% in Spain and Italy. It is premium space.

What the multiple counts. The 16 to 35 times comparison measures how often each service appears in the section. It does not measure an individual merchant's product visibility or performance. BrightEdge has not tested what changes when a merchant switches service. Google Shopping led the section across those three weeks. Read your own selection before deciding whether to change it.

Confirm which CSS represents your domain, resolve related disapprovals, and check whether priority products appear.

Where the selection does not match the destination, Merchant Center disapproves the products with the message "CSS not selected for this destination". Resolve the CSS mismatch, then check for any remaining eligibility issues.

Merchant Center and third-party catalogs provide separate routes into the section

Coverage figures cover 1 September to 13 October 2026. Share and position figures cover 9 to 29 September 2026. Routes by which a product can appear in the Comparison Sites section. Source: BrightEdge Professional Services, with CSS mechanics from Google Merchant Center documentation.

Third-party comparison services use their own product catalogs, separate from Merchant Center. Retailers should check which services carry their products and whether the information is accurate.

Google Shopping appeared substantially more often than other services during the period measured. The section exists to give third-party services comparable prominence. Monitor changes in service appearances alongside your own product coverage.

Coverage returned to its previous level by 30 September

On 16 September, the number of tracked keywords showing a Comparison Sites section was 38% below the previous Product Grid baseline. Coverage increased through the remainder of September and matched the previous baseline on 30 September. The baseline is 53,035 keywords, day one counted 32,771, and 30 September counted 53,227.

Since 3 October, the combined count has remained 3% to 8% above that baseline.

These figures measure how often a product section appeared in tracked search results. They do not establish whether individual retailers retained their visibility or traffic. Changes in how the new section was collected may also have affected the early trend.

Markets did not move together

Selected markets, indexed to each market's own Product Grid average for 10 to 14 September. Source: BrightEdge Professional Services, 1 September to 13 October 2026. The six shown are the clearest examples of each pattern. The other eight tracked markets mostly follow the large-market or Poland shape.

Germany, Italy and Spain took the deepest day-one hits and recovered slowest, then sat above their old baselines on 3 October. Finland and Norway took smaller early hits and missed the 30 September step.

Finland and Norway remained below baseline on 13 October, the last day of the window. Both are small markets where a few dozen keywords move the index, so neither reading is a reliable guide alone.

What to do before the holiday peak

• Establish which CSS represents each merchant domain for free listings. Confirm the service, resolve eligibility issues, and assess that choice against your own product visibility. A market-wide share does not answer it.
• Annotate 16 September in European product reporting. The grid and the Comparison Sites section are different layouts, so measures either side are not like for like. Mark the date, report layout-specific measures separately, and keep the earlier history.
• Establish which third-party services carry your priority products, market by market. Those boxes fill from each service's own catalog. Record the service, the feed behind it, and whether the product data is accurate. Order the work by which markets carry your peak trading. The recovery index does not tell you where your catalog gaps are.
• Make the feed, the page and the structured data agree. Product information may appear across Google Shopping, third-party catalogs and AI shopping experiences. Match titles, prices, availability and identifiers across all of them.
• Compare clicks either side of 16 September. Compare product-page and non-product-page clicks before and after the change.

Start by checking CSS setup and product eligibility. Then prioritize the markets and products that matter most for holiday trading. Building coverage across third-party catalogs is slower, and how far up the list it belongs depends on which markets and products carry your peak.

Related check: product-page accessibility

Product-page accessibility is a related readiness check, and it sits outside the research above.

Crawlers vary in whether and when they execute JavaScript, and several AI crawlers fetch raw HTML without rendering it. Content that appears only after client-side rendering may be missed, so serve price, variants, availability and reviews in the page code itself.

Test this on your own pages. An industry average does not tell you which of your templates fail. Crawl a sample of product pages with JavaScript disabled and compare what appears with the browser view. Then check server logs for which crawlers fetched the full product content.

How to check this for your own site

Before you compare anything, note what changed in the reports. From the week of 27 September 2026, BrightEdge platform Product Grid reports reflect the new layout. Grid title, grid rank, product count and seller count can shift for reasons unrelated to SEO performance.

Find the keywords that lost a grid listing. In BrightEdge Product Grid reporting, pull the EEA keywords where your products appeared in the grid before 16 September, by market. Those are the keywords to follow through the switch.

Compare by market, with 16 September annotated. In Data Cube X and Product Grid reporting, compare keyword counts by listing type for your domain on either side of 16 September. Index each market to its 10 to 14 September average, as this edition does.

Compare clicks across the switch. In GSC Reporting, compare clicks to product pages and to non-product pages for your EEA markets before and after 16 September.

Frequently asked questions

What is the Comparison Sites section?

A section Google began showing on 16 September 2026, observed in the 14 EEA markets BrightEdge tracks, where comparison shopping services list products. Google Shopping, Google's own service, is one of them. It replaced the free product grid.

What is a comparison shopping service?

A service that places products in Google results in Europe, such as Google Shopping, idealo or billiger.de. A retailer selects one for its free listings.

Is the product grid gone everywhere?

No. The 14 EEA markets BrightEdge tracks all switched on 16 September. The UK, US, Canada and Australia kept the grid throughout the window.

Which markets did BrightEdge measure?

Austria, Belgium, Czechia, Denmark, Finland, France, Germany, Greece, Italy, the Netherlands, Norway, Poland, Spain and Sweden. These are 13 EU countries plus Norway. Other EEA markets exist and were not measured, so this edition does not describe them. Share and position figures cover Germany, France, Spain and Italy.

Which comparison shopping service should I select?

This research does not answer that. Google Shopping appeared 16 to 35 times as often as the next-largest service. That is across Germany, France, Spain and Italy, from 9 to 29 September 2026. That figure measures how often each service shows up in the section. It does not measure how a merchant's products perform inside it, and BrightEdge has not tested what changes when a merchant switches service. Start by reading which service represents your domain.

Did EEA traffic drop when the grid was removed?

This edition measures visibility and does not measure clicks, so it cannot answer that. Compare clicks to your product pages and to your non-product pages either side of 16 September in your own reporting.

Are merchants keeping their visibility?

The counts cannot answer that. Keyword coverage of the Comparison Sites section matched the old Product Grid level by 30 September. The counts show how many keywords display the section. Whether a given merchant's products appear inside it is a different question. That depends on your CSS selection and on the feeds third-party services ingest.

Is the grid coming back?

Unknown. The change is a compliance step and Google disagrees with the ruling. Compliance with the decision does not establish that this particular layout has to remain.

Technical methodology

Key takeaways

What is still open

Three questions would change this advice if they resolved differently.

• Whether the supplier unit, Google's separate section for direct sellers, appears on product queries at all. No capture in this collection shows it.
• How Google ranks the services inside the section. We have not located documentation.
• Where clicks from the section are reported in Search Console and Merchant Center. We have not located documentation.

More from this series

See the rest of the Weekly AI & Search Insights series. 
For more on the grid removal and the Google Shopping lead, read EEA Product Grid Is Gone: Google Shopping Still Leads.
For the access and rendering checks behind machine-readable product pages, read our guide to AI agents.

BrightEdge tracks product visibility across search and AI surfaces. See how your products appear in the markets that changed with Data Cube X, or request a demo.
 

This edition compares two AI surfaces, Google AI Overviews and ChatGPT, on holiday shopping prompts. We pulled the top 50 cited rows per retailer on each engine, across four major US retailers. Both engines were filtered to the same holiday terms. Then we pulled the prompts themselves.

What this measures: This measures which pages each engine cites as sources for holiday questions. It does not measure what the answers say, and it does not measure clicks. Citation weight counts page citations, so a prompt that cites two newsroom posts counts twice. Every share in this edition is a share of citation weight. A share of distinct prompts is a separate pull and would give different numbers.

Shoppers bring different questions to each surface

Half of ChatGPT's top holiday prompts are logistics questions. On Google AI Overviews, 28% are logistics questions and 72% are product and deal queries.

Google AI Overviews gets keyword strings such as black friday tv deals and store hours thanksgiving. ChatGPT gets full sentences such as Is the store open on Thanksgiving? and What time does Black Friday start?

The average ChatGPT holiday prompt runs 6.0 words against 4.4 on Google AI Overviews. A keyword product query is answered by a category page. A logistics question is answered by a press release or a help article.

Each engine receives different questions and cites different pages, so each needs its own measurement.

Each engine cites a different mix of page types

Share of holiday citation weight by page type, Google AI Overviews against ChatGPT. Source: BrightEdge AI Hyper Cube. ChatGPT and Google AI Overviews, United States, June to August 2026. Four of six panel retailers, with both engines matched at the top 50 rows per domain, so both engines are compared at the same depth. Ratios use unrounded shares, so they differ slightly from ratios computed on the rounded values shown. Page owners are our characterization of a large retail organization. The shares are measured.

The last column names the engine with the higher share. ChatGPT gives more citation weight to newsroom posts, guides and help pages. Google AI Overviews gives more to commercial pages and on-site search results. Both engines cite all five types.

Commercial pages and on-site search results make up 74.4% of Google AI Overviews holiday citations and 35.5% of ChatGPT citations. Newsroom posts, guides and help pages make up 64.5% of ChatGPT citations and 25.6% of Google AI Overviews citations.

Two findings hold at every retailer in the pull. Commercial pages and on-site search results together run higher on Google AI Overviews for all four, by 10 to 53 percentage points. Help and policy pages run higher on ChatGPT for all four. The other three page types do not agree across all four. Those are panel averages, and the newsroom share on ChatGPT runs from 2% to 64%. Each retailer needs its own pull.

Pages owned outside marketing carry half of ChatGPT's holiday citations

In a large retail organization, marketing and merchandising own the commercial pages and guides. Those make up 72.1% of Google AI Overviews citations and 48.3% of ChatGPT citations. Communications and customer support own the newsroom and help pages, which make up 18.1% of Google AI Overviews citations and 51.5% of ChatGPT citations. On-site search results, owned by the ecommerce platform, make up the remaining 9.7% and 0.1%.

That changes who needs to be in the holiday planning meeting. On Google AI Overviews, updating the pages marketing owns covers most of the citations. On ChatGPT it covers about half.

Every retailer in the pull already has cited pages across at least four page types. Start by reviewing those cited pages for outdated information.

Example. Take an announcement post that carries last year's event date. If it is cited for the question of when a sale starts, anyone who opens it sees last year's date.

What this means for agentic commerce and AI shopping agents

These findings also matter for AI shopping agents. An agent needs four facts before it can buy: the sale date, the return window, the price-match terms and the shipping cutoff. Those four sit on newsroom posts and help pages, which carry 51.5% of ChatGPT's holiday citations.

The shopper typing a full sentence into ChatGPT today needs the same four facts an agent will need. Publishing them as pages a crawler can read gives both shoppers and AI systems more reliable information to work from.

On-site search results in Google AI Overviews call for a crawl and indexation check

Google AI Overviews cites retailers' own internal search result pages at 9.7% of its holiday citation weight. ChatGPT cites them at 0.1%. Two of the four retailers account for all of it, at 14.2% and 24.3%, and the other two show none. If a retailer's internal search pages appear in citations, the question for technical SEO is whether those pages should be crawled and indexed.

What to update before Black Friday

• Pull the cited-URL list for both engines. Group each list by directory and compare the two profiles. If they come back close to identical, check that the engine filter was applied before reading anything into the result.
• Share the ChatGPT list with the owners of those pages. The newsroom and help pages behind half of ChatGPT's citations sit with communications and customer support.
• Date every announcement and review last year's post. A cited announcement that carries last year's date shows that date to anyone who opens it. Sale start date, return window, price-match terms and shipping cutoff belong on pages a crawler can read.
• Update pages that already exist before commissioning new ones. Six weeks is a short window for new content to build visibility. Refreshing a page that is already live and already cited is the more practical priority.
• Plan around Google first and let the same page serve AI. Google still sends the traffic, so the commercial pages come first. They make up 64.6% of Google AI Overviews citations. Keep them current, then cover the newsroom, guide and help pages that make up 64.5% of ChatGPT citations.

How to check your holiday and agentic shopping visibility on both engines

Group the cited URLs by directory. In AI Hyper Cube, pull the cited-URL list for your holiday prompts and group it by directory. Each directory shows how much of the footprint comes from the newsroom, the help center and the guides.

Compare the two engines. Run the same pull on both and compare the two directory profiles. If they come back close to identical, confirm the engine filter was applied before acting on the result.

Size the organic footprint. Under the same question set, use Data Cube X to size the space before deciding how much to invest.

For the longer build program behind all of this, crawler access, product data and schema, see our guide to optimizing e-commerce for AI search.

Frequently asked questions

Does being cited mean I get the click?

This analysis does not measure clicks. It measures which pages are cited as sources.

Is this a share of prompts or a share of citations?

Citations. A prompt that cites two newsroom posts counts twice, so every share is a share of citation weight. A share of distinct prompts is a different pull and would give different numbers.

Should we stop investing in the Black Friday hub?

No. The hub sits inside the commercial pages that Google AI Overviews cites most, at 64.6%, and Google still sends the traffic. The finding is about which other page types carry the answer on ChatGPT.

How does this change what we do for agentic shopping?

It names the pages. An AI shopping agent needs the sale date, the return window, the price-match terms and the shipping cutoff. Those sit on newsroom and help pages, so those pages carry the agentic commerce work.

Why are the shares based on four retailers?

The page-type pull covers four of the six panel retailers, and the shares describe those four. They do not describe the other two.

Does this hold on other engines?

The pull covers ChatGPT and Google AI Overviews. Other engines are untested.

How current is this?

It reads June to August 2026. Holiday prompts run all year, so the pages cited in the summer are the pages already published going into November.

Technical methodology

Key takeaways

More from this series

See the rest of the Weekly AI & Search Insights series. For the build side of the same problem, read our guide to optimizing e-commerce for AI search.

BrightEdge tracks which pages ChatGPT and Google AI Overviews cite for your brand. Find out which holiday pages each engine cites for you before Black Friday with AI Hyper Cube, or request a demo.

BrightEdge referral data shows AI search volume still climbing. Against that, a shorter list of brands per answer means each named slot is worth more than it was six months ago.

This edition covers the B2B Tech vertical. The panel holds the same non-branded prompts constant across two dates six months apart. Movement in the numbers reflects how ChatGPT answers changed. What was asked stayed the same. Usage and answer quality can both climb while the named list gets shorter. Measured in AI Catalyst.

What brand mentions measure, and what they do not

Mentions per answer counts how many distinct brands ChatGPT names in a single response, averaged across the tracked prompt set for a funnel stage. It is not citation volume, and it is not market share. A single answer can name many brands or very few, so the count moves independently of how many sources get cited for the same answer.

Source: BrightEdge AI Catalyst. A fixed panel of tracked non-branded B2B Tech prompts, ChatGPT engine, held constant across 15 March 2026 and 13 September 2026.

Where the list got shorter

Where a prompt sits in the buyer's journey now decides how short the list gets. The panel stayed fixed. The prompts stayed fixed. Only the answers changed. Every figure below covers prompts that held the same intent classification on both dates. On those prompts ChatGPT named 5.36 brands per answer in March against 3.87 in September, a 27.9% decline. The fall concentrates at the top of the funnel.

Figures are the fixed-cohort cut: every prompt that carried the same intent classification on both dates. Source: BrightEdge AI Catalyst. A fixed panel of tracked non-branded B2B Tech prompts, ChatGPT engine, held constant across 15 March 2026 and 13 September 2026.

Data collected

Post Purchase carries too few prompts to read as a trend and is excluded. Transactional is printed above and held out of the conclusions below, for the reason given in its own section.

The questions buyers start with got the most selective

Informational prompts are the broadest questions, such as what a technology is or how it works. They went from naming 5.15 brands per answer to 3.50, a 32.0% decline, the steepest fall of any stage.

Microsoft, Google, and Amazon lost the most ground, down 9.5, 6.7, and 6.4 points. A cluster of developer and data-infrastructure names lost ground alongside them: Apache, dbt, Snowflake, Elastic, and HashiCorp.

IBM gained the most, up 9.9 points. NIST.gov, TechTarget, FBI.gov, Cisco and FTC.gov gained too, so institutional and government sources moved up alongside a publisher. The expected publisher squeeze does not appear in this data.

Consideration became a shorter list

Consideration prompts are where someone is comparing named options, such as accounting software for a small business. They went from naming 8.21 brands per answer to 6.39, a 22.1% decline.

Google, Amazon, GitHub, Trend Micro, Apple, and GitLab lost the most ground, down 9.3, 6.1, 3.7, 3.3, 2.0, and 2.0 points.

Every brand gaining ground here is a single-purpose tool.

Why buying-intent prompts are held back

Transactional prompts are where someone is ready to buy. They moved differently over the same window. A third measurement point shows that movement reversing, so the stage is held out of this edition's conclusions until the full weekly series is in. Both figures are printed above, so the omission is visible.

Two dates can show a direction that a third date takes away. So we waited, and printed the figures anyway.

Everything else in this edition holds at every measurement point checked.

Mentions and citations do not always move together

The relationship between how many brands get named and how many sources get cited is not consistent across the funnel.

In Informational, citations per answer rose from 2.30 to 3.12 even as mentions fell. Fewer brands were named, and each was backed by more sources.

In Consideration, citations fell in step with mentions, from 5.70 to 3.25.

In Transactional, citations stayed close to flat, from 1.92 to 1.84, while mentions rose.

What marketers need to know

1. Fight for the winnable Consideration prompts. Pull Consideration-intent prompts for your category in AI Catalyst or AI Hyper Cube, then check where you already show up. Every brand gaining ground here is a single-purpose tool. Find the use case your product owns, and build content that answers that exact question.
2. Audit where you appear stage by stage. A single visibility score hides which stage you are missing from. Split your tracked prompts by intent and find the stage where you are absent, because being absent from a stage is a different fix from ranking low inside it.
3. Hold your top-of-funnel investment. Awareness answers hold their value. They just got harder to win, and the goal there is now earning a place on a shorter list. Pull awareness prompts for your category in AI Catalyst. Identify one to three where you have a right to win but do not appear, then prioritize those.
4. Google is still the channel to plan around. The canonical, specific content that earns organic visibility is also what gets a brand named in an AI answer. Build it once and it works on both. Re-run this funnel comparison quarterly, because funnel-stage presence keeps moving.

How to check this for your own brand

Path one. Open Mentions, filter to your tracked B2B Tech prompt groups, and split by funnel stage. Compare two dates six months apart to see whether your category shows the same shift. Do it in AI Catalyst.

Path two. In AI Hyper Cube, pull the mentioned-brand list for your category across funnel stages for the cross-engine, brand-anchored view.

One caveat. A category with very few Consideration or Transactional prompts will not show a clean trend either way. This check is most useful where your category has enough tracked prompts at each funnel stage to compare, as B2B Tech does here.

Frequently asked questions

What changed in how ChatGPT answers B2B Tech questions?

Over six months ChatGPT named fewer brands per answer, and the decline concentrated at the top of the funnel. Overall presence fell 27.9%, from 5.36 to 3.87 brands per answer.

Which funnel stage lost the most brand presence?

Informational, the broadest and earliest-stage questions, fell 32.0%, from 5.15 to 3.50 brands per answer.

What happened at the buying-intent stage?

Transactional moved differently and is not reported as a finding here. A third measurement point shows that movement reversing, so the stage is held until the full weekly series is complete.

Does this mean publishers are losing ground to owned content?

The data does not support it. TechTarget gained ground in Informational over the same period, and other major publishers stayed flat. That pattern does not carry a publisher-versus-owned-content story.

Is this specific to ChatGPT?

Yes. This measurement is ChatGPT only. Whether Google AI Overviews and AI Mode follow the same funnel-stage pattern is untested and worth checking separately.

How reliable is the funnel-stage classification?

About 15% of prompts were reclassified into a different intent stage between the two dates. Every figure in this edition uses the fixed cohort instead: only prompts that carried the same classification on both dates. Reclassification therefore cannot explain the result.

Technical methodology

Measurement: mentions per answer, the count of distinct brands named in a single ChatGPT response, averaged across the tracked prompt set for each funnel stage.

Panel: tracked non-branded B2B Tech prompts on the ChatGPT engine, matched exactly across 15 March 2026 and 13 September 2026. Every prompt is present in both periods.

Classification: each prompt is assigned a funnel stage by intent. About 15% of prompts were reclassified between the two dates. The headline figures use the fixed cohort, meaning only prompts that held the same stage on both dates.

Cross-check: the same comparison run across all prompts at each date's current classification gives a 25.1% overall decline, against 27.9% on the fixed cohort. Brand-level movements quoted here are computed on all prompts at each date.

Scope: ChatGPT only. Whether the same funnel-stage pattern holds on Google AI Overviews or AI Mode is untested. Google remains the larger channel and the one to plan around.

Key takeaways

Nothing about the content changed. Reddit changed its robots.txt, and every citation gain built on being crawled went with it. A source that had answered one in six Education questions in ChatGPT stopped appearing.

This series has been tracking Reddit's presence in AI citations closely, including an edition on 13 August covering Reddit's rise in search visibility. What follows is what happened in the two weeks after.

Six domains moved into the space inside a week. Two of them were gone again by week 37. The two weeks after the collapse are the useful part, measured in AI Catalyst.

What citation presence measures, and what it does not

Citation presence is the share of tracked prompts that cite a domain at least once. It is not share of citation volume, and it is not market share. A single AI answer cites several domains, so presence figures across domains add up to more than 100%. Reading them as a pie will produce the wrong conclusion.

Source: BrightEdge AI Catalyst. ChatGPT only, a fixed weekly panel of tracked Education prompts, United States, July to September 2026.

The collapse, week by week

Source: BrightEdge AI Catalyst. ChatGPT only, a fixed weekly panel of tracked Education prompts, United States, July to September 2026.

Presence held steady for six weeks, then fell 81% in a single week and reached zero the week after. The Education panel fell further than the account-wide measure. That is what you would expect in Education, where Reddit already sat near the top of the cited set.

What we analyzed

A fixed weekly panel of tracked Education prompts in ChatGPT, measured every week across the same prompts. Because the panel does not change, week-to-week movement reflects the engine's behavior. What was asked stayed the same.

Data collected

Key finding

reddit.com citation presence in the Education panel fell from 17.2% to 3.3% to zero across two weeks in August 2026. Account-wide, the same measure fell from about 9% to 0.5%. The inflection lands in the same week Reddit's robots.txt change took effect.

The panel is fixed, so this corroborates the mechanism behind the drop. It does not prove it. What the data can show is the timing and the size of the drop, and both match when the robots.txt change took effect.

Reddit did not fall on its own

Source: BrightEdge AI Catalyst. ChatGPT only, a fixed weekly panel of tracked Education prompts, United States, July to September 2026.

Community sources and general commercial publishers receded together. Coursera lost 19 points of presence, Indeed 15 and Forbes 14. Reading the event as a Reddit story alone misses that other sources fell in the same window.

What replaced them was narrower and more institutional. Government sites, a university, and professional associations moved into the top set in the same two weeks.

Not everything that moved in stayed in

Source: BrightEdge AI Catalyst. ChatGPT only, a fixed weekly panel of tracked Education prompts, United States, July to September 2026.

The two biggest immediate movers did not hold. The four that held are government, university and professional-association domains. So are the two that faded. onetonline.org and careeronestop.org are both Department of Labor properties. Some institutional sources held and others did not, so domain type alone does not explain durability. Anyone who rebuilt a strategy on the first week's leaderboard rebuilt it on noise.

One name in the new top set is a trap. bls.gov did not grow at all. It was already third before the collapse at 21.2% presence, and it became first because everything around it fell faster. Rising in a ranking and gaining presence are different events.

This was a ChatGPT-specific event

Source: Promptwatch via Search Engine Journal, 19 August 2026; Forbes and Gizmodo, 20 August 2026.

A one-engine read would have told a marketer their Reddit strategy had failed everywhere. It had not. Google's surfaces declined gradually across the same window while ChatGPT fell off a cliff, which is why the cross-engine view matters before anyone acts.

Reddit carried a trait the model wanted

Reddit's high citation rate became a playbook of its own. Teams seeded threads in order to be cited and treated presence on the platform as the objective. The trait the model actually wanted was first-hand, specific, unpolished answers. That content already existed on Reddit.

When the channel closed, it closed through an access policy. Content quality was not the reason. Everything built on it disappeared inside two weeks, and no warning was available to anyone optimizing for it. Reddit-based visibility was never a durable asset.

Some of the domains that moved in held position through week 37 and some did not. Both of the domains that spiked and faded are Department of Labor properties, so institutional status alone does not explain which sources stayed.

Citation share earned through one platform's willingness to be crawled is rented. Visibility built on a third-party platform is exposed to that platform's access decisions. Build the underlying expertise and canonical content on a domain you control. Run one test against your top cited sources. Does the citation exist because of what you are, or because a platform currently allows it to be crawled.

What marketers need to know

One. Build the trait, not the placement. What the model rewarded on Reddit was first-hand, specific, unpolished detail. Replicating that on a domain you control takes original data, named practitioners, and direct experience. Publish the material only you have.

Two. Quick wins from one platform's indexing behavior are fragile, whoever the platform is. Ask whether your current AI citation gains would survive a crawler-policy change tomorrow, and assume you would get no notice.

Three. Wait for the second week before crowning a replacement. The biggest immediate movers here did not hold, and two institutional domains faded alongside them. One week of leaderboard movement is not a trend.

Four. Google is still the channel to plan around. The canonical, specific content that earns organic visibility is also easier for a model to recommend by name. Build it where no third party's access policy can remove it.

How to check this for your own brand

Path one. In AI Catalyst, open Citations, filter the domain to reddit.com and scope to your tracked prompt groups. Pull the weekly trend. A single snapshot will not show it. You are looking for a steady baseline followed by a sudden break. A slope will not produce that pattern.

Path two. In AI Hyper Cube, pull the cited-domain list for reddit.com across your category for the brand-anchored, cross-engine view. That is where you compare Google AI Overviews and AI Mode against ChatGPT.

Three things to look for. Whether your category's Reddit presence collapsed in the same week or whether this is specific to Education. Which domains gained presence in the same window, and whether they held into a second week or spiked and faded. Whether your brand already has a foothold in whatever filled the gap.

One caveat. A category where Reddit had a low baseline will not show a dramatic story. This check is most useful where Reddit already sat in the top three or top five cited domains, as it did here.

Frequently asked questions

What happened to Reddit citations on ChatGPT?

Reddit's presence in ChatGPT citations fell sharply in the second half of August 2026. Public trackers reported a fall from 3.8% to 0.5% of ChatGPT Search citations account-wide, an 86% relative decline (Source: Promptwatch via Search Engine Journal, 19 August 2026). The change is attributed to Reddit blocking crawler access through robots.txt.

When did it happen?

The break lands in the week of 17 August 2026, with presence reaching zero in the Education panel the following week.

What replaced Reddit in Education specifically?

Government, university and professional-association domains. state.gov, asu.edu, apa.org and aacnnursing.org entered the top set and held position. Two others, onetonline.org and careeronestop.org, spiked hardest at the moment of collapse and then receded.

Is this permanent?

Unknown. The outcome depends on an access decision. Content has nothing to do with it. The measurable point is that a policy change removed a source in two weeks, and that is the risk worth planning around.

Does this mean brands should stop trying to get cited on Reddit?

It means a Reddit citation should not be the objective. What made Reddit useful to the model was specific, first-hand detail, and that kind of detail is harder to manufacture on an owned domain. Replicating it takes original data, named practitioners, and direct experience.

Was Reddit's high citation rate ever a reliable quality signal?

It was a signal about access and content type. Reddit's own authority as a source never entered into it. That distinction is what the collapse made visible.

How can I check whether this happened in my own industry?

Follow the two paths in the section above. Pull the weekly trend in AI Catalyst and the cross-engine view in AI Hyper Cube.

Technical methodology

Measurement: citation presence, the share of tracked prompts citing a domain at least once. This is not share of citation volume. Presence across domains sums past 100% because one answer cites several sources.

Engine: ChatGPT. Market: United States. Period: week 28 to week 37, 2026. Panel: a fixed weekly set of tracked Education prompts, held constant so week-to-week movement reflects engine behavior.

The series is plotted as absolute presence and is never indexed to a baseline week. A short series indexed to one week makes every later movement a function of that week.

Attribution. The panel is fixed and cannot observe cause directly. The inflection lands in the same week Reddit's robots.txt change took effect. That timing supports the mechanism. It does not prove it.

Scope. One category, one engine, one account's tracked panel. Directional for Education. It is not a claim about every category, and the account-wide comparison is included so the reader can see the difference.

Every figure quoted anywhere in this edition is a share or a change in a share. We publish no raw citation counts and no panel sizes.

Key takeaways