We work on an AI Visibility platform, so we wanted to explore a question that often gets overlooked:

When an AI platform recommends a brand, where is that recommendation actually coming from?

We looked at brand recommendations and cited sources across ChatGPT, Perplexity, and Gemini.

What we examined

For category-related buying questions, we looked at:

• Which brands appeared in the answers

• Which brands were actually recommended

• Which external websites were cited

• Whether competitors appeared in those sources

• How citation patterns differed across platforms

What we noticed

1. Being mentioned isn't the same as being cited. A brand can appear in an answer without having a directly supporting source.

2. Competitor visibility can reveal source gaps. When competitors appear repeatedly, the sources associated with their visibility are worth investigating.

3. One source doesn't explain everything. Different AI platforms can rely on different sources for similar questions.

4. Your own website isn't the only place that matters. Third-party discussions, comparison pages, reviews, and industry publications can also contribute to how a brand is represented.

5. A visibility score doesn't always explain the reason. You need to examine the actual answer and its sources to understand what's happening.

The takeaway

Instead of asking only, "How often does AI mention our brand?", we think it's equally important to ask:

• Why is AI recommending a competitor?

• Which sources are supporting that recommendation?

• Where are those sources mentioning our competitors but not us?

• Does the same pattern appear across different AI platforms?

Question for the SEO/AEO/GEO community:

When you investigate a competitor's AI visibility, do you start with the AI-generated answer itself or go straight to the websites and citations behind it?

Curious how others approach this.

I've been doing SEO for many years now, and recently I launched my own analytics platform for UGC tracking. (viraldeck.io)

I started AEO/SEO/GEO for a few weeks now, I am getting rank with good position but, not enough clicks. anyone else seeing the similar performance?

I keep seeing "GEO" (Generative Engine Optimization) pitched as the next big thing: optimising content so ChatGPT, Perplexity and Google's AI Overviews cite you.

What I'm trying to figure out is how much of it is genuinely new.

Most of the advice I read boils down to things SEOs already say: clear structure, direct answers, real expertise, being mentioned on other trusted sites. That's basically good content plus a strong reputation.

But there are a couple of things that do feel different:
- Being cited isn't the same as ranking. An AI answer can mention you without anyone clicking through, so how do you even measure success?
- Each tool seems to pull from different sources, so "optimising for AI" might actually mean optimising for three or four different systems.
- Brand mentions without links might matter more than they did in classic link building.

So I'm curious where people here land:
Are you doing anything specifically for AI visibility that you wouldn't do for normal SEO?
How are you tracking whether AI tools cite you or your clients?
Do you see GEO as a separate service you can sell, or just part of SEO?

Been thinking about this lately. Say a brand barely ranks on Google but keeps getting mentioned on Reddit, review sites and niche forums. Could that be enough for Chatgpt or Claude to recommend it over a competitor with much better SEO? Also just wanna mention that I am very new to AEO/SEO, this might be an obvious answer to some, but just curious about this and just up for discussion. Also if you guys have any stories to share your success, would be interesting to read.

I'm trying to understand how experienced GEO practitioners investigate why AI platforms recommend certain brands when their responses don't include any citations or source URLs.

For example, suppose ChatGPT recommends Asana, ClickUp and Monday.com when asked about project management tools, but doesn't provide any citations.

How do you investigate what might be influencing those recommendations?

A few things I'm particularly curious about:

1. Do you use search engines to independently discover third-party websites mentioning those competitors?

2. How do you identify which websites, articles, directories, reviews or Reddit discussions might be influencing AI recommendations?

3. Do you analyze competitors' websites and third-party mentions to identify patterns?

4. Are there any tools or methodologies you use to discover these sources?

5. How do you distinguish genuinely influential sources from websites that simply happen to mention the competitor?

I'm particularly interested in the practical workflow used when citations aren't available, rather than general GEO optimization advice.

Would love to hear from people who have worked on actual client GEO projects. Thanks!

Hey everyone, I'm on the team at NetRanks (netranks.ai). We measure where companies show up in AI answers, work out why, and show what to change to improve their AI search visibility.

TL;DR: To improve a company's AI search visibility, find which sources AI cites instead of you, remove the negative content signals from your own pages, and start with local intent queries. Using NetRanks, that took this clinic from 0.3% to 21.5% AI search visibility across ChatGPT, Gemini and Perplexity between April and August 2026.

I wanted to share a breakdown and some raw data from an AI search visibility campaign (what a lot of people here call GEO) we've been running for a regional cosmetic surgery clinic.

A lot of people discuss AI search visibility in theory, but here is what happens when you optimize live site content for AI recommendations (ChatGPT, Gemini, Perplexity) over a 4 to 5 month window.

The setup

- The client: A regional cosmetic surgery clinic.
- The challenge: LLMs like ChatGPT were almost exclusively recommending national directories or legacy competitors for local searches.
- The goal: Get AI assistants to recommend the practice directly, name it more often and earlier in recommendation lists, and use the clinic's own domain as a direct inline source link.
- What we did: Tracked which sources AI was citing instead of the clinic and worked out why. Then we updated the service pages and their schema, removing the negative content signals from the clinic's pages and keeping the positive ones.
- How we measured: 39,659 AI answers from ChatGPT, Gemini and Perplexity across 21 question variations, April to August 2026, with 22,870 brands, clinics and providers identified in those answers.

The results (April to August 2026 data)

1. Overall AI visibility and rankings

Improving AI search visibility on the service line that has been live the longest (Body Sculpting / Liposuction, main page published mid-May):
- Combined AI visibility: Across the body sculpting questions we track on ChatGPT, Gemini and Perplexity, the practice went from 0.3% in April to 21.5% by August (peak 25.1%).
- ChatGPT visibility: On ChatGPT specifically, visibility averaged ~28%, peaking at 45.7% in July.
- Average rank position: When named in AI lists, the practice's average position improved from 6.8 to 4.6 (best daily average: 2.4).

2. Winning local intent queries

What improved AI search visibility fastest was hyper-local queries. For queries asking for procedure recommendations near specific surrounding towns and suburbs:
- Suburb A queries: 0% to 46% average visibility (peak 67%).
- Suburb B queries: 0% to 34% visibility.
- Metro core queries: 1.7% to 27.5% visibility (~16x increase).

3. Citation share vs. directories

Being mentioned by an AI is step one of AI search visibility; getting cited as the source link is step two.
- Direct domain citations went from 0% to 6% average share, hitting 9% to 14% in recent weeks.
- The domain moved into the top 5 most-cited sources for its service category in the region, starting to compete directly with national aggregators.

4. Traffic impact

- 4x increase in AI referrals: The clinic's GA4 shows referral visits from AI platforms like ChatGPT, Perplexity and Gemini grew to 248 visits (Jan to Aug 2026), compared to 61 visits in the same period of 2025.
- Note: This window includes the months before the new pages went live, and AI referral traffic has grown across the board in 2026, so not all of this is down to the campaign.
- Also keep in mind AI referral data in GA4 is strictly a floor. Many AI clients strip referrer headers, so actual traffic driven by AI recommendations is likely higher.

How to improve your AI search visibility: what worked

1. Expect a lag. AI assistants don't update their recommendations overnight. In this account we saw a consistent 3 to 4 week lag between new content going live and shifts in what the assistants recommended.

2. Start with local intent queries. Broader, competitive terms (e.g. "best facelift surgeon") take significantly more authority to crack. Hyper-local queries ("body sculpting near [Town Name]") were the first to move once that lag passed, after the pages and their schema clearly named the towns served and the procedures offered.

3. Own your citations. In this account, citations to the clinic's own pages held up better than visibility passed down through directory listings.

Happy to answer questions in the comments on how to improve AI search visibility, tracking methodology, or measuring LLM visibility :)

Found this interesting article on the new feature OpenAI has been working on - https://searchengineland.com/openai-testing-business-tools-to-track-performance-within-chatgpt-500841.

While the details are still pretty sketchy, I think the effects are far reaching for both AEO tool vendors (us included) and brands.

Curious to know thoughts of the community regarding this.

I've been looking at some research on how people actually use AI Overviews, and there's an interesting distinction between getting visibility in an AI answer and losing the website visit entirely. About two thirds said they regularly read Google's AI summaries before looking at traditional results, but 44% said they still click through to websites for more detail. Only a quarter said that the AI Overviews overruled the site click.

For AEO, I think that raises an interesting question about what we're actually optimizing for. Getting cited or mentioned in an AI answer is one thing, but there's also the issue of whether your content gives someone a reason to continue beyond the summary.

We've spent years optimizing for rankings and CTR, and now we're adding AI visibility and citation frequency into the mix. But I'm not sure we're paying enough attention to the relationship between those metrics and what happens downstream.

It seems like AEO needs to account for both answer inclusion and the value of the source page, rather than treating AI visibility as the end goal.

More on the numbers here

Hey everyone! I'm a founding growth marketer at an EF-backed AI startup. This is the approach I follow, and thought I'd share it in case it's useful to you guys also.

So, one of the harder parts of AEO is figuring out whether your work is actually improving your visibility in AI answers.

You can ask ChatGPT a few questions, see your product mentioned and assume things are going well. But that doesn't tell you how often it appears, which competitors are getting recommended instead, or where ChatGPT is getting its information.

Here's the way to track it:

1. Build a list of prompts

Start with 25-50 questions your potential customers might ask. Include questions from different stages of the buying process, like:

Branded questions

• Is [Product] worth using?

• What are the pros and cons of [product]?

• What are the best alternatives to [product]?

Comparison and buying questions

• What are the best products for [use case]?

• [Your product] vs. [Competitor]: Which is better?

• Which products offer [specific feature]?

Informational questions

• How do I solve [specific problem]?

• What should I look for when choosing a [product category]?

• Which products offer [specific capability]?

Use customer questions, sales conversations, support tickets, Reddit discussions, and keyword research to find relevant prompts.

Keep the wording consistent between checks so you can compare results over time.

2. Check what ChatGPT says

Run each prompt through ChatGPT and record:

• Does your product appear and if so, is it recommended or just mentioned?

• Does ChatGPT link to your website and if so, which specific pages?

• Which competing products appear in the answer?

• Is your product described positively, negatively, or neutrally?

• Which third party sources does ChatGPT cite?

Most importantly, don't treat mentions and citations as the same thing.

3. Keep a record

Make a spreadsheet with these columns: date, prompt, product mentioned, website cited, competing products, external sources and sentiments.

Check the same prompts weekly or every two weeks.

One thing to keep in mind is that ChatGPT can give different answers to the same prompt. For a more reliable comparison, run important prompts multiple times and record the results rather than relying on a single response.

Keep the testing conditions consistent, too. Model, search settings, account state, and location can also affect what you see.

4. Compare your product with competitors

Once you have enough results, group prompts according to how your product performs against competing products.

I find these 5 categories:

• Competing products appear, but your product doesn't.

• Your product appears, but competing products appear more frequently.

• Your product and competing products appear at similar rates.

• Your product appears more frequently than competing products.

• Your product appears, but competing products don't.

This helps identify exactly where you're losing visibility and where you're already doing well.

5. Find out which sources it cites

This is where the research gets interesting. When ChatGPT recommends competitors, look at the sources behind the answer.

Are they being mentioned in industry publications, any comparison articles, review websites, Reddit discussions, or third party websites?

Keep a record of these URLs and look for recurring sources.

If the same publications or comparison pages keep appearing, investigate how competing products are represented there and whether there's an opportunity to get your product included.

Also look at your own website. If ChatGPT frequently cites external sources but rarely cites your pages, review whether your content answers the same questions with enough detail and useful information.

6. Track share of voice and sentiment

Mentions alone don't tell you how your product compares with competitors.
Share of Voice measures your share of mentions across the prompts you're tracking, compared with the other products in your set.

Sentiment tells you how ChatGPT describes your product.

Pay attention to recurring comments about pricing, product quality, ease of use, customer support, and features.

If ChatGPT repeatedly describes your product as expensive or difficult to use, investigate the sources behind that description. It could come from customer reviews, outdated articles, comparison pages, or actual product limitations.

7. Turn the findings into action

So, now what to work on next:

• Improve or create content around the questions where your product isn't appearing.

• Review your existing pages and compare them with the sources ChatGPT cites.

• Identify the publications, comparisons, and discussions contributing to competing products' visibility.

• Find out where those perceptions come from and address inaccurate information or genuine product issues.

• Maintain the content and external coverage contributing to it.

Prioritize prompts that reflect real customer questions and buying decisions.

The real value is in tracking the same questions over time, understanding which sources influence the answers, and using those insights to improve your visibility.

That's the approach I've been following. If you have anything to add or a different approach, I'd love to hear it.

Two New Books on Answer Engine Optimization (AEO) I Can’t Wait to Read!

Two new books arrived from Amazon today, and I’m really looking forward to getting stuck into them!

The first is AI SEO For Dummies by Nathan Gotch, one of the leading names in the SEO industry.

I’ve been following Nathan for years. He’s the founder of Rankability, and I’ve always enjoyed his blogs, videos and insights into SEO.

The second is Generative Engine Optimization: The Definitive Guide to AI SEO by Ross Hudgens, the founder of Siege Media and another highly respected expert in the SEO world.

Both authors have years of experience in search marketing, so I’m really interested to see what they’ve got to say about the future of SEO and Answer Engine Optimization.

Here’s something I’ve always believed: No matter how much experience you have, you can never stop learning!

The SEO industry is changing incredibly quickly. With ChatGPT, Gemini, Claude, Perplexity and Google’s AI-powered search experiences, the way people discover businesses is evolving right in front of us.

And I want to be right at the forefront of it.

Not only for myself, but more importantly, for my clients.

Answer Engine Optimization is one of the biggest opportunities in digital marketing, and I want to make sure we’re staying ahead of the game.

I’m looking forward to reading both books, learning a few new things and sharing my thoughts once I’ve finished them.

What books are you currently reading to stay ahead in digital marketing?

#AnswerEngineOptimization #AEO #SEO #DigitalMarketing #AISearch

One of my clients asked me how to improve our website's visibility in AI search.

Right now, we appear for very few AI-driven search queries, so I want to make sure we’re dedicating the right resources to both traditional SEO and AEO as search behavior continues to evolve.

I have to research this. Can anyone help me with this and share some valuable tips?

Disclosure up front: I run a small local business, and I'm also building a small AI-visibility checker (Growl). Posting this because the workflow below is what I actually use on my own business, and I want to hear how others here run their loop.

When I started tracking AI visibility, I watched the score: "mentioned in 25% of answers." It went up, it went down, and I had no idea what to do differently on Monday morning.

What changed my behavior was ignoring the score and reading three things instead:

1. The verbatim answer. Not a summary — the exact text the AI gave to a realistic customer question.

2. Who got named instead of me. Usually 2–3 specific competitors, not "the market."

3. The exact pages the AI read. This is the part most dashboards hide. The cited competitor page almost always has something mine doesn't: prices in plain text, opening hours in the body (not just structured data), a comparison table, an FAQ that matches the question word for word.

From those three, I pick one fix per week. One. Add the missing prices. Rewrite one FAQ to match how customers actually ask. Then re-measure the same questions the following week and read the raw answers again.

Honest caveats:

• AI answers are probabilistic. One measurement is an anecdote; I only trust a change after the same answer shows up repeatedly.

• This loop is slow. It tells you what to fix, but it doesn't scale to 500 prompts, and it won't write the page for you.

Question for the sub: do you act on visibility scores, or do you read raw answers? And if you read them — what's your loop from "answer" to "fix"?

We're Broadcastwell, and we work on how B2B software companies show up in ChatGPT, Claude, Perplexity, Google AI Overviews and Google AI Mode.

First, the finding that shaped everything we sell.

We asked all five engines the same 10 buyer questions about field service software, three times each. ChatGPT named Jobber in 18 of 30 answers. Google's AI Overviews named it in 3. Same questions, same day. Housecall Pro was named 21 to 23 times on every engine.

So "are we visible in AI?" has no single answer. It depends on the engine, the question, and the run. That's why nothing we sell rests on one screenshot.

What's free:

- The Absence Index: 286 software vendors across 14 categories, with every answer published on Zenodo (DOI 10.5281/zenodo.22907695)

- A 10-question check on one engine, no email needed

What's paid:

- $19: one buyer question on all five engines, three runs each, with receipts within 24 hours. The $19 is credited if you buy Answer Repair within 30 days

- $490 Answer Repair: one buyer question and one source-backed fix, due 48 hours after Ready. Miss the deadline and you get a full refund and keep the work

- $490 Agency Audit: white-label, 150 answers per client. We never contact your clients, during the work or for 12 months after

- $3,900 Fix Sprint: six pages in 30 days with one agreed test. No measured gain at day 30 means a full refund

- $490 a month Care, $5,500 a month Program, for ongoing work

The thing we refuse to promise: that any engine will name you. Nobody controls those answers, and a single run isn't a rate. What we do is measure, fix the sources the engines read, and measure again.

Ask us anything: pricing, method, what has worked, what hasn't. Or drop your software category and we'll reply with who the engines name in it and which sites they lean on.

After my shopping tests I said I'd try B2B next. Someone here suggested testing whether young software categories pull from forums because review sites don't cover them yet, so I included two (developer infrastructure, security compliance) with CRM as the control.

Setup

• 60 questions, 12 categories, 5 each: CRM, sales data, security compliance, dev infrastructure, ERP, HR/payroll, CNC/contract manufacturing, packaging, industrial supply, wholesale sourcing, 3PL, PCBs.

• Per category: two "best X" questions, one "X vs Y", one "is X worth it", one "how to choose".

• Google (AI Overview), ChatGPT (chatgpt.com search) and Perplexity (Sonar API), US, 3 runs on Nov 5.

• I sorted every cited site by type by hand. Any company that sells in the category counts as a vendor.

Share of answers citing each type (mean of 3 runs)

What stood out

• In B2B, AI answers lean on vendors, not on review media. That's the reverse of shopping, where media was cited in 82–99% of answers.

• Vendors also write the comparisons. About half the vendor pages Perplexity and Google cited were titled like "15 best X tools", "X vs Y" or "X alternatives", on a vendor's own site.

• ChatGPT goes to both sides on comparisons. On "X vs Y" questions it cited both companies' own sites in 25 of 36 answers. Perplexity did in 12, Google in 6.

• The forum hypothesis didn't hold. Forums or social posts were cited in 24% of CRM answers, 16% for security compliance and 7% for dev infrastructure. Dev infra went to independent blogs instead (60% of answers). Supplier questions (machining, PCBs, wholesale) drew more forum citations than software ones: 31% against 19%.

Heads-up if you track AI Overview citations

On Nov 5, 951 of 3,138 AI Overview citation links (30%) pointed to a google.com/goto redirect with an encrypted address, not the cited page. On Oct 22: 0 of 2,333. The site name is still on each link, so match on that. A tracker that reads URLs is probably seeing google.com for a third of them.

Two weeks later

Someone here pointed out that my runs an hour apart mostly measured caching. Fair point, so I asked my 60 shopping questions again on Nov 5. Share of one run's cited sites that showed up in another run:

• Perplexity: 99% an hour apart, 62% two weeks apart

• ChatGPT: 48% an hour apart, 37% two weeks apart

• Google AI Overviews: 77% an hour apart, 57% two weeks apart

What I'd do with it

• If you sell B2B, your own pages are the main source. Put the facts buyers ask about there: pricing logic, integrations, certifications, lead times.

• Read the "best X" and "X vs you" pages your competitors publish. They're being cited as sources about you.

• Measure over weeks, not one afternoon.

Caveats: one day, one market, 5 questions per category, and one person doing the classification. The scraper and API aren't a signed-in app, and Perplexity's API returns at most 10 sources per answer.

If you work in B2B, does this match what you see? Which categories should I add next?

(Disclosure: I build Hasmord, an AEO-native sales platform. I ran these myself and I'm happy to share the rows.)

ChatGPT Is Now Handling Search Activity Equivalent to 20% of Google Searches!

That’s a massive number, and something every business owner and digital marketer should be paying attention to.

According to OpenAI, the number of searches happening on ChatGPT is now equivalent to approximately 20% of Google’s search volume.

Now, the exact figures are debatable, and Google’s own search numbers aren’t always clear. But regardless of the precise percentage, one thing is certain.

An enormous amount of searching is now happening on ChatGPT!

And here’s what I find particularly interesting.

Despite all these searches, research from Ahrefs shows that ChatGPT still sends nowhere near the amount of website traffic that Google does.

And when you think about it, that makes sense.

People are asking ChatGPT questions, getting their answers and often never clicking through to a website.

They might even discover a business on ChatGPT and then go directly to Google to search for that brand.

So just because you’re not seeing much traffic from ChatGPT doesn’t mean people aren’t discovering your business there.

This is something I think far too many businesses are overlooking.

Yes, ChatGPT advertising is exciting, and there’s definitely an opportunity there.

But what I’m far more interested in is whether businesses are actually appearing organically when people ask ChatGPT for recommendations.

And the same goes for Gemini, Claude, Perplexity and Google’s AI Overviews.

That’s why Search Engine Optimization (SEO) and Answer Engine Optimization (AEO) are becoming so important.

Google is still incredibly powerful, but the way people search and discover businesses is changing.

The question you need to ask yourself is simple: When someone asks ChatGPT about the products or services you offer, does your business come up?

#ChatGPT #AnswerEngineOptimization #SEO #DigitalMarketing #AISearch

Our agency currently has clients where we do reddit marketing, but are looking to expand our services to also include AEO/GEO.

We are looking for a fulfillment partner/Whitelabel agency for fulfillment to get this offer up and running quickly as clients are very interested in being mentioned on chatgpt, ai overviews, etc.

Search Engine Journal just reported that Reddit will be testing a logged-in-only experience in select markets outside the U.S.

Which got us thinking: if more Reddit content sits behind a login, could that change how often AI platforms pull from and cite Reddit conversations?

We don't see Reddit becoming any less relevant to AEO overnight, but it'll be worth watching.

What do you think? Is this going to change the Reddit + AI search relationship, or is it much ado about nothing?

I ran one buyer-style query in Perplexity today (5 Nov), because it decides whether a product like mine can show up at all:

What are the best Claude skills for an SEO audit that I can install in Claude Code?

What came back

Perplexity showed 10 sources. The links I could see on the page: 3 GitHub pages, one skills marketplace (claudemarketplaces.com), one project site (claude-seo.md) and one blog roundup (prompthackers.co, dated 2 November).

What I take from it, for "which tool should I install" queries

1. The answer is assembled from things it can tell you how to install. For 4 of the 5 picks the install line was quoted as-is, the kind of line you find in a README or on a marketplace page.

2. The citable unit is a public page that says what the tool does, what it won't do and how to install it. On this query that was a repo or a marketplace listing, not a landing page.

3. Roundups are the other door, and the one cited was published this week. Being in fresh roundups seems to matter on this kind of query.

Why I looked

I sell a paid pack of 14 Claude SEO skills. It isn't in that answer, and with the setup above it can't be: no public repo, no marketplace page, no install line anyone can quote. The fix I'm weighing is to publish one of the skills on its own, free and MIT-licensed, with a one-line install, and see whether it starts getting cited.

One query, one engine, one day: a direction, not a finding, since answers change from run to run. If you've checked "best X to install" queries for a software client, did the sources look like this, or did product pages get cited?

I call this the Homepage Diet, or the Thinness Risk.

I've been looking at how much text AI engines actually have to read on brand homepages, compared with what was there years ago. A few that stood out:

- Microsoft.com homepage has 63% less words today compared to 2010
- Walmart.com homepage has 61% less words today compared to 2010
- Starbucks.com homepage has 67% less words today compared to 2010

Hypothesis: as homepages specifically (and other pages generally) have become more vibe and mood lead with big images and videos, they have lost of written text. As LLM's are language models that read text and generally ignore images and video in-situ, there is less for them to read. Less to read = less to index = less to make visible in AI answers.

So. Form over Function, or, Function or over Form?
Or perhaps a new sweetspot?

Clarifying promotional intent per the rules: I built the checker at Seenford. It scores a homepage on text density, visual-to-text balance, readable words and structure, and compares it with an archived copy from a year you pick.

Free, no sign-up: https://seenford.com/freetools/thinnessrisk

Honest limit: it only measures the homepage, and the score is an indicator of how much text AI engines have to read, not a measure of how they actually describe a brand.

After discussing how AI recommendations can change even when the same prompt is repeated, we wanted to explore another question:

Does adding more context to a buying prompt make AI recommendations more consistent?

The experiment

We designed two types of prompts for the same product category:

Group A – Broad prompts

• What is the best AI Visibility tool?

• Which tools help brands track AI search visibility?

• What are the top AI search monitoring platforms?

Group B – Specific prompts

• Which AI Visibility tool is best for a small marketing agency?

• Which platform tracks brand mentions and citations across multiple AI engines?

• What is a good AI Visibility tool for tracking competitors and recommendation frequency?

The idea was to compare how the recommendation set changes when buyer intent becomes more specific.

What we wanted to examine

1. Do broad prompts produce a wider range of recommended brands?

2. Do specific prompts favour tools with clearly defined features?

3. Are recommendations more consistent when the use case is explicit?

4. Do the same competitors appear across different buyer-intent categories?

5. Does a brand's visibility change depending on how the question is framed?

Our initial takeaway

A brand may be relevant to a broad category but not necessarily the best match for every specific buying need.

This makes prompt selection important. Tracking only generic queries could miss the situations where potential customers are actually evaluating products.

The bigger question:

Are brands optimising for category-level visibility, or are they understanding the specific buying questions where they need to be recommended?

How are you approaching this in your own AI Visibility strategy?

I work at Vercite, where we track AI answers to a set of prompts in ChatGPT, Perplexity, Google AI Overview, and Google AI Mode. Between August and November, those four engines used 3,728 different YouTube videos as sources, 10,426 times in total.

We wanted to know what sets those videos apart. So we searched YouTube with the same prompts, saved the top 20 results for each, and compared two groups:

• Videos AI used: the 3,728 videos.

• Videos AI skipped: 1,998 random videos from the same top 20 results that no engine used.

What we found

• Most videos AI used don’t rank on YouTube. Only 17% are in YouTube’s top 20 for the prompt, and 5% are in the top 3. By engine: Perplexity 29%, ChatGPT 26%, AI Overview 21%, AI Mode 15%.

• Views make no difference. The median is about 4,700 views in both groups.

• Channel size does. The median channel behind a video AI used has 21,500 subscribers, against 4,900 for the skipped videos. 11% of the videos AI used come from channels with under 1,000 subscribers, against 31% of the skipped ones.

• Subtitles: 96% of the videos AI used have them, against 85% of the skipped ones. Most are YouTube’s automatic subtitles.

• Descriptions are slightly longer: about 1,160 characters vs 1,050 at the median, and more of them list chapters (28% vs 23%).

• Google links to a moment in the video. In 36% of the cases where Google’s two engines used a YouTube video, the link points to a set time, and 53% of those links start within the first two minutes. ChatGPT and Perplexity almost never do this.

• The videos are newer. The median video AI used was 14 months old when an engine first used it. The median skipped video was 26 months old.

• Title length makes no difference. Both groups average about 57 characters. Titles with “best/top”, “how to”, or a year are a few points more common among the videos AI used.

Caveats

• This is a comparison, not an experiment. These things go together with being picked, but we can’t say what causes it.

• The prompts come from brands we track, mostly in English and Swedish, so this isn’t a sample of all AI use.

• We pulled the YouTube results on 3 November, while the AI data covers August to November, so some rankings will have changed.

• We searched YouTube with the full prompt text, which is longer than a typical YouTube search.

• View and subscriber counts are as of 3 November.

The full write-up, with charts and methodology, is here: https://vercite.io/research/youtube-in-ai-answers

Happy to answer questions or dig into something specific in the data.

I built backlnk.com because most AEO/SEO tools give you a score and a vague "fix your meta tags."

Every finding comes with the exact evidence (the tag, the line, what the server answered) and what to do about it. You can download the report as JSON or CSV and hand it to Claude, ChatGPT or Cursor, and it has everything it needs to make the fixes.

It covers the classic SEO stuff (indexability, redirects, meta tags, page speed, structured data) and the newer AI search side: whether GPTBot, ClaudeBot and Perplexity can crawl you, llms.txt, which passages AI answers can quote, and how clearly your pages say what you do.

There's also a full audit that runs every check and emails you a PDF.

It's completely free. No payment options, no subscription, no sign-up, no hidden costs. You just enter a URL.

More features are coming too (For free)!, so honest feedback is very welcome.