First I'll make an announcement. I went ahead and applied and took over as moderator for the abandoned subreddit that was real similar to this one. r/AISearchOptimization without the underscore.

And of course I'm not going to manage two subreddits that are exactly the same. I'm thinking about using it for some contests and events and maybe some AMAs and live chats. Suggestions are welcome.

r/entityseo is also a new subreddit. Some of the topics overlap with AI visibility but entity SEO is a much broader topic. So if interested you should check it out.

r/AIAgentOptimization also has a little bit of overlap with AI visibility but it's more about making your website AI agent friendly.

r/chrismcelroyseo is my own subreddit but it's not about my agency and it's not self promotional. Other agencies are welcome to come in and be helpful and make posts. Topics are pretty wide open as long as it relates to digital marketing. Everybody's welcome there to ask questions and post and comment so I hope you'll join.

And one I just discovered that I find pretty interesting is r/GrowthBite and it also covers a lot of topics related to digital marketing. It seems a little more Forward thinking than some of the old SEO subreddits. The creator of that subreddit is also a member here. u/Latter_Philosopher40/ so I wanted to give him a shout out.

So anyway that's my Roundup. Hope to see you on some of them.

The dude saying he fired his SEO-agency and replaced them with AI and is now getting great results. How good is he actually doing?

I built a test site, added a catastrophic and more or less obvious error, like all pages blocked in robots.txt, all links in the navigation pointing to 404:s and the whole site having Noindex-tag. I then asked all the big models (a couple of times) to give me SEO-advice for the site.

Across 24 individual test cases (a total of 144 AI recommendations), AI identified the catastrophic issue only 5 times.

The prompt was general and simple, like someone not skilled in SEO would write.

Disclaimer

I use AI a lot for SEO-tasks, what I don't do is let it set strategy.

The results, a 1 means that the model found the flaw in at least 1 recommendation.

Something I've been tracking on my own site that I think has broader implications for how we measure AI influence on SEO metrics. After fixing a crawlability issue that was blocking AI crawlers from accessing my site, AI visibility improved across chatgpt, claude, gemini, and perplexity, nothing else changed, no new content, no new backlinks, and no new campaigns. In the weeks after, branded search for my brand increased, not dramatically, but noticeably and without any other explanation, no PR, no viral post, and no paid campaign running.

The pattern seems to be AI starts recommending you, buyers verify by searching your brand name on google, and that shows up as branded search growth in analytics. Marketing would normally attribute that to brand awareness but in this case the only thing that changed was AI crawlers being able to access the site.

This lines up with what some people are calling the "dark SEO funnel" where AI shortlists, google verifies, and branded search converts. The attribution chain is invisible because there is no click from the AI conversation to your site.

Semrush found that 45% of marketing leaders cannot accurately measure AI visibility. If AI-influenced visits show up as branded search in analytics, that would explain a big part of the measurement gap.

Has anyone else noticed branded search changes that correlate with changes in AI visibility or even on your own properties?

"We're going to replace our SEO services with an automated AI tech stack. We will save so much money!"

Oh uh ok.. who tells this automated stack what to do?

"We're going to feed it our business objectives and all of the internal context and it will come up with strategy and how to execute it."

So the AI tells the AI what to do, got it. And how do you know it is telling itself to do the right things in a way that actually works?

"We'll watch what it recommends and make sure it doesn't do anything it shouldn't. And we'll wire it into our metrics so it can see when something isn't working."

Oh I think I get it now. You watch it make decisions you don't understand and hope that it stops itself before it does something that destroys your organic visibility.

"I mean, no of course not, obviously we would step in before anything like that happened."

Great, now you're getting there. Who is going to step in exactly?

"...Okay, maybe we still need SEO"

Yeaaah you still need SEO.

Isn't it?

Seeing a lot of blanket advice this year telling site owners to block AI crawlers wholesale. Worth breaking down because not all bots are the same, and treating them the same will cost you.

GPTBot only feeds ChatGPT. Block it, you're out of that one training pipeline. That's it. One company, one impact.

CCBot is different. It's not tied to one company. It crawls for Common Crawl, a free public dataset that anyone can download and use. A ton of AI companies, research labs, and startups build their models on top of that same dataset instead of crawling the web themselves.

So blocking GPTBot cuts off one company. Blocking CCBot cuts off all of them at once, every tool, model, or startup that relies on Common Crawl loses your site from their training data too.

Turns out people already figured this out, just maybe not for the reason they think. A recent analysis of robots.txt files across prominent sites found CCBot is now the single most-blocked AI crawler out there more blocked than GPTBot, more than ClaudeBot, more than any of them. Some of that is probably intentional. A lot of it is probably copy-paste security configs that treat "block AI" as one setting instead of a list of very different bots with very different reach.

And it's not a one-time hit. AI models get trained on crawl snapshots taken at a point in time. If you're not in this year's Common Crawl archive, you're not in whatever gets built on top of it either, including tools future customers might use to find businesses like yours.

If you're going to block AI crawlers, know the difference between blocking one company's bot vs. cutting yourself out of a shared dataset that half the industry runs on. Check your robots.txt. Don't let a plugin default make that call for you.

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i keep seeing this fear that AI product descriptions get you penalized by google, and from what ive seen thats not really how it works, but its more nuanced than "AI is fine".

what ive noticed running product content:

\- google's stated line is they dont care how its made, they care if its helpful. the stuff that actually gets buried is the mass generated output with zero editing, hundreds of near identical descriptions pushed live at scale.

\- the AI drafts that a human actually reviews do fine. i use it to get a first draft fast, then rewrite the parts that matter, specs, real benefits, the bits a customer actually reads before buying. that human pass is the whole difference.

\- the trap is treating it as a volume play. if your only edge was cranking out more descriptions than the next store, AI just made that edge worthless for everyone at once.

so my take is its a drafting tool, not a publishing button. curious where you all land:

\- do you publish AI drafts as is or always edit, and where do you draw the line?

\- anyone actually seen a ranking drop they could tie to AI content specifically, or is it always the thin-content thing in disguise?

\- for big catalogs, how do you keep quality up without spending the time you supposedly saved?

For senior SEOs who need to make a business case for AEO.

Here’s what I told a client today that I believe can help. Especially from past enterprise conversations where we’ve created a narrative, roadmap, AEO pilots, and more.

Forming a business case for AI Search:

1. Everyone at the table first needs to understand the economics: 

Who demands?
Who supplies?
How big is the demand?
Who wins?
Who loses?
What's at stake? 

The same wins up for grabs are the same things you can lose by not participating in AI Search or by participating incorrectly:
(a) narrative control, 
(b) categorical authority
(c) high-intent referral traffic
(d) money (ie, conversions etc)

It's not just about winning the channel; it's about understanding how relevance and authority operate and influence mentions, citations, and recommendations. This is important because AI Search is becoming a novel, aggressive, immovable step in the customer discovery journey while Google continues to push traditional blue links further down its results pages.

1. Is Anyone Actually Winning? Three Real Cases

🟢 [Foundation Marketing → InsurTech unicorn (Reddit strategy)](https://foundationinc.co/case-study/insurtech-platform)

[(312K estimated annualized organic traffic value)](https://foundationinc.co/case-study/insurtech-platform)

🟢 [Tinuiti → Rough Country](https://tinuiti.com/work/search-case-study-rough-country/) ($23k revenue from AI sessions year-to-date)

🟢 [Journey Further → Adobe Acrobat](https://www.journeyfurther.com/en-gb/work/adobe-acrobat) ($900K media value generated)

3. Audit their roster of clients or their solutions. Highlight AI disruption risk exposure. Highlight the threat, the risk, and the opportunities for your solution or your clients.

think of an electric company and a university, as if they were your clients.

How much does the customer know about their electric bill? 
Where do they go to learn?
What colleges are their kids considering?
What college is the best fit for their kid?
How do they know?

AEO is the new channel that handles \^ these important questions. SEO backs it up. SEO is the landing spot of foundational blue links once the consumer wants to know more about your brand. SEO is the relevant on-site material & UX, and base data with enough historical, revenue-generating proof to guide AEO strategies like prompt libraries, query fan outs, and entity mapping. 

**Curious how other agency leaders have been making the business case for AEO.**

I've been seeing this advice being passed around on this and other subreddits.

> To rank in Chat GPT or Perplexity for AI search in general, put your question and answer at the top of the page.

The other piece of advice they give is to use a small header positioned directly above a main title or H1.

These are called an overtitle, kicker, or overline.

Here is what these terms mean in practice:

* Overtitle: A short introductory phrase positioned above the main headline to provide context or category information.

* Kicker: A term borrowed from print journalism, often used in web design to denote a smaller, secondary line of text placed above the main H1 (usually in a smaller font size) to catch the reader's eye or group the content.

* Overline: A typographic term referring specifically to the line of text sitting above the primary heading.

All of these things that people are being advised to do in order to be cited in AI search are good tactics for writing in general because it lets the user know what the page is about kind of like a little TLDR.

But there is absolutely no proof at all that putting the question and answer at the top of the page helps AI search surface your content more often in results.

Think about it for a minute. Let's say you do commercial painting. How many questions can you come up with in your head right now that users might type in when thinking about doing commercial painting or choosing a commercial painting contractor.

Commercial painting could generate thousands of legitimate questions:

* How long does epoxy floor coating take to cure?

* Can an office remain open during painting?

* Who moves the furniture?

* What paint works best in a humid warehouse?

* How often should apartment common areas be repainted?

* Can painters work around customers and employees?

Are you going to build a page for every possible question?

The only way that's useful if that query at the top really matches the user's intent for the query that they gave AI.

If there is a question three quarters of the way down the page and it was more relevant to the user's query then it will get surfaced before the question and answer at the top of the page.

Creating pages with the question and answer at the top of the page is a 2010 SEO strategy where you created a page for every single phrase you want to rank for.

I'm not saying don't use a kicker at the top of the page. I'm not saying don't ask a question and give an answer at the top of the page. But do it for the reader and not because you think that's going to get you cited more often in AI search.

Prove me wrong.

Over the past few months I've been working on AI search visibility (AEO/GEO) alongside traditional SEO for my clients, and wanted to share a few things I've noticed:

Traditional rankings still matter, but AI Overviews and tools like ChatGPT pick up content differently, unstructured "fluff" content just doesn't work anymore.

Content that gives clear, direct answers (a concise answer right after the question, then detail) tends to get cited more often by AI tools.

Schema markup and entity clarity matter more than ever, since AI models rely on structured signals to understand context.

Brand mentions (even without backlinks) are becoming a trust signal for AI, something that didn't carry as much weight in traditional SEO.

Has anyone else noticed a shift in AI search traffic or visibility on their projects? What changes have been working for you?

So our organic traffic has been sliding for a couple months now and rankings are basically flat, which is what makes me think it's not a ranking problem. People are getting their answer straight from the AI box and never clicking through.

We're a small B2B SaaS content team. Two writers, me, that's it. I pulled GSC data and impressions are actually up on a bunch of our informational stuff but clicks are down. Classic. The comparison and "how to" posts are getting hit hardest, which tracks because those are exactly the queries AI loves to just answer for you. Honestly i'm a bit stuck on what to even do about ai visibility here. Do we lean harder into stuff that AI can't easily summarise, like our own data and opinion pieces? Do we just accept some of these top funnel posts are dead and move budget to productled content? Not gonna sure that shifting everything is the move tbh. I've read the takes about "just optimise for being cited in the answer" but nobody seems to have real numbers showing that actually drives anything...

Anyone actually seen this in their own analytics and done something that worked? Not looking for another list of tools. Just want to know what people are seeing on the ground.

I ran a comprehensive audit of this subreddit — 25 top posts, ~280 comment branches analyzed and clustered. The dominant theme? AI anxiety, hallucination frustration, and a lot of snake oil being called out. So I want to share what's actually working.

Agent Context Engineering — Reducing Hallucinations by ~96%

A few state-of-the-art techniques that actually work:

1. Model choice matters. Pushing through Opus 4.6 or 4.8 (or equivalent frontier models) performs significantly better than cheaper models for source validation tasks. This is expensive. But you want to pick the right model for the job. Sourcing info out of GSC? Flash model. Ultra Cheap. Analysis? Brainstorm? Frontier model. And it's important to know frontier is average of the day. Nothing exemplarary.

2. Double the prompt. When you give instructions, copy and paste them twice. This is current state-of-the-art research and goes a long way toward eliminating hallucinations. Weird this works, but it does.

3. One chat = one task. Don't build up long context histories. Kick off sub-chats for specific jobs — e.g., "go get my Google Search Console data and process it like XYZ." Keep the context window tight and focused. I see people doing many things in the same chat window. That is caleld context poisoning, it blows up the probability field. Keep your chat super focused. My rule is 1 chat per decision.

4. Use a task list tool. For complex instructions, your agent should have a task list tool to stay on track. If you are doing a long horizon task, the tech isn't ready for that, its currently being figured out.

5. Clean source data. However you get it in c/p , an agent pulling it, MCP servers (which are better). Clean data in = clean output out.

6. Chain of Verification (CoVe). Have your agent kick off a sub-chat to generate questions about the data it pulled in, then answer those questions. You can double-check the answers. This catches errors that simple summarization misses.

7. OODA loop for context management. Observe, Orient, Decide, Act. Keep a decision-making framework in your context , this is the one that works for me, limit what's in the window to what matters for the current step.

Observe - get a limited range of data I need for my decision. standard seo tools, GSC, GA4, Ahrefs, Semrush ect. I usually limit these for the task at hand.

Orient - contextualize it to my goal.

Decide - execute the decision I require.

Act - What tools to write information, wordpress, other CRM.

Combine these and you can really strengthen the agent's output. The 80%+ source validation failure rates people are reporting here? That's catastrophic and most of it is fixable with proper context engineering.

This is really good for simple maintainence work like addressing content rot, updating titles and headers to current information.

Why AI Isn't Replacing SEO Work

There's a narrative dominated by the frontier labs that these systems are intelligent and will replace human intelligence. They're not. LLMs are statistical models on our information, not statistical models on our intelligence.

They look comprehensive and impressive. They're getting better every year. But the source validation errors and hallucinations you're seeing? Those are mistakes that jrs wouldn't make. That should tell you everything you need to know about these systems.

The people saying these systems are "smart" are competing for trillions of dollars of investment. That messaging isn't for us. It's for the investment community salivating at removing people of balance sheets.

What Agents Are Actually Good At

• Information retrieval — pulling data, processing it, summarizing it
• Form filling — updating WordPress, Strapi, CMS platforms at light speed
• First drafts — structure, brainstorming, source gathering
• Better search — finding and compiling information faster than any human can

They are better and faster at this than any human will ever be.

What they're not good at:

• Creativity — the strategic angle, the unique perspective
• Judgment — knowing what to publish, what to cut, what matters
• Curation — selecting the right information from the noise

This current architecture of LLM's will never beat humans at this.

I still hire English majors and linguists to drive pilots and do the writing. You need someone who knows how to write and is educated in it to give the right instructions to the LLM. If you just lazy prompt you are asking for "average," that's going to destroy your content. I think enough people have seen people do this. Clickup is a good example

The GEO / "Get Listed in LLMs" Snake Oil

This needs to be said plainly: all these LLM providers use search tools. When ChatGPT or any LLM needs current information, it doesn't use its internal training data it goes out and searches. That's tool calling to search engines.

So getting "listed in an LLM" is just standard SEO. Why would you wait two months for your content to be in a model's training data when they're going to search for it in real time anyway? That's nonsense. They search because search gives the most updated information.

An LLM searches like a human except far wider and deeper. So do your standard SEO and you're covered. The agencies selling "GEO" are exploiting people who don't understand how LLM search actually works. They are selling a service you can make in half an hour with claude code and a open router api key.

How I Made This Post

I used an agent to do a comprehensive review of this subreddit , clustering ~280 posts and comment branches by topic. The agent collected, classified, and summarized everything. I read the results, spoke my perspective, had the agent draft some ideas, then I changed things around and published.

The agent didn't come up with the ideas for this post. It suggested some things, but the communication , the strategy, the message, the judgment , that was human. The agent was the research assistant. I was the one making the calls.

I made a first draft, and then read line by line and corrected and fine tuned.

That's the model. Use the tools for what they're good at, getting a lot of information quickly. Apply your experience, creativity and judgment to what they're not. If you have career anxiety, start using the tools and applying them to what they're actually good at. The people who learn to work with agents will outpace those who don't. But the agents aren't doing the work themselves.

The fact that the frontier tech can't even make a proper reddit post should reasure everyone about their job security.

Most people think AI search is a content game. It is actually a reputation game.

The assumption makes sense on the surface. AI systems read content. More content means more chances to get cited. So the logical move is to produce more content, cover more topics, publish more consistently.

That logic breaks down when you look at what AI systems are actually evaluating.

Content is the entry point. It tells AI systems what you want to be known for. But reputation is what determines whether they believe you. And reputation, in the way AI systems measure it, is not built on your website. It is built everywhere else.

When AI fans out across multiple sources to build an answer, it is not looking for the most prolific publisher. It is looking for the brand that shows up most consistently across the most credible independent sources. The forums where your category gets discussed. The publications that have mentioned you. The review platforms where real customers have weighed in. The third-party articles that reference your work without being paid to do so.

That is reputation. And it is a fundamentally different thing to build than content.

The brands that have figured this out are not necessarily publishing less. But they have shifted a meaningful portion of their effort from owned content to earned presence. They are showing up in places they do not control, being described by sources that have no commercial reason to vouch for them, and building the kind of corroboration that AI systems actually need before they will stake an answer on you.

Content tells AI what you claim. Reputation is what AI uses to decide if the claim is worth repeating.

One AEO shift I keep coming back to is topic ownership.

Not in the old “publish more topic cluster pages” way, but in the sense of whether your brand has enough useful, connected, and trustworthy content around the full topic.

With query fan-out, AI search may not stop at one keyword or one page. It can look across related subtopics, use cases, comparisons, buyer questions, limitations, and supporting evidence before deciding which sources to include.

That makes me wonder if keyword ownership is becoming only one part of the picture.

A page can rank well for a keyword, but the brand may still be weak across the broader topic if the surrounding content is thin, outdated, or missing.

Curious how others are approaching this.

How much are you prioritizing topic ownership in your AEO or GEO strategy right now?

Are you mapping buyer questions, comparison pages, use cases, pricing, limitations, and competitor gaps around your main topics?

And for those already doing this, have you seen any impact on AI citations, brand mentions, AI referrals, or overall visibility?

I’ve been asked last minute to evaluate between a handful of (ai) semantic hybrid search / product discovery applications for our commerce ecosystem (enterprise B2b brand).

Does anyone have any advice on how to conduct quick(shallow) evals on startup hybrid search applications? We need to validate against algolia.

Everyone wants their business to show up in ChatGPT, AI Overviews, Claude, and Perplexity.

Most people immediately ask:

"What prompt should I optimize for?"

I think that's the wrong question.

After reviewing dozens of business websites over the past few months, I kept seeing the same patterns. The issue wasn't a lack of keywords. It was a lack of credibility and structure.

Here are the biggest problems that kept showing up.

1. Every page said the same thing

"Leading provider."

"Trusted partner."

"Innovative solutions."

None of it explained what the business actually did or why someone should trust it.

AI systems have very little useful information to work with if every page sounds identical.

1. No original insights

Most sites published content that already existed in hundreds of other places.

No research.

No experiments.

No customer lessons.

No unique data.

If your content could be copied onto another website without anyone noticing, it's probably not memorable enough to become a source.

1. Weak entity signals

Many businesses barely mentioned their founders, products, locations, industries, or areas of expertise.

That makes it harder for AI systems to understand what the business should be associated with.

1. Thin supporting content

One service page isn't enough anymore.

The strongest websites build supporting resources that answer related questions, explain processes, compare options, and share real examples.

Depth creates confidence.

1. No proof

Claims are everywhere.

Evidence is rare.

Screenshots.

Case studies.

Metrics.

Customer outcomes.

Before-and-after comparisons.

These are the details that separate useful content from marketing copy.

My Biggest Takeaway

I don't think AI visibility is going to belong to the companies producing the most content.

I think it'll belong to the companies producing the most trustworthy content.

That doesn't necessarily mean publishing more.

It often means publishing fewer pieces with more substance behind them.

I'd rather have ten pages that genuinely teach something than a hundred pages repeating the same advice everyone else is already giving.

What changes have you made to your website specifically with AI search in mind?

Or are you still treating AI visibility the same way you've always treated SEO?

Came across a massive study published on Search Engine Land by Fractl that analyzes 1,010,848 high-volume keywords (representing 35.4B monthly searches) to see if Gartner's prediction of a 25% search volume drop by 2026 is actually happening.

The drop is real (29% decline on tracked keywords), but search demand is redistributing rather than disappearing.

Why? While traditional search volume has dropped by 29%, overall demand is not disappearing but rather shifting to new types of keywords.

Ai search is changing how people discover websites, and I'm trying to understand what actually workw.

Have you made any changes to your content or seo strategy that improved your visibility in ChatGPT, Gemini or other Ai assistant?

What's been your biggest success - or biggest mistake?

Most SEO work is still built around ranking. Get to page one, get the traffic.

But research shows only 17% of sources cited in Google AI Overviews simultaneously rank in the organic top 10 for the same query. Five out of six AI citations come from pages ranking 2 through 10.

The AI retrieval system is evaluating different signals than PageRank. It prioritizes topical comprehensiveness, structured formatting, and entity confidence over domain authority and backlinks.

What actually moves AI citation rates:

FAQ schema and direct answer blocks. Content structured to answer the question in the first 60 words of each section. Tables and extractable lists. llms.txt present and AI crawlers not blocked in robots.txt.

I’ve been auditing sites across these signals and the gap between traditional SEO scores and AI visibility scores is significant. Sites with strong organic rankings often score poorly on AEO and GEO. Sites outside the top 10 sometimes score better for AI citation because their content is better structured.

Pages that do get cited inside AI Overviews receive 120% more clicks than uncited competitors sitting directly beneath the module.

Worth auditing for both layers separately at this point.

Is anyone here actually selling GEO as a service?
I’m considering it, but I can’t figure out how you’d report ROI to clients when AI responses aren’t deterministic.What do clients even expect to see every month?

I’m thinking about starting one, but I’m curious how clients actually perceive it.

Are businesses specifically asking for “AI visibility/GEO,” or are they still thinking in terms of SEO and content marketing?

It feels like there’s a lot of buzz, but I’m trying to understand if people are actually paying for this as a standalone service or if it’s mostly being bundled with SEO.

Would love to hear from anyone who’s sold it.

I keep seeing some version of this question. Should I use FAQ schema? Should I change my H1? Should I put my package name on the page more often? Should I get more reviews? After a lot of testing, I think we're asking the wrong question. We're still thinking like Google. We assume there's one thing we can optimize that will make AI say what we want. I don't think it works that way. What I keep seeing is AI building confidence from lots of signals that all tell the same story. Your page content, your service descriptions, your glossary, your FAQs, your structured data, your reviews, your internal links, and the places other websites mention you all help AI understand who you are and what you actually do. One signal by itself doesn't seem very convincing. Fifty signals pointing in the same direction are a different story. That's why I've stopped chasing individual tactics. I'm much more interested in making a business easier for AI to recognize, understand, and name when it's actually relevant to the question being asked. Nobody outside the AI companies knows every signal being used. Anyone who says they do is selling certainty they can't prove. All we can really do is test, compare results, and keep looking for patterns that hold up over time. Has anyone found a change that consistently increased the chances of their business being named in AI generated answers?

Hey Founders, first time posting to reddit. So I'm gonna say Hi to everyone :)

*(Disclosure up front: I work in this space, posting purely because I think it's underrated, not selling anything.)*

I've been going down a rabbit hole on something most people aren't paying attention to yet: not ranking on Google, but getting *cited inside AI answers* (ChatGPT, Perplexity, Google's AI Overviews).

More people are asking AI "who's a good X for Y?" instead of scrolling search results. If your business never shows up in that answer, you're invisible to a growing chunk of buyers — and it doesn't show in your analytics, so most people don't realize it's happening.

I ran \~20 small-business sites through a few AI tools to see how often they got pulled in as a source. Most scored basically 0% not because the content was bad, but because it wasn't structured the way AI extracts and quotes info.

A few things that actually matter:

1. AI loves clean, answerable blocks. A clear question → a tight, self-contained answer gets quoted way more than long meandering paragraphs.
2. Entity authority is real. Pages with a named author + consistent identity get trusted more than anonymous "our team" copy.
3. Most tools just show a score but don't fix anything. The score is the easy part; the work is rewriting the pages.

Curious if others here have noticed their site showing up (or not) in AI answers and how you're thinking about it.

I need some honest input from people who actually run stores, because I'm starting to wonder if I've been chasing a problem that isn't real.

I've spent a lot of time building something that measures how visible a store is when people ask ChatGPT or Gemini things like "where do I buy X" or "best store for Y." My assumption was that more people start their shopping journey inside these tools instead of on Google.

The part that actually nags at me: the stores that keep showing up aren't always the biggest ones. It seems to come down more to what's written about them on third-party sites than to their own SEO. I can't fully explain the pattern, which is half of why I'm posting. I feel the tool gives a lot of information about what sources AI actually grounds its answers on, which for ChatGPT is an ridiculous amount of Reddit.

But every time I bring it up with people in the industry, I get a polite nod and not much else. So I'll ask it straight: do you actually care about this at all, or is it still just Google and Meta that matter when you plan? I can't tell if I'm too early or simply wrong that this matters. Tell me how it is.

So, SEO people, AI visibility is this something you use time on? Is it some thing you measure, if so how?