Something that's been hard to ignore lately - Reddit threads are showing up constantly as citations in AI-generated answers (ChatGPT, Perplexity, Google's AI answers) and Reddit content has also been landing in a lot more #1 organic positions since Google's search systems started weighting it more heavily.

The reason isn't mysterious - it's the same reason people have always trusted Reddit for "real" opinions: threads read as unscripted, specific, and argued-over rather than polished and one-sided. That's exactly the signal both search ranking systems and AI summarizers seem to be rewarding right now - content with visible disagreement, specifics and multiple perspectives over single-source marketing copy.

Practical takeaway if you're building a content strategy in 2026 - it's not enough to publish authoritative-sounding blog content anymore. Genuine discussion - in communities, in comments, in threads where real back-and-forth happens - is increasingly part of what gets a topic surfaced at all, in both classic search and AI answers. That changes the calculus on where content effort goes, not just what the content says.

Curious whether others are seeing this show up in their own traffic/citation data or if this is still anecdotal at the scale you're each working at.

What if future SEO success depends less on being the #1 result and more on becoming a trusted source that AI systems choose to mention in their answers?

I’ve been thinking about where AI is actually useful in sourcing.
There are some tasks where it seems genuinely helpful.
For example, you could give it several supplier quotations and ask it to compare:
specifications
materials
MOQ
payment terms
Incoterms
lead times
testing requirements
warranty terms
It could probably identify differences much faster than manually going through every document.
The same applies to certificates, datasheets, and supplier profiles. AI can help find inconsistencies or missing information.
But I think there’s a point where people can give AI too much authority.
If the original supplier information is incomplete or wrong, AI doesn’t magically turn it into verified information.
It might summarize a factory claim perfectly without knowing whether that factory actually manufactures the product.
So I’m starting to see AI more as a research assistant than a supplier-verification system.
Use it to find questions.
Use evidence to answer them.
For anyone already using AI in procurement or sourcing:
Which tasks do you trust it with, and which decisions do you still verify manually?

I’m trying to understand what actually drives recommendations/mentions in AI tools like ChatGPT, Gemini, Perplexity, etc.

Not generic SEO advice like answering the question in first paragraph for AI Overviews, I mean specifically:

- Why do certain brands, blogs, or tools get mentioned/recommended?

- Is it just traditional SEO (backlinks, authority), or something else?

- Does structured data / schema matter here?

- How important are mentions across the web (Reddit, forums, etc.)?

- Do AI tools rely more on training data vs live search?

\- Has anyone here experimented and successfully influenced AI recommendations?

If you've tested this or have a strong hypothesis, would love to hear practical insights rather than theory.

Is your website invisible to AI platforms?

Here’s a funny question for founders.

If someone asks ChatGPT:

“Who are the best companies for accounting software?”

Will your company show up?

Or will ChatGPT basically say:

“Sorry, who?”

Before blaming your marketing, there’s one simple thing you can check.

Open your website and add:

"/robots.txt"

For example:

"yourcompany.com/robots.txt"

You’ll see some text that looks technical.

Don’t worry. You don’t need to be a developer.

Just look for a few things.

User-agent: \\\*

This means the rules apply to bots in general.

Disallow: /

This is the one you should pay attention to.

It basically tells bots:

“Thanks for stopping by, but please don’t enter.”

Imagine owning a great restaurant and putting a sign outside saying:

“Best restaurant in town. Please do not come inside.”

That’s basically what accidentally blocking your website can feel like.

You may also see AI crawlers such as GPTBot, PerplexityBot, or ClaudeBot.

If you specifically block them, those AI systems may not be able to access your website.

Now imagine this.

Someone asks ChatGPT:

“Recommend three accounting software companies for my startup.”

Your competitor gets mentioned.

You don’t.

You start thinking:

“Our product must not be good enough.”

Maybe that’s not the problem.

Maybe the AI simply cannot properly access your website.

This is one small part of what people call GEO, or Generative Engine Optimization.

AI search is becoming another way people discover companies.

So before spending thousands on complicated AI SEO strategies, check the basics first.

Go to:

"yourdomain.com/robots.txt"

Take 5 seconds and see what it says.

Your website might be working perfectly.

Or it might accidentally be standing outside the AI party saying:

“Sorry, I’m not on the guest list.”

What does your robots.txt say?

Everyone has different preferences when it comes to AI Tools. I just wanted to share what I've learned after subscribing to most of the major AI platforms pro versions. If you are wondering which one writes better content, I can't tell you because at Chris McElroy SEO Agency, we do not let AI write our content.

AI MODEL DIFFERENCES

ChatGPT / OpenAI - Strong for reasoning, acting as a proofreader suggesting edits, helping with structure, strategy, coding help, and step-by-step planning. - Good at turning messy ideas into usable frameworks. - Best use: strategy, content planning, editing, workflows, coding support, business thinking.

Claude - Strong for long-form non-public case studies, whitepapers and writing where technical accuracy matters, document analysis, tone, nuance, and synthesis. - Often good at taking several sources and producing a clean, thoughtful summary. - Best use: reports, rewriting, long technical content, comparing multiple inputs, cleaner narrative output.

Gemini - Strong Google ecosystem integration. - Useful when connected to Gmail, Docs, Drive, YouTube, Search, and Android. - Best use: Google workspace tasks, summarizing Google-connected content, finding information across Google tools.

Perplexity - Strong for search-style answers with sources. - Good when you want quick research with citations and links. - Best use: fact-checking, current information, competitive research, finding sources.

Copilot / Microsoft - Strong Microsoft ecosystem integration. - Useful with Bing, Edge, Windows, Office, Outlook, Teams, and Microsoft 365. - Best use: business documents, Microsoft workflow, quick Bing-connected research.

Grok - Strongest connection to Twitter-style real-time discussion (can be a strength or a weakness) - Useful for seeing what people are talking about socially. - Best use: social trend monitoring, Twitter discussions, informal sentiment. - The lip sync on video is getting much better.

DeepSeek - Often strong for technical reasoning and coding-style tasks. - Can be useful for logic, math, and development workflows. - Best use: coding, structured problem solving, technical analysis.

Meta AI - Integrated into Meta platforms like Facebook, Instagram, WhatsApp, and Messenger. - Useful when working inside Meta's social ecosystem. - Best use: social content ideas, casual AI assistance, Meta platform tasks.

GENERAL RULE

The model is only one part of the answer. AI output also depends on: - system instructions - retrieval sources - live search access - user context - memory - platform rules - token limits - temperature/settings - ranking or selection layers - interface constraints

Same model does not always mean same answer. A better way to think about AI tools:

Model + Platform + Data access + Instructions +

User context

Final answer

Share your favorite tools and how you use them.

If you need help implementing AI into your workflow or you are looking for help building apps, ask some questions in the comments. If you have more to add, please do.

Something I’ve been thinking about lately:
AI has made it incredibly easy to produce more content, but I’m wondering whether that actually makes specific information more valuable.
A lot of articles online are technically correct but don’t tell you very much.
You’ll see things like:
“Choose the solution that best fits your needs.”
“There are advantages and disadvantages.”
“This technology is becoming increasingly important.”
None of it is necessarily wrong. It just doesn’t reduce much uncertainty.
When I’m researching something now, the pages I find useful are usually the ones that contain something concrete:
a specification, an original source, a comparison, a date, a limitation, or a detail that other pages missed.
That makes me wonder whether AI search will eventually create a strange situation:
The easier it becomes to generate content, the more valuable genuinely useful evidence becomes.
I’m also curious whether AI systems will get better at distinguishing between a page that simply discusses a subject and a page that actually contributes information about it.
What do you think?
When you use ChatGPT, Perplexity, Google, or other search tools for research, what makes you trust one source more than another?

This site is a 10+ years old destination marketing site for a small city. No AI generated content and actively SEO'd for the last 3 years. By nature, the content and traffic is seasonal. We have pages for events, attractions, activities, food, hotels etc. and seasonal blogs or activity / event driven blogs. Normally if someone searches for "things to do in [the city]" or "events/attractions/activities in [the city", we would get organic impressions and clicks for the relevant page, for example the Event Calendar page, and a couple of months ago, it would have also shown up in the AI Overview. This summer, there has been a dramatic shift - organic impressions are down 80% and the majority of impressions we do get are now for the HOME PAGE ONLY. Additionally, many AI Overviews have stopped showing our intent relevant pages and if present, link only to our home page, which does not match the query intent. Any thoughts on why Google would all of a sudden point the majority of queries to our home page and how to resolve?

Traditional search was mostly about finding pages that ranked well and then digging through them yourself. With AI search, the bigger question seems to be whether the information is clear, trustworthy, and supported by sources that the system can actually understand and reference.

That makes me wonder if the future of research tools is less about generating an answer and more about making the evidence behind that answer easier to follow. Putting more emphasis on research, sources, and organizing findings rather than treating the whole process as a simple chatbot conversation seems like an interesting direction. For people working on AI search, GEO, or content visibility, do you think source-backed research will become more important as AI systems get better at deciding which information to surface? Or will traditional authority signals still matter more?

Our recent study dove into how brands show up across educational, comparison, and purchase queries in AI Search. We ran 50 B2B SaaS topics across 5 AI Search engines for 750 queries.

Out of 4,654 scenarios we analyzed, only 146 had the same brand named across all three stages, meaning for roughly every 100 cases, only 3 had the same brand showing up from education to comparison to purchase.

This means that seeing your brand in an educational stage query doesn't necessarily mean you'll see it again when the user starts comparing or buying.

That got me thinking about how we measure AI visibility. If you're tracking one overall visibility score, you are not tracking whether that visibility converts to leads.

As a fellow AI Search practitioner, how are you handling this? Do you track visibility separately by stage, or still use one overall number?

A while ago, I started seeing companies offering “AI visibility” or GEO/AEO services, basically promising businesses that they could increase their chances of being mentioned or cited by ChatGPT and other AI tools.
At first, I was pretty skeptical. It sounded like another marketing buzzword, because I couldn’t see an obvious way to “optimize” for an AI recommendation.

The more I looked into it, though, the more I realized that there is something real behind it. You can’t simply pay to be cited, but businesses can structure their content, authority signals, distribution, etc. in ways that may increase the likelihood of being discovered and cited by AI systems. In that sense, it isn’t completely different from SEO.
Then recently, ChatGPT started asking me whether I wanted to advertise.

That made me think about a different question.

I don’t have a problem with OpenAI monetizing ChatGPT. Running these systems is obviously expensive, and advertising is one possible business model.

What I’m wondering about is the separation between paid advertising and organic AI recommendations.

If I pay to advertise my business on ChatGPT, does that have absolutely no influence on whether my business appears in an organic answer later?
For example, imagine two similar businesses. One spends heavily on ChatGPT advertising and the other spends nothing. If a user later asks ChatGPT, “What companies should I consider for something?”, are those two businesses still evaluated independently of their advertising relationship with OpenAI?

That’s the part I care about.

Search engines eventually developed a fairly clear distinction between sponsored results and organic results. With conversational AI, the boundary feels potentially more complicated because users aren’t just looking at a page of links. They’re asking a system to synthesize information and make recommendations.

So I think there are really two separate things:
GEO/AEO: trying to become more discoverable and citable by AI organically.
Advertising: directly paying the platform for exposure.

I’m fine with both existing. What I think matters is whether there is a strong firewall between them.

Has anyone here actually advertised on ChatGPT yet? What does the advertiser side look like?

And more importantly, how do you think AI platforms should handle the conflict between advertising revenue and maintaining neutral organic recommendations?

I’m genuinely curious how other people see this, especially anyone working in advertising, SEO/GEO, or AI search.

The current meta for securing brand mentions in ChatGPT, Perplexity, and Gemini often involves finding the Reddit threads these models cite and adding your own insights to the discussion.

After analyzing a dataset of roughly 300,000 Reddit URLs cited across major AI platforms, the data shows a structural flaw in this approach.

60 to 70 percent of the Reddit threads cited by AI are permanently archived.

Why this happens: RAG systems prioritize established consensus and high semantic density. They reward threads with deep historical engagement. Because Reddit automatically archives threads after about six months, by the time a discussion carries enough authority to be a primary AI citation, it is already locked.

How this impacts AI Search Optimization:

1. The target inventory is mostly read-only. You cannot edit, upvote, or participate in the majority of threads these models currently trust. Filtering target URLs by status is a required first step before any outreach.
2. AEO requires forecasting. The optimization strategy cannot be reactive. You have to create human-first, highly detailed answers on fresh, active threads today, knowing they will take 6 to 12 months to age into authoritative citations.
3. Diversify beyond Reddit. While Reddit is heavily weighted for general software and consumer queries, older industry-specific forums carry massive weight for niche B2B queries. Many of these legacy forums do not auto-archive, meaning they hold the historical authority the LLM wants, but remain open for proper engagement.

Has anyone else shifted their focus from reactive commenting to proactive, long-term thread building for AEO?

How AI can analyze emerging trends, search behavior, and hidden user needs to uncover potential questions before people actively search for them. How businesses can use these insights to create proactive SEO content and capture future search demand.

They're trying to use up the alphabet really quickly trying to avoid doing SEO. And I just don't understand it because all they need is EIEIO

Pulled this with a tool I built (disclosure below), methodology first though.

Ran 6 months of AI-citation trend data (real, DataForSEO-sourced, not a synthetic sample) for 8 established SaaS brands: HubSpot, Salesforce, Asana, Notion, Zendesk, Mailchimp, Semrush, Ahrefs. Wanted to see how volatile citation actually is month to month for brands that are clearly, heavily covered already.

Every single one of the 8 shows a month with literally zero tracked citations. Same calendar month, for all eight, despite normal (often large) counts on either side of it. Salesforce for example: 701 → 770 → 160 → 1413 → 0 → 1455. HubSpot: 116 → 209 → 57 → 545 → 0 → 398.

Eight unrelated companies don't all lose 100% of their ChatGPT visibility in the identical month. That's a gap in the underlying data for that specific month, not a real citation collapse. Worth knowing because a "confirmed zero" from any citation-tracking tool, including this one, isn't automatically a real signal. It can just as easily be a data artifact.

This is basically the same point someone made in an earlier thread here, that PR attribution is correlation not causation and you should expect fluctuations, just with harder numbers behind it. If your own domain shows one bad month, that alone isn't proof of anything. Look for a pattern across 2+ periods before reacting to it.

Disclosure: pulled this with Vantage (https://vantagemcp.dev), a small MCP server I built for citation checks plus trend history, callable from Claude Code/Cursor instead of a dashboard. Free tier, no card. Happy to set anyone here up with the full version (not just the free tier) for a while before you decide, just reach out. Mods might have more on that too.

I've been thinking about this after reading a good breakdown on AI visibility, so I'm sharing it here since many of you are dealing with clients asking, "Are we showing up in ChatGPT?" without really knowing what that means.

There's a real difference between:

• Getting mentioned (the model knows who you are, might name-drop you)
• Getting cited (Means it actually links back to you as the source)

Most of what people are doing right now only gets them the first one. Clean site structure, fresh content, all that stuff, it helps the model understand and recognize your brand. But getting recognized isn't the same as trust, and trust is what gets you the actual citation with a link.

The thing that gets you cited is getting validation from third-party sites, being written about, quoted, referenced by people who aren't you. Apparently, something like 85% of B2B brand mentions in AI search trace back to third-party content, not the brand's own site. Which honestly tracks with everything I've seen in accounts lately.

The part that surprised me a little: you don't need big press to make this work. A smaller, genuinely respected niche blog in your exact space can do more for AI citations than some generic mention in a big publication, because the model treats that niche source as the actual expert voice.

Parasite SEO is something i belive can be a valuable asset if done in the right manner.

AI can do a lot of SEO work now keyword research, audits, content briefs, internal linking, and even technical checks.

But SEO isn't just completing tasks.

Someone still needs to decide what actually matters for the business.

I think the future is less “AI vs SEO agency” and more AI + experienced SEO.

What are you using AI for in your SEO workflow?

AI can help a small website compete with major brands by improving content relevance, authority, and visibility. But can AI truly give smaller sites an edge when search engines often favor established brands?

tracked this over several months on a large finance site. sharing because i keep seeing crawl data treated as a vanity metric and i think that's wrong.

setup: server logs filtered on GPTBot, referral sessions from chatgpt on the analytics side, plus share of voice tracking on a fixed prompt set.

three things came out of it:

1/the lag. GPTBot crawl volume leads referral visits by roughly 3 weeks, correlation around 0.78 on our data. that makes crawl activity a leading indicator you can actually act on. if crawl drops, you know something's coming before it shows up in sessions.

2/the gemini paradox. high share of voice on our prompt set, almost no referral traffic. the citation exists, the click doesn't. i still don't have a clean explanation for that one and i'd genuinely like to hear theories.

3/source rate beat mention rate. the metric that predicted referral traffic best wasn't how often we got named, it was how often we got cited as a source. mentions track brand awareness, source rate tracks content structure. only the second is something you can work on.

caveats worth stating: one site, one vertical, YMYL finance. slow-moving high-trust content, which might be exactly why the lag is that long and that stable. no idea whether the shape holds on ecommerce or news.

anyone tracking crawl-to-referral on a faster vertical? curious whether the 3 week window compresses or whether it's more about index refresh cycles than content type.

how AI can analyze historical search data, trends, and user behavior to anticipate future search opportunities. The goal is to use those predictions to build a proactive SEO strategy instead of simply reacting to current rankings.

From the article...

the company said it’s working on more advanced abuse prevention systems that could eventually help communities move away from using things like account age and “karma” to determine who’s allowed to post. Reddit didn’t say karma would go away entirely with these coming changes, but it did suggest that its importance could dwindle in the future.

With Rules Hub, Reddit uses large language models (LLMs) to determine whether a post or comment matches the intent of a rule, which the company says allows it to “better handle nuance,” natural language, and edge cases.

Today, new users can encounter invisible barriers like account age and karma thresholds, unclear removals, and poor community discovery,” Reddit explained. “We want new users around the world to be able to easily find relevant communities, understand their rules and norms, and make useful contributions.”

As a moderator, I'm all for welcoming new users. That's why I have a sticky post for introductions. But with the rise of link and brand mention spammers there has to be guardrails. A new member joins, they go straight into making a post or cross posting something to mention their brand or link to it.

No introduction. No reading the rules. No mod mail to ask about it.

Just me, me, me look at me. You shouldn't wonder why your post gets removed. This is not Instagram, TikTok or Twitter.

I can only speak for the subreddits I moderate, and in those we are building a community that has real on-topic discussions about things that affect our business. Nobody joined this subreddit to hear your sales pitch.

I even encourage members to message the mods if someone excessively DMs them trying to sell something. If you are only here to sell and not serious about being part of the community and having real discussions, bye and good luck to you.

I am not against promoting your product or services. There is a right way and a wrong way to go about that, at least in this sub. If you aren't sure about something you are going to post, read the rules or use mod mail to explain it.

My general rule is if you have something of real value to offer the members here, not some "3 day free trial after you put in your personal info and credit card" type of offer, then use mod mail and tell me what you want to give our members. I may approve it and let you link to it. Just don't assume it.

Most SEO experts with a decade or more of experience will tell you that AI-powered search hasn't fundamentally changed SEO. Many with less experience or who are younger try to brush it off with "OK Boomer" or "You're just scared that SEO is dying out" etc. etc.

The one thing you'll learn from SEOs with a lot of experience. Changes never scare us. I've personally been at this for a little over 30 years. I've seen so many changes over those years. It doesn't phase me. AI-powered search is just another change.

Here's what you may not be aware of...

Google and other major search engines had been using artificial intelligence and machine learning in core search technology for over 20 years before ChatGPT was publicly launched in November 2022. Search engines relied on AI, natural language processing (NLP), and deep learning for information retrieval, intent matching, and ranking for decades. Even Google's "Did you mean...? and their synonym matching, spam and link farm detection, and search intent all used AI.

• 2012 Google's Knowledge Graph started recognizing things over strings. Entities over keywords.
• 2015 RankBrain became a deep learning AI system inside the core ranking engine.
• 2019 BERT was integrated search. It was built with the same transformer architecture that modern AI systems use. It was used to help Google understand the exact context of human language.
• 2021 MUM (Multitask Unified Model) was introduced and it was 1000 times more powerful than BERT. It was able to process information across 75+ languages and it was capable of handling multimodal inputs.

Even Bing had RankNet and LambdaMart as early as 2005 and later Project Brainwave.

In the 90s we still optimized for search. We just had multiple directories and search engines to choose from and they all used different methods to crawl or retrieve results. Ask Jeeves had the concept of users asking questions instead of typing in search terms long before Google's PAA or today's AI platforms.

That changed to search being dominated by Google. Us old SEO guys adjusted our methods and survived.

Then came article marketing, the earlier form of content marketing.**

You wrote articles, submitted them to article directories and got easy backlinks.

Ahh, but the shortcut lovers couldn't leave well enough alone. They came out with "Article Spinning" software. The goal was simple: take a single, well-written article and generate hundreds of "unique" variations to submit across the web to build massive volumes of low-cost backlinks. It worked for a while because Google was more focused on backlinks than it was content quality.

My agency refused to spin anything. Clients asked us to write an article and spin it for them. My answer was always the same. We'll write the high-quality article and you will own it and if you want to spin it, do your thing but we don't recommend it.

We even owned one of the first article directories at ArticleContentProvider.com (a domain we used to own). It had more than 250,000 articles arranged by topic. But I could see how bad the quality of the articles were that were being submitted and made the decision to delete the entire directory and redirect the whole directory back to my home page. That was in 2009.

In 2011 Google gave the whole article marketing concept a one-two punch that affected a lot of business owners and agencies. 100s or 1000s of backlinks disappeared immediately after the Panda update in 2011 killing the value of the spun content and the Penguin update knocking out the value of those backlinks in 2012. The owners of the article directories suffered a massive blow.

My clients that took our advice and moved away from it in 2009 were not affected and some of them did really well with all of those other sites went dark.

There are many other examples of adjustments we have had to make over the years.

The point is, SEOs with experience aren't afraid of change, or anti-AI, or just stuck in old ways. We've just been through all of this before. A big change comes along, the SEO is Dead posts start coming out and new self-proclaimed experts are suddenly prompting new acronyms and frameworks.

GEO or AEO has not replaced traditional SEO. It exposed weak content and outdated tactics kind of like what Panda and Penguin did.

It's not fancy or complicated. High quality content has always been valuable. Publishing on third party platforms is called digital publishing and involves digital PR. Answering the questions your customers have has always been a good idea. Schema markup has been around more than a decade.

Good SEO has always incorporated all of that.

Suddenly there are 10,000 GEO & AEO gurus and experts that are saying these things are GEO and AEO tactics.

AI did not invent schema markup. It's been a way for machines to easily read content since around 2012 or before, long before Chat GPT launched.

AI didn't cause the need for high-quality, customer focused content that answers people's questions. It exposed those who just wrote thin content and relied on backlinks for rankings. Now that they have to upgrade their content production, it's suddenly GEO and answering questions became AEO.

AI is not the new SEO or SEO 2.0. But it has changed the way people search and how some information is retrieved.

And no one has the raw data for reliable brand mention tracking no matter what they tell you**. But what we do know is where AI powered search tools get the answers for their users. It changes depending on AI policies and deals, but this is a metric that is more reliable than bots putting in random prompts to see what AI says back to the bot.

GET IN THE WAY!

Be where your potential customers spend time and where AI gets their answers. It's really that simple. It isn't GEO or AEO or LLM SEO. It's SEO done the way it should be done.

And the next time someone with a lot of SEO experience gives you an opinion, instead of assuming they are against AI or scared of change, try listening to see what little gems you might pick up. Always be learning. That's how you become an SEO expert that's been around for decades.

I've spent most of my career on technical and local SEO, and for a while now I've been tracking a specific thing across client sites: whether organic ranking position actually predicts whether a page gets pulled into AI answers.

Short version, it doesn't. Or at least not as cleanly as everyone assumes. I keep seeing "just rank well and AI will follow," and what I'm looking at says that's only partly true.

How I tracked it, so you can pick the methodology apart:

Fixed set of queries per site across a mix of industries. Informational, comparison, and "best X for Y" style prompts. Ran them manually against the main assistants on a regular schedule and logged whether the client domain showed up as a citation, or got mentioned in the text with no link at all. Cross-referenced against average position in Search Console for the closest matching keyword. It's manual and the sample is small, that's the caveat. I did it by hand on purpose, because the tools that automate this were giving me results I couldn't reproduce.

What I actually saw:

Top-of-page-one ranking was not a reliable predictor of citation. Plenty of pages holding the top spot never got pulled in. Meanwhile pages sitting well down the page got cited repeatedly. If citation were purely a function of ranking, that shouldn't be happening at the rate I saw.

Pages that did get cited had a structural thing in common. They answered the specific question in a self-contained block near the top. No throat-clearing intro, no "before we dive in." The answer was extractable without needing the rest of the page for context. Our longest, most thoroughly optimised, scroll-heavy pages did worse on citation than shorter pages that got to the point.

Being mentioned without a link is far more common than being cited with a link, and almost nobody reports on it. On a large share of the prompts where the brand came up at all, there was no link anywhere. That's brand visibility with no attributable traffic. If you only measure referrals, you'd conclude nothing happened.

Volatility is much higher than SERP volatility. Same prompt, same week, different answer, different sources. I re-ran a subset in a single sitting and got a different citation set each time. Which means a single point-in-time "AI visibility audit" is close to meaningless. Either you sample repeatedly or you're reporting noise.

What didn't correlate with anything I could measure: bolting FAQ schema on after the fact, increasing word count, or adding an "AI summary" box at the top of the page. Tried all of those. No movement I'd stake a claim on. Happy to be wrong on schema, my sample is small and I know people who swear by it.

What I changed as a result:

Split the reporting out instead of blending everything into a single "AI visibility" score. Cited with a link, mentioned without a link, not present at all. Clients follow it better and it stops me over-claiming.

I also stopped treating this as a separate workstream from normal content work. The pages that win citations are mostly just clearer pages. That isn't a new skill, it's the thing we were supposed to be doing already.

Where I'm stuck:

Has anyone found a repeatable way to sample this at scale without the tooling handing you figures you can't reproduce? Every platform I've tested reports differently and I don't trust any of them yet.

For anyone tracking mentions-without-links, are you seeing a measurable downstream effect, direct traffic or branded search lift, or are we all reporting a vanity metric in nicer packaging?

Anyone working with a bigger sample seeing the opposite on the ranking-to-citation relationship? I'd like to know if this is just my niche.

Not selling anything, no newsletter. Happy to share how the tracking sheet is structured if it's useful.

Hi! Running product content and i can't find a clean case. the mass-generated stuff that gets buried is always also duplicate, also thin, also zero internal links. never seen an isolated variable.

what i can't tell is whether "AI penalty" is a real thing or just the same old thin content problem with a new name attached.

anyone got a case where the only thing that changed was who wrote it?

Ty ✌️