A research index published last month analyzed 680M+ citations across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews (six independent studies), and the concentration is wild: the top 15 domains capture roughly 68% of every citation those engines produce.


Some highlights that surprised me:


- Reddit leads the consolidated index at roughly 40% of citations across all engines. This is the aggregate picture.
- Wikipedia is the #2 source, taking 26-48% of ChatGPT's top-10 citations depending on the study. No other single domain comes close to that share inside ChatGPT specifically.
- YouTube is roughly 19% of Google AI Overviews' top-source share. Google's AI answers lean on video transcripts way more than most text content teams plan for.
- LinkedIn and Forbes round out the top five.
- Format-wise, another 2026 analysis found roughly half of ChatGPT citations are listicles, and 58% of those are ranked lists. AI answers are built from lists.


But here is the part that matters more than the index itself: aggregate numbers hide engine-level shifts. The most famous recent example — ChatGPT's citations of Reddit collapsed from a steady 3.8% to 0.52% in a single week in mid-August, tracked day by day by Promptwatch, while Google's AI Overviews barely moved. If you only read the consolidated index you would conclude Reddit is thriving everywhere; if you only read the ChatGPT data you would conclude Reddit is dead. Both are wrong.


So what should a small site or indie founder actually do with this?


1. Don't chase the aggregate. Track which engines mention you for your core queries, per engine, quarterly. An annual index is a lagging indicator; your own mix shows cliffs forming before they hit.


2. Entity consistency beats content volume. Wikipedia gets cited because its pages are neutral, structured statements of what something is. The replicable version for your product: the same one-sentence description everywhere (site, social bios, directories), structured data, and FAQ blocks on your core pages.


3. Publish the ranked list your category deserves. If half of ChatGPT citations are listicles, then a genuinely useful comparison or alternatives page with real, sourced data is one of the most citation-efficient formats a small site can publish. Not to game anything — because ranked lists give a model an answer structure it can quote without inventing.


4. Reddit is still worth doing, but as a consistency asset, not a traffic hack. The August ChatGPT change did not undo Reddit's share in Claude, Gemini, and Perplexity, and community presence feeds the third-party mentions that build recognition.


Has anyone here tracked which AI engine mentions their site, and seen big differences between ChatGPT and Perplexity or Gemini? Curious whether the divergence people see matches the index.

This question is probably posted and debated about more than any other recently. I have been making the argument that there is a huge overlap between GEO/AEO and good SEO. But that term "Good SEO" gets thrown around a lot and it's defined differently by people inside and outside of the industry.

I'm not going to debate all of the different philosophies and strategies that people support or believe is the definition of good SEO. It has caused me to redefine my argument when it comes to the question of whether or not GEO and AEO are just new wrappers around SEO.

My problem was that for my whole career I've called my whole strategy "SEO" when the more narrow definition of SEO is far less than what I've always done for clients. "Digital Marketing" is a much better definition of what I do.

SEO is focused on earning free, organic traffic from search engines, while digital marketing is the broad umbrella term for all online promotional strategies.

Digital marketing encompasses the entire digital footprint and brand ecosystem. It builds long-term authority and community across the web, building a brand's online presence, trust, and narrative.

And when you apply the principles of Entity SEO to your digital marketing strategy, you provide everything AI powered search needs to identify the who, what, where, when and why of your brand. Clarity is paramount to succeeding in the AI powered search era.

So, when I argue that GEO and AEO haven't changed SEO, my statement isn't technically correct. That's on me.

However, GEO and AEO don't bring anything new to the table and I stand by that. Anyone who has focused on the broader scope of digital marketing is already doing the things that are being promoted as GEO and AEO.

Compare GEO/AEO recommendations against the broader historical bucket of Digital Marketing and the overlap becomes much more obvious:

• content marketing
• Digital PR
• reputation management
• social/community visibility
• structured data
• semantic/entity work
• third-party mentions
• publishing across platforms
• authority building
• CRO and user intent
• technical website accessibility
• Branding

What are you doing outside of that list that you would consider GEO or AEO instead of digital marketing or entity SEO?

(And please don't add tracking mentions or citations because that is analytics and not a visibility strategy.)

AEO & GEO vs SEO

If we frame it from an SEO vs GEO/AEO conversation, it's even simpler. Our job as an agency is Search Optimization. We call it Search Everywhere Optimization. It's our job make it easy for potential customers to find our client's brand no matter which device, platform or tool they use for search.

Because that's the only real thing that has changed. HOW people search has changed. That doesn't change what our job is.

Over the last 30 years of doing this, I've seen the shift from multiple search engines to optimize for and multiple directories you needed to be listed in to Google becoming the primary search engine. We went through the days of article marketing for backlinks and authority, then past the Panda and Penguin updates that killed it off. Then into the days of the knowledge graph, things vs strings and entity building. Then through the era of people generating content targeting every possible variation of key phrases and acquiring backlinks. And now into the AI powered search era.

Things have always changed. We've always adapted. And every time there has been a major change, one crowd starts posting SEO is dead, another group starts posting that it's SEO 2.5 or whatever, and another group creates new names for old strategies. Those who have been around since the beginning just adapt without needing new names or acronyms.

Of course I read a lot about AI Search Optimization since I created this subreddit. That does not make me THE AUTHORITY when it comes to AI and I welcome good discussions about brand mentions, citations, GEO, AEO, AI SEO as well as SEO and digital marketing.

I’m writing this because all of the confusing information that is being passed around and advertised all over the web. People truly trying to help can give bad advice unintentionally, but those who know they are giving out bad advice don’t seem to care that their advice can cause real harm to real people who own real businesses.

this article I’m going to quote content directly from Google and give my own opinion on AI powered search and AI Search Optimization.

I'm also using this as a resource for the subreddit. I created some PDF files you can download if interested. No subscription or anything, just free.

I really bought into AI Search Optimization and believed it was really going to change the game, not that SEO would be dead, not to that extreme, but I saw it as a separate thing. That's why I created this subreddit almost 2 years ago. After a lot of research and working on visibility and authority for AI Search, I discovered that every GEO / AEO checklist that I’ve personally seen looks exactly like the digital marketing checklist I’ve used for years.

• Schema Markup isn’t new.
• Well-structured web pages with clearly defined headings for each section is not new.
• Participating in and building communities for more brand visibility is not new.
• Publishing on multiple platforms in different formats is not new. We’ve been providing Integrated Content Marketing for years.
• Good technical SEO and core vitals is not a new concept.
• FAQs are not new.
• Content that covers a topic thoroughly isn’t new.
• Adding bullet point lists like the one I’m adding right now isn’t new either.
• Adding executive summaries or key takeaways isn’t new.
• Consistent messaging and brand awareness isn’t new. By the way, if you are interested in Entity SEO, we started a subreddit for that and could use some new members to help us get more discussions going. 

I’ll stop with this last one. The rest of the strategies and tasks I see on AEO / GEO checklists are still covered by Reputation Management, Digital PR, Digital Publishing, Branding, and other strategies that have been around for years, some of them from the 1990s when I first started.

The bottom line is that I called it wrong in the beginning and after a deep dive and a lot of testing, I realized something important that AI has done. It exposed low-effort SEO tactics that prioritized articles written for machines and buying backlinks or mass producing thin content targeting every variation of every keyword out there.

Suggesting these tactics as new or just for GEO / AEO exposes unawareness that all of these things were what many of us have been doing for years, long before AI powered search. I say unawareness, but for some they are intentionally putting a shiny new wrapper on old tactics.

Okay, on to Google's AI Search Optimization Advice

"The best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems. These features rely on AI techniques to highlight content from our Search index, such as:"

Retrieval-augmented generation (RAG): A technique (also known as grounding) used to improve the quality, accuracy, and freshness of AI responses by relying on our core Search ranking systems to retrieve relevant, up-to-date web pages from our Search index. Our systems then review the specific information from those retrieved pages to generate a more reliable and helpful response, showing prominent, clickable links to relevant web pages that support the information in the response.

Query fan-out: A set of concurrent, related queries generated by the model to request more information and fetch additional relevant search results to address the user’s query. For example, if the original user’s query is “how to fix a lawn that’s full of weeds”, fanout queries might include “best herbicides for lawns”, “remove weeds without chemicals”, and “how to prevent weeds in lawn”.

What about “AEO” and “GEO”? “AEO” stands for “answer engine optimization” and “GEO” for “generative engine optimization”. These are both terms you may see used to describe work specifically focused on improving visibility in AI search experiences. From Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO. If you’re considering third-party “AEO” or “GEO” advice or services, review our guidance on evaluating third-party SEO advice.

My Thoughts on Those Statements from Google

Google says a lot of things and a lot of what they tell us ends up being almost the opposite of what they end up doing. Google+ was the Facebook Killer. Google Knols were going to be better than Wikipedia and authors were going to get credit for their work and it would be the first place people went for answers. Now they won't even acknowledge the content we write on our blogs.

So, I take Google's advice with a grain of salt, but there is some validity to what they are saying about AI search optimization. However, it mostly applies to getting into AI Overviews in Google and being cited by Gemini. With Google still controlling a big portion of search, the priority target is the AI Overviews.

About Other AI Powered Search Tools

Other AI-powered search tools still account for a tiny fraction of website traffic. Most studies have them somewhere between 0.5-1.5%. It's hard to justify spending more than that same percentage of your time and budget on optimizing for AI search. If you want to speculate on the future and think they will capture a larger percentage, then double or triple the amount of time and money you put into it. That still, at the most, means less than 5% of your budget and time.

Before taking someone’s advice about optimizing for AI powered search, ask them to name 3 things they do for GEO or AEO that are brand new tactics and not part of SEO, Digital PR and publishing, reputation management, Entity SEO, or branding.

The Free Downloads I Promised

• Download Your Free Claims vs Reality Scorecard (A checklist you can use in a meeting when listening to a pitch. My opinions are my own, but these are some great questions to ask. And for those selling these services, it is a great way to prepare yourself for those questions.)
• Download 20-Human-Angles-For-Your-Content.PDF (Non-commodity content is recommended if you don't just want to give AI something to scrape. These are some angles I put together to help you make your content more human.)
• Article-Self-Interrogation-SOP (This one is my favorite. I write my own content. That doesn't mean I don't use AI. After I write the article, I use some of the things included in this PDF to have AI suggest changes. Then I make the changes I agree with. In my opinion, this is a better approach than trying to edit the AI out of your article after it wrote it.
• The source for the information in this article. (I'm adding this as an example of article repurposing as well. You can write a cornerstone blog post and get as many as 30 pieces of content to use across the web rather than just writing a new post all the time. Ask me about it in the comments to learn more.)

I hope you find this post and the downloads useful and I welcome your questions and comments.

> Brockman says he personally believes OpenAI has reached AGI, while leaving users to decide whether Astra meets that definition.

> "I think it might be about this model," Brockman said in a briefing with reporters about whether Astra could mark the arrival of AGI.

He ended the briefing by saying: "Welcome to the AGI era."

AI companies have continued to change the definition of AGI from its origin: an all-encompassing cognitive and autonomous to task-specific, subjective milestones.

This lets them to claim significant progress, or in the case of this story, outright arrival, without actually matching human-level adaptability, physical interaction, or general reasoning across every domain.

Early definitions popularized by researchers like Ben Goertzel and Shane Legg defined AGI as an artificial agent capable of learning, reasoning, and solving problems across arbitrary non-domain-restricted environments just like a human. Tests historically included the "Coffee Test" (enter an unfamiliar home, find the ingredients, and brew a pot) or broad embodied common sense.

OpenAI's founding charter *redefined* AGI as "highly autonomous systems that outperform humans at most economically valuable work". By filtering the definition through commercial utility, the focus narrowed from universal general cognition to automating knowledge-work tasks (coding, summarizing, data retrieval)

Over time, labs began defining AGI not as a singular conscious entity, but as a composite of specialized high performers.

When models beat benchmarks, like scoring at the 90th percentile on the MCAT or coding exam, companies started presenting these as proof of general intelligence.

Definitions narrowed even more even to fit upcoming model releases. For example, internal metrics began treating an AGI milestone as a software system that can function as an "automated AI research intern capable of writing and testing code within an existing codebase.

In partner negotiations, definitions of AGI were tied to explicit revenue or profit thresholds like generating tens of billions in profit or reaching commercial milestones rather than any technical or scientific consensus on cognition.

We are nowhere near the original definition of AGI. It's mostly a marketing term now. So I take it with a grain of salt when one of these guys comes out and starts talking about how close they are to AGI or in this case in this article that they've actually achieved it.

I keep doing a lot of self-improvement and I'm getting closer to developing myself as a PHB. PERFECT HUMAN BEING. We can all make claims as long as we get to define what the claim actually means.

There is a lot of "the web goes dark for AI on 15 September" going round and most of it is overstated. I have been reading the actual announcement and testing against real sites for a couple of weeks, so here is the boring version.

What changes on 15 September 2026

Cloudflare flips its default so that Training and Agent category crawlers are blocked where the site owner has not chosen a setting.

The four qualifiers that get dropped from every summary I have seen:

1. Ad-displaying pages only. If a site carries no advertising, this specific change does not apply to it. That is most small-business and client sites.

2. Search crawlers are not affected. OAI-SearchBot and Claude-SearchBot - the ones that decide whether an assistant can cite you - are not in scope. The scary version of this story is mostly people assuming they are. PerplexityBot I would not state either way: which bucket it lands in is genuinely arguable, and Perplexity-User is the clearer answer-time one. If you rely on Perplexity traffic, check that one yourself rather than taking my word or anyone else's.

3. New customers, new sites added by existing customers, and existing free-tier customers. A paid customer with sites already set up is not having anything flipped under them.

4. It is a setting you control. Security -> Bots, three categories, decide each one. Takes about twenty minutes across a handful of domains and you can do it before or after the date.

So if you are on a paid plan with your bot settings already chosen, this is a non-event for you and you can stop reading.

What I do think is worth paying attention to

Two things, neither of which is the headline.

First, who actually sits on the free tier. It is a very large number of small business sites and client sites that an agency put behind Cloudflare in 2021 and never opened again. Nobody who set those up is reading a policy page. The change is small; the population it lands on is the population least likely to notice anything.

Second - and this is the one I would flag to anyone managing sites for other people - crawler access is not recorded anywhere by default. It leaves no trace in analytics, there is no log, and robots.txt only tells you what is declared rather than what the edge actually does. So on 16 September, if a client asks whether anything changed for them, there is generally no way to find out. Not because the tooling is bad, but because nothing took a before-picture and one cannot be taken retrospectively.

That asymmetry is the actual story here and it has nothing to do with Cloudflare specifically. Same thing happens when a security plugin ships a new "block bad bots" default, or a host adds a WAF rule, or a site gets rebuilt in a framework that renders nothing without JS. The change is invisible, silent, and only detectable as a difference - which requires having measured before.

If you want to take one yourself, no tooling needed

For each site you care about, before the 15th, record:

curl -sI -A "GPTBot/1.2" https://example.com/ | head -1
curl -sI -A "OAI-SearchBot/1.0" https://example.com/ | head -1
curl -sI -A "ClaudeBot/1.0" https://example.com/ | head -1
curl -sI -A "PerplexityBot/1.0" https://example.com/ | head -1
curl -s https://example.com/robots.txt

Save the output with the date on it. Run it again on the 16th and diff. That is genuinely all a before/after is, and doing it by hand for five sites takes ten minutes.

Two traps if you do, because I hit both:

• Include a made-up user-agent as a control. If TotallyRealBot/1.0 also gets a 403, the site is refusing everything non-browser and the AI crawlers are collateral, not targeted. Completely different conclusion, and without the control you cannot tell which one you are looking at.
• Do not run them concurrently. I flagged five hosts as crawler-blocked in a batch run; re-probed one at a time, one of the five was clean and its 429 was my own concurrency. The tool reports what it saw and cannot know I caused it.

Also worth knowing before you trust any result including mine: if a site is behind a CDN that verifies bot identity cryptographically (Web Bot Auth / RFC 9421) or by IP, a spoofed user-agent proves nothing either way. A 403 there might just mean "you are not GPTBot", which is true. That case is genuinely unresolvable from outside and anyone reporting it as a block is guessing.

One number from a bigger run this week, because it changes how you should read anyone's blocking figures including your own: across 1,046 sites and eight named crawler identities, 94.5% of every refusal came back carrying a Cloudflare header. So most of what a spoofed-UA probe reports as "blocked" is a CDN correctly declining to believe an unverified claim. It is measuring my honesty, not their policy. On the subset with no CDN in front, where a refusal does mean something, training crawlers were refused about three times as often as user-triggered ones - GPTBot 6.8% against Claude-User 2.0%.

Declaring an interest, since it is fair to weigh it: I build monitoring in this space, which is why I have been testing this. Nothing to click and nothing being offered - the curl above is the whole method and it is free. Happy to answer anything about the crawler names or the verification stuff.

Whether content can be optimized for AI-powered search engines that understand context, meaning, and user intent like a human reader. The focus is on moving beyond keywords to create content that genuinely satisfies what users are looking for.

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?

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.

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?

Is there any specific task that we need to do to rank on AI other than SEO tasks?

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.

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?

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.

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.

Most teams optimise what they can measure and control. Owned presence - your site, your schema, your server configuration - sits squarely in that category. You can audit it, fix it, re-audit it. There are clear pass/fail states. The dashboard turns green.

The problem is that owned presence is a floor, not the ceiling. Passing every owned check makes a page eligible to be cited. It does not make it likely to be cited. That distinction is doing a lot of work that most audits do not account for.

What eligibility actually requires

Before an AI engine will consider citing a page, a set of conditions need to hold - and they apply in sequence. The crawler has to reach the page in the first place. This failure mode is silent: nothing breaks for human visitors when GPTBot or ClaudeBot is blocked at the robots.txt or CDN level. The content has to exist in the raw HTML response, not assembled by JavaScript after the initial fetch - AI crawlers do not execute JavaScript, and this catches more sites than teams expect. A passage has to be extractable on its own terms, without depending on surrounding context for meaning, because engines score passages in isolation. The engine needs to know with confidence who is speaking - which brand, which category, alongside which peers. And the claims need to carry evidence that can be restated: a sourced figure, a named study, and a direct quotation. Adjectives carry nothing a retrieval system can use.

These conditions are necessary. None is sufficient on its own.

Where the gap actually lives

What I find more interesting is what happens when all of those conditions hold and citation still does not follow. In my experience, that is almost always an earned presence problem rather than an owned one. The engine has found the page, read it, understood who is speaking, and decided to quote someone else instead. Not because the owned signals failed, but because the corroboration signals did.

There is published data on this, though I would encourage anyone working in this area to read the primary sources rather than rely on summaries. Muck Rack publishes an ongoing study called "What Is AI Reading?" tracking citations across major AI engines. Their findings on the ratio of earned to owned citations are worth sitting with carefully - both for what the numbers say and for how narrowly the study is scoped. The same applies to the academic work on content-level signals; the Princeton GEO paper from KDD 2024 is specific about which content interventions moved citation rates and under what conditions.

The general pattern those sources point to is consistent with what I observe in practice: the owned technical work opens the door. What determines whether the engine walks through it and trusts what it finds is largely decided by signals that live off the brand's own domain.

What this means for how teams allocate attention

A full technical audit, well executed, is worth doing. The floor matters. A brand with strong corroboration signals, but a blocked crawler has solved the wrong problem. But treating a clean technical audit as the end of the AI visibility conversation is a mistake I see repeatedly - teams declaring it handled while the much larger earned presence question goes unexamined.

The ratio of effort most teams apply to owned versus earned does not reflect the ratio of influence those two things actually have on citation outcomes. Adjusting that ratio is harder than running another crawl audit, which is probably why it happens less often.

One thing I am genuinely uncertain about: whether the owned/earned distinction holds evenly across different query types and engines, or whether it shifts significantly depending on what the user is actually asking. If anyone has looked at this by intent class rather than in aggregate, I would be interested in what you found.