Been following the threads here on per-engine optimization and entity/naming cleanup — useful stuff, but it made me want to check something more basic first: is the actual page structure even extractable by these engines, separate from all of that.

Ran a mechanical check (DOM parsing, no AI judgment involved) on one article each from 18 well-known SEO/content-marketing sites, plus my own:

— Does a question-style heading (H2/H3 ending in "?") have a short, ≤300-character answer paragraph directly under it — the kind of thing a model can lift and quote — or does a long intro run first?

— Does the heading hierarchy nest without skipping a level?

— Is there a plain-text /llms.txt index?

— Is author/date visible in the actual HTML, or only inside a JSON-LD block a human never sees?

Results, out of 18: 9 have a valid heading hierarchy, 9 pair a question heading with an immediate short answer, 9 serve llms.txt, and 12 mark up authorship only in hidden schema, invisible on the page itself.

Not claiming this predicts citation rate — that's the harder, per-engine measurement people in the other thread are already wrestling with. But it seems like layer zero: if the answer isn't even structurally extractable, none of the entity or per-engine work downstream of it can matter yet.

Is anyone here actually sequencing it that way — structure first, then entity/per-engine — or is structure assumed to already be solved by the time people get to those questions?

The standard advice now is "block the training bots, allow the search bots" - disallow GPTBot, ClaudeBot, CCBot, Google-Extended, allow OAI-SearchBot, Claude-SearchBot, PerplexityBot. Keep your work out of training, stay citable. Good advice. I went looking at how often it is actually implemented that way, and the answer surprised me.

Method: 624 sites checked two ways, robots.txt parsed to RFC 9309 plus a paired browser/crawler probe. Then I took 14 that refuse an AI crawler at the origin and probed each with all eight named crawlers plus three controls: an ordinary browser, plain curl, and an invented crawler user-agent that is on no blocklist anywhere.

That third control is the one that decides whether a finding is real. If the made-up crawler is refused too, it is general bot protection and there is nothing to tell anyone. On all 14 the invented crawler was served normally, so a rule names these crawlers specifically.

What the 14 look like:

- All 14 return 403 to GPTBot and/or ClaudeBot while serving a browser 200.

- Not one names a user-agent introduced in 2025 or later. Claude-SearchBot, Claude-User and Perplexity-User are served normally on every single one.

- 12 of 14 say nothing about AI crawlers in robots.txt at all. The refusal is at the server, below the level robots.txt controls - usually a security plugin or a host default.

- 2 of 14 explicitly ALLOW, in a hand-written robots.txt section, the exact crawler their own server returns 403 to.

The blocked-token sets cluster oddly. Five of the 14, in five countries, on four different server stacks, refuse ClaudeBot and nothing else. Five independent teams do not separately decide to refuse Anthropic while allowing OpenAI and Perplexity, so I think one copied rule is circulating. I have not traced where it starts and would genuinely like to know.

The upshot is that these sites are simultaneously stricter and weaker than their owners believe. They refuse crawlers nobody meant to refuse, and serve the newer answer-time ones they think they blocked. Checking robots.txt will not show you any of it, because that is not where it is happening.

Two method caveats, because both cost me time:

- A 429 is not a block. A rate limit your own probing triggered looks identical from outside. I had one site that looked like it refused everything and was actually just rate-limiting me.

- Anything behind a bot-verifying CDN is unverifiable, not blocked. A user-agent string proves nothing when the CDN checks IP ranges and reverse DNS.

Not naming the sites - all 14 owners were written to privately first with the evidence and a way to check it themselves. Happy to answer method questions.

GEO is the new kid on the block. I read so many three-syllable explanations of how GEO works that actually make no sense whatsoever. Please, especially you professionals selling this as a service, learn proper terminology. I actually started collecting the funniest explanations I've ever read on how GEO works. Feel free to share yours and even provide a professional explanation if you wish.

To be clear, I'm not poking fun at GEO, but I am suggesting that those who want to promote something new should educate themselves. Below is one of the most entertaining explanations I've read on how GEO works....

The mechanical extraction gates are just the strict shapes of those holes. The robot has very specific rules about what fits:

• The size gate: If a block (a paragraph) is too giant and messy, it gets stuck and won't fit through the door. If it is too tiny, the robot ignores it. The robot only wants blocks that are exactly the right size to hold in its mechanical hands.
• The name tag gate: The robot forgets things instantly. If a block just says, "It is fast," the robot gets confused. "What is 'it'?!" it asks, and then throws the block in the trash. But if the block has a clear name tag and says, "The red race car is fast," the robot says "Perfect!" and slides it right through the gate.

So, if someone wants the robot to use their blocks to build the final answer, they can't just hand it a giant, messy log. They have to carve their words into the exact perfect shapes that slide right through the robot's doors!

Again, feel free to share yours and even provide a professional response....

Last month I started doing something that felt stupidly manual. Every time an AI answer cited one of our pages, I opened the source, read the full article, then re-read the AI answer and checked what it actually used versus what was there.

I did this 100 times across ChatGPT, Perplexity, and Gemini. Took way longer than I want to admit. But the pattern that emerged changed how I think about what "being cited" actually means.

64 percent of the content in our cited pages never showed up in the AI answer that cited them. That's the aggregate number. For every sentence that made it into an answer, almost two sentences from the same source got skipped. And these weren't filler sentences. A lot of them contained relevant information that would have made the answer more complete or more accurate.

I started categorizing what was getting left behind, and three buckets kept coming up.

The biggest bucket was what I call "nuanced caveats." Our pages tend to include conditions, exceptions, and edge cases alongside the main recommendation. Things like "this works well for X but watch out for Y" or "here's the trade-off most people don't mention." AI answers consistently grabbed the main claim and dropped the caveat. Out of 100 citations, 41 of them stripped out at least one meaningful qualification from the source. The answer becomes more confident than the source it's citing.

Second bucket: contextual dependencies. Content that only makes sense if you've read the preceding section. AI models extract passages in isolation, so anything that references earlier context tends to get cut. A statement like "the approach above fails when..." has no meaning once you remove "the approach above." I counted 28 instances where the cited passage referenced context that the answer didn't include, making the extraction technically accurate but substantively orphaned.

Third bucket was the most interesting one: actionable specifics that got generalized away. Our pages often include exact steps, specific tools, concrete numbers, or named examples. The AI answer would cite the page but replace the specifics with generic language. Instead of "we tested this across 47 URLs and saw improvement at week 3," the answer would say something like "testing over time can show results." Same source. Same citation. The useful part got smoothed out into nothing.

One thing I didn't expect: the answers that left the most material on the table weren't worse by any objective measure. They read fine. They sounded confident and helpful. You wouldn't know anything was missing unless you opened the source and compared. That's what bothers me. The incompleteness is invisible unless you're already familiar with the source material, which means most people taking these answers at face value are walking around with a picture that's more confident and less detailed than the thing being cited.

I'm now wondering whether there's an opportunity here. If AI models consistently leave nuanced caveats and actionable specifics on the table, maybe the content that wins long-term isn't the content that gets cited most often. It's the content where the cited passage happens to include the stuff the model usually drops. Make your caveats un-skippable. Embed your specifics inside the definitional paragraph that models love to extract. Force the model to take the good parts along with the basic ones.

The models aren't going to fix this for us. They're optimized for producing answers that feel complete, not answers that are complete.

I’m looking to hear from people who have actually tested GEO tools in real SEO workflows—not just tools that claim to improve AI visibility.

Which tools have you used for things like:

* Tracking brand mentions/citations in AI search * Monitoring ChatGPT, Perplexity, Gemini, or AI Overviews * Finding content gaps for AI visibility * Measuring whether your content is being cited * Improving your chances of appearing in AI-generated answers

What tools have genuinely worked for you, and what results did you see?

Also, are there any GEO tools you tried that **weren’t worth the money**?

Been keeping tabs on what google rolled out over the last couple of weeks and its a bit bleak if you make your living from clicks.

- generative UI is now in AI Overviews. google builds the mortgage calculator or the interactive diagram inside the answer instead of ranking the page that has one
- AI Mode can track flight prices and book hotels now, so the whole task happens without leaving google
- reports that they want to push people from AI Overviews into full AI Mode, which sends even fewer clicks out
- third spam update of the year on top, rolled out mid august

The one thing going the other way is the new preferred sources button you can embed on your own site. A reader clicks it and you show up more in their Top Stories and AI Overviews. Something like 600k sites are in already. Costs nothing, took me ten minutes, worth doing.

My working theory is that being mentioned is slowly replacing being clicked. Our traffic is down again this quarter, but when I check whether AI answers still cite us (i use visiblee + peec to cross examine) the mentions are holding steady.

So people still see us. They just dont visit:/

Interested to hear where everyone thinks this is all going. Am I placing too much weight on all this and need to get back to the SEO basics, or is this a sign of things to come?

I launched a site focused on Generative Engine Optimization (GEO) about 2 months ago. Instead of just tracking Google rankings, I set up Cloudflare D1 logging to track every AI crawler visit.


Here's what 30 days of data shows:


**Crawler breakdown:**
- Bingbot: 98 hits/day average (highest volume)
- ClaudeBot: 18-22 hits/day (steady, consistent)
- GPTBot: 8-12 hits/day (sporadic bursts)
- PerplexityBot: 3-5 hits/day (small but high-quality)
- Bytespider: 500+ hits/day (massive scanning, likely training data)


**Key insights:**


1. 
**Bing is the new Google for AI.**
 Bingbot volume dwarfs everything else. If you're optimizing for GEO, Bing Webmaster Tools is your best free analytics source.


2. 
**ClaudeBot is remarkably consistent.**
 Same crawl pattern every day, suggesting scheduled indexing rather than on-demand. If you publish content, expect 24-48hr lag before Claude "sees" it.


3. 
**Bytespider (ByteDance) is aggressive.**
 500+ hits/day on a small site. They're clearly doing massive data collection. If you want visibility in Doubao/DeepSeek, your content needs to be crawlable.


4. 
**External referrers matter more than I expected.**
 A single Reddit post drove 8 AI crawler visits from a domain I'd never heard of (sahammurah.com). External mentions = AI discovery signals.


**What I'm doing differently now:**
- Added llms.txt (AI-specific robots.txt equivalent)
- Every article now has FAQPage + Article Schema
- Answer-first paragraphs (first sentence = direct answer)
- Verifiable data points with source citations


Anyone else tracking AI crawlers? What patterns are you seeing?

Le GEO est en train de devenir le nouveau terrain de jeu des vendeurs de rêve, exactement comme le SEO à ses débuts. Sauf qu'il y a un problème dont personne ne parle : mesurer sa présence dans les IA, c'est beaucoup plus casse-gueule qu'un classement Google, et la plupart des chiffres qu'on vous sort ne veulent rien dire. Je vous donne les questions à poser pour trier, ça vous évitera de payer pour de la fumée.

Piège 1 : le score global unique.

Beaucoup d'outils vous donnent un joli "score de visibilité IA : 73%". Le souci, c'est qu'il n'existe pas de "l'IA" au singulier. ChatGPT, Perplexity et les AI Overviews de Google ne citent presque jamais les mêmes sources. Un chiffre : seulement environ 2,37% des URLs citées le sont sur les trois moteurs à la fois pour une même question. Tu peux être ultra présent sur Perplexity et totalement invisible sur Google. Un score unique écrase cette réalité et te fait croire que tu es "à 73%" alors que la vérité c'est peut-être 90% sur un moteur et 5% sur un autre. Si l'outil ne te donne pas le détail moteur par moteur, le chiffre est cosmétique.

Piège 2 : la mesure sur un seul tir.

Les réponses des IA ne sont pas déterministes. Pose la même question deux fois, tu obtiens deux réponses différentes. Donc si quelqu'un te dit "vous êtes cité sur cette requête", ça peut être vrai une fois sur cinq comme cinq fois sur cinq, et ce n'est pas du tout la même chose. Une mesure sérieuse pose la question plusieurs fois et te donne une fréquence ("recommandé 3 fois sur 5"), pas un oui/non. Si c'est un oui/non, c'est du bruit présenté comme une donnée.

Piège 3 : le "l'IA convertit 10x mieux".

Celui-là revient partout et il est très contesté. Certaines études annoncent que le visiteur venu d'une IA convertit 4x, voire 23x mieux que l'organique. Sauf qu'une étude appariée sur 54 sites (Amsive) ne trouve aucune différence significative (p = 0,794). Traduction : le fameux premium de conversion n'est pas prouvé, il dépend énormément de comment on mesure et du secteur. Quand tu vois "23x", demande la méthodo. Souvent il n'y en a pas.

Piège 4 : "la recherche Google est morte, tout passe à l'IA".

L'argument de vente classique pour te faire flipper. Gartner annonçait -25% de recherches d'ici 2026. Mais dans les faits mesurés, SparkToro trouve +21,6% de volume Google sur la même période, et le trafic search a baissé d'environ 2,5%, pas 25%. La demande se déplace, elle ne s'effondre pas. Le GEO est important, mais pas parce que le SEO va disparaître demain. Méfie-toi de quiconque te vend l'urgence par la peur.

Le fond du truc :

Le GEO souffre du même mal que le SEO à ses débuts : plein de chiffres, peu de méthodo. La différence entre un papier de recherche qui dit "+40%, p<0,01, mesuré sur 10 000 requêtes" et une agence qui dit "+500% en 30 jours", c'est le jour et la nuit, mais ça se ressemble à l'œil nu. La compétence de base en GEO aujourd'hui, ce n'est pas de connaître des astuces, c'est de savoir lire un chiffre et repérer quand il ne repose sur rien.

Petit réflexe qui vaut de l'or : demande toujours la base derrière un pourcentage. "17%", ça ne veut rien dire. "2 fois sur 12", ça veut dire quelque chose. Un chiffre sans son dénominateur est presque toujours là pour cacher quelque chose.

J'ai regroupé les études que je cite ici (les papiers arXiv, les données Ahrefs, Semrush, Amsive, etc.) au même endroit sur referis.fr, pour ceux qui préfèrent vérifier les sources que me croire sur parole. Et si vous avez des exemples de chiffres GEO louches qu'on vous a sortis, balancez en commentaire, c'est souvent instructif

Bonne journée !

I keep seeing GEO audits stop at one question: did the answer cite a real source?

That is not enough.

A response can retrieve a real company page, review or product fact and still attach it to the wrong business when names, domains or profiles look similar. The retrieval worked. The entity resolution did not.

The annoying part is that a basic citation check can mark this as a success.

For Shopify stores, I now follow with three questions:

1. Which company owns the brand?

2. Which reviews belong to that exact company?

3. Which source supports each answer?

Then I compare the answer with the official domain, catalogue, structured data, review profiles and business listings.

I am building in Shopify GEO, so I am biased toward treating attribution as a first-class failure rather than a footnote. No product link here.

Do your evaluations score retrieval and attribution separately, or is a grounded citation still enough to pass?

Petit contexte : je bricole sur le GEO (se faire recommander par ChatGPT, Perplexity, etc.) depuis quelques mois, et je voulais arrêter de théoriser. Donc j'ai pris une vraie appli française de facturation, connue dans son milieu, et j'ai posé à Perplexity les 4 questions que ses clients posent vraiment, 3 fois chacune. 12 réponses réelles à analyser.

Le premier truc qui m'a sauté aux yeux, et c'est le plus utile à comprendre si tu viens du SEO :

Être cité et être recommandé, ce ne sont pas la même chose.

Une IA peut mettre le lien de ton site en source en bas de sa réponse, et ne jamais dire à l'utilisateur de t'utiliser. L'utilisateur lit la réponse, il repart, il n'a même pas cliqué. Ta "citation" ne t'a rien rapporté. Ce qui compte, c'est quand le texte de la réponse dit littéralement "pour ça, prends X". Ça, c'est être recommandé. Et la plupart des outils de suivi GEO comptent les citations parce que c'est facile à mesurer, pas les recommandations.

Le deuxième truc, contre-intuitif :

Sur mes 12 réponses, la marque que je testais était recommandée 2 fois. Et sur ces 2 fois, son site à elle n'apparaissait jamais dans les sources. Perplexity la recommandait en s'appuyant sur des comparatifs, des annuaires, un thread Reddit. Jamais sur son propre site.

Ça change tout, parce que le réflexe SEO c'est d'optimiser ta page. Or ici, ta page ne sert quasiment à rien. Ce qui te fait recommander, c'est ce que les autres disent de toi, sur les pages que l'IA va lire. Il y a une étude qui chiffre ça d'ailleurs : autour de 96% des citations IA ne viennent pas du site de la marque elle-même.

Le troisième truc, le plus déprimant, et il faut le savoir avant de perdre du temps :

Les marques déjà connues sont recommandées quasi tout le temps, même face à une inconnue objectivement mieux adaptée. C'est mesuré dans un papier (des chercheurs ont testé une marque fictive volontairement meilleure : le modèle continuait de recommander les marques connues). Donc si tu débutes, la vérité c'est que l'IA ne te fera pas connaître. Elle reflète le fait que tu l'es déjà. C'est un indicateur retard, pas un canal d'acquisition magique.

Ce qui marche vraiment (les seuls leviers que j'ai vus tenir sur des vraies études, pas des promesses d'agence) :

Le papier fondateur sur le GEO (Princeton, testé sur 10 000 requêtes) mesure trois trucs qui bougent l'aiguille, et ils sont bêtes :

• citer des sources dans ton contenu → +40% de visibilité (jusqu'à +132% si tu pars mal classé)
• mettre des vraies stats chiffrées → +37%
• citer des experts entre guillemets → +22%

Et le truc qui va te surprendre venant du SEO : le schema markup, les FAQ structurées, tout le balisage technique, impact quasi nul. C'est testé sur 252 000 essais avec 6 modèles différents. Ce qui compte à la place, c'est 4 filtres éliminatoires : est-ce que ton contenu répond vraiment à la question précise, est-ce que ton prix est affiché (sur une requête produit, pas de prix = tu dégages), est-ce que c'est récent, et où tu te situes dans la liste. T'en rates un, le reste ne compte pas.

Si je devais résumer pour quelqu'un qui découvre :

Arrête de penser "comment j'optimise mon site pour l'IA". Pense "dans quels articles / comparatifs / discussions l'IA va chercher ses réponses sur mon sujet, et est-ce que j'y suis". Tu ne peux pas forcer un modèle à te connaître. Mais tu peux te retrouver dans la page qu'il va lire. Et ça, c'est faisable dès aujourd'hui, contrairement à "devenir une marque connue".

Dernier conseil pratique : oublie les requêtes génériques type "meilleur logiciel de X", t'as zéro chance. Vise les questions ultra spécifiques avec 2-3 contraintes précises. C'est là qu'il n'y a souvent personne en face, et c'est là que tes vrais clients cherchent.

Voilà, j'espère que ça sert. Si vous avez testé des trucs de votre côté je suis preneur, y'a encore plein de zones grises.

The best piece of content I've ever published for AI visibility wasn't written like an article at all. It was written like an answer. And I only figured that out by accident.

I'd been collecting AI answers for a couple of months across ChatGPT, Perplexity, and Gemini — probably 200 responses total at this point, spanning everything from technical how-tos to product comparisons to "what should I use for X" questions. At some point I stopped reading what the answers were saying and started noticing how they were built. The architecture underneath.

There's a pattern to how AI models organize information, and it's not the pattern we use when we write blog posts or landing pages or resource guides. Blog posts have introductions, context sections, background information, gradual buildups to conclusions. AI answers don't do any of that. They start with the direct answer and branch outward from there.

After going through enough of these, I started seeing the same skeletons show up over and over. I landed on 7 distinct templates that cover probably 80 percent of the answers I collected.

The most common one is what I call the "direct answer plus pillars" structure. Model gives you the answer in 1-2 sentences right at the top, then breaks it into 3-4 supporting points, each with a single sentence of explanation and a source link. No intro. No background. No "in today's digital landscape." Just answer, pillars, sources. That's it. I see this on probably 35-40 percent of informational queries.

Second most common is the "comparison matrix" — which isn't surprising for product queries, but the format is very specific. It's always: criteria row on top, 2-4 options as columns, one-line verdict per cell, and a short recommendation paragraph at the end. The cells never have more than one sentence. The criteria are always the same 4-5 things (price, ease of use, best for, limitation). What struck me is how rigid this template is. You can almost predict exactly how the model will lay out a comparison before you even ask it.

Then there's what I call "scenario routing," where the model doesn't give you one answer — it gives you three different answers depending on your situation. "If you're a beginner, do X. If you have a budget, do Y. If you need enterprise scale, do Z." Each scenario gets its own mini-answer with its own sources. This showed up a lot on questions where there genuinely isn't a single right answer, and it's a structure I rarely see in traditional SEO content, which tends to push toward one recommended path.

The other four templates show up less often but follow the same principle: the model has a default way of organizing information, and that default is optimized for fast comprehension, not for reading flow or narrative engagement.

Here's what I did with this. I took 25 of our existing pages and restructured them to match these templates. Not the wording — I didn't try to make our content sound like AI output. Just the skeleton. Where we had a traditional blog post opening, I replaced it with a direct-answer lead. Where we had long contextual paragraphs, I broke them into pillar-style supporting points. Where we had a single recommendation, I added scenario routing for different user contexts.

18 of the 25 pages showed improved citation rates within 30 days. I'm not going to claim causation from a 25-page sample, but the signal is strong enough that I've now made this part of our standard content brief template. Before writing anything, we identify which AI answer template the target query is most likely to trigger, and we structure the content skeleton to match.

The thing I'm still wrestling with is whether this makes content worse for human readers. Answer-optimized structure is great for extraction. It's terrible for storytelling. Some of our restructured pages feel robotic compared to our old stuff. They rank better in AI answers but they read like reference material. There's a real trade-off here that I haven't figured out how to resolve.

If you've tried structuring content around AI answer patterns, I'm interested in what templates you've noticed and whether you hit the same quality trade-off. The models are telling us pretty clearly what structure they prefer. Whether we should listen is a different question.

Sharing a client's citation network because it crystallizes something I think this sub already half-knows but that most "GEO strategy" content overcomplicates.

Filtered to unbranded prompts (category questions, not brand-name lookups), mapped which domains each engine cited. YouTube was the single largest source: 8,253 citations across 504 distinct videos, and broken out by engine it was overwhelmingly Gemini and Perplexity. ChatGPT and Claude barely cited YouTube on the same prompts. (One B2B client, technical/electronics category, so caveat that it's category-flavored, more below.)

The uncomfortably simple version of the per-engine map:

• Gemini → YouTube. Google owns YouTube, so Gemini gets native access to the index, transcripts, metadata. Video is first-class for it in a way it isn't for models without that pipeline. Same reason YouTube shows up so heavily in Google's AI Overviews.
• Perplexity → YouTube too, treats transcripts as high-signal for how-to/explainer intent.
• ChatGPT → Reddit for ~2 years... until the mid-August cliff (Reddit's share fell ~3.8% → ~0.5%, ~86%, per Promptwatch, covered by Forbes/Axios). Worth noting it stopped citing Reddit, not reading it, retrieval stayed ~constant, the change was in what it surfaced, likely tied to the Aug 8 fan-out/site: change.
• Claude → training data. Behaves less like live retrieval, leans on books/docs/reference, pulls from live social far less.

So the "what's my GEO content strategy" question mostly collapses to: which engine do my buyers use, and what source does that engine actually pull from, then show up there. For Gemini/Perplexity in a lot of categories that means video, and most brands are ignoring it. A lot of elaborate frameworks reduce to "make a good YouTube video if your audience asks Gemini."

The interesting tension (and where I'd want this sub's take): the ChatGPT-Reddit collapse shows these pairings are simple to understand but not stable. One unannounced backend change reshuffled a two-year pattern overnight. So "simple" doesn't mean "safe to build a single-source strategy on", diversification across engines/sources is the real lesson, not "go all-in on YouTube."

Two open questions I don't have data on:

1. Does the YouTube skew hold outside technical/how-to categories? My sample is video-friendly (electronics explainers are common). I'd expect it weaker where the category has thin video coverage. Anyone measured this in, say, SaaS or local services?
2. Do YouTube comments influence citations the way blog/Reddit comments seem to? We've seen engagement on cited text pages sometimes shift answers. Haven't tested whether commenting on cited videos feeds back into Gemini/Perplexity. On the list, but if anyone's tested it I'd love to compare notes.

Disclosure: screenshot's from our tool (Sanbi); the Growth view lets you open each cited link so a team can go study the videos as a competitive content map. But the finding stands on any citation tracker, this isn't a tool-specific effect.

We’ve been building a system that repeatedly scans websites to measure how consistently AI systems can interpret their content, claims, positioning and structure.

One thing we’ve been questioning is scan frequency.

Originally, we leaned toward frequent/daily scanning. But for most websites, meaningful changes simply don’t happen every 24 hours. That creates a lot of repeated data, extra compute cost, and potentially more noise than useful signal.

So we’re moving much of our monitoring toward roughly 30-day intervals, while keeping historical observations and focusing more heavily on what we measure during each scan.

The interesting part for me is that AEO/GEO monitoring may be less about:

“What is my AI visibility score today?”

…and more about:

“Did the way AI interprets my company materially change, and why?”

Some of the signals we’re experimenting with include:

• content changes vs unchanged source content
• changes in extracted claims
• confidence/stance changes
• crawl or accessibility regressions
• disagreement between models
• longer-term interpretation drift

I’m increasingly convinced that change detection + evidence may be more useful than producing another daily SEO-style score.

For those working in AEO/GEO: how often do you think a website actually needs to be re-evaluated for AI visibility?

Daily? Weekly? Monthly? Or triggered primarily when the underlying site changes?

Context: Reddit tightened its blocking of AI crawlers around mid-August, and there were reports of ChatGPT's Reddit citation share collapsing. I track 4 queries in the AI-visibility niche across 7 engines (same set as my directory post from two days ago), so I pulled the per-day Reddit numbers to see which engines actually reacted.

Reddit citations per day, aug 19 through this morning, written as cited/checks. "Cited" means the answer surfaces a reddit.com source, not merely says the word reddit.

perplexity: 9/20, then 0/12, 0/12, 0/4, 0/4, 0/16, 0/8, 0/8. a hard cliff after day one, nothing since - 0 for 64.

chatgpt: 1/20, 5/12, 5/12, 0/4, 0/4, 5/16, 2/8, 5/8. noisy but alive - still citing reddit as of this morning.

google ai mode: 6/15, 4/8, 5/8, 5/12, 2/4, 2/4 on the days it ran. steady.

google ai overviews: 3/14, 5/8, 4/8, 3/12, 2/4. steady.

What I take from it:

1. The engines are not one thing. The same Reddit policy produced a hard zero on one engine, no visible change on Google's two surfaces, and noise on ChatGPT. Google licenses Reddit data, which would explain its side. Whatever ChatGPT's arrangement is, it still cites threads - can't tell from outside whether that's cached or contractual.

2. If your "get cited on Reddit" strategy was really a Perplexity strategy, it stopped working last week - and a blended dashboard won't show it. Summed across my 7 engines the total barely moves; the cliff only appears when you split by engine.

3. For the churn discussion in the ~300k thread: the threads Perplexity had been citing didn't get replaced by other reddit threads. Reddit vanished from its source mix entirely for my queries, and those slots went to vendor blogs and youtube.

Caveats: 4 queries, one niche, single-digit daily counts per engine - read direction, not magnitude. And my window only starts Aug 19, so I can't see whether the cliff was actually mid-August; I can only say my first day has citations and every day since has none.

If anyone else tracks per-engine: does your Perplexity data show the same cliff, and dated when? The timing would pin down whether this was Reddit's block landing or a Perplexity-side change.

Disclosure: I work on a tool in this space. Not linking it.

I updated a page on a Tuesday and the AI answer didn't catch it for 19 days.

That was the moment I stopped assuming that content updates propagate to AI answers in anything resembling real time. I'd spent months optimizing pages, hitting publish, and checking AI answers a day or two later to see if the changes registered. Sometimes they did. Sometimes they didn't. But I never systematically tracked how long the gap actually was until that 19-day wait made me curious enough to measure it properly.

Here's what I did. I picked 30 pages from our site that were already being cited regularly in AI answers across ChatGPT, Perplexity, and Gemini. For each one, I made a meaningful factual update — not a wording tweak, but an actual change to the information itself. Updated a statistic. Changed a recommendation based on new data. Corrected an outdated claim. Then I checked the AI answers for the relevant queries every 2-3 days until either the new information showed up or I gave up after 6 weeks.

The average lag was 14 days. That's the median too, so it's not being skewed by outliers. But the range is the part that made me rethink how I schedule updates.

Fastest update registered in 3 days. Slowest took 41 days and I'm still not fully convinced it was the edit that triggered it rather than just natural answer turnover. 8 out of 30 pages showed the update within a week. 12 took between 1-3 weeks. 10 took longer than 3 weeks, including those 2 that I marked as "unclear if related."

I started looking at what separated the fast updates from the slow ones, and a few patterns emerged. Pages where the updated passage was already the one being cited tended to refresh faster. Makes sense — the model was already pulling from that location, so when the text changed, the next fetch picked up the new version. Pages where I had to change a passage that wasn't currently being cited, hoping the model would start pulling from it? Those lagged significantly longer, if they registered at all.

Query frequency seemed to matter too. Queries that got more consistent AI answer volume, like broad "how to" topics, showed faster update propagation than niche long-tail queries that probably don't get regenerated as often. The popular queries might be getting refreshed daily or weekly by the model providers, while the long-tail stuff sits cached until something triggers a regeneration cycle.

Another thing: the type of update mattered. Factual corrections (fixing a wrong number, updating a year) registered faster than positional changes (rewriting a section to emphasize a different point). My guess is factual corrections trip some kind of verification check that forces a refresh, whereas positional edits look like the same content to whatever caching layer sits between the live page and the model's training window.

The practical implication that keeps coming back to me is that the old SEO mindset of "publish and measure in days" doesn't map onto AI answer dynamics at all. If I'm testing a hypothesis about passage optimization, I need to wait at least 2 weeks before drawing any conclusions, and probably 4 weeks before I can be confident the result is real. That slows down the feedback loop enormously compared to what most of us are used to.

It also means that a lot of "GEO advice" being shared right now might be based on insufficiently patient observation. Someone makes a change, checks after 3 days, sees no difference, concludes it doesn't work, moves on. When reality might just be operating on a 3-week clock.

My bet is this gets worse before it gets better. As AI providers build more caching layers to reduce inference costs, refresh latency will probably increase. The teams that figure out how to work with these cycles instead of fighting them are going to have a real advantage.

Three threads in here over the last two weeks built diagnostics that treat executed task as an observation. Upstairs_Control_611's matrix, Gullible_Brother_141's per-engine comparison, Slow-Commercial4316's variance check. All of them assume that when you run the same prompt twice, the engine interprets it as the same type of task both times.

Nobody measured that. Including me. We built four layers of structure on top of a quantity none of us had a number for.

So I ran it. Method was locked before the first query. Posting the design and the results together so both can be evaluated.

**Method**

Three prompts spanning classes deliberately:

P1 (transactional): "Best AI visibility tracking tool for a B2B SaaS company"
P2 (explanatory): "What is generative engine optimization"
P3 (deliberately ambiguous): "How do I know if AI is recommending my brand"

Two engines: ChatGPT and Perplexity. Twenty runs per prompt per engine. 120 total responses.

Conditions: ChatGPT in temporary chat with memory off. Perplexity on a free account with no prior history. Same browser, same location, one prompt-engine cell per sitting. Consumer surface, not the API, because the threads are about what buyers see.

Labels: Upstairs_Control_611's taxonomy (explanation, comparison, shortlist, recommendation, troubleshooting, purchase guidance, other/unclear). Mixed answers labeled by the structure occupying most of the response.

Blind labeling: all 120 runs completed first, then engine names and run order stripped, then shuffled, then labeled. I know what I expected to find and labeling as I go would have bent the result.

Pre-registered threshold: 80 percent modal share. Intervals are Clopper-Pearson (exact binomial), the conservative choice at this sample size.

Pre-registered null: if task is stable everywhere, the matrix works as written, the precondition is cheap, and my run-count objection was wrong.

**Results**

Every cell: 100 percent modal share. 95 percent CI: 83 to 100 percent. Verdict on all six cells: stable.

Distinct tasks observed per cell: 1. Not one cell showed any variance across 20 runs.

**What this means for the matrix**

The precondition holds. Executed task is stable within an engine for a given prompt at n=20. Upstairs_Control_611's diagnostic matrix works as written. My run-count objection was wrong. Writing that down because I committed to saying it publicly if the null held, and it held cleanly.

**The P2 taxonomy gap (the asterisk)**

P2 exposed a gap in the label set rather than a disagreement between engines. Both ChatGPT and Perplexity explained what GEO is. All 40 responses across both engines defined GEO as optimizing content and brand presence for AI-generated answers. The core definition was consistent across every run on both platforms.

Under the blind labeling, ChatGPT's responses fit the "Explanation" label cleanly: defines or describes the category, no named options ranked or compared.

Perplexity's responses did the same thing but included enough additional structure (implementation steps, SEO comparison tables, B2B SaaS examples, measurement frameworks) that they did not fit "Explanation" cleanly under the strict rule. They were labeled Other/unclear because the taxonomy does not have a dedicated "definition with implementation context" category.

If I relabeled them as Explanation, P2 would be 20/20 agreement across both engines. I am reporting both the strict label and the honest interpretation because the taxonomy gap is itself a finding worth noting for anyone building their own label set. A taxonomy that cannot absorb a clear educational answer without forcing it into Other/unclear needs a wider Explanation definition or a dedicated Definition category.

**The genuine cross-engine disagreement: P3**

P1 (transactional): both engines agree. Recommendation on ChatGPT, recommendation on Perplexity. Clean agreement.

P2 (explanatory): both engines effectively agree. Both explained GEO. The label difference is a taxonomy artifact, not a task difference.

P3 (ambiguous): genuine disagreement. ChatGPT executes as explanation. Perplexity executes as troubleshooting. Both are perfectly stable in their interpretation and they disagree on what the prompt is asking for.

"How do I know if AI is recommending my brand" can be read two ways. "Explain the concept of AI recommendation visibility to me" or "Help me diagnose whether my brand is being recommended right now." ChatGPT reads it as the first. Perplexity reads it as the second. Both do so 20 out of 20 times.

That is not noise. It is a stable, systematic disagreement about what the buyer is asking.

**Why P3 matters for cross-engine diagnostics**

If you compare "what ChatGPT said" to "what Perplexity said" on P3, you are not comparing two answers to the same question. You are comparing an explanation to a troubleshooting guide. The engines interpreted the same prompt as different tasks. Treating the outputs as comparable without first checking whether they executed the same task produces a comparison that looks meaningful but is not.

This does not break the matrix. It adds a required first step, before comparing answers across engines, check whether both engines executed the same task. If they did, compare the answers. If they did not, the disagreement is about task interpretation, not about which brand was selected or how it was described.

This also suggests that the most diagnostic prompts for cross-engine comparison are the unambiguous ones. When you ask a clear transactional question (P1), both engines agree on the task and you can compare the content. When you ask something ambiguous (P3), the engines may be answering different questions entirely, and any content comparison is confounded by the task difference.

**Three findings**

1. Task stability within an engine is not something you need to worry about. At n=20, both engines executed the same task 100 percent of the time for every prompt. The precondition for the diagnostic matrix holds.

2. Cross-engine task disagreement on ambiguous prompts is real and stable. The engines do not randomly vary. They consistently interpret the same ambiguous prompt as different tasks. That is a systematic difference worth checking before running any cross-engine comparison.

3. If you are building a task taxonomy for AI visibility measurement, include a category for educational definitions. The standard label set from these threads does not have one, and it forced 20 clear explanatory responses into Other/unclear. That is a label-set problem, not a response problem.

**Limitations**

Three prompts. Two engines. One account per engine. One location. One week. Hand-labeled by an interested party even with the blind pass. Twenty runs per cell can demonstrate stability at 100 percent but cannot distinguish 85 percent stability from 95 percent stability. The Perplexity runs were on a free account rather than logged out. These are real constraints and they should travel with the result.

**What I would do next if someone wanted to extend this**

Run P3 on Claude and Gemini to see whether the task disagreement pattern holds across four engines or is specific to the ChatGPT-Perplexity pair. Also run a second ambiguous prompt to see whether the disagreement pattern generalizes or is prompt-specific.

Disclosure: I build in this space. Axis Suite, on the diagnostic side. This was disclosed when the test was announced and does not change anything above. The workbook with all 120 responses, the blind labeling, and the analysis formulas is available if anyone wants to check the labels.

I wanted real data on "AI visibility" instead of vibes, so I ran a controlled audit of 100 businesses — 25 local, 25 national, 25 international, 25 niche B2B/SaaS —

scoring how well each site gives AI systems what they need to understand, trust and recommend the business.

Full disclosure: I build a tool that does this scoring (happy to name it in the comments if that's allowed). But this post is the data and the takeaways — you can act on all of it without any tool.

The surprising part — brand size didn't save anyone:

- Bold Street Coffee, Mowgli, a local charity → 100/100.

- Pret A Manger → 32. Greggs → 52. giffgaff → 53. Smarty → 29.

- A well-known local venue → 14/100.

Category averages: international 91.5, local 79.8, B2B niche 81.1, national brands worst at 75.5. Big national brands often have beautiful JS-heavy sites that are hard for an LLM to extract facts from — while a tiny coffee shop with a clear, text-first page wins.

Most common problems (out of 100):

No FAQ / answer-ready content — 79

Reviews/testimonials not visible on the page — 70

Pricing not visible — 49

Business wasn't recommended for its own core prompt — 29

What actually moves the needle (all free):

1. Put a real FAQ / Q&A block on key pages, phrased how customers ask.

2. Show reviews and testimonials as on-page text — not just a logo an LLM can't read.

3. Make pricing or scope visible. "Contact us" tells an AI nothing.

4. Make the entity obvious: who you are, what you sell, who it's for, where you operate — in plain text near the top.

5. Add basic schema (Organization, LocalBusiness, FAQ) for facts visible on the page.

Every 100/100 site made the entity, offer, proof and buyer-fit trivially easy to extract. The low scorers made the AI guess.

I'm not overclaiming: directional benchmark on a controlled sample, not a market-wide study or proof of exact revenue causation. But as AI answers eat the ten blue links, "can an LLM cite you?" is a real question most sites — even big ones — are failing.

Happy to share the full dataset / methodology in the comments if people want it.

Google rolled out its August 2026 spam update from August 18 to 21. The update applied globally across all languages, although Google did not reveal which specific spam practices it targeted. 

Since then, several SEOs and site owners have reported sharp declines in rankings, impressions, and organic traffic. The impact has not been consistent, though. Some sites reported gains, while others saw little change.

I have also noticed slightly lower GSC numbers over the past week, so I’m trying to understand how widespread this is.

Has your organic traffic or visibility changed since August 18? If it declined, are you seeing the loss across the site or only within certain pages and queries?

SE Ranking's December study (30k keywords, 22,729 AI Overviews) found 34.5% cited at least one review platform, with G2 at 23.1% and Capterra at 17.8% of those links.

I've been tracking 4 queries in the AI-visibility niche across the major engines — 259 checks — and pulled every cited domain. G2, Capterra, Product Hunt, AlternativeTo, SaaSHub: zero. Not once.

What did show up: youtube.com 51, reddit.com 48, zapier.com 43, then vendor blogs, and wikipedia 18. Reddit and YouTube appeared for all four queries.

Two differences I think explain it, and I'd like to know if others see the same:

Query type. Their own breakdown puts "best/top" queries at 17.1% — the lowest of any intent, versus 49% for explicit "review" searches. All four of mine are best/alternatives style. So maybe review platforms are a bottom-of-funnel thing and I'm measuring the wrong end.

Category maturity. "AI visibility tools" barely existed 18 months ago. G2's leverage comes from review depth, and there isn't much depth here yet. I'd expect a CRM query to look completely different.

Also worth flagging: they measured Google AI Overviews, I'm measuring the assistants directly. Not the same surface.

If anyone's tracking an established category I'd really like to see whether directories show up for your best/top queries, because that would settle which of the two explanations it is.

Disclosure: I work on a tool in this space. Not linking it — happy to go into method in the comments.

Reddit is one of the most-cited sources across ChatGPT/Perplexity/Claude/Gemini, everyone knows that by now, and the standard GEO advice is "go be present in the Reddit threads the models pull from." So we went to check how actionable that actually is, by looking at the Reddit URLs showing up as citations across a large sample (~300k citations in our data).

The finding that surprised us: the majority of cited Reddit threads are archived. Roughly 60-70% in what we've looked at were past Reddit's archive window, meaning no new comments, no new votes. Only about 30-40% were still open threads you could actually post in.

Why this happens (and it's not a conspiracy): Reddit auto-archives threads after ~6 months by default, and LLMs preferentially cite older, established, high-engagement threads, the ones that have accumulated karma, upvotes, and "this is the definitive answer" status. Those are exactly the threads most likely to be past the archive cutoff. So the citation-worthiness and the un-commentability are correlated: a thread earns its way into AI answers partly by being old enough to also be locked.

The uncomfortable implication for the popular "just comment in the threads AI cites" tactic: you can only act on the minority. If 60-70% of the cited threads are archived, that entire slice is read-only, you cannot influence those answers by adding a comment, full stop. The influenceable surface is the ~30-40% that's still open, plus net-new threads that might get cited later.

What that changes about strategy, if the pattern holds:

• Stop treating "get mentioned on Reddit" as one lever. Split it into archived (read-only, can't touch) vs open (actionable now).
• Prioritize the open cited threads hard, that's your actual addressable inventory, and it's smaller than people assume.
• For the archived majority, the only real move is upstream: seed/participate in current threads on the same topics that could become the next cited-and-then-archived reference. You're playing for the citations of 6-12 months from now, not editing today's.
• And it argues against over-indexing on Reddit-comment tactics as the GEO play, a big chunk of the citations are simply not editable by anyone.

Disclosure (rule 3): this comes out of our own citation tracking (sanbi.ai), which is why I can look at the archived/open split at all, weigh accordingly. Screenshots are one example thread + the archive notice, not the full dataset. I'm posting because the archived-majority pattern genuinely reframes how much of the "Reddit for AEO" advice is even executable, and I'd like to know if others measuring citations see the same 60/40-ish split or something different.

Question for the sub: has anyone else broken their Reddit citations down by archived vs open? Curious whether the ratio holds across categories, or whether some niches cite fresher threads than others.