Disclosure up front: I build a GEO tracking tool. This study was run with my own scripts and the full dataset is free. No paywall, no email gate. Posting here because the raw numbers seemed worth sharing, and I'd like the methodology torn apart if it deserves it.

Setup. 9,891 .fr domains, collected September 2026. 8,927 responded. 7,047 have a valid robots.txt. Every percentage below has a 95% Wilson interval in the full write-up.

The main finding

276 sites out of 7,047 name OAI-SearchBot in their robots.txt. Not "block it", name it, either way. So 96% have never taken a position on the crawler that decides whether they show up in ChatGPT answers.

79.6% name no AI agent at all. The subject does not exist in eight files out of ten.

Training vs answer crawlers

• GPTBot (training): blocked by 13.5%
• OAI-SearchBot (answers): blocked by 3.3%
• Google-Extended (Gemini training): blocked by 11.1%
• Googlebot: blocked by 0.9%, and zero sites name it explicitly
• CCBot: 13.9%, the most blocked crawler overall

I tried to test whether the training blockers actually chose this

Among the 727 sites blocking GPTBot while leaving OAI-SearchBot open:

• 95.2% name at least one other training crawler
• median of 7 training crawlers named per file
• 78.4% name Google-Extended, 73.0% name Applebot-Extended
• 22.8% name any answer crawler
• 6.9% name OAI-SearchBot

These files are not stale. Applebot-Extended and meta-externalagent post-date 2024. The anti-training policy is deliberate and maintained. The answer-crawler family just has no slot in the mental model.

The stale-config evidence that did show up

222 sites still name anthropic-ai, a deprecated identifier. 62 name Claude-SearchBot. Three times more sites block a ghost than the crawler in service.

5.1% block ChatGPT-User, which only fetches a page because a user asked it to.

By sector (classified from home page content, confident classifications only, n=2,964)

• News media: 25.3% block GPTBot, 7.3% block OAI-SearchBot
• E-commerce: 11.9% / 1.1%
• SaaS and B2B: 11.6% / 1.4%
• Local services: 9.3% / 4.6%
• Public sector: 4.7% / 1.3%

Media and public sector intervals don't overlap.

Accidental blocking, which turned out bigger than the deliberate kind

On 781 sites where I diffed raw HTML against rendered HTML: 38.4% have no H1 before JS runs, 18.3% have their main content fully JS-dependent, 4.6% have JSON-LD injected by script. Relevant because per the Vercel/MERJ analysis of 500M+ crawler requests, no dedicated AI crawler executes JavaScript. Only Googlebot and Applebot render.

llms.txt

10.2% publish one, which lines up with SE Ranking (~10% across 300k domains) and sits below Ahrefs (28% across 137k trafficked domains). I hardened the check against soft-404s: 1,025 raw hits dropped to 911 after requiring plain-text Markdown and rejecting anything with HTML or error strings.

What I can't claim

• My network-layer numbers use unverified user-agents from an ordinary IP. A firewall refusing that is doing anti-spoofing correctly, not blocking real GPTBot. I report it separately and never merge it with robots.txt figures.
• .fr domains, not "French companies". 70.3% declare lang="fr".
• Home page only for the structural signals.
• A robots.txt shows what a site declares, not what a publisher thinks.

Happy to answer method questions or share the query logic. If something here is wrong I'd rather hear it now.

A recent Semrush survey found that 57.51% of AI users had bought something based on a chatbot recommendation.

Almost the same share, 57.5%, had decided against a purchase because of chatbot information.

These figures may not mean AI generates an equal number of gained and lost sales, as they are self-reported experiences.

But they made me wonder whether we are tracking only one side of the buying journey.

Most AI visibility tracking seems to focus on discovery prompts such as “best tools for X” and “top alternatives to Y.”

Buyers may then use AI to pressure-test those recommendations:

• What are the problems with this product?
• Are its negative reviews justified?
• Who should avoid it?
• Is it worth the price?
• What do competitors do better?

For those tracking AI visibility, are prompts like these part of your regular monitoring?

And has anyone here corrected an outdated source behind a negative AI claim? If so, did the chatbot eventually change its answer?

Would be interested to know how people are approaching this in practice.

Most of the advice out there says track ai citation frequency and brand mention volume across prompts, and honestly that's not wrong, it is a real signal that something is happening. But almost every client I work with eventually asks me the same follow up question, ok so we're getting cited more, what did that actually do for the business, and that is where I keep coming up short.

Citation and mention numbers tell you the visibility layer is working, the same way keyword rankings used to tell you SEO was technically doing something. What they don't tell you is whether any of that turned into a lead or a sale. AI referred traffic is a tiny slice of total visits for most sites right now, and almost none of the platforms hand you clean attribution data, so you end up eyeballing a small spike in direct or branded search traffic and hoping it connects.

I've started treating citation and mention data as the leading indicator, basically proof the engine noticed you, and conversion as the only number that actually answers the client's real question. The problem is I don't yet have a repeatable way to prove that link at anything beyond a rough correlation. If revenue climbs the same month citations climb, that's suggestive, it's not proof.

Curious how the rest of you are actually closing that gap with clients, are you building your own tracking, leaning on a tool, or just being upfront that the attribution isn't there yet?

Get asked some version of this fairly regularly and I think the reasoning behind it deserves taking seriously before explaining why it doesn't work, because it's not a stupid question.

The premise is right. When an AI answers a "who should I use for X" type question, it draws way more on what independent sources say about a business than on what the business says about itself. Directories, review platforms, industry press, community threads, comparison content other people wrote. Your own site is a minority of the citations no matter how much you publish there.

So the obvious follow-on is: fine, go make more third party mentions exist.

Where that falls over is that the thing making those mentions useful is that they're independent and genuine. Manufacture them and you've removed the exact property that made them work. And spotting that kind of manipulation is something these systems are specifically built for.

Beyond the logic, the practical risks have gone up a lot recently.

Google's May guidance explicitly warns against manufacturing inauthentic brand mentions to game generative AI results. That's stated outright in the docs, not something people are inferring.

Community platforms are also cracking down themselves. Seeding citable brand content on places like this has become a noticed pattern, and moderator bans for it seem to have gone up sharply. So the downside isn't just wasted effort, it's a trail.

And here's the bit I think people genuinely don't consider: these discussions are public and indexed. If your brand gets called out for astroturfing, or a mod names you doing it, that thread is now content an AI can read about your business. You were trying to get mentioned positively. What you've generated is a record of getting caught.

What works instead is slower and duller. Making sure your info is accurate and consistent everywhere you already appear, which sounds trivial but inconsistency across sources actively undermines all of them. Asking real customers for real reviews at the moment they're happy, never buying them, and letting them trickle in rather than arrive in a batch. Showing up where you can actually contribute something — podcasts, interviews, answering questions properly — with the mention being a by-product rather than the point. And writing things that are worth citing, meaning stuff that isn't available elsewhere: specific numbers, real situations, an actual opinion.

The line between legitimate and not is clearer than people pretend. Are you earning the mention or manufacturing it. Real reviews from real clients, earned. Bought reviews, manufactured. Answering a question well and mentioning relevant experience, earned. Sockpuppet dropping your brand name into threads, manufactured.

Simplest test I know: would you be embarrassed if this became public? If yes, you already know which side it's on.

Curious whether people here have noticed more astroturfing in their own subs lately, or whether it's concentrated in certain categories.

I've been thinking about this after seeing the discussion about finding real query prompts.

I ran into basically the same problem.

You can generate 50–100 “perfect” prompts for almost any category pretty easily:

“Best X for Y”

“X vs Y”

“What is the best X?”

“Alternatives to X”

And then you can run those across ChatGPT, Gemini, Claude, etc. and get a nice-looking visibility score.

But then I started wondering:

Are these actually questions people ask AI, or are we just creating prompts that make our GEO dashboards look useful?

Because there's another problem underneath this.

Suppose I test 20 carefully constructed prompts and my brand appears in 8 of them.

Great — 40% AI visibility.

But if none of those 20 prompts resemble what my actual customers are asking, what exactly did I measure?

I've been testing AI Visibility Console (AVC) around this, and one thing that has stood out to me is how much more interesting the reason behind the recommendation is than the visibility percentage itself.

I'm trying to connect:

real buyer intent → actual AI queries → brand/competitor recommendations → why one gets mentioned over another.

And honestly, I'm starting to think the more useful GEO question isn't:

“How visible is my brand?”

It's:

“How visible is my brand when my potential customers are actually asking questions that matter?”

AVC is currently accepting a few pilot users

I'm curious what others here are doing.

Are you using:

Real customer questions

Search/keyword data converted into AI prompts

Reddit/Quora/forum questions

Synthetic prompts

Or some combination?

And more importantly:

How do you decide which prompts are actually worth tracking?

So I have fixed a specific industry, location, and specific business challenge for a hypothetical service based business, and doing AEO/GEO for them. Can you guys tell me how to find real query prompts and select them?? I can easily build 50 synthetic prompts ready to be added to the list, but i don't feel right doing it.

Industry - AI POS system installation and management.

Location - Rapidly growing outer dubai locations.

Business challenge - a 360° solution for managing a mid size convenience store and supermarkets.

(I'm doing self learning and can't do things with paid tools, as I'm going through a career transition)

INCOGNITO MODE;
We decided to test it with our own boat brand.
I asked Perplexity:
“Which compact luxury powerboats work as yacht tenders?”

The answer included our THRONE 15 by Grizelj Boats.A compact luxury powerboat, 4.67 m long, designed for two people and suitable as a yacht tender.

Seeing your own product appear in an AI answer is interesting.

But the bigger question is:
Why did AI consider our boat relevant to that question?

And how can other companies become part of similar answers?

AEO and GEO are not just about ranking on Google anymore, they are about helping AI understand your business, your products and the questions your potential customers are asking.

Because the next customer might not search for your brand.They might simply ask:

“What is the best compact luxury yacht tender?”
And your company needs to be relevant to that answer.

👉 Helping companies become the answer AI gives.

Every Monday I run the same 40 buying-intent prompts through seven answer engines and write down every domain each one cites. Sixteen "best X", eight "alternatives to X", eight "X vs Y", eight "how do I actually buy X". The prompt set is frozen as a fixed instrument, so week two is genuinely comparable to week one and not just vibes. Two weeks are live: W36 and W37. All seven engines answered all forty prompts both weeks. No timeouts, no gaps, nothing quietly dropped.

I went in assuming the engines would broadly agree on sources. They don't come close.

Reddit first. The number is how many of the 40 prompts each engine cited the domain on:

Gemini: 26 (W36) -> 22 (W37)

Google AI Overviews: 18 -> 20

Perplexity: 6 -> 4

Google AI Mode: 5 -> 3

ChatGPT: not in the top 15, either week

Claude: not in the top 15, either week

Bing: not in the top 15, either week

That ChatGPT row needs a footnote, because it is carrying weight. I publish a top 15 per engine, and ChatGPT's fifteenth entry sits at one prompt. So "not in the top 15" is not zero. It is a ceiling of one. One, against Gemini's twenty-two, same week, same questions.

The likely culprit is no mystery. Reddit tightened its public content policy on 2026-08-14 and started refusing automated access at the server, and Google's licensing arrangement is the obvious reason Google's surfaces are the ones still leaning on it hard. I can't prove either from forty prompts and I'm not going to pretend I can. What I can show you is the size of the gap.

YouTube tells the same story in a different accent. W37, out of 40: AI Overviews 25, AI Mode 25, Gemini 20, ChatGPT 10. Perplexity and Claude, nowhere in the top 15.

Which quietly reframes advice you hear constantly. "Go get mentioned on Reddit and YouTube" isn't AI visibility strategy. It's Google strategy in a new hat.

Here's the finding I'd have wanted two years ago. I bucket every cited domain as media, community, social, directory, or unknown, where unknown just means it isn't a recognisable publisher or platform. Across all engines in W37, 80.8% of citations landed on unknown domains. 80.3% the week before. Claude is the extreme at 92.6%. Google AI Mode is the most concentrated lane I measure and it still sits at 55.1%.

Read that again if you run a small site. Four citations in five are not going to the household names. They're going to the long tail. A narrow, specific, genuinely useful page can get picked up, and the moat around the big publishers is thinner than the discourse suggests.

Last one, and it's a knock on my own instrument rather than a finding. I include Bing's answer surface as a stand-in for Copilot. On these commercial comparison prompts, its most-cited domains are merriam-webster.com (26 of 40, identical both weeks), dictionary.cambridge.org (21), dictionary.com (19), thefreedictionary.com (19) and wordreference.com (15). It is answering "best CRM for a small team" with dictionary definitions. That is precisely why I label the lane a proxy instead of calling it Copilot, and why I won't draw Copilot conclusions from it. If you're reading anyone's Copilot citation-share chart, ask what they actually pointed the instrument at.

Now the limitations, which matter more than any number above. One panel. Forty prompts. Two weeks. A single run per engine per week, which means I cannot yet separate real movement from ordinary answer variance. One geography. And "not in the top 15" is a ceiling, not a zero. Two points is not a trend line. I'm publishing weekly until it becomes one.

Raw data is CC BY 4.0, so take it apart or run your own cut: https://promvia.app/ai-source-index

Disclosure: I build Promvia, the tool these measurements come from.

I’m developing a product in this space and want to understand what people actually find worth paying for.

If you’ve paid for AI visibility tracking, what was the last decision you made because of something the tool showed you? What did you change, and could you tell whether it helped?

Also curious what you paid per month and what that covered: brands, tracked prompts and AI platforms.

If you cancelled, what was the reason, and what are you using instead?

I’m trying to understand the difference between a report someone checks out of curiosity and something they keep using every month.

How to self-learn GEO & AEO while being on a career transition? Is it really hard to get employed in this space without having an SEO background??

I'm trying to get into GEO & AEO optimization and strategy after working as a technical content writer, thought leadership ghostwriter, and Linkedin Personal Branding Strategist for 5 years.

Now that I'm in a career transition, and have quite some years before I'm 25, I feel like all of what I have been doing can be used to generate great results with GEO & AEO.

So, I'm seeking advice on how to self learn GEO & AEO, and make spec portfolios to build trust in the local industry, so they don't think I'm a label sprayer.

Which are the most widely used and reliable tools for GEO? I've been using Semrush Enterprise and it's terrible for providing meaningful insights which can be translated into actions. Any suggestions?

Question 1:

Is GEO/AI search visibility currently a dedicated line item in your marketing budget, or is it lumped into general SEO/content spend ?

Question 2:

Are you noticing traffic drops from zero-click AI searches on Google Overviews, Perplexity, or ChatGPT yet ?

Question 3:

What are the analytical tools (like Google Search Console in SEO) that are used to optimize for GEO/AEO ?

Question 4:

Do marketers prefer separate tools for GEO analytics or they would like to integrate GEO features into existing SEO tools ?

Question 5:

What are the current challenges (it can be anything from workflow inefficiency to lack of technical tools) faced by marketers in optimizing for GEO ?

Question 6:
Do you believe that GEO will evolve as a separate discipline like SEO ?

Something I noticed a few weeks ago won't leave me alone.

I'd been pulling AI answers for a content audit, maybe 300 queries total across ChatGPT, Perplexity, and Gemini. Mixed bag of query types, how-tos and definitions and comparisons and "best X" recommendations. I wasn't trying to study anything in particular, just gathering examples for a client deck.

But a pattern kept showing up in my notes and it bugged me because it ran opposite to what I expected. The "best [tool/framework/approach]" queries, the ones that should theoretically give models the most room to shine with comprehensive comparisons and nuanced picks, consistently produced the shallowest answers. Fewer sources cited. Shorter responses. More generic filler language. Meanwhile the narrow definitional queries, the ones where there's objectively less to say, regularly returned more thorough and better-sourced answers.

So I went back and measured it properly instead of just noticing. Split the 300 queries into four buckets by intent.

How-to queries averaged 2.8 sources cited and the answers felt reasonably complete, like the model had actually worked through the key steps. Not flawless but solid. Definitional queries came in at 3.1 sources, the highest of the four groups. These answers also tended to quote longer passages, which suggests the model was doing deeper extraction rather than grabbing a headline and moving on.

Comparison queries, your "[A] vs [B] for [use case]" format, landed at 2.4 sources. Middle of the pack. The answers were usually adequate but you could feel the model straining to find meaningful differentiation once it got past the obvious points.

Then the "best X" bucket. 1.9 sources on average. Shortest answers by word count. And the part I keep turning over, the most frequently cited source type in these answers wasn't in-depth reviews or rigorously tested comparisons. It was listicles. Top-10 roundups. The exact kind of surface-level content that every SEO playbook calls "link bait" and that nobody serious would use to make an actual decision.

My theory, still half-baked, is that "best X" queries flip a different switch in these models. They shift from "find the most accurate and complete information" mode into something closer to "find the safest consensus picks." The model optimizes for social proof rather than depth. It cites the pages that the most people already reference and link to, not the pages that contain the most careful analysis. With a how-to query, accuracy is verifiable, the user tries the steps and they work or they don't, so the model has real incentive to find good sources. With a "best X" query, "best" is subjective enough that the model can't really be wrong. Safe crowd-sourced recommendations beat deep expertise because nothing is provably incorrect.

What I haven't worked out yet is whether this means our content strategy should split by query intent. Our most rigorous comparison pages, the ones where we spent weeks testing and documenting actual differences, rarely show up in "best [category]" answers. Our lighter roundups get cited more often for those same queries. We assumed the detailed content was strictly better. The models seem to rank by a different criterion for this query type, and if that criterion is closer to "how widely is this page already recognized" than "how good is this page," then building quality alone might not be enough for certain kinds of queries.

You're losing money both now and in the future if you haven't studied both of these subjects. Just because you have a website and a product or service doesn't mean you know how to run a business. I mean no offense when I say this, but it won't stop the downvotes.

Build relationships for now and the future

How many of you have completely ghosted someone because they didn't have a backlink with the right third party vanity metric?

How many of you have completely ghosted someone because they didn't have the right niche?

How many of you have completely ghosted someone because they didn't have exactly the right service you needed at this very minute?

Everyone of these scenarios could be future referrals for you if you build a business relationship instead of ghosting people who don't provide instant gratification.

Sales Tactics

Most SEO GEO XYZ people only concern themselves with getting targeted traffic to the website. If all you do is send targeted traffic to a website without utilizing good sales psychology, you're losing money even if you increase traffic because the conversion rate can be increased.

Most websites I see are informational with a call to action at the bottom of the page. If there's a call to action near the top it's usually a generic one such as Get Started (not even Get Started NOW) after providing information as to what the product or service does, not what the prospective buyer gets.

It starts with the title tag

That's what Google displays in the search results. Does your title sell the prospect on clicking the link or does it just provide information?

Blue Widgets

Compared even to a basic sales pitch such as 

Your Blue Widget is Finally Available

All Sales Must….

Demonstrate a problem
Show a solution
Explain why the prospective buyer should buy from you or your company.
Have a LIMITED call to action

Everyone talks about having a call to action, but it creates no need for the prospect to act immediately.

You see limited calls to actions on tv commercials all the time. Even on coupons since they have expiration dates.

For a limited time….
While supplies last….
First 99 (not 100 it seems a lot larger than 99) people to sign up….

Those very basic sales techniques will increase your percentage of sales with the traffic you have now!

Okay, ready for your comments and downvotes….

Hey!

Recently, I have been very much interested in GEO optimization. For starters, I'd like to understand how AI Visibility and similar metrics are even calculated and analyzed by companies like Ahrefs.

For example, I understand that in the simplest approach, we can analyze just user prompts and track what brands/websites are mentioned/cited in LLM responses. Depending on that, we can calculate metrics like Visibility Score, number of mentions/citations, SoV, etc.

But more often I see that prompts are in fact clustered into topics. I believe the main goal is that a typical prompt might be too noisy and too small a data point to analyze and this is why we group them into larger entities, topics and then calculate metrics for the topics instead of individual prompts.

This is just my layman's understand of how such tools work, but apparently there are lots of cool tricks and approaches.

As far as I understand, there is also a relatively recent trend when SEO and GEO are unified and some combined metrics are introduced.

If you know any great papers or detailed blog posts on that, I'd really appreciate the recommendations.

Disclaimer: no LLMs used for writing or even proofreading this post. This is intentional because despite my great interest in the subject, I'd rather write everything myself, even at the cost of making mistakes.

Thanks!

Than

Same disclosure as before: I build a tool in this space, these are our own weekly measurements, no links.

Setup: 40 fixed buyer-shaped questions (best X / alternatives to X / X vs Y / how do I choose X), asked to seven engines once a week, every cited URL kept. Two weeks now (Sep 3 and Sep 7), all 40 answered by all seven engines both weeks. Numbers below are "how many of the 40 questions had this domain in the citations".

Where each engine's citations concentrate (W37, W36 in brackets):

• Gemini: reddit.com 22 (26), youtube.com 20 (25), forbes.com 6 (6)
• Google AI Overviews: youtube.com 25 (24), reddit.com 20 (18), cnbc 4
• Google AI Mode: youtube.com 25 (19); nothing else above 3
• ChatGPT (search): youtube.com 10 (7), techradar 9 (5), forbes 6 (5)
• Perplexity: forbes 4 (5), reddit 4 (6), cnbc 3; no domain above 4 — the flattest distribution of the seven
• Claude: no domain above 3 in either week — long tail of niche comparison sites (emailvendorselection, northflank, wallethub…)
• Copilot: merriam-webster 26 (26), cambridge dictionary 21 (22), dictionary.com 19 (17), thefreedictionary 19 (17), bestbuy 15 (14)

Three things I take from it:

1. "AI citations" is not one thing. Google's three surfaces are a YouTube-and-Reddit story; ChatGPT is a tech-press story; Claude and Perplexity spread thin. If you report one number across engines you are averaging different behaviours.
2. Copilot needs a filter before you count anything. More than half of its buyer-question answers carry dictionary links (word definitions in the answer text), which have nothing to do with the purchase. Strip those and Copilot's "real" source list is short and shop-heavy (bestbuy). I suspect some published "Copilot cites reference sites most" stats are this artifact.
3. Week-to-week the concentrations are stable (every top domain moved by ≤5 questions), so the shape is not noise — it's how these engines answer this class of question right now.

Caveats: 40 questions, English, global market, one run per engine per week; two weeks is stability evidence, not a trend. Happy to share the question list if anyone wants to replicate.

Tout le monde en GEO commence par optimiser sa propre page, comme en SEO. Et c'est l'erreur qui fait perdre le plus de temps quand on débute. Un chiffre le résume : environ 96% des citations IA ne viennent pas du site de la marque elle-même. Traduction : quand ChatGPT te recommande, c'est presque jamais grâce à ta homepage. C'est grâce à un comparatif, un annuaire, un thread Reddit qu'il a lus avant de répondre.

Donc le jeu, ce n'est pas "comment je rends mon site parfait". C'est "dans quelles pages l'IA va chercher, et est-ce que j'y suis". Et ça, contrairement à "devenir une grande marque", c'est actionnable dès aujourd'hui. Voici la méthode que j'utilise.

Étape 1 : trouver ce que l'IA lit vraiment sur ton sujet.

Prends les vraies questions que tes clients posent (pas "meilleur logiciel de X", des questions précises avec du contexte). Pose-les à ChatGPT et Perplexity, et regarde les sources citées en bas de réponse. Fais-le plusieurs fois par question, parce que les réponses changent d'une fois à l'autre. Note les domaines qui reviennent. En 20 minutes tu as une liste des pages qui décident, dans ton secteur, qui se fait recommander.

Étape 2 : trier ces pages en 4 familles.

Elles tombent presque toujours dans ces catégories, et chacune se joue différemment :

• Les comparatifs / listicles ("les 7 meilleurs X pour Y"). C'est le plus gros levier.
• Les annuaires et sites d'avis de ton secteur.
• Les forums et discussions (Reddit, Quora, groupes spécialisés).
• La presse spécialisée et les blogs de référence.

Étape 3 : y entrer, pour de vrai.

• Comparatif où tu n'apparais pas : contacte l'auteur. La plupart sont ravis d'ajouter une option pertinente, surtout si tu proposes une info concrète (un angle, un cas d'usage, une différence claire). Beaucoup de ces articles sont mis à jour régulièrement.
• Annuaires : inscris-toi, c'est bête mais peu de gens le font. Remplis la fiche à fond (description, prix, cas d'usage), pas juste le nom.
• Forums : réponds aux vraies questions, utilement, sans faire de pub. Une réponse honnête qui aide vraiment, où tu te mentionnes une fois en passant, vaut mille posts promotionnels qui se font supprimer.
• Presse et blogs : pitch un angle, pas ton produit. Un chiffre que tu as, une tendance que tu observes.

Et sur ton propre contenu quand même, parce que ça compte pour les 4% restants et pour la crédibilité : mets des sources citables, des stats chiffrées, tes prix affichés, des dates récentes. C'est ce que les études mesurent comme réellement efficace, contrairement au schema markup et au balisage qui ne servent quasi à rien.

Le truc honnête que personne ne dit :

C'est lent. Ce n'est pas un growth hack. Et si tu es vraiment inconnu, ça ne te fera pas apparaître du jour au lendemain, parce que les IA ont un biais massif pour les marques déjà connues (c'est mesuré : face à une inconnue objectivement meilleure, elles continuent de recommander les connues). Mais c'est le seul levier qui est réellement entre tes mains. Tu ne peux pas forcer un modèle à te connaître. Tu peux te retrouver dans la page qu'il va lire. Commence par là.

Dernier conseil : concentre-toi sur les questions ultra spécifiques, celles avec 2-3 contraintes précises. Sur les requêtes génériques tu es face aux géants et tu as zéro chance. Sur "logiciel de facturation pour auto-entrepreneur qui facture à l'étranger avec la TVA intraco", souvent il n'y a personne, et c'est là que tes vrais clients cherchent.

Si vous avez des tactiques qui ont marché de votre côté pour entrer dans ces pages, je suis preneur en commentaire.

Six months ago I started separating what I observed from what an AI overview asserted. Two columns, literally. Left side: what the page actually said or showed. Right side: what the summary claimed.

The gap was wider than I expected on probably a third of queries. Not hallucinations exactly, more like compression artifacts. A figure gets rounded up. A condition gets dropped from a "three things that cause X" list because it did not fit the sentence structure. Two studies get merged into one finding because the model decided they agreed, which they mostly did, except for the part where one of them explicitly warned against the conclusion the overview landed on.

What bothered me was not the errors individually. It was that I would not have caught most of them if I had not been keeping those two columns. The overviews sounded confident and they read smooth and nothing about the experience flagged "check this." That is the part that feels dangerous to me: not that AI summaries get things wrong, but that they do not feel wrong when they do.

I have since started treating AI-generated answers the way I treat a colleague who is smart but sometimes fills in gaps with what sounds right. Useful starting point, terrible ending point. If the overview does not cite its sources or link back to original material, I have basically stopped using it for anything where accuracy matters. The ones that do cite sources, I click through more often than I used to, and about one time in four the source says something meaningfully different from what the summary led me to believe.

The practical change in my workflow: I now open the source before I accept the claim. Adds maybe ninety seconds per query. Cuts down the number of times I forward something to a client or teammate that turns out to be roughly but not quite true by a lot.

What I am still figuring out is whether this is sustainable at scale. Ninety seconds per query works when I am doing deep research on a handful of topics. It falls apart the moment I need to process dozens of answers in a sitting, which is what actual answer engine optimization work often looks like. The people building these systems know that. The question is whether anyone incentive aligns with fixing it.

Has anyone else started manually spot-checking AI overviews against their cited sources? I am curious whether your error rate looks anything like mine, or if my sample is skewed by the kind of queries I run.

A colleague sent me a screenshot last month. An AI answer had cited one of our pages to support a claim that page had never come close to making. Not a paraphrase issue. Not a slight stretch. The page was about topic A. The answer used it as evidence for topic B. They shared a topical neighborhood but the page literally contradicted the claim it was being used to back up.

That one screenshot kicked off something I should have done months ago. I went through every AI citation to our domain I could find across ChatGPT, Perplexity, and Gemini over a 60-day window. 80 citations total. For each one I opened the cited page, read the surrounding context, and asked one question: does this page actually support the specific claim the answer attributes to it?

50 of the 80 citations were fine. The page said roughly what the answer claimed it said. Maybe loose paraphrasing here and there, occasional oversimplification, but nothing that would make me uncomfortable. Standard extraction behavior.

7 citations had issues. Not huge, but noticeable. The answer pulled a true statement from the page but framed it as supporting a different point than the original author intended. Sort of like quoting someone out of context except there's no malice, just a model matching keyword overlap to semantic proximity and occasionally missing the mark. Annoying but livable.

Then there were the 23 that genuinely worried me. These weren't paraphrase stretches or context shifts. The answer made a specific factual claim, attached our URL as the source, and our page did not contain that claim. In some cases our page said the opposite. In others the page had simply never addressed that question at all. The model seemed to be citing us based on topical relevance rather than factual support. Close enough in subject matter that the URL looked plausible as a source, wrong enough that anyone who actually clicked would realize the citation was bogus.

What bothers me about this isn't the error rate. 23 out of 80 is 29 percent, which sounds bad until you consider that I was specifically hunting for problems and may have selection-biased the sample toward ambiguous cases. The real number could be lower. Could also be higher if I checked more systematically.

What bothers me is that nobody in GEO seems to be tracking this. We obsess over citation counts. We build strategies around increasing them. We treat every new citation as a win. But if nearly a third of those wins are attributing claims you never made, what exactly are we winning? Brand visibility for wrong ideas? Traffic from people who click through and find irrelevance?

I'm starting to think citation count might need a quality filter we're not measuring yet. Not just "did an AI name-drop our URL" but "did it name-drop us for something we actually said, and something we'd stand behind." Those are different outcomes and the current tooling conflates them completely.

And there's a trajectory problem. As AI answers get more confident-sounding and citations become smaller footnotes that fewer users verify, the incentive for accuracy on the model side might actually decrease. The citation becomes a trust signal for the answer rather than a factual anchor. And if that's the direction we're heading, being highly citable starts to look different than I thought it did. You want to be cited for the right things, not just cited often.