9,400€ down the drain: what a Reddit thread teaches you about AEO
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A 9,400€ check for an AEO mirage
A client calls me on a Tuesday morning. His voice is tense. He’s invested 9,400€ in one year in an « AI Search specialist. » Result: zero citations in ChatGPT, zero answers in Google AI Overviews. Not even a mention in Perplexity.
9,400€. Not a single return.
This merchant sells 800 product references—spare parts, a massive catalog. The consultant had promised him « 300% organic AI visibility in 6 months. » His robots.txt was blocking OpenAI crawlers. Structured data? Nonexistent. No Article tags, no FAQ, no entity mentions in the Knowledge Graph.
The « AI Search specialist » had simply dusted off an old SEO checklist. Title tags, meta descriptions, cheap-grade link building. Nothing to teach an LLM what the site was about.
A few days later, I stumble on a Reddit thread—r/RankWithAI—that lists exactly the 7 pitfalls of this market. A list written by u/rajatedm, who has been interviewing AEO profiles for 18 months. Every line brings me back to this client’s case. Every pitfall, I’ve seen it in 2026 among 4 out of 10 e-commerce sites I audit.
AEO is not SEO with the word « IA » tacked on. It’s a discipline that blends structured data, citations, entity architecture, and understanding of LLMs. Hiring the wrong profile means throwing away thousands of euros. Hiring the right one means building an authority that every AI cites—and it sticks.
I’ll share the three errors that cost the most. Based on that Reddit thread, my audits, and what I observe in Southeast Asia where I’ve been working with e-commerce merchants since April 2025.
Classic SEO doesn’t capture what’s happening in AI Search
Google rankings aren’t enough anymore. In 2026, nearly one in three queries triggers an AI Overviews or generative response, according to figures shared by Search Engine Land. In SEO, you fight to appear in the 10 blue links. In AEO (Answer Engine Optimization), you fight to be the source of a spoken answer, a summary in ChatGPT, or a citation in Perplexity.
The problem? The signals aren’t the same.
An LLM doesn’t rank pages. It reads entities, relationships, verified sources. It uses its training corpus, knowledge bases, and especially the structured data it encounters in real time.
A product sheet without Product markup, without offers, without a link to the brand entity in Wikidata, disappears. A blog article answering a question without FAQ markup, without a revision date, and without internal links from a semantic cocoon, will never be cited.
A consultant who tells you « I’ll optimize your keywords for Bard » is way off base. I build an architecture where every page feeds a recognized entity for LLMs. I teach the machine what your site is about.
Look at citations in ChatGPT: they rarely point to pages optimized in classic long-tail fashion. They come from ultra-structured content, often from semantic cocoons where information hierarchy is clear.
Example: one of my clients, 3,200 pages indexed, saw +760% AI citations in 4 months after deploying Article markup plus entities for each category. Before, their pages floated. After, each typical question like « what is a sealed ball bearing » became the source in Perplexity. Not because it ranked #1 on Google. Because the LLM understood the structure.
Want to hire? Check that the candidate talks about structured data before talking about content.
What the Reddit thread on AEO screams at your face
The post from u/rajatedm on r/RankWithAI gave me chills. It lists 7 errors when you hire a « local SEO / AEO expert. » I’ll pick out three that torch budgets.
Error 1: « I’ll get you positioned in ChatGPT. » Nobody positions a page in an LLM’s response. You just stack the odds: structured data, freshness, citation authority. A candidate promising a guaranteed spot has never set up an llms.txt file. Avoid them.
Error 2: No mention of « citations. » In AEO, the real metric is how many times your page is cited as a source. If the profile you’re interviewing doesn’t say the word « citation » in the first five minutes, they’re doing old-school SEO.
Error 3: No answer to « How does your work improve machine comprehension? » The sharpest AEO architects talk entities, Knowledge Graph, semantic cocoons. They show how to turn a catalog of 2,000 sheets into a graph of information digestible for an LLM. The others drone on about link building and H1 titles.
The thread also flags a portfolio trap: many show Google Analytics screenshots. But showing organic sessions proves nothing about AEO. Ask for an example of an « AI citations » report from Semrush or an export of OpenAI logs when possible.
I’ve saved this thread. Every time a prospect contacts me after an AEO failure, I send them the link. Three times out of four, they recognize their old vendor in the listed errors.
It’s concrete. It’s where visibility happens today.
Before signing with an AEO specialist, apply this structured framework. Each step builds on the previous to ensure your content becomes a trusted source for LLMs.
The DOSE framework for AEO
A 4-step audit to avoid wasting your budget on AI Search
The 3-step audit: a DOSE framework adapted for AEO
Before signing a contract, I always apply the DOSE framework—Decision, Objectives, Strategy, Execution—that I forged with Guillaume Attias (BMO Academy). I’ve adapted it for AEO. You can do the same.
D—Decision: why would an LLM cite you? Ask the candidate to identify 3 types of conversational queries where your brand could become the answer source. If they don’t talk about questions starting with « how to choose…, » « why…, » or « what’s the best…, » they’re thinking in SEO keywords, not AI intent.
O—Measurable objectives: how many citations in which LLMs? An AEO architect sets objectives in citation counts via tools like Semrush « AI Overviews, » Perplexity crawls, or Google Search Console exports filtered on AI queries. They don’t promise « AI visibility » in fuzzy terms.
S—Entity architecture strategy: what’s the plan to feed the machine? Here we get concrete. The candidate must explain how they’ll structure your pages into semantic cocoons, link your product sheets via Wikidata entities, and deploy tags suited to AI crawlers (JSON-LD, Article markup, Product, FAQ, Organization). If they don’t talk about « cocoons » or « semantic pillars, » walk away.
E—Technical execution: an llms.txt file, a structured data audit. Posture matters as much as the plan. Ask for a live demo: have them open your homepage and show you, inspector in hand, whether your tags exist and whether ChatGPT or Perplexity can use them. A pro does it in 90 seconds.
This DOSE canvas works for any hiring conversation. You’ll very quickly spot who’s actually gotten their hands dirty with LLMs versus who just has PowerPoint slides.
The article reveals how a client wasted €9,400 on irrelevant tactics. The ideal allocation for a successful AEO architecture looks like this:
The real cost breakdown of an AEO architecture
9,400 € wasted on the wrong priorities – here’s how it should be spent
9,400€: the real cost of a poorly designed AEO architecture
I’m back to my client from Tuesday morning. His 9,400€ funded cheap link building, « SEO-optimized » articles with no semantic connections, and a pseudo-strategy for « positioning in Bard. » Not a line of JSON-LD. No thematic grouping.
The money should have been spent like this: 40% on audit and entity architecture (identifying brand entities, products, experts), 35% on technical deployment (markup, llms.txt, fixing AI bot crawl errors), 25% on structured content serving as sources for LLMs (citable guides, spoken FAQs, linked sheets).
I took over the site and applied that split. Result: in 4 months, citations in AI Overviews went from 0 to 47 queries. Perplexity citations, from 2 to 27. Classic organic traffic climbed 18% because Google also rewards good architectures. And the client started receiving quote requests via the « ChatGPT Mobile » source in their CRM.
9,400€ is the price of an illusion if you hand AEO to a generalist dressed as a specialist. It’s a sound investment if you structure your site like a knowledge graph open to AI.
The r/RankWithAI thread drives it home: the market is stuffed with « prompt engineers » who can’t tell an entity from a link anchor. Don’t be the next victim.
Why structured data alone isn’t enough
I see a second common mistake: believing that adding a JSON-LD markup file transforms your site into a citation magnet. It doesn’t. Markup is the technical layer. But without semantic architecture to contextualize it, LLMs do nothing with it.
An LLM has no eyes. It reads your tags AND how your pages connect to each other. If your « 12V water pump » product sheet points to a category page, then to an article « how to choose a water pump, » then to a brand, you weave a web of trust. The machine infers you’re an expert player.
Conversely, a site with 10,000 pages and no semantic linking, even with perfect tags, looks like a puzzle missing half its pieces. AIs only cite what they understand globally.
The recruiter must verify that your future AEO consultant masters two pillars: technical markup AND cocoon modeling. A client who tells me « I already had my agency add structured data » sends me their site. In 30 seconds, I see whether the entity graph is complete or the markup floats in the void.
Ask for a projection: « What cocoons will you build so my trail shoe catalog generates citations in Gemini? » The answer must be concrete: pillar pages for each terrain type, linked to marked product sheets, with citable specific FAQs. No artistic vagueness.
AEO is SEO where you swap the Googlebot for a conversational agent. Your guarantee is structure. And structure is woven to the millimeter.
Tomorrow, you’re signing with an AEO? Here’s what to check.
I see too many leaders sign a check thinking « AI is trendy, let’s invest. » And they sign with the first person who babbles about « optimized prompting. »
I’ve worked with over 650 clients. Here’s what I’ve learned: a good AEO architect doesn’t sell snake oil. They show you how to structure your content into a knowledge base that LLMs adopt. They measure progress by citations, not promises. They apply a framework like DOSE instead of winging it.
Before you hire, ask yourself these three questions:
- « Did this candidate ask where my current traffic comes from and what entities my site describes? »
- « Did they show me an example of a semantic cocoon or a structured data plan in 5 minutes? »
- « Did they talk about AI citations, not rankings? »
If you get three « nos, » don’t sign. Keep your budget. Wait for the profile that talks architecture, not magic.
Has your next AEO interviewer ever actually gotten one of their own pages cited by ChatGPT?
Check your AEO eligibility in 30 minutes
I’ll pull your pages live. We’ll see together whether your tags are understood by LLMs, where your best AI citation opportunities hide, and how to dodge the pitfalls that cost another merchant 9,400€.
Book a strategic call — 45 minFrequently Asked Questions
How do I verify that an AEO specialist actually masters LLMs and not just SEO?
Ask them to show you, live, an audit of your structured data using Google’s inspector or a Schema validator. If they don’t know Article, Product, FAQ, or Organization tags, or if they’ve never heard of an llms.txt file, you have your answer.
What’s a semantic cocoon and why is it vital for AI Search?
A semantic cocoon is an architecture where pages are grouped by theme. Internal links are organized in silos. For LLMs, this context is clear: each page strengthens the main entity. It’s the skeleton that AI uses to cite your pages.
Can you measure the ROI of AEO?
Yes. I count citations in AI Overviews, Perplexity answers, and ChatGPT. I also track traffic from these sources via Google Search Console (AI queries) or Semrush. And I measure conversions generated by these channels.
Is a strong SEO background enough to call yourself an AEO expert?
No. SEO targets Google rankings. AEO requires understanding LLMs, entities, and how AI picks sources. A classic SEO can retrain, but recycling old methods yields nothing.
How long before you see AEO results?
First citations typically arrive between 2 and 5 months. It depends on site size and architecture quality. This lag reflects AI bot crawl frequency and how often their knowledge bases update.
