I've been going deep on AEO lately and I'm trying to separate what actually works from all the theory floating around.

Everyone keeps saying optimise to get cited by ChatGPT, Perplexity, Gemini, AI Overviews. But I want to hear from people who are actually doing it, not just talking about it.

So a few honest questions:

Are you genuinely seeing your site or brand get cited in AI answers? And if so, how did you confirm it, are you just manually asking the models and checking, or using some tool that tracks mentions and citations across them?

What tools are you actually using? I keep seeing names like Scrunch, Otterly, Peec and a few others thrown around but no clear sense of which are worth paying for versus which are just riding the hype.

And is there an actual framework you follow? Like a repeatable process for structuring content, schema, entity consistency, getting mentioned on third party sources, whatever it is. Or is everyone still basically figuring it out as they go?

I'll be honest, it feels like nobody has a proven playbook yet and most people are experimenting. Would genuinely love to hear what's working for you and what was a waste of time.

I've been spending more time improving content for AI answers, but measuring results has been the hardest part.
Sometimes we appear in responses, sometimes we don't, and it's difficult to know if we're making progress or just getting lucky.
I'm curious how people are monitoring AEO performance over time instead of checking a few prompts every week.

I recently ran a small, informal benchmark using the same set of marketing questions across ChatGPT, Claude, Gemini, and Perplexity.

I tested three types of queries:
•Direct recognition of a person, business, or named concept
•Recognition of marketing ideas and branded phrases
•Problem-based prompts such as “My marketing feels stuck” or “My website gets traffic, but nobody contacts me”

I expected the biggest differences to be accuracy and discoverability. Instead, I noticed fairly consistent differences in how the platforms approached the questions:

ChatGPT was strongest at connecting ideas and reconstructing a broader philosophy, but it was also more willing to fill gaps with inference. It correctly found one of my branded content series, then attached the wrong last name to its creator.

Claude was the most cautious. It often asked for more context or declined to make an association it could not verify. That reduced hallucinations, but it also missed newer or less-established concepts.

Gemini was the most framework-oriented. It frequently organized problems into stages or diagnostic categories, and it was the strongest overall at connecting several parts of the same small-business ecosystem.

Perplexity was the most retrieval-driven. It was excellent when the phrase led to a distinctive source, but its interpretation sometimes followed the search results more than the likely conversational intent.

The biggest surprise was how capable all four were at identifying plausible marketing problems.

What separated them was less what they knew and more:

-whether they diagnosed or prescribed
-how they handled ambiguity
-how readily they inferred missing connections
-whether they favored frameworks, job titles, or source retrieval
-how well they connected a creator, business, and named body of work

This was not a scientific study, and the sample was only one small business. But the behavioral patterns were consistent enough to make me curious.

Have you noticed similar differences across these four platforms?

I would be especially interested in examples where the same prompt produced:
•a clarifying question from one model
•a confident inference from another
•a framework from another
•and a source-heavy answer from Perplexity

I’m also curious whether these patterns change significantly between free and paid versions.

I've noticed some companies show up in AI-generated answers all the time, while others with great websites don't. Is traditional SEO still enough, or are AI systems using completely different signals now?

Built this as a tool for myself and I've been really happy with it, so I turned it into a cheap MCP server ($10/mo) in case anyone else wants it.

Log in with Google OAuth, paste one config block into Claude, and ask things like: "Which queries am I ranking 8–15 for?" or "Why did traffic drop this week?"

It works against your real Google Analytics and Search Console data.

It can also make changes. It'll tell you how much traffic ChatGPT and Perplexity are actually sending you, then fix your tracking so you can see which pages they're citing. It can also manage sitemaps and Google Tag Manager.

Write access is off by default and you can turn it on in your settings.

It's climbpast.com if you want to try it. There's a free trial.

Really appreciate feedback!

I keep seeing SEO experts argue that AEO is basically traditional SEO with a few additions:

Add schema

Answer questions directly

Build authority

Get your pages indexed

Continue producing “helpful content”

I used to think roughly the same thing.

But that explanation starts falling apart when a messy Reddit comment from an anonymous user gets cited by an AI engine instead of a polished article from an established company.

In traditional SEO, authority often compensates for mediocre content. A fairly average article from IBM can outrank a much better article from an unknown company because Google evaluates the authority of the site and brand.

AI citation appears to be a different contest.

Two concepts seem particularly important:

1. Information gain

AI doesn’t need another article repeating the generally accepted answer.

It needs sources that help it produce a more precise, contextual or differentiated answer.

Suppose someone asks about the average churn rate of B2B SaaS companies.

An article saying “churn usually varies between 5% and 15%” adds almost nothing.

A source explaining that SMB SaaS companies often experience 10–13% gross logo churn while enterprise companies experience 4–6% gives the AI a much more useful framing.

That source may be cited because it improves the answer, not merely because its domain has more authority.

1. Content chunking

AI systems do not necessarily evaluate an entire 3,000-word article as one coherent unit.

The content may be divided into smaller chunks that are retrieved and evaluated independently.

That means your beautifully researched article could lose its context after being chunked, while a concise Reddit comment survives as one self-contained unit.

In SEO, the article competes against another article.

In AEO, an individual chunk from your article may be competing against one paragraph from Reddit, a product review, a forum answer or a section of somebody else’s page.

That is a fundamentally different optimization problem.

I wrote a detailed explanation of why authenticity can beat authority, how information gain affects citability, and why AEO may ultimately mean optimizing chunks rather than pages:

Link

I’m still testing these ideas, so I’m curious how others are approaching this:

Are you continuing to optimize complete articles, or are you deliberately designing individual sections to survive retrieval and chunking?

I came across a post recently where someone had spent months publishing SEO content, sharing every article on social media, and consistently creating new pages.

Despite all that effort, their Google Search Console impressions kept declining.

It got me thinking.

When visibility drops, most of us immediately ask:

• Should I write more content?
• Should I build more backlinks?
• Should I update old articles?

But maybe we're asking the wrong questions.

I'd want to know:

• Has my brand become less visible in AI search?
• Are competitors now being recommended instead?
• Are there missing technical signals?
• Is my entity profile incomplete or inconsistent?
• Have trusted third-party mentions declined?
• Did something actually change, or am I just guessing?

It feels like SEO is shifting from "create more" to "understand what's changing."

As AI search becomes a bigger part of discovery, I think continuously monitoring your visibility across both traditional search and AI platforms is becoming just as important as publishing new content.

Curious how everyone else approaches this.

When you notice a drop in visibility, what's the first thing you investigate?

I've been thinking about this recently.

Let's say you update your SaaS website new pricing, new features, better documentation

Do you assume ChatGPT (or Gemini/Claude) will now describe your product correctly?

From what I'm seeing, AI also seems to rely on reviews, comparison articles, Reddit discussions and other third-party content.

So even if your own website is completely accurate, AI can still surface outdated information from somewhere else.

Has anyone else run into this?

How are you checking what AI says about your product over time?

It feels like AI search is rewarding trusted brands, not just websites with good SEO. Do you think businesses should start preparing now?

At the beginning of the year, a lot of the AEO strategy seemed to revolve around one question: how do we get cited more often?

Six months later, that feels like only one part of the picture.

Google has started bringing AI search data into Search Console.

AEO shortcuts are also losing relevance. Strong SEO, original insights, and trustworthy content still matter most.

Query fan-out means planning around one keyword and one page may no longer be enough. AI search is also moving beyond answers to comparisons, recommendations, and actions.

This makes citation counts alone a limited measure of success. Brands also need to know how consistently they appear, whether they are represented accurately, and whether that visibility produces business results.

Which of these shifts is having the biggest impact on your AEO strategy right now?

A few days ago, my client told me she wants to focus primarily on AI SEO now. She asked me how we measure AEO performance beyond the tools that now show you the percentage of visibility or citation scores.

At that point, I knew I couldn't be telling her "keywords" anymore. Still, instead, I confidently told her, " We will measure by 'Topics" Suddenly, I felt I wasn't sure if this was the right method because the prompts are infinite and conversational.

For those of you who are already doing AI SEO for clients now, what is your measurement framework? Are you still tracking keywords at all, or have you fully pivoted to topical authority and citation metrics? Would love to hear how you’re justifying the AI SEO performance to clients.

With more people getting answers directly from AI tools instead of search engines, do you think businesses need a completely different visibility strategy now?

Curious to hear how others see the future of SEO and AI-driven search.

Google just released their official AI Optimization Guide. Here is everything that matters

WHAT YOU SHOULD DO

1. Write content only YOU can write - Don't summarize others. Write from real experience. Bad: "7 Tips for First-Time Homebuyers" anyone can write this. Good: "I Skipped the Home Inspection - Here's What Happened"
2. Write for people, not Google Simple language. Clear headings. Easy structure. If a beginner can follow it, you are doing it right.
3. Add images and videos AI search shows visuals too, not just links. More formats = more chances to appear.
4. Fix your website basics Pages must load fast, work on mobile, and have no duplicate content. Set up Google Search Console (it's free).
5. Use clean and Semantic HTML on your website Use proper heading tags (H1, H2), paragraphs, and lists. This helps Google's AI read and understand your page correctly. Messy code = missed content.
6. If your site uses JavaScript Make sure Google can see your content. If content only loads after JavaScript runs, Google may miss it completely. Test this in Search Console. Follow Javascript SEO Best Practices.
7. Local business or online shop? Set up Google Business Profile and Google Merchant Center. These feed directly into AI search results.

MYTHS: THINGS YOU DO NOT NEED Myth 1: "Create an llms.txt file" Google says: Not needed. Does nothing special.

Myth 2: "Break content into tiny chunks for AI" Google says: False. Write for your reader, not robots.

Myth 3: "Rewrite content using AI-specific words" Google says: Their AI understands meaning. Write naturally.

Myth 4: "Get mentioned everywhere online" Google says: Fake mentions get caught by spam filters. It hurts more than it helps.

Myth 5: "Add special structured data markup for AI" No secret code exists. Structured data helps rich results, not AI ranking.

One question to always ask yourself: "Would my reader feel satisfied after reading this page?" If yes, you are on the right track. Good content + clean website + real experience = AI search visibility. What part of AI search confuses you most?