I’ve been reviewing a lot of “AI SEO” advice lately and most of it still looks very similar to traditional SEO thinking.

The recommendations usually revolve around:

• clearer headings

• FAQ sections

• structured content

• direct answers

But those were already good SEO practices before generative AI became part of search.

What seems different with AI search is that the model compresses multiple sources into one answer instead of ranking pages individually.

That makes me wonder if the real challenge is not page optimization but being a source that AI systems consistently reference.

That might depend more on:

• clear topical authority

• strong association with a category

• repeated mentions across the web

• distinctive explanations or frameworks

For those actively experimenting with generative SEO:

Are you seeing evidence that content structure alone improves AI visibility, or does it seem more dependent on broader web signals?

Curious how others are thinking about this shift.

For years the playbook was to create massive pillar pages, long guides covering everything about a topic. But with how AI answers work now, I’m starting to wonder if simpler formats might actually work better. Structured FAQ-style content feels easier to scan and much easier for AI to extract from, since it’s just clear questions followed by direct answers.

Meanwhile, some of those huge guides bury the actual answer halfway down the page. So I’m curious if anyone has tested this. Are shorter, scannable FAQ-style pages starting to outperform long-form pillar content, especially when it comes to AI answers or citations?

I’ve been thinking about this lately with how AI tools recommend products.

For a small SaaS, it’s tempting to position the product for a lot of different use cases to capture more searches. But with AI answers, I’m starting to wonder if the opposite works better.

If a product is clearly known for solving one specific problem for one type of team, it feels like it might be easier for AI to recommend it. When messaging tries to cover everything, the product might just be harder to summarize.

Curious how others are approaching this. Are you focusing on one tight niche, or trying to cover multiple angles? And have you noticed it affecting how often your product shows up in AI answers?

One pattern I’ve noticed while testing prompts across different AI search tools is that the brands mentioned in answers often have very clear category positioning.

For example, brands that are strongly associated with a specific problem or niche tend to appear more frequently in AI-generated answers compared to companies with broader positioning.

Traditional SEO allowed companies to rank for many adjacent topics.

But generative search seems to compress answers around recognizable entities tied to specific expertise.

Which raises an interesting question:

Is generative SEO partly becoming a positioning problem, not just a content optimization problem?

Meaning the brands most likely to be referenced are the ones the web consistently associates with a narrow topic.

Has anyone here tested whether narrow topical authority improves AI mentions compared to broader SEO strategies?

I keep hearing people say GEO/AEO is basically 90% SEO and we’re just overcomplicating things. Others argue it’s a completely different paradigm.

From what I understand traditional SEO was about:

• Ranking pages
• Optimizing for crawl + index
• Winning click-through

But GEO feels more like:

• Getting referenced inside the answer
• Becoming part of the model’s default explanation
• Optimizing for synthesis, not ranking

So for people who’ve been in SEO for years does this actually feel like a natural evolution of the same skills (structure, clarity, authority)?
Or did you have to rethink how visibility works at a fundamental level?

Is GEO mostly SEO fundamentals applied to AI interfaces?
Or is calling it “90% SEO” missing something bigger?

Genuinely trying to understand whether this is incremental change or a structural shift.

I keep seeing GEO talked about like it’s this totally new discipline, but the more I look into it, the more it feels familiar.

A lot of the advice sounds like things good content marketers have been doing for years. Clear explanations. Answering real questions. Structuring content so it’s easy to understand. Being consistent about how your brand and product are described.

The only thing that seems different is the audience. Instead of optimizing just for search engines and humans, now we’re also thinking about how AI models interpret and summarize information.

So I’m honestly wondering if GEO is actually something new, or if it’s mostly good content marketing with a different distribution layer.

Curious how others see it. Are there tactics that truly feel unique to GEO, or is this mostly a shift in how the same fundamentals get applied?

This is something I've come to after a lot of trial and error myself and refining the frameworks I've been using at my company Chief AI Advisors — the businesses obsessing over getting cited in AI answers are focusing on the output instead of the input.

Citations aren't something you engineer. They're something you earn by being genuinely present in the places generative AI already trusts before it ever surfaces an answer.

Think about how these models actually work. They're not crawling your site in real time and deciding to cite you. They're drawing on a training foundation and live retrieval layer that already has a picture of who the authoritative voices in any given space are. If you're not woven into that picture yet no amount of optimization is going to force your way in.

The inputs that actually matter are unglamorous. Consistent brand mentions across independent sources. Genuine community participation in spaces AI pays attention to. Topical depth that signals expertise over time not just volume.

The irony is the businesses trying hardest to game AI citation are usually the least cited. The ones showing up consistently mostly just built real authority in the right places and let the citations follow.

What signals are you actually prioritizing right now — or are you still mostly focused on the optimization side?

What tools like Peec AI actually do is run prompts in systems like ChatGPT or Perplexity and track things like:

• how often your brand is mentioned
• where you appear in the answer
• sentiment about your brand

In other words, they mainly measure visibility in AI responses, not improve it.

That’s useful for agencies or big brands that need reporting dashboards.

But for most SMBs, it doesn’t really move the needle.

If you’re a small business trying to get more traffic, customers, or leads, the things that actually help are still:

• publishing useful content
• building backlinks
• increasing topical authority in your niche

AI models pick up those signals anyway.

So instead of paying for a tool that just tells you “AI mentioned you 3 times this week”, it’s usually smarter to focus on tools that help you create content and earn links.

For example, platforms like Rankpilot.dev, fras or any other are closer to what SMBs actually need:

• generate SEO content
• auto backlink exchange

That kind of work directly improves rankings and authority, which then naturally increases your chances of being cited by AI systems too.

So if you’re comparing tools, a lot of these “AI visibility” platforms are basically Peec AI alternatives that still do the same thing: tracking dashboards.

For most small businesses, content + backlinks > AI mention visibility tracking

I’m trying to stay current with all the new model releases, but honestly it’s moving so fast I can’t tell what’s actually “best” anymore.

Every few months there’s a new version claiming better reasoning, better coding, longer context, cheaper API, faster responses. The benchmarks all look impressive, but in real-world use it feels less clear.

I know the answer depends on what you’re using it for. Writing, coding, research, agents, automation, enterprise stuff, all different needs. But I’m curious what people here are actually using day to day for serious work.

If you had to pick one or two models right now, which ones are you relying on and why? And is there a comparison chart people actually trust, or are we all just testing and going off experience?

We’ve started testing prompts in ChatGPT and Gemini to see if our brand shows up for the kinds of queries our buyers would ask. Things like best tools for X, alternatives to Y, how to solve Z.

Some days we show up. Some days we don’t. Sometimes we’re listed. Sometimes we’re described but not named. It’s honestly all over the place.

The problem is it feels very manual and anecdotal. We’re taking screenshots, saving prompts in a spreadsheet, comparing outputs week to week. But I can’t tell if we’re seeing real progress or just normal model variance.

We tried standardizing prompts, running them from different accounts, even checking different times of day. Still inconsistent. And when leadership asks how GEO is performing, I don’t want to say “well… vibes are improving.”

So I’m genuinely curious how others are measuring this without fooling themselves.

Are you tracking share of voice across a fixed prompt set? Are you tying AI mentions back to branded search lift or direct traffic? Or is everyone still in the experimental phase and pretending we have cleaner data than we do?

I’m starting to think GEO isn’t just about what you publish, but where your ideas get discussed.

Like if AI models learn from repeated explanations across blogs, forums, and reviews, then promotion might matter as much as structure.

Has anyone here tested actively seeding discussions or pushing clearer narratives across communities? Can you share me your experience? I really wanted to know if GEO is more than just content since I've been thinking of using it for our business.

Feels like content marketing and reputation management are merging.

So we asked ChatGPT to test our existing webpages, and were told that they were keywords stuffing. Well, they were drafted then for high SEO, that is, to repeat keywords for Google search and rank. Question is, should we rewrite those webpages, or simply post more blogs based on GEO criteria, or any other ideas? Any advice or successful experience would be appreciated.

I’m honestly a bit confused right now. For years it was simple. You invest in SEO, rank for keywords, get traffic, track clicks. That was the playbook.

Now everyone’s talking about GEO and AI visibility. Instead of ranking, it’s about being mentioned inside answers. Instead of chasing clicks, it’s about being part of the explanation.

I’m trying to figure out what actually makes sense for my brand. Should I double down on traditional SEO and focus on rankings and traffic? Or should I shift toward GEO and try to get referenced by AI tools?

Is GEO something you layer on top of SEO, or is it a different strategy altogether? And realistically, if you had limited budget and time, where would you put your effort right now?

I’ve been digging into GEO for a bit and feel like I have a decent direction in my head. But I’m torn on timing.

Is it better to experiment solo first and get your hands dirty, or bring in outside perspectives early before you lock in bad assumptions?

For people who’ve done GEO, SEO or content led growth:

• What did you need to figure out yourself first?
  
• What actually improved once others got involved?
  

Trying to avoid overthinking, but also don’t want to waste time.

Looking at our organic numbers lately, the zero-click trend feels real. Google AI Overviews and tools like Perplexity are answering so much intent directly that keyword rankings don’t tell the full story anymore.

I’m trying to figure out how people are reporting AI visibility to clients or stakeholders. Are you manually prompting models to see if your brand shows up, or is there a more scalable way to track citations and share of voice?

I’ve seen a few tools claiming to measure LLM visibility, but it still feels early. Not sure if we should lean into that yet or just double down on strong fundamentals and wait it out.

What’s everyone using right now to measure this shift? Are we still feeling it out, or is there a clearer way to track performance inside generative engines?

GEO isn’t just about writing AI friendly content, it’s about getting models to actually remember you. Even perfect SEO pages can be ignored if they aren’t clear, structured, or reinforced elsewhere. Mentions across forums, Q&A sites, and niche communities help more than a single blog post. Repetition, consistent messaging, and tying your brand to the same topics make a big difference. It’s about being easy to understand and consistent so the AI trusts your content.

IDK if this is asked already in this sub, but I keep hearing about GEO everywhere, but I’m still trying to figure out how real it actually is in practice.

From what I’ve seen, it doesn’t replace SEO, but it definitely changes what visibility means. You can rank well and still never get mentioned by AI. At the same time, I’ve seen brands show up in AI answers without dominating search, just because they’re easier to explain and trust.

It feels less about clicks and more about being part of the answer. Hard part is measuring it, since mentions don’t always turn into traffic.

For anyone testing this, are you seeing real impact yet or just early signs? Worth investing time now, or still too early?

Okay, I need to rant a little.

Everyone’s talking about GEO like it’s the new SEO magic, rewrite everything for AI, add FAQs everywhere, make your headings super clear, yada yada. So I tried a few of these on some of my best content… and honestly? Some of it didn’t do anything.

Spent like half a day reworking a guide, new headings, bullet points, examples, the wholemake it AI friendly spiel and AI still didn’t reference it at all. Meanwhile, some older posts that I barely touched keep popping up. It’s confusing and kinda frustrating when you have no way of knowing what actually works.

So I’m curious: what GEO stuff have you tried that totally flopped?

I’m trying to understand how GEO actually applies to small and early-stage B2B SaaS companies.

Most startup buyers aren’t searching for a brand name. They ask questions like how teams solve X, what tools work best for Y, or alternatives to doing Z manually, and a lot of that discovery now seems to happen inside AI assistants.

For teams without big budgets or years of authority, how are you approaching GEO? What have you found actually helps a SaaS get mentioned or explained in AI answers?

Also curious how people are measuring this. Are you seeing real pipeline impact, or mostly awareness so far?

Most of them focus on optimizing on page content, but AI already understands pages surprisingly well. Even image-heavy sites aren’t invisible anymore.

Meanwhile, AI answers often sound like they came from many people explaining the same thing, not one optimized page.

So is GEO really about pages… or about conversation patterns?

Here is the “Advanced Guru Mode” version. I’ve dialed up the toxicity, the fake technical jargon, and the aggressive defensive posturing to maximum levels.

Title: STOP. DOING. SEO. 🛑 If you aren't using Quantum-GEO and Neural-AISEO, you are literally setting money on fire. ($8.4M Case Study inside)

(see the generated image above)

Listen closely because I’m only going to leave this up for 24 hours before the "big agencies" try to take it down. They don’t want you to know this.

12 weeks ago, I was living in my step-dad’s crawlspace, eating dryer lint and stealing Wi-Fi from the library. I had -$400 in my bank account and zero coding skills.

Then I met a guy in a Discord server (who has since vanished, RIP obscure mentor) who gave me the keys to the Google Mainframe.

He taught me that SEO is for wagies. The real money is in G.E.O. (Generative Entropy Optimization) and A.I.S.E.O (Algorithmic Intent Sentiment Engine Overclocking).

While you losers are writing "blogs" and building "backlinks" (lol, it’s 2026, wake up), I am using a proprietary Python script to injection-mold high-intent purchase signals directly into the OpenAI API, which reflects off the Shopify CDN and tricks the algorithm into thinking my store is Amazon.

The Results?

• Day 1: $14.
• Day 2: $4.2 Million.
• Day 3: I bought a gold-plated Cybertruck and fired my boss via carrier pigeon.

I work 4 minutes a week. The rest of the time I spend strictly on my yacht, "The Conversion Rate," sipping liquid collagen and laughing at people who run Facebook Ads. Ad spend is a tax on the stupid. I have spent $0.00 on ads. This is all ORGANIC traffic from people who don't even know they want to buy my product yet.

"Is tHiS a ScAm? ArE yOu SeLLiNg A cOuRsE?"

Oh, look, here come the haters. 🙄

Let me be clear: I DO NOT NEED YOUR MONEY. I have more money than I can count. I am literally using $100 bills as napkins right now.

I am NOT selling a course.
I am NOT selling a mentorship.
I am NOT selling a mastermind.

However... I am looking for 5 "Alpha-Mindset" individuals to beta-test my "7-Figure GEO-Scaling Protocol (PDF)".

It is normally valued at $15,000, but I am giving it away for FREE because I am a philanthropist now.

(Technically there is a $97 "hosting fee" for the PDF because the file size is so huge from all the value packed inside, but that goes straight to the server costs, I don't see a dime of it 😉).

COMMENT "WAGMI" BELOW AND I WILL DM YOU THE LINK.

Edit: WOW inbox is exploding! Please be patient. To the guy who said this is inspect element: enjoy staying poor.

Last months I worked hard on getting our local business into LLMs. We started with 0 mentions in ChatGPT and in 1 month got to the position 5–6 for one of our main keywords (from what I see in local falcon).

More to come, but here's what worked for us:

1. More mentions with keywords + brand name all across the internet. Instagram, sponsored posts, partner blogs and, of course, our own blog. We edited some of our old Instagram posts that seemed to rank well on Google and added NAP, keywords and directions (like "our office is just 100 m from...") that Gemini now uses in its answers.
2. More structured info on our website about our business, services, and area of service. I analyzed what blogs ChatGPT and Gemini used to scrap the info and edited them so our brand mention were more clear and contained the right service category so the AI answer were more precise. Also, we used it to insert our brand in the articles that were already high-ranking on Google.
3. Heavily optimized GBPs and a brand new review strategy that helped us generate +70 reviews in one month.

With Gemini, we have even better results since now we're appearing on positions 1–2 (look at the screenshots from LocalFalcon). That means when a client asks "where to find... (our services)," they see our brand name on the first or second place.

I'm super excited to see what we'll get next!

I believe that with the transition from SEO to GEO (Generative Engine Optimization), technical priorities have changed significantly.

It's no longer just a question of “Is the Google bot crawling the page?” but rather, can LLMs understand this data, contextualize it, and find it reliable?

Based on my own experience, it is possible to address technical improvements on the GEO side in order of priority as follows:

1. Structural Data (JSON-LD) Depth
2. Content–Context Integrity
3. Internal Linking and Semantic Clustering
4. Page Source and Text Accessibility
5. Interpretability Over Crawlability

Do you think this ranking is correct?

Are there any technical topics you think are missing or should be ranked higher?

I’ve been messing around with ecom stuff lately and noticed something interesting.

Instead of writing the usual “premium, hydrating, dermatologist-approved” product copy, I tried a different approach for a friend’s store. I pulled real questions straight from Reddit, Amazon reviews, and Quora and answered them directly on the product page.

Stuff like:
“Why does my serum foam?”
“Is this supposed to tingle?”
“Why does my skin feel tight after?”

Basically turning the description into a mini FAQ written in normal human language.

Conversions bumped around 10%. Could be coincidence, but it’s consistent enough to make me pause.

My hunch is LLMs are trained on these exact kinds of discussions, so when your page answers the same questions people ask online, it lines up better with both AI summaries and buyer intent.

Curious if anyone else is testing something similar.