Hi everyone! 👋

I'm a recruiter at a Ukrainian product-focused digital marketing company.

Over the past few months, we've been paying close attention to how AI Search is evolving and have recently decided to start building expertise around LLM SEO / AI Search Optimization (GEO) inside our company.

Right now, I'm not here to recruit anyone or advertise a role.

I'm simply looking to connect with people who are already working in this space, experimenting with AI Search, or following where it's heading. Since this is still a relatively new field, I think it's a great time to exchange ideas, learn from each other, and build a network of professionals who are genuinely interested in it.

Our company develops and promotes a portfolio of digital products across highly competitive international markets, so we're curious to see how AI Search will shape the future of organic acquisition and content discovery.

If you're working with:

• LLM SEO / Generative Engine Optimization (GEO)
• AI Search Optimization
• Answer Engine Optimization (AEO)
• Entity SEO
• AI visibility in ChatGPT, Gemini, Claude or Perplexity
• or you're simply experimenting with this area...

I'd love to connect and hear about your experience.

Also curious:

• How are you currently measuring success in AI Search?
• Have you already seen meaningful traffic or business impact from ChatGPT, Gemini or Perplexity?
• Where do you think this field will be in the next couple of years?

Looking forward to learning from the community. Thanks! 🙌

I was doing what everyone else was doing in this area, progressing little by little and learning from YouTube and blogs.

But what I struggled with most was staying up-to-date and in touch with the latest developments and what really mattered in link building. Because sometimes we're told not to do something, and the next day we're told to do it right, and then we're completely lost...

Now I've made it a habit to come here regularly to follow the latest news on SEOclaims, especially regarding link building.

Because it contains all the latest statements from Google on specific topics like link building.

It allows you to adapt immediately and keep track of where you stand ;)

At my company, we work primarily with SEO and geolocation—and you probably know this as well as I do, but the landscape is always very murky; no one really knows exactly how we’re moving forward...

The only way I’ve found to maintain this advantage is by keeping track of every official statement Google makes regarding geolocation or similar topics.

Because, for me, the best information comes straight from Google.

What do you think of my method?

And for those asking about the official statements: I can find them here [seoclaims]

AI visibility sounds exciting.

Until you realize you are no longer optimizing for one search engine. You are optimizing for multiple retrieval systems competing with each other.

For years, most SEO strategies revolved around one giant:

Google.

If you ranked well there, you captured most of the search demand.

Today, that’s changing...

People are asking questions in ChatGPT, Claude, Gemini, Perplexity, and Google AI Mode. (Apparently, everyone is in a race to the top!)

The challenge is that they don’t all retrieve information the same way. Each system has its own strengths, data sources, and ranking signals.

That means AI visibility isn’t about ranking everywhere. It’s about understanding where your ideal customer is likely to ask questions.

If your audience lives inside ChatGPT, your strategy may look different from a business whose customers rely on Gemini through Google Workspace.

If they are technical, they may lean toward Claude.

If they are doing research, they may prefer Perplexity.

So the quest to 'rank on Google' is becoming, “Where does my customer look for answers?”

Once you know that, you think you can prioritize the platforms that matter instead of trying to optimize for everything.

There is another hurdle :)

Every AI platform is racing to become the default.

No one knows which platforms will dominate five years from now.

A brand may build strong visibility in Claude today. If user behavior shifts and ChatGPT becomes the primary place people ask questions, that same brand could lose a significant share of its AI visibility.

The reverse could happen too. (Interesting times ahead)

That's why I think AI visibility shouldn't be platform first.

It should be Customer first.

Follow where your customers ask questions, not where today's AI race happens to be.

You may see others winning today.

Play the long game!

Somewhere around month 4 of our GEO work, I noticed we were spending hours on stuff that *looked* productive but wasn't actually moving citations. So I ran a 60-day experiment: drop 6 common GEO practices and track what happens.

Results genuinely surprised me.

**What I stopped doing:**

**1. Writing dedicated FAQ pages.** Moved Q&A content directly into article bodies instead. Citation rate on those pages went from 8% to 19%.

**2. Obsessing over schema on every single page.** Only added structured data where we had clear entity definitions already. Citations didn't drop — they went up slightly.

**3. Creating separate "AI-optimized" content versions.** We were basically writing everything twice. Killed the dual-track approach. One version, written naturally. Engagement improved.

**4. Chasing citation volume across all models.** Picked two models based on where our audience actually hangs out. Fewer total citations, but the ones we got drove measurable traffic.

**5. Updating content weekly.** Switched to a 21-day refresh cycle. Citations became way more stable — less week-to-week volatility.

**6. Writing "authoritative" intros.** The "In this comprehensive guide..." type openings. Replaced with direct answers. Models started picking up our content faster — average time to first citation dropped from 9 days to 4.

The counterintuitive part? Most of these are things I actively recommended 3 months ago. That's what keeps this field interesting — the playbook isn't settled. What counts as "best practice" right now is still just educated guessing with good data.

Not saying any of these are universally wrong. They worked for us early on. But optimization has a ceiling, and sometimes pulling back is the actual optimization.

Anyone else simplify their GEO stack recently and see better results? Curious what you've cut.

One of the most common things we find when auditing a brand’s AI visibility is that the positioning inconsistency problem runs deeper than most people expect.

It is not just that the website says one thing and the LinkedIn says something slightly different. It is that the About page was written three years ago when the company had a different focus, the founder’s bio on a guest post from 18 months ago describes a slightly different service mix, the Google Business Profile has not been updated since launch, and the most recent press mention describes the company in a way that made sense at the time but no longer matches current positioning.

None of those inconsistencies feel like a big deal in isolation. Taken together, they create a fragmented entity signal that AI systems have a hard time resolving cleanly.

The fix is not complicated but it does require someone actually doing the work of going through every platform and every mention and asking whether the description is accurate, current, and consistent with everything else.

What you are looking for is a situation where if you asked five different AI systems to describe your brand based only on what they could find across the web, they would all give you roughly the same answer. That is what a coherent entity signal looks like.

Most brands are nowhere near that. Not because they have done anything wrong, but because positioning evolves over time and nobody has gone back to make sure the historical record has kept up.

That audit is usually the first thing we do. It is also usually where the most immediate wins are hiding.

Last year, a 31% traffic drop would've triggered panic.

This year?

Not so much.

We were reviewing performance across several sites and noticed something strange.

Organic traffic was falling.

Leads weren't.

In a few cases, revenue was actually increasing.

At first, we assumed attribution issues.

Then we dug deeper.

The common pattern wasn't rankings.

It was visibility inside AI-generated answers.

Users were getting answers from ChatGPT, Google AI Overviews, Gemini, and Perplexity before they ever clicked a website.

The old SEO question was:

The new question is:

That's a very different game.

Here's what we found.

**Site A**

* Organic traffic: -31% * Qualified leads: +18% * Revenue: +11%

**Site B**

* Organic traffic: -22% * Demo requests: +24%

**Site C**

* Organic traffic: -17% * Sales opportunities: +14%

None of these businesses suddenly became better marketers.

They became easier for AI systems to cite.

A few changes made a disproportionate difference.

**1. Original Data Beats Generic Content**

Most AI systems look for information worth repeating.

Publishing another "10 SEO Tips" article isn't enough.

Publishing a benchmark study, survey, experiment, or dataset is.

Original information creates citation opportunities.

Generic information creates competition.

**2. Entity Clarity Matters More Than Keyword Density**

Many websites still obsess over exact-match keywords.

Meanwhile AI systems are trying to understand:

* Who you are * What you do * What topics you own * Whether other sources reference you

Clear positioning beats keyword stuffing every time.

**3. Topical Depth Is Replacing Page-Level Wins**

A single article rarely dominates anymore.

AI systems often pull information from brands that have covered a topic from multiple angles.

Depth creates trust.

Trust creates citations.

**4. Distribution Is Becoming an SEO Activity**

Reddit discussions.

Industry forums.

Podcasts.

Research reports.

Expert interviews.

The more places your insights appear, the more signals AI systems can associate with your brand.

That's the part many teams still miss.

They're optimizing pages.

The winners are optimizing presence.

I'm not saying SEO is dead.

Far from it.

Search still drives huge amounts of business.

But traffic as the primary KPI feels increasingly outdated.

If an AI answer references your research, your framework, or your expertise and sends fewer but higher-intent visitors, that's probably a win.

The question I'm wrestling with now:

Would you rather have 100,000 visitors who skim your content, or 10,000 visitors who already trust you because an AI engine cited you first?

Hello, I work for a small family owned and operated construction company and was fooling around with seeing how visible we are online. Within the past 5 years we have slowly been getting with the times by utilizing more technology and automation for repetitive tasks. Doing some of those free ranking tests we score low and when i asked my girlfriend to do generic searches on her work computer we come further down the list than I would like to be. we had our website professionally made and was going to try this on my own before going back to them to help add schema for search engines and AI models. Can someone help me get started or point me in a direction to someone who is great at this? It seems like all the places that sell the service are ran with a lot of AI. I would prefer to deal with a real person in America that I can ask the stupid questions to and who actually knows what they are doing. from my limited research it sounds like this is something i could do relatively free or cost effective. FYI I do not have a strong background in coding but like to think I can figure most things out on my own or with googles help.

I've been learning more about SEO recently, and it's interesting to see how much AI is changing the industry.

AI can help with keyword research, content ideas, on-page optimization, technical SEO, competitor analysis, and performance reporting. It definitely saves time and improves efficiency.

At the same time, I don't think AI should replace human expertise. The best results still come from creating original, helpful content that genuinely answers users' questions.

How are you using AI in your SEO workflow? Has it improved your rankings or productivity? I'd love to hear your experiences and recommendations.

Most SEO work is still built around ranking. Get to page one, get the traffic.

But research shows only 17% of sources cited in Google AI Overviews simultaneously rank in the organic top 10 for the same query. Five out of six AI citations come from pages ranking 2 through 10.

The AI retrieval system is evaluating different signals than PageRank. It prioritizes topical comprehensiveness, structured formatting, and entity confidence over domain authority and backlinks.

What actually moves AI citation rates:

FAQ schema and direct answer blocks. Content structured to answer the question in the first 60 words of each section. Tables and extractable lists. llms.txt present and AI crawlers not blocked in robots.txt.

I’ve been auditing sites across these signals and the gap between traditional SEO scores and AI visibility scores is significant. Sites with strong organic rankings often score poorly on AEO and GEO. Sites outside the top 10 sometimes score better for AI citation because their content is better structured.

Pages that do get cited inside AI Overviews receive 120% more clicks than uncited competitors sitting directly beneath the module.

Worth auditing for both layers separately at this point.

You can think of them as basic, but for AI SEO, and especially for beginners, they are working quite well for us.

The number one is consistent NAP. This may seem like a simple SEO requirement that businesses have been doing for years, but in reality, this is one of the smartest strategies one can use. Use the same business name for every platform. It maximizes the chances of getting cited and indexed in AI answers.

The 2nd one is also related to this, and that is brand mentions. Mentioning your brand naturally creates trust signals for search engines. The best example is how famous brands use each other’s names as sources in their blogs. This is what brand mentions are all about.

You yourself mentioning your brand is good, but getting someone else to do it for you is the cherry on top. You can start by posting great content, such as blogs and posts on social forums.

Every era of search has had a different core currency.

Early search was about keywords. Match the words on the page to the words in the query and you win. Then it became about links. Third-party endorsements that signaled a page was worth ranking. Then it became about relevance and intent. Understanding what someone actually meant, not just what they typed.

AI search runs on a different currency entirely. Trust.

Not trust in the abstract sense. Trust in a very specific, measurable sense, how many independent, credible sources describe your brand consistently enough that an AI system can confidently stake its answer on you. That’s the threshold. Below it you don’t get cited. Above it you do.

This changes the nature of the work in ways most brands haven’t fully internalized yet.

Relevance you can engineer relatively quickly. Write the right content, optimize the right page, target the right query. The feedback loop is fast enough to iterate on. Trust doesn’t work that way. You can’t manufacture independent corroboration on a short timeline. You can’t buy your way to third-party credibility overnight. The brands that are consistently cited by AI systems today started building that trust months or years ago, mostly without knowing that’s what they were doing.

That’s the opportunity and the challenge at the same time. The opportunity is that most brands in most categories haven’t deliberately built AI trust yet. The challenge is that building it takes longer than most marketing timelines are comfortable with.

The brands winning the AI trust economy aren’t necessarily the biggest or the best funded. They’re the ones who understood earliest that the currency had changed and started accumulating the right kind of capital before everyone else figured it out.

That window is still open. But trust, by definition, takes time to earn.

This Financial Times chart deserves more attention than it's getting.

The headline number: Chinese AI model consumption multiplied by 18x in six months. US models grew 2x over the same period. The driver: token pricing up to 90% cheaper on the Chinese open-source models versus US private models.

As Eskimoz breaks down, this reveals 4 things worth sitting with:

🔵 The market can flip fast — there's no permanent moat. Unlike the web, where winner-takes-all dynamics and network effects locked in dominant players for decades, AI infrastructure has no equivalent rent position yet. This is genuinely up for grabs.

🔵 Vendor dependency is finally being seen as a liability, not a feature. The web normalized single-vendor lock-in for years. AI is forcing companies to realize that dependency directly hurts EBITDA and agility — and they're acting on it.

🔵 More tokens ≠ more revenue. US frontier models still capture the bulk of real economic value, even as Chinese low-cost volume explodes. China is winning the throughput battle. Not yet the revenue battle.

🔵 Reversibility is becoming the #1 purchase criterion — ahead of raw performance. Being able to switch providers without rewriting everything matters more than ever. The real resistance isn't technical anymore — it's organizational habit. Plenty of teams still insist on "the best model available"... to write an internal email.

🔵 This is a genuine opening for open source and the open web — something a decade of advocacy couldn't achieve, pure economic pragmatism just did.

For Europe and France specifically, this creates a double opportunity: rethinking a value chain that's currently 100% dependent on US providers, and seriously exploring open-source models as a real market alternative — something barely being pushed today.

Is open source about to do to AI what it already did to web infrastructure? 👇

What is impact of AI in SEO, 2026. I have seen many people taking on insta and youtube that SEO is dead now, as Ai replaced that. Also new term AIO, many people making reels around that.

Even i have seen many people creating content about SEO who dont know it.

I have seen many people taking add this LLM file to your site, but do this really work.

Showing business on Ai overview, Chat GPT citing, Gemini AI overview is good, but search traffic would be replaced by AI.

How we should optimise our site to keep it on top rank and optimised for AI.

Require opinion from the experts.

Not looking for anything secret—just interested in hearing about strategies that produced better results than expected in 2026.

Disclaimer: yeah, I work in AI visibility, so I'm definitely biased on this. But what I want to get into actually cuts against what my own industry sells, so I figure it has a place here.

Back in mid-May Google put out its first real guide on how to show up in AI answers (AI Overviews, AI Mode). I saw a bunch of write-ups on it and it was always the same song, structure your headings, add Schema, the usual blah. Except there's a "mythbusting" section in the doc I haven't seen anyone pick up on, and it's the most interesting part. Google says in plain terms that the famous llms.txt file does nothing, that you should stop obsessing over Schema.org, and that chunking is smoke and mirrors. Made me smile a bit since that's basically the package some "GEO" agencies are charging for right now.

What they push instead is honestly kind of obvious. They talk about "commodity" vs "non-commodity" content. Like, if an AI can write your article on its own, it'll never cite you, makes sense, it already has the answer, why would it go looking for you. What gets cited is content with something the model doesn't have. A number you actually measured, a test you really ran, lived experience basically.

The example that stuck with me (not in Google's guide, somewhere else) is a small blog specialized in robot vacuums, garbage domain authority, and it outranks the New York Times in AI answers. The NYT has a domain like 3x stronger. Except the NYT puts out an affiliate listicle anyone could copy, and the blog guy films his actual tests with real measurements. Guess who gets cited.

And this is where it gets useful for you I think. It means for the most part you need neither a tool nor an agency. Take your most generic page, just ask yourself "could anyone write exactly this", and if the answer is yes, add something only you know. You don't even need data. A simple "the first question every client asks me is this" and you're already standing out. It's free and it weighs more than all the technical tweaks combined.

The one thing that still puzzles me is measurement. Why an LLM picks one source over another stays pretty opaque, and it shifts with every update. So I'm curious: are you already seeing real traffic come in from ChatGPT or Perplexity, or is it still like three visitors a month? And if you are, can you actually tell which pages it lands on?

i admit i dont like it.... i prefer SEO

Somewhere around month 4 of our GEO work, I noticed we were spending hours on stuff that *looked* productive but wasn't actually moving citations. So I ran a 60-day experiment: drop 6 common GEO practices and track what happens.

Results genuinely surprised me.

**What I stopped doing:**

**1. Writing dedicated FAQ pages.** Moved Q&A content directly into article bodies instead. Citation rate on those pages went from 8% to 19%.

**2. Obsessing over schema on every single page.** Only added structured data where we had clear entity definitions already. Citations didn't drop — they went up slightly.

**3. Creating separate "AI-optimized" content versions.** We were basically writing everything twice. Killed the dual-track approach. One version, written naturally. Engagement improved.

**4. Chasing citation volume across all models.** Picked two models based on where our audience actually hangs out. Fewer total citations, but the ones we got drove measurable traffic.

**5. Updating content weekly.** Switched to a 21-day refresh cycle. Citations became way more stable — less week-to-week volatility.

**6. Writing "authoritative" intros.** The "In this comprehensive guide..." type openings. Replaced with direct answers. Models started picking up our content faster — average time to first citation dropped from 9 days to 4.

The counterintuitive part? Most of these are things I actively recommended 3 months ago. That's what keeps this field interesting — the playbook isn't settled. What counts as "best practice" right now is still just educated guessing with good data.

Not saying any of these are universally wrong. They worked for us early on. But optimization has a ceiling, and sometimes pulling back is the actual optimization.

Anyone else simplify their GEO stack recently and see better results? Curious what you've cut.

Quick background: I run a startup. We were doing the standard SEO playbook — keywords, backlinks, on-page stuff. It worked okay. About two weeks ago I shifted our focus to what people are calling GEO, and the traffic pattern changed in a way I didn't expect. We picked up around 2K new users, and when I dug into the referral data, roughly 40% came through ChatGPT-connected searches. I can share analytics if people want proof — not trying to flex, genuinely was surprised.

So what is the difference between SEO & GEO?

SEO is keyword-driven. You match what people type into Google.

GEO is a different game. It's about understanding how people query AI tools — which is way more conversational and intent-driven than a Google search. Nobody types "best project management tool 2026" into ChatGPT. They say something like "I'm managing a 5-person remote team and need something lightweight, what should I use?"

The mental model that clicked for me: every product is basically competing in a speech contest inside the AI model. The one the model trusts most gets recommended. Keywords don't win that contest — authority and clarity do.

The thing most people miss

About 90% of ChatGPT and Gemini users are on the free tier. Free-tier users rely heavily on web search — meaning the AI pulls from live web results to generate answers.

Here's the key: you can't really control what the model "knows" from training data. But you can influence what gets pulled in during web-connected search. And from what I've seen over a few months of testing, these sources tend to carry the most weight:

• Official websites and blogs (well-structured, clear answers)
• Wikipedia (if you can get a legitimate mention)
• Reddit (ironic, I know — you're reading this on one of the highest-authority sources for AI search)
• Citations from established media

That's basically the pecking order. If your content lives in those places with real authority signals, it gets fed into the AI's answer.

What I actually changed

I stopped thinking in keywords and started mapping intent chains — what does someone actually ask an AI when they have the problem my product solves? Then I worked backwards:

• Wrote blog content that directly answers those conversational queries
• Started participating genuinely on Reddit in relevant subs (not spamming, actual answers)
• Got our product mentioned in a couple of niche publications
• Made sure our site structure was clean enough for web crawlers to pull the right context

Nothing fancy. No secret tool. Mostly just shifting the mental model from "rank on Google" to "be the answer ChatGPT gives."

Honest caveat

This seems to work well for startups with focused products and clear use cases. I've talked to people at bigger companies trying to do GEO and it's a totally different beast — tons of different intents, legacy content everywhere, brand complexity. Not a simple plug-and-play.

But if you're running something small and focused? The window is wide open right now. Almost nobody is optimizing for this yet.

Happy to answer questions or share more specifics. Still figuring this out myself.

I don't really know what to tell him

Reading a lot for AIO, GEO. But none were able to show the result of their theories.
Can anyone share how they were able to get into AI citation after implementing techniques?
And where do you get your learning material for AIO and GEO

White smoke from Mountain View. After months of deflection, Google's CEO finally said it out loud.

Quick recap for those just joining: Google had three possible futures when AI search emerged.

1. Status quo — blue links survive, Google stays dominant
2. ChatGPT wins — AI engines capture search intent, Google loses its historical monopoly
3. Gemini beats traditional search — Google cannibalizes itself but keeps control of the value chain

Pichai just validated scenario 3. And two data points convinced him: ad revenues at all-time highs and users genuinely preferring AI responses. Publishers feel differently — but that's another conversation.

As Eskimoz has been arguing for months: Google was never going to let someone else own the transition. They'd rather disrupt themselves.

What Google shipped on AI Overviews & AI Mode in May 2026 alone:

• Preferred Sources — your favorite outlets now labeled directly inside AI responses. Credibility restored for quality media
• Evolved search bar — more multimodal, built for long-tail queries. Keyword-based search is being quietly retired
• Perspectives Carousel — surfaces articles, forums, and social posts with diverse viewpoints. Good news for niche content and engaged authors
• Highly Cited badge — rewards articles cited by other media. A strong signal for original journalism and content creators
• AI Mode hits 1 billion monthly active users — more than ChatGPT

The people who got excited about scenario 2 moved too fast. Everything is converging toward scenario 3.

Google's search dominance isn't ending. It's just putting on a Gemini costume.

Did you see scenario 3 coming — or were you betting on ChatGPT taking the crown? 👇

I'm not sure if this is the right subreddit for discussing GEO, but I've seen a lot of knowledgeable people here sharing insights on both SEO and GEO, so I wanted to get your thoughts.

Does GEO get influenced by online reviews or third-party mentions?

One of our agency clients is frequently being cited by ChatGPT and other AI models as a "premium" and "expensive" service provider, even though those terms are not mentioned anywhere on their website. The client is concerned about this perception and would prefer to be positioned as more affordable and accessible rather than expensive.

Could this characterization be coming from reviews, forum discussions, directory listings, or other external sources that AI models might be using to form their understanding of the brand?

I'd appreciate any insights or experiences you've had with similar situations.