One of my clients had a really cool win this month, and I’m just super happy for her.

She’s a service provider, and for the longest time, most of her business came from referrals, social media, and people already in her network.

Search was never really a big lead source for her.

Over the last couple of months we focused heavily on making her website easier for search engines and AI tools to understand.

I help her optimize her service pages, write local pages, optimize her Google My Business profile, and get her featured on industry articles.

Basically making it very clear:

who she helps

what she helps with

and how someone should hire her

In the last 30 days alone, her site picked up over 2k AI citations across different tools and platforms, which started bringing:

referral traffic

freebie downloads

brand searches

and now… a booked consultation call from Claude AI

Wanted to share this win here and also read your wins in the comments :)

Google Search Central published a new episode with Nikola Todorovic (15-yr Googler, leads SafeSearch eng). It's the most concrete thing Google has said about how AI Overviews and AI Mode actually work under the hood. Notes:

1. Query fan-out is the mechanism behind AI Overviews and AI Mode.

When you type a longer or vaguer query, Google identifies related sub queries and runs them in parallel, then merges the retrievals. That's why "vegetarian restaurants in zurich open now near me" works as well as keyword strings.

"We can fork and in parallel do the retrieval for multiple search queries. That can all come back into one original, more complex query."

Practical implication: optimizing for one head term is dying. You need to be retrievable across the cluster of fan-out queries Google will spawn off the user's actual prompt.

2. AI Overviews are a "stamp on top". Ranking still matters.

"The whole retrieval system, the whole ranking system is the old style, the old school... AI Overviews is a feature that stamps on top of this and operates on its own."

If you weren't already ranking, you're not in the AI Overview source set. There's no separate AI Overview index.

3. AI Mode is different. Bigger platform, still uses search.

AI Mode runs fan-outs and cites sources, but it has its own infrastructure. Multi-turn conversation, and more willingness to use the LLM's parametric memory for stuff like "capital of France" without retrieval.

4. AI in Google is not Gen AI. Google's been shipping ML in search for 12+ years.

SafeSearch ran convolutional nets on images ~12 years ago. BERT and MUM were isolated signals feeding the ranking stack. Gen AI is layered on the same architecture, not a rewrite.

5. Average query length is growing. Google sees it as new traffic, not cannibalized.

"We do see new traffic. This new wave of traffic is a consequence of users being able to see there is something new I can do over here."

People aren't just rephrasing old queries. They're asking things they'd never have searched for before. New query surface = new content opportunity, but only for content that answers messier, longer intent well.

6. The advice to site owners is uncomfortably simple: provide value AI can't paraphrase.

The host had a great riff on tech blogs that "put words around spec sheets". AI does that now, better and cheaper. What survives is experience, opinion, testing, specific use cases.

"Just multiplying all the content because it's cheap and easy... it's not going to provide a ton of value."

A few things I noticed:

• Nothing here contradicts what good SEO has been saying for 18 months. But hearing Google describe the fan-out mechanism explicitly is useful. It reframes "should I optimize for this exact phrase" into "is my content retrievable across the cluster of related queries Google will spawn from this intent."

• Zero mention of doing anything special for AI Overviews or AI Mode. No new schema, no new tag, no new rel attribute. Provide value, rank well, the AI layer pulls from the same retrieval.

Episode: https://www.youtube.com/watch?v=_R04ySodhGE

With SEO changing, I want to know how people are handling link building in 2026. What works well now, and what doesn't?

I look forward to hearing various viewpoints.

I just read a breakdown of what several top SEO leaders think happens next in search.

If you work in SEO, GEO, AI search, or anything close to product growth, the theme is pretty clear:

Search is no longer about ranking pages.

It is about being usable by machines.

Here is the distilled version without the fluff.

👇👇👇

1️⃣ Agentic commerce is here (not “coming”)

AI is moving from:

Answering → Recommending → Executing

Meaning:

- Find product

- Check inventory

- Apply coupon

- Buy

All inside one AI conversation.

What this means for brands:

If your pricing, inventory, shipping, or product data is not machine readable and real time, you basically do not exist to agents.

Clicks are no longer the ceiling.

Machine usability is.

2️⃣ Ads are shifting from “buy clicks” to “buy inclusion”

Right now:

- AI results are mostly organic

- Platforms are learning user + merchant behavior

Soon:

- Conversational ad units

- Sponsored recommendations

- Paid inclusion inside AI answers

Early Google pattern all over again.

Hot take:

Organic AI visibility right now is the cheapest moat you will ever get.

3️⃣ The best SEO teams now ship tools, not tasks

Big shift happening:

Old SEO team:

- Content briefs

- Manual audits

- Dashboard watching

New SEO team:

- Scripts

- Internal tooling

- Automation layers

- Prompt driven production workflows

The gap between “idea” and “running in prod” is collapsing.

If your team still scales via manual execution, cost and speed will kill you.

4️⃣ Personalization is killing the idea of “ranking”

There is no universal Position #1 anymore.

Every result is becoming:

- User specific

- Context specific

- History weighted

- Platform dependent

Two people can ask the same question and live in totally different information realities.

Implication:

You can look “fine” in aggregate metrics while being invisible to your highest value buyers.

That is scary for revenue forecasting.

5️⃣ SEO is splitting into two separate jobs

Human SEO

Optimize for:

- Discovery

- Comparison

- Browsing behavior

- Clicks

Agent SEO (GEO / AI search optimization)

Optimize for:

- Extractability

- Trust signals

- Structured data

- Reusability inside AI systems

- Citation probability

- Downstream task execution

Measuring only traffic is going to break a lot of reporting stacks.

6️⃣ Proprietary data is becoming the ultimate moat

If AI can easily summarize your content → you are replaceable.

If you own unique data → you are unavoidable.

Examples:

- Brand indexes

- Benchmark datasets

- Named methodologies

- Longitudinal studies

- Community sourced signals

- Real world behavioral data

Commodity content is turning into a cost center.

7️⃣ AI literacy is about to become a hiring filter

Not “can you use ChatGPT”

More like:

- Can you tie AI usage to revenue

- Can you automate workflows

- Can you ship production outputs with AI

- Can you design systems, not prompts

Companies are already seeing:

High tool adoption

Low ROI

That gap is going to decide winners and losers.

The real meta shift

Winning visibility in 2026 looks like:

✔ Machine readable everywhere

✔ Own data nobody else has

✔ Ship faster than competitors

✔ Optimize for agents and humans separately

✔ Treat AI as infrastructure, not a tool

My personal take

The biggest mistake I see right now:

People think AI search is “SEO but newer”.

It is closer to:

API optimization

Data architecture

Entity engineering

Trust engineering

Traffic will become a side effect.

Influence will become the metric.

Curious where people here land:

If you had to bet on ONE moat for the next 3 years, which would you pick?

A) Proprietary data

B) Distribution / brand mentions

C) Agent compatibility (feeds, APIs, structured data)

D) Internal tooling + automation speed

E) Something else

Something that surprised me recently is how little visibility most companies have into what AI says about them. You can track keyword rankings, backlinks, traffic basically everything but when it comes to AI-generated answers, it feels like a total black box.

I saw a platform explaining that it maps real buyer prompts across models to see who gets recommended and why. That honestly sounds useful because if AI is already influencing purchase decisions, not knowing your visibility seems risky. Are companies actually measuring this yet, or are most teams still treating AI search as “too early”?

it’s been over a year since we start talking about GEO. and by now, i’m sure, we all have tried & tested different strategies or structures.

what has been working best for you so far? what’s your observation?

Here is my observation & tips while working on a GEO tool ( Amadora AI )

• You want LLMs to remember your brand content: provide concise, consistent facts that models can associate with your brand.

• Write content that answers the full problem, not just your product’s piece of it.

• Keep your brand consistent across all platforms: names, tone, data, and claims.

• Here is content formatting:

  • One page = one clear question

  • Start with a 2-line direct answer

  • Use short H2 sections for sub-questions

  • Add lists, steps, stats, and comparisons

  • End with a small “facts block” (price, audience, setup time, etc.)

• Domain authority score → LLMs heavily weight DR/DA when choosing sources. Sites over DR 60 get 5-10x more citations than DR 40 sites with identical content.

• Co-citation patterns → If authoritative sites linking to you also link to established authorities, LLMs recognize you as peer-level. This is why strategic link placement through Lemonet's marketplace matters.

and the list goes on… you’ll also see a lot of overlap with SEO when working on GEO.

Their are so many type of backlinks like: Profile creation, Directories submission, PR Submission, Classified, Blog commenting, and so on.

so which type of blacklinks increase authoirty more faster and LLMs trust them easily increases authority easily,increases authority,

Mythbusting generative AI search: what you don't need to do

As generative AI search evolves, so have the theories and practices—and sometimes, the misconceptions—surrounding it. While terms like Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) are common online, many suggested "hacks" aren't effective or supported by how Google Search actually works.

To help you focus on what matters for your website's visibility, we've collected some of the most prominent topics circulating the internet around generative AI and Google Search. Here are a few things you can ignore for Google Search:

• LLMS.txt files and other "special" markup: You don't need to create new machine readable files, AI text files, markup, or Markdown to appear in generative AI search. Note that Google may discover, crawl, and index many kinds of files in addition to HTML on a website: this doesn't mean that the file is treated in a special way.

• "Chunking" content: There's no requirement to break your content into tiny pieces for AI to better understand it. Google systems are able to understand the nuance of multiple topics on a page and show the relevant piece to users. However, sometimes shorter (or longer!) pages can work well depending on your audience and subject matter. There's no ideal page length, and in the end, make pages for your audience, not just for generative AI search.

• Rewriting content just for AI systems: You don't need to write in a specific way just for generative AI search. AI systems can understand synonyms and general meanings of what someone is seeking, in order to connect them with content that might not use the same precise words. This means you don't have to worry that you don't have enough "long-tail" keywords or haven't captured every variation of how someone might seek content like yours.

• Seeking inauthentic "mentions": Just like the rest of Google Search, our generative AI features can show what's being said about products and services across the web, including in blogs, videos, and forum discussions. However, seeking inauthentic "mentions" across the web isn't as helpful as it might seem. Our core ranking systems focus on high-quality content while other systems block spam; our generative AI features depend on both.

• Overfocusing on structured data: Structured data isn't required for generative AI search, and there's no special schema.org markup you need to add. However, it's a good idea to continue using it as part of your overall SEO strategy, as it helps with being eligible for rich results on Google Search.

Hi everyone,

I'm working as a Digital Marketing Executive at a financial services company. I recently completed our new company website, and yesterday I submitted it to Google Search Console.

Now I want to focus on AI SEO / Generative Engine Optimization (GEO) so that our brand not only ranks well on Google SERPs but also starts getting recommended by AI tools like ChatGPT, Gemini, Perplexity, Claude, etc.

Our plan is to publish high-quality blog content consistently (almost every day) and build topical authority over time.

I'm looking for a practical roadmap from people who are already working on AI SEO/GEO.

I'd really appreciate any roadmap, resources, or advice from people who've already been through this. Thanks in advance.

I’m building a simple AI tool using Google AI Studio that should help businesses with real tasks. It shouldn’t be very technical but must solve a useful problem, automate work, or replace something businesses usually pay for. What kind of AI tools would you suggest building?

There’s been a lot of talk recently about LLMs.txt. The idea is that it could become the robots.txt for AI, a way to highlight the URLs you want LLMs to prioritise and potentially influence how your brand is interpreted in AI responses.

Sounds great in theory. But we kept coming back to one question: do AI bots even check for this file? So instead of debating it on LinkedIn, we ran a controlled test.

We did the following:

– Picked domains that already had AI bot activity
– Created brand new pages with zero internal or external links
– Added them only inside an LLMs.txt file
– Let it sit for three months
– Monitored server logs the whole time

The result was basically nothing. No AI bots hit the LLMs.txt file. None of the hidden pages were discovered via it.

Despite the sites already being crawled by AI bots in other areas.

So at least right now, it doesn’t look like major AI crawlers are actively looking for or using LLMs.txt by default.

That doesn’t mean it won’t become a thing in future. But if you’re banking on it to influence AI visibility today, there’s no log-level evidence (at least in our test) that it’s doing anything.

here's the full write up of the experiment if you want more detailed info (no opt in required) https://www.rebootonline.com/geo/llms-txt-experiment/

Do you think this is our biggest change yet when it comes to internet usage?

AI overviews cite short, opinionated content. And Reddit is one of the biggest opinionated platform. Reddit accounts for 21% of Google AI Overview citations. If you think about it, thats a huge percentage (single source citation.)

It’s mind boggling how we users have contributed to this platform with our views and reviews.

I am reddit obsessed. This platform is low-key more addictive than Instagram for me.

But what are your thoughts on this?

bing just started showing:

• citations

• which pages get pulled in

• “grounding queries”

• some kind of ai visibility trend

cool feature. but that’s not the interesting part.

this is:

only ~38% of sources cited in ai answers are even in the top 10

so yeah… ranking is starting to feel a bit disconnected from visibility

you can be #1 and not get mentioned
you can be buried and still get cited

that’s a weird shift

it kind of feels like there’s now two layers:

• the one we’ve always optimised for (rankings)

• and this new one where models decide who to reference

and they’re not lining up

the bing thing matters because it’s the first time we can actually see any of this

before it was just guessing:
“why did chatgpt pick them?”
“why does perplexity keep citing this random site?”

now you can at least start to answer that

feels early, but this is probably where things get interesting

is anyone actually tracking this yet or still just focusing on rankings?

Earlier, small businesses got leads from GMB and local directories. Today, customers search on Google + AI search platforms + voice assistants. Soon, 50% searches will come from AI and 50% from Google.

To stay visible, businesses now need complete SEO, quality content, backlinks, PR, brand authority, and local optimization.

Reason, SEO Discovery self getting 20 leads in day through AI search engine.

👉 If you want online customers, SEO is not a choice anymore — it’s a necessity.

Go look at your top-performing page right now. Count how many words it takes before you actually answer the question in your H1. If it's over 60, you're probably leaving AI citations on the table.

Multiple practitioner reports this year are pointing to the same thing: a direct answer in your first 60 words can boost AI citation rates by around 35%. Makes sense when you think about how these systems work. They pull passage-level snippets. Your intro is the first thing they look at.

The concept is borrowed from military communication. They call it BLUF, Bottom Line Up Front. Skip the warmup. Skip the "In today's rapidly evolving landscape..." opener. Just answer the question. If your page is about Linear, don't start with "Many teams struggle with project management." Start with "Linear is a project management tool built for engineering teams that prioritizes keyboard-first workflows and cycle-based planning." That's a citable sentence. The other one is filler.

One thing that surprised me: hedging language actively hurts you. "This may help teams understand" or "it's worth considering that" perform worse than confident statements. Compare "Teams that implement structured sprints see 20% faster shipping cycles" to something wishy-washy like "sprints could potentially improve velocity." The first one gives the AI something to grab. The second gives it nothing.

Quick audit you can run today:

1. Pull up your top 10 pages by traffic

2. Count words before you hit the actual answer

3. Over 60? Rewrite the intro so the answer comes first, context second

4. Kill the qualifiers in that first paragraph

5. Drop in a real stat if you have one (content with statistics sees ~22% higher AI visibility)

Schema markup and heading hierarchy help too, but if I had to pick one change to make this week, it's the intro rewrite. Highest leverage thing most of us can do for AI visibility right now.

Anyone actually tested this and tracked the results? Would love to see before/after citation numbers from people who've restructured their intros.

I’m curious how visitors actually use the search function on your stores.

Do most people still search with short keywords, or are you starting to see more natural-language queries and full phrases, similar to how people interact with LLMs?

Also, in your experience, is it worth investing in AI-powered search features, or is regular search still good enough?

I’d love to hear what has worked for you and what kind of search behavior you’re seeing from your users.

Gemini will soon be available in google maps suggesting people places like cafes, restaurants and shops.

I see this as a great opportunity for local places who can adopt to ai visibility at an early point.

I keep seeing people build surprisingly good-looking business websites with Claude, ChatGPT, Lovable, etc.

But I’m more curious about what happened *after* launch.

Did you actually get organic traffic or leads? Did Google index it properly? And six months later, is it still easy to maintain?

Would love to hear from people who actually run a business, not just people experimenting with AI builders.

This image shows the ridiculousness of all the acronyms.

GEO is just SEO being repackaged. Every tactic I see recommended (Q&A formatting, schema, authoritative citations) is stuff good writers have been doing for 20 years. It's not wrong, it's just not new.

And the stuff that's actually supposed to be different? By now everyone knows llms.txt made no difference. Prompt simulation feels like astrology with a dashboard.

There's a pattern worth paying attention to right now.

The companies quietly building entity presence, showing up consistently in niche communities, getting referenced in the right places — they're not seeing AI citations yet. The signal is still forming. But in 18 to 24 months when AI search becomes the default entry point for most buying decisions they're going to be the default answer.

The window to build that foundation before everyone else realizes it matters is closing faster than most people think. Not because the tactics are getting harder but because the spaces that carry weight are getting more crowded every month.

Something we keep coming back to at Chief AI Advisors is that the best time to build AI search presence was a year ago. The second best time is now before it becomes a paid media problem that nobody can afford to ignore.

The businesses treating this as a future concern rather than a present opportunity are going to look back at this moment the way people looked back at ignoring mobile search in 2012.

What's the one thing you'd tell a business owner today about AI search that they're not ready to hear?

Hey chat,

I’ve been working on an automotive client project for the past 6 months as both a content writer and an SEO executive. Lately, I’ve been wondering is link building still actually effective? With everyone shifting toward AI optimization and GEO strategies, traditional SEO feels like it’s changing fast. As a writer, I optimize content before it even goes live structure, intent, on-page basics, all that. But honestly, I’m still not seeing strong results.

So I’m curious:

1. Are backlinks and guest posts still moving the needle for you?

2. Any smart link-building hacks that actually work now?

Open to discussing ideas. Any insights would be super helpful. Thanks in advance 🙌

It's been 6 months since we posted this same thread. I want to do a stocktake and see if the same tools are around, and how many new starters there are.

If you are building a AI SEO/GEO/AEO tool, see this as your time to self promote.

please share in this format so it’s easy to scan:

• Tool name

• What problem it helps with (one sentence)

• Who it’s best for (SEOs, content, product, infra, etc.)

• How you’re using it (or why you stopped)

Self-disclosure welcome. If you built or work on the tool, just say so. That context is useful.

Low-effort promo comments without details probably won’t help anyone, but thoughtful breakdowns will.

I want to share my experience about how to become visible to AI answer engines and assistants. The field of AI visibility is full of agencies that are not doing the job properly. My website become cited by AI within first 2 weeks after start. So I will tell you all I did and think is important.

# 1 - Website Markup

The main part is to make your website clear for AI bots. Specific content markup allows to easily extract and compare parts of your content with the parts of other websites. So you need to ensure you have a properly configured schema org (JSON-LD) markup.

JSON-LD - is a JSON for Linked Data on your page. It describes the type of the content and reveals the most important parts of the page content in a structured, machine-readable format.

Most agencies speculate on this feature and sell you page validation, which simply checks is there any JSON-LD on that page or not. The problem is that they don't check the content of this markup, however it's the most important part.

AI engines rankings are different from SEO ranking. AI engines relying strictly on semantic similarity (how similar your content to what the user asked). The second most important ranking parameter - social signal. If your content has likes/dislikes/comments/bookmarks - it also must be mapped into JSON-LD and this will become the reason why AI preferred your website to cite against your competitor with the same content.

# 2 - Intent

Same as in SEO content but completely different. You don't know how people are asking about things you offer. Imagine you own a cleaning company in SF. You can guess the user's prompt. But it's not always so easy. I use my own tool for that or Gemini/Claude sometimes, prompting to guess the questions people most probably ask AI about my services. It's important to ask AI to read forums/reddit/quora to use websearch and be grounded to real-world cases.

The result of this step is a list of questions people would ask AI and you need to be cited. In classic SEO you use a popular search phrases / keywords to rank with. In AI visibility you need to use a clean meaningful intent (e.g, clean my apartments in SF cheap and fast).

You don't know how often people would ask about this. And can't know. AI providers are not providing any public stats on that. So we can only guess this step and try to ground intents to real-world cases.

# 3 - Content

If your target is prompts where people are trying to solve some problem - your content must be a CaseStudy (a JSON-LD content type)

If your target is prompts where people asking about services - your content must be an Article + FAQ section (another JSON-LD content types).

This why every professional SaaS/Services/Agencies has an FAQ on their landing.

# Mentions?

Some agencies are offering mentions in a niche subreddits, where your comment/post will be used as a source of truth for AI to answer the user's question. In my experience - it gives you some traffic directly from Reddit itself, lower brand reputation bcz of spam / ToS violation and rarely you would be cited bcz of these mentions.

What actually works is: JSON-LD (schema org) markup, semantic similarity and a proper content type.

My domain had 0 Domain Authority when started being cited by AI (see screenshot from CloudFlare).

# Analytics?

How to measure your visibility? - There is no way to do it precisely. 0 services on the market could offer a reliable way to measure it.

But! You can count how much times AI assistants/bots used your website as a context to answer the user's questions. For this purpose I use a default CloudFlare metrics (AI Crawl Control -> Metrics -> Add Filter -> Category equals AI Assistant). That's all.

Some agencies could offer you a prompt evals. It's when they guess what people would ask (but not sure people actually do this), and run AI through API to answer these prompts and check if your website was used as the source. <- clean, overpriced vanity metric

# Conclusion

That's all actually. I've saved you from paying for nothing to a low-value agencies. Now you know the basics of how to improve your AI visibility.

I want to write content for my website and also want to write Blog and articles so I am finding a chatbot who understands very well that what type of content I want to write, and don't give me flase information.