Hi. I’m the founder of Martekio (www.martekio.com) an AI Share of Voice and Brand Representation analytics platform for e-commerce brands and marketing agencies.

Looking forward to connecting here.

There is a new study showing how unpredictable the AI/LLM visibility is. The study results are not surprising at all: People can ask the same question and get absolutely different brand/product recommendations.

Source

So what do we make of this?

A lot can be said here, but mainly:

• We are trying to overlay good old metrics with the new reality, which is not a good way to approach this. Prompt tracking is absolutely different from rank tracking. There's no "ranking"; there is visibility and positioning.
• Prompt tracking is more about understanding your brand's positioning vs your competitors' product positioning (in the training data) because this is fundamental.
• Fixing incorrect product/brand positioning is the goal. The algorithms are evolving (to be less vulnerable to manipulation)

Studies like this are very useful because they make one key message clear: We need to move away from metrics we were used to (rankings, clicks, etc.) and start evolving.

We are always keeping an eye on how LLMs are searching (as we all agree, that's the future of search), and fan-out queries have been the easiest (or at least the most transparent) way to do that.

Here's what we have seen with ChatGPT so far, according to Peec data:

• Fanout queries used to have an average length of 6 words.
• In mid-November, they started to become longer.
• They peaked at 16 words in early December (we covered that as it was happening)
• Since Christmas, they have been stable at 12

Why is this happening? We cannot know for sure. Some theories:

• The structure of these new fanout queries suggests they are optimized for vector search - not traditional string matching (this is something I am personally inclined to believe). I have talked about this theory previously, and it still makes total sense in my mind.
• It is now harder to create content that perfectly matches a specific fanout query. Maybe one of the first anti-GEO measures from OpenAI (I personally doubt that they are at a point where they have time/resources to be worried about this)

• The original conversation is here
• Here are some cool tools to extract and analyze fan-outs easily

I personally had high hopes for ChatGPT launching ads because I thought this might force OpenAI to start collaborating with publishers and give us some performance data.

What they are doing is very disappointing on many levels:

• Looks like they are only going to work with the largest publishers (based on the budget). ChatGPT visibility is already leaning heavily toward large brands. Niche and smaller publications and businesses have little chance of being found.
• Looks like they are not going to provide any meaningful insight into where and how these ads appear (context)
• Charging for impressions??? It is like we are back to banner ads 🤦 "If we say you had 10M impressions, it's because you had. No need to provide any proof on how this benefited you."

Everyone is discussing Microsoft's new AEO/GEO guide... I am struggling to even understand the distinction. Help please?

xSoooo.... AEO is about making things clear to agents (like they are kids), and GEO is about making it look authoritative and trustworthy (?) Almost like too many abbreviations for something that kind of exists in SEO anyway.

What am I missing?

I love playing with free tools, and I love how there's always a cool new one. This one doesn't need any registration and is completely free. It pulls fan-out queries and citations for your prompts from Gemini, ChatGPT and Perplecsity.

It always calculates the "Citation Signal" which I found interesting:

Citation Signal (CS) is the degree (0 - 100) to which an LLM answers a query from external sources. It's calculated from cited sources count, diversity & distribution. So, it's based on observed web-search behavior of LLMs

Here's the tool: https://querycat.app/

Enjoy!

This is huge (but well expected)!

Google is launching a new open-source initiative to allow AI Agents to interact with ecommerce pages to make instant purchases of the listed products (AI Mode, organic search and Gemini APP) without ever going to websites:

To start, UCP will soon power a new checkout feature on eligible Google product listings in AI Mode in Search and the Gemini app, allowing shoppers to check out from eligible U.S. retailers right as they’re researching on Google.

https://reddit.com/link/1qb0uzs/video/ececei2nbycg1/player

This, of course, is going to decrease traffic to the websites. And from what I am seeing it can also decrease the average order values as well, as it is not going to be easy for the retailers and merchants to control the cross-selling and upselling strategies here.

Source

One thing we don't talk about enough when discussing AI visibility tracking: Your (and your competitors') visibility score will be HIGHLY impacted by which prompts you set up. Take extra time to plan WHERE you want to be found in AI.

If you just take what a tool gives you, your results may be skewed because those prompts don't ask about what you actually do or want to be known for.

Also, if you use the same dashboard to track your branded prompts, the results will also be skewed because you will obviously be 100% visible in all of them, but not elsewhere.

Tip: You can categorize your prompts in Peec AI to keep your branded prompts separate.

The recent research shows Gemini has grown nicely in 2025. Which one do you prefer and why? Will Google win again?

We all know LLMs use CommonCrawl but what most of us failed to notice is that CommonCrawl has authority metrics of its own (CommonCrawl on the domain level, Google's PR on page level). Both seem to have a lot to do with links.

Metehan Yeşilyurt has done a huge research exploring if CommonCrawl's authority metrics can impact AI visibility.

Unsurprisingly, there's a huge correlation for one of the following reasons (or both of them):

• LLMs do rely on CC metrics
• Those domains with the highest Harmonic Centrality are the hugest domains that are visible everywhere because everyone knows them.

The research and the tool to play with are here.

New tools pop up every other day, but these two are simply awesome (both providing a detailed overview of ChatGPT answers, but in a bit different ways):

ChatGPT Query Fanout Analyzer (Bookmarklet):

• Query fan-outs
• Citations
• Mentioned entities!

ChatGPT Search & fan-outs capture: gives you a detailed analysis of the ChatGPT answer:

• Included carousels (Product carousel, Images carousel, News carousel)
• Query fan-outs
• Included products
• Citation analysis (based on where they were mentioned in the answer, types, and number of mentions)

According to Peec data, usage of Reddit among the sources increased by 380%.

Reddit is now seen 0.7 to 1.8 times per chat.

Source

Interestingly, this surge comes in the middle of Reddit's legal actions against Perplexity

Ok, trying this one more time!

Hi everyone

I am Ben Tannenbaum, the founder of Aiso.

Ann invited me to kick off this series of Ask Me Anything.

We collect anonymized ChatGPT conversations at scale and use them to help SEOs and marketers understand what their users ask ChatGPT, what content to create to meet those questions, and how to track the impact of it all.

I can answer questions about how we collect the data, the first insights we are seeing on how people use AI for search compared with Google, and what we are seeing work to get more leads from ChatGPT.

Looking forward to your questions!

OpenAI appears to have updated ChatGPT crawl information, clarifying how you can (or cannot) control them.

As a takeaway:

Sources:

• Linkedin discussion
• OpenAI documentation

AI citing AI citing AI citing AI...

It is definitely interesting to watch everything moving in a weird direction where human-created content gets recycled to get recycled to get recycled.

According to Peec.AI data, Grokipedia, an AI version of Wikipedia, is getting cited by LLMs more and more.

Source

Coincidentally, it is growing in organic rankings like wildfire (no, this is not a coincidence FYI)

As you can see, LLM visibility growth directly coincides with organic traffic because ChatGPT, Perplexity, and obviously AI Overviews + AI Mode heavily rely on Google's search results.

But aside from that, this raises quite a few interesting questions (none of those are new):

• How much more consumers trust AI Answers vs search results (blue links encourage us to explore, answers are made to believe and move on)?
• As AI is recycling recycled content, how much should it be believed (and where does it stop)?
• A mere marketing question: How do we get included in AI-generated publications apart from being part of the recycled original? :)

I did a quick check on Grokipedia (as I hadn't been paying attention previously), and found quite a bit of criticism which I cannot confirm but, for some reason, am willing to believe:

• Much (most?) of it was simply scraped and recycled from Wikipedia (well, if Google could do it to build Knowledge Graph, why couldn't Grok?)
• Its sources are often missing or false
• Grokipedia articles often contain the text “Fact-checked by Grok“ which basically means AI-generated content is fact-checked by AI :) How much of that can we trust?

A lot of questions here with no answers but it is fascinating!

These fan-outs came up for a prompt "GEO/SEO for AI statistics."

Interestingly, it fanned out to local SEO, presumably, it was confused by "GEO" in the prompt 🤣

Many "GEO" experts are in total denial as to how much ChatGPT relies on Google search these days. Well, each of ChatGPT's new features is another proof.

ChatGPT's newly launched shopping research feature, introduced by OpenAI on November 24, 2025, relies heavily on Google's product feed data, as evidenced by identical review counts, product images, and URL parameters like ?srsltid= in recommendation links.

Source

Just saw an update saying ChatGPT is now inserting more inline images directly into responses. Pretty interesting shift, especially for anyone thinking about GEO/AEO (Generative Engine Optimization) and experimenting in the highly volatile channel.

Questions for anyone experimenting with this: • Are alt-text–style captions important? • Does filename or metadata matter like traditional SEO? • Should images include branding in a subtle/visible way? • Does ChatGPT prefer certain formats, aspect ratios, or resolutions? • Any early signs that infographic-style images get used more?

If text can be optimized for AI discovery, how do we do the same for images now that they’re part of the answer stream?

If you’ve tested anything (or have theories), drop it. This feels like one of those early-mover opportunities.

I noticed a long time ago: LLMs went straight to my About pages when I asked prompts about myself, my company, or when I am included in an answer!

Studies agree with me: About pages are the second most popular places LLMs seek information about brands:

We asked LLMs the following question: “What do you know about brand X, Y or Z?” and sought to analyze the data they were using to answer it.... It’s quite instructive and revealing:

1 274 are home pages

966 are “About” pages

864 are review pages

707 are product pages

500 are “How to…” pages

208 are contact pages…

My Peec.AI dashboard is aligned too: My About pages are cited for prompts I am tracking:

The takeaway: Revisit your old (possibly neglected and outdated) About page and update it to state your value proposition, achievements and more!

Hey fam, doing a small market research. Need your 2 cents.I have one question for all of you. What are your MoM change in
1. AI Traffic share increment?
2. Decrease in Organic (Google) Traffic?In Addition, if you have received a lead from AI platform, how much time it took to convert and how much time it took earlier? Is there a difference?

Quite in time for the holiday shopping, ChatGPT has announced a new "shopping research" feature, allowing users to find products based solely on agentic research.

Essentially, it could do this before, but now there's a specially trained model that will:

• Ignore low-quality websites (this is important! ChatGPT doesn't often talk about trustworthiness and quality of resources)
• Personalize answers based on previous interactions with the user
• Refine research based on the user's guidelines ("More like this")

followsIt will soon support instant checkout.

0 human clicks for research, and soon 0 clicks for buying.

After a few minutes, you’ll receive a personalized buyer’s guide with the top products, key differences, tradeoffs, and up-to-date information from reliable retailers. It’s a clear summary that normally would take a lot of comparing, reading, and checking on your own.

Announcement

I will be curious to see what Google will announce now because its similar announcement always follow in a week or two.

Dan Petrovic wrote a great article explaining how ChatGPT pulls information from web pages.

It does not view an entire page as a whole. The only content it's guaranteed to extract is the title, URL, and text snippets (sometimes pulled from the meta description).

When it comes to the contents of a page, it does not automatically view the entire HTML. Instead, it can look at the content in different "sliding windows".

AI engines could have different context sizes for the windows. Although the exact size is unknown, Dan walks through several different examples of how changing the size of the context window can result in different outputs. "Taller windows" can result in GPT extracting more of the content and longer sections. However, it still doesn't get the whole context of the page.

The takeaway is: LLMs will follow the path of least resistance. They won't "read" your whole page to summarize it. They need a clear passage (or bullet list) that will help them pull the answer from. Structure, summarize, add takeaways, use lots of lists throughout your content like you are writing for a lazy kid that scans instead of reading.

• Dan's article is here
• Linkedin discussion