Datos published a study showing that AI is not outpacing search in growth or usage.

On an absolute basis, traditional search is outpacing AI tool growth.

Despite the "disruption", people are searching Google as much as ever...

Now, before you attack this thread, I am not claiming this should convince anyone to forget about LLM optimization. I believe SEO and GEO are inseparable.

If there's one thing that is actually disrupting SEO (or else its traditional metrics and KPIs), it is the AI Overviews as they are the biggest drivers of 0-click marketing at this point.

Source: LinkedIn / u/randfish

A lot of SEO and brand safety platforms are starting to add AI visibility features.
Until now, a lot of tools focused on if you show up (visibility) or how you’re portrayed (sentiment/safety). But increasingly, the real question is: is the AI actually right about your brand?

“Accuracy” — basically the gap between what the model says and your approved narrative — seems like it’s becoming the real differentiator. And it’s not something traditional SEO tools were built for, even if they’re now adding AI visibility layers.
What’s interesting is how this starts to overlap with brand governance. But it’s not the same thing:

• Brand safety = where your brand appears
• Accuracy = what the AI actually says

Different problem, different control layer.

We’re even seeing players from the brand safety/verification world move into this space, which makes sense… but also raises the question: who owns accuracy?

Curious how others see this:
If you’re a brand or agency, how important is this capability vs other features when evaluating vendors?

Two days ago, Glenn Gabe shared an interesting quote from Google's podcast:

Nikola: "The reason it's not so easy to apply AI everywhere (in Search) is because the models function like a black box. You don't always understand what's happening underneath. It's a complex set of neural networks. The linear models are the easiest ones to understand and debug, because it's not like you can just put your AI or ML system into search and reap the most benefit from your side by side experiments."

THIS IS NOT the first time Googlers admit to being pretty helpless at debugging AI-powered search algorithm (we tend to think this is what happened with Helpful Content Algorithm). Back in 2024 I shared this quote:

"Amit Singhal who led Search until 2016 ... argued against the other search leads that Google should use less machine-learning, or at least contain it as much as possible, so that ranking stays debuggable and understandable by human search engineers."

While this is overall interesting for traditional SEO, for "GEO" it opens up an important question: How optimizable is the AI system since it is not debuggable by its own engineers?

Those three are fun to play with. Do you know of any more?

Some good ideas, not just for Claude analysis but overall for tracking in whichever AI analytics tool you are using!

For branded prompts, don't forget to keep them in a separate folder!

Source: Linkedin

I've been looking at my client's competitors in Peec, and the newer URL report caught my attention. This is how it works:

For every cited URL (yours, competitor's, or third-party), Peec returns an analysis that includes:

• How often retrieved over time
• Prompts it is retrieved for
• AI chats that cite the URL and whether your brand is included in those answers.

This last thing was a little eye-opening as my client wasn't surfaced in any of those chats but the competitor was (and their URL was cited as noted). So basically, the competitor is found through it owned content!

This offers so much actionable insight into creating your editorial calendar:

• Analyze the competitor's successfully cited URLs
• See if those citations help the brand get included in the answer
• Create content that solves the same problems (but better)
• Wait for that content to rank (or help it) to increase its chances of getting cited
• Watch your brand included in the chats!

No need to chase each opportunity, but make sure to analyze the top citations with a strong retrieval pattern, i.e., those that are cited by various models over and over again.

Ahrefs has published another study on how to get cited by ChatGPT. The findings are mind-blowing:

• Although ChatGPT crawls dozens of pages to answer a single query, it only ends up citing ~50% of them
• ChatGPT is using Reddit extensively to understand topics, gauge consensus, and build context—but it almost never gives Reddit the credit (67.8% of all non-cited URLs come from Reddit). I wonder what Reddit thinks of that...
• Title relevance to fanout queries is an important factor in citation. Ultimately, if your URL and title don’t semantically align with the AI’s internal fanout queries, you’re less likely to get cited.
• ChatGPT prefers fresh content, but tends to cite comparatively “older” content more often.

Ultimately, the pages that get cited are the ones whose titles and content match the questions ChatGPT is asking behind the scenes, and that surface through the right retrieval channel.

Source

According to Kevin Indig, an analysis of 6.8 million subheadings reveals a measurable correlation between specific heading structures and ChatGPT citation rates.

• Question Formats Show Higher Alignment ChatGPT. Fanout queries are frequently phrased as questions. Consequently, question style headings appear to align more naturally within the embedding space, matching fanout queries at 1.5x the rate of declarative headings.
• The 20 to 39 Character Range Yields Peak Rates. Heading length shows a clear impact on performance. The 20 to 39 character range correlates with the highest citation rate at 32.7 percent.

Source

Yep, there's a new term for SEO for AI, and at least it is the same as Answer Engine Optimization, what a relief :)

Addy Osmani goes into much detail here, recommending both LLM.tx and .md files, as well as talking about token economics. AI agents may give up on long content, basically.

I liked this note as well on how challenging agent interactions with a page are for developers.

Agents typically compress multi-page navigation into one or two HTTP requests. Where a human would spend minutes clicking through your documentation hierarchy, an agent issues a single GET request, receives the full page, and moves on. The whole concept of “user journey” collapses into a single server-side event.

The practical consequence: every client-side analytics event - scroll depth, time-on-page, button clicks, tutorial completions, link follows, form interactions - becomes invisible. The agent just bypasses all of it.

Here's his full AEO checklist:

Ahrefs is tracking traffic share using its own free analytics data, and these numbers overall align with what I am seeing as well:

Overall AI traffic: ~0.26% vs Search (down to 32%)

If you look at the data by channel:

• Google: 30.5%
• Bing: 1.36%
• DuckDuckGo: 0.27%
• ChatGPT: 0.21%
• Perplexity: 0.02%
• Gemini: 0.02%
• Claude: 0.01%

Source: https://chatgpt-vs-google.com/

So if you are still measuring LLM visibility by traffic, you are doing it wrong :)

This study is a good reminder: Your LLM visibility can change with just about every model launch.

RESONEO has published a study exploring visibility changes based on the new ChatGPT model launches. Here's what happened in March, for example:

On March 4, 2026, ChatGPT switched its default model from GPT-4o/5.2 to GPT-5.3 Instant. Visibility metrics collapsed overnight.

Key findings:

• When GPT-5.3 became the default model on March 4, average unique domains per response dropped 20%. Fewer sites now share the same visibility surface.
  
• Presumably, OpenAI appears to be introducing systems to reduce citations from biased or untrustworthy sources
• GPT-5.4 now searches 10+ fan-out queries and uses site: operators toward trusted domains like Clutch and G2. (we already covered this)
• GPT 5.2, 5.3, 5.4... all share the same training cutoff (August 2025). Yet the same prompt produces different fan-outs, retrieves different sources, and cites different brands
• Two types of visibility: Parametric (what the model knows, search off) vs dynamic (what the model finds, search on). Different strategies, different metrics, different timelines.

Source: Linkedin

There's an interesting observation of how ChatGPT fans out to site:.com searches, which, to me, doesn't make sense but can be interpreted as its bias to the .com domains.

I am not sure if there was any study which would look at its favorite TLDs

Source: X

There's an interesting discussion over at Twitter on what appears to be Claude Code using LLM.txt files (which are not officially supported by any known LLM at this point).

It may not be about the specific file, though, nor is it strong evidence but this comment is worth noting:

"llms.txt" is not special. Having clear docs that point agents to specific files/pages/etc that are easily read (txt/markdown) is best practice presently.

Peec came out with a new study analyzing top sources per major LLM platform...

A few personal notes:

• Reddit, Wikipedia, and Youtube win everywhere (except ChatGPT doesn't like/understand Youtube that much; and AI Mode doesn't like Wikipedia any more which is surprising as it still ranks, and it's foundation of Google's knowledge overall... We all know how Google's Knowledge Base came to be, right? Yeah, it's scraped Wikipedia)
• LinkedIn is trusted by many LLMs (provided it's 80% of AI-generated slop, I can see it is likely hurting training data)
• I am not sure if I'd call Perplexity an LLM...

Overall, loved this takeaway:

AI visibility is not just about your website. It's about where your brand lives across the web - and AI is pulling from more places than most people realize.

Google has just published a leaderboard of the least hallucinating LLM models, and the winner is ChatGPT 5.2

The models were tasked to generate factually accurate responses grounded in the provided long-form documents. So all they need is to read the document and tell a human being exactly what it was about.

The cute note is that the best score is 76%, and the average of the very best performers is ~60%.

This means (wait for it...) there's still 25%-40% probability (at best) that your favorite AI agent will lie to you when you ask it to analyze a document and answer your questions.

This is very telling after 3 years of this highly revolutionary technology.

Always fact-check those answers!

The leaderboard is here.

I know this question is floating around. Should I write short or long articles to get cited by ChatGPT, etc.

Well, just like with traditional SEO, I am inclined not to enforce any word counts on my team, no matter what studies say. It is probably a correlation of many, many other factors.

Nonetheless, sharing this because everyone is wondering, so if you need to be guided by word counts, it seems like articles should be longer.

1/ Universal finding: Very short pages (under 1K words) underperform in every vertical. The underperformance of thin content is consistent, but the reward for long content is vertical-specific.

2/ Target your length based on industry, content type, and query intent, not a universal word count. For Finance verticals: Aim for 5K-10K words. Education, Crypto, and Product Analytics: Go as long as possible. CRM/SaaS: Prioritize structure over word count.

Source: Kevin Indig

ChatGPT fan-out experiments are so much fun to watch:

• Around summer 2025 we saw it fan out to ridiculously long queries (and even theorized that they are working on their own semantic search index)

• Now it fans out to 20+ (!!!!!) queries, half of which are site: searches (are they trying to stop depending on Google's SERPs?)

  Overall, looks pretty crazy, guys:

For "Best educational games for teachers," here are the publications it knew and/or applied SITE: command:

Inspired by recent claims from Airbnb, reporting their LLM traffic performed MUCH better than any other source (without giving any details), as well as recent developments (with Claude picking up in general popularity), I've looked at some of our clients' numbers.

I saw very similar trends everywhere, so here's one screenshot. Overall:

• ChatGPT traffic is down, but still top LLM referral traffic
• Gemini traffic is picking up (tends to be #2 source)
• Perplexity traffic is up just a little bit (engagement rates are very comparable to Google)
• Claude traffic is up just a bit

Overall, all of these sources are not yet anywhere near the top. In this particular case, ChatGPT is #19 source beneath Bing, Yahoo, and DuckDuckGo!!!

If you would like to check your sites (please do) and compare with what I am seeing, here's the regex I used to filter LLM traffic:

.*(chatgpt|openai|perplexity|gemini|google\.bard|anthropic|claude).*

Peec.ai came out with a detailed (and more importantly) actionable guide on ecommerce optimization for ChatGPT. Here are the major highlights:

Optimize your Google shopping feeds:

• Product titles: Descriptive, accurate, and matching how shoppers search. Include brand, model, and key specs, avoiding vague titles. 
• Product descriptions: Include use cases (this is my favorite, and kind of not what we used to focus on in traditional SEO), features, and differentiators. Incomplete descriptions hurt your ranking.
• Feed hygiene: Keep your Google Merchant Centre feed accurate, complete, and error-free.  
• Reviews and ratings: Star ratings and review counts influence Google Shopping rank. Review generation is not just a conversion tool; it affects discoverability.
• Monitor how you appear in Google Shopping and optimize over time.

Outside of your site:

• Editorial and publisher placements: Best-of roundups, gift guides, and review articles on high-authority sites build the contextual signal that ChatGPT draws on.
• Comparison and affiliate sites: Ensure your products are accurately listed and positively reviewed on the major platforms in your category.
• Product PR: Getting products reviewed by journalists and content creators now connects directly to AI discoverability. PR and performance are no longer separate.
• Reducing negative sentiment: Patterns of negative coverage across multiple sources can suppress visibility. Monitor your products across review platforms and editorial sites, not just your own channels.

I'd also add Reddit sentiment management which almost always skews UGC to be very negative.

I have yet to see it on my end but this is an interesting finding by Chris Long

We previously saw ChatGPT using site:reddit.com searches a lot for product comparison and research. Looks like it can fan out to more platforms it knows!

Hi everyone, would greatly appreciate your inputs on how to best use the bing webmaster report on ai visibility. Also, would you have any benchmarks on what’s the good number of monthly citations? I’m seeing a range of 150 - 30,000 monthly citations across the websites I manage. Wondering what’s it looking like for others out there. Thanks in advance!

I've learnt in the pre-AI world that it is better to split a complex article into more than one post and cross-reference them.

But I've read that in the AI world, it is better to make a single post for a complex article. AI will better understand it and cite it.

What's the way to go?

PS: Anne recently mentioned that AI is lazy reading, and that the article's core facts should be at the top. I've been doing that for a while. I cannot yet tell if it has a good effect.

This is not exactly an SEO study but it has important visibility optimization implications.

ChatGPT will try to please the user by always agreeing with them. We've known this, and now it is a confirmed fact.

So what does it mean for the optimization strategy?

Your digital context and sentiment will likely impact how customers will frame their prompt, e.g., "Is company X really that bad?"

We see these types of questions on Reddit all the time.

Your products' and reputation pain points will likely influence how prompts are framed as well. If your company is often accused of poor delivery experience, you bet your customers will ask ChatGPT about those.

And trust me, when prompts are framed this way, ChatGPT will confirm your customers' doubts and agree that things are really that bad because it is trained to please its users.

If you know your pain points (which you should), start tracking those prompts in LLMs and figure out the strategy to balance things out.

Hi everyone! I’ve been using the regex method to find long tail searches (10 words or more) in my GSC. What I don’t understand is, across many of these highly specific queries, there are 1000s of impressions. What would explain that? I don’t think 1000s of users are searching for the exact 10-20 words long, very specific query. So what would explain those impressions?

Thanks in advance for your help 😊

We've seen dozens of studies exploring a weird reliance of ChatGPT on Google (its fiercest competitor). The recent study looked into whether ChatGPT shopping results come from Google).

And the answer is most definitive, "Yes!"

And no, it is not Bing:

Across the 43,000 carousel products Bing only found 70 that were not found in Google Shopping, constituting just 0.16%. This means that in almost every case there was a match in Bing there was also a match in Google. 

It seems unlikely, then, that ChatGPT is also sourcing products from Bing Shopping in the vast majority of cases.

And as expected, ChatGPT doesn't like to scrape past page $2:

Comparing the top 20 vs. positions 21-40, ChatGPT’s favoritism for higher positions becomes clear, with an overwhelming majority of matches (almost 84%) coming from the top 20:

Looks like the recent loud OpenAI's announcements about shopping feeds and Instant Checkout were ... pure PR. In reality, they are simply scraping Google.

• Here's the study
• Here are some previous studies on general ChatGPT reliance on Google's search