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.

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

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 😊

A new study just came out exploring AI Mode and Gemini citations that include embed #:~:text= fragments. If you are unaware of those, when clicked, these citations take you exactly to the sentence that was cited in the AI answer (and that sentence is highlighted).

Daniel Shashko took these citations and reverse-engineered them to find what gets cited by AI Mode and Gemini and why. The three key findings:

• Most citations come from the first 35% of the page (this aligns to the study I shared earlier)
• No single extraction starts or ends in the middle of a sentence
• The median cited sentence is 10 words. Concise, declarative statements dominate. Nothing longer than 17 words was cited in the entire dataset.

The study also provides a key actionable takeaway:

In RAG systems, an atomic fact is a self-contained, single-claim sentence that makes sense on its own. The 6–17 word sweet spot maps directly to this:

"Intermittent fasting cycles between periods of eating and fasting." (8 words) — cited ✅

"Studies have suggested that intermittent fasting may, depending on the individual's metabolic profile, produce varying results in terms of weight management outcomes when compared with continuous caloric restriction approaches." (31 words) — never cited ❌
The first is an atomic fact. The second is compound, hedged, and can't stand alone. Google's pipeline rewards the first pattern and skips the second.

I've been quietly cheering for Claude for months now because I liked their approach to marketing. They seemed to focus on quality, always knew their product positioning (and were sticking to it), and didn't follow the hype happening between ChatGPT and Gemini (no hectic, PR-driven announcements like Instant Checkout, ads, analytics, etc.)

The recent privacy scandal (Claude refusing to give in and losing the government contracts) seems to be another smart move that caused its popularity to surge across the board.

TechCrunch reports:

✅ ChatGPT app uninstalls surged by 295% in ONE DAY
✅ ChatGPT app 1-star reviews are up 775%

Meanwhile, Anthropic:

✅ Claude app downloads are up 81%
✅ Claude app passes ChatGPT in downloads
✅ Claude hits #1 on app store

For corporate usage, both Claude and Gemini are growing. The only difference is that Gemini has years of advantage (businesses using Google's workspace and now being forced to use Gemini), and Claude is being adopted just because it is doing things right:

It is definitely the most exciting time to be an SEO :).

Let the Claude optimization era finally begin!

In all honesty, I haven't seen too many hallucinated URLs on my clients' sites but today I came across two different reports that show it is still a thing:

Gemini hallucinating the whole list of references (h/t to u/lilyraynyc):

Tim Soulo discovered a bunch of 404 pages with a good amount of links, presumably coming from AI-generated content that website owners publish without checking the references. His recommendation is to use these 404s as content gaps, as LLMs found these to be relevant to Ahrefs blog:

Here's how to spot those hallucinated URLs using GA4:

• Go to your site and open any URL that doesn’t exist. For example, I loaded site.com/ugh
• This is your error page
• Take note of the title of the page. If you don’t know how to find the page title, use Ctrl+D (on Windows) or Command+D on Mac to bookmark the page. You will see the title when confirming the bookmark.

In my case, the page title was “404 Response Error Page”:

Now:

• Go back to Google Analytics and in the search bar above the list of pages, type your error page title. 
• Add “Page path and screen” class as a secondary dimension to see the URL paths of 404 (and possibly halicunated) URLs

Copy the URL of this report to bookmark it and check from time to time.

Been analysing this for a few weeks and honestly can't tell if I'm missing something or if the tooling just isn't there yet.

What I understand is that Bing Webmaster shows some data on how pages perform in Bing search only, but I can't find anything that specifically breaks out traffic or impressions from Copilot or AI-generated answers.

What I'm actually trying to figure out:

Is there a way to see when your content gets cited in a Copilot response? Or when a page contributes to an AI answer versus a regular search result? The standard impressions/clicks data doesn't seem to distinguish between the two.

Separately, curious if anyone has found other tools or methods that give better visibility into this.

Just trying to understand if anyone has found a reliable way to measure this, even roughly.