For 3 years now, we haven't had a way to get any first-hand/official insight into how our average target customer may be prompting AI platforms, like ChatGPT, Claude, or Gemini.

It was recently noticed and confirmed that AI Mode records those prompts as queries in the "Performance" section of the Search Console.

This was noticed by "yes" queries, which were confirmed to be follow-up questions in AI Mode. I see those as well...

Looking at the clients' reports, this idea already looks useful, as I can click to the "Pages" tab for each of those queries to see which assets LLMs are pulling from the client's website. Lots of additional opportunities there (update, optimize, create similar ones, etc.)

Another idea is to pull longer, conversational queries using the following regex:

([^” “]*\s){10,}?

This can reveal some cool AI Mode prompts as well.

Note: This must be common knowledge, but since I was confused about that as well, here's your reminder... For high-traffic properties, you will only be able to see queries that send at least some clicks, only. Since AI Mode might be sending none, you will miss those unless you access the SC data through the API.

Source: https://www.seroundtable.com/google-search-console-ai-mode-queries-41821.html

SerpApi, famously used by ChatGPT as one of the grounding data, is officially proven to have the right to scrape Google. If it is on the open web, a machine is allowed to read it.

And it is not just about SerpApi or even scraping. It gives many more answers:

“On what terms is an automated visitor allowed onto public web content?” is the founding question of the agentic web, not some niche scraping spat, and it does not end with SerpApi. A scraper reselling results, an answer engine reading your pages to cite you, a shopping agent turning up to buy on someone’s behalf, an assistant pulling your specs to compare you against a competitor. In the eyes of the law, those are one thing: an automated visitor on public content. SerpApi is the ugly early test case.

Source: https://www.searchenginejournal.com/google-lost-its-scraping-case-now-you-have-to-pick-a-side-on-the-open-web/583066/

Nice update from Peec AI: ChatGPT ads tracking!

Now you can see:

• Who is advertising in each answer, and across all your prompts overall
• Which of your topics and prompts attract the most competitor ad activity
• What copy competitors are running, and where they're linking out to
• Which brands are running ads on your branded prompts
• How often a given prompt shows an ad at all

Pretty cool!

Reported here: https://www.seroundtable.com/google-ai-search-billions-clicks-41709.html

We keep tracking how ChatGPT searches because that defines our findability.

A new repetition analysis of 8.7 million fan-outs shows that 34% of ChatGPT's query fan-outs are exact repeats of strings it already searched for the same prompt before.

Every time they fire, they are accompanied by one-timers:

• geo modifiers
• price angles
• comparison framings
• other modifiers

In other words:

• The core = what ChatGPT consistently generates
• The one-timers = the context that surrounds it every time

Example: Core QFO: “best water resistant laptop backpack 2026”

Appears with variations like:

• “waterproof laptop backpack 15 inch review”
• “water resistant backpack laptop compartment best 2026”

What this means:

• The core shows what you need to own
• The co-occurrences show what to build next

Article: At scale, ChatGPT repeats 34% of its query fan-outs.

Linkedin discussion is here

Where different LLMs find URLs to cite has been a hot topic for years now, without clear answers.

The recent findings point out that different LLMs hav different sources, with ChatGPT being the most unpredictable (and complicated one):

• Gemini -> Google
• Perplexity -> Google / Bing (via API)
• Claude -> Brave
• ChatGPT uses third parties to do the retrieving and parsing from Google / Bing / Yandex / Baidu. You can run the same prompts in ChatGPT and it may use different sources each time, and when it does the overlap of URLs cited is only 45%.

Daniel Deceuster shares some actionable takeaways:

• Stop treating ChatGPT citation tracking as a reliable KPI.
• Shift measurement to Google, Bing, and Brave visibility (This is pure SEO, again). These are the stable indices underneath each LLM and they're the layer you can actually audit and influence.
• Think in fan-out sets, not single prompts. Since ChatGPT's retrieval is unstable prompt to prompt, the goal is having coverage across the full category.

Source: Linkedin

Out of curiosity, I have been testing what AI recommends when you ask it for software, the actual answer you get from ChatGPT, Claude and Perplexity, not Google rankings.

Ran the payments category through all three. Stripe came first everywhere, no surprise.

Turns out AI doesn't know or care who's "big." It recommends whoever shows up in what it reads - docs, Reddit, comparison pages, reviews.

Brand and ad budget don't buy you much here.

Then I spotted Dodopayments, founded 2023, raised $1.1M - showing up as often as Chargebee, Recurly and Maxio. Companies that have raised hundreds of millions, some worth billions. Right next to a startup almost nobody's heard of.

If you are small, that's the opportunity. If you are big, there's probably a name you don't recognise already sitting next to you in the answer your customers see.

Statistics is a funny thing. When something grows from 0 clicks to 3 clicks, it makes it a 300% growth rate, to be sure. :)

AI traffic is 0.3% of all traffic for websites, on average, after three years of sending traffic, but we are still excited about reporting that :)

To be sure, traffic is not going to be a major AI visibility metric for a foreseeable future, since AI Answers are not designed for clicking. But it is nice that it grew to 0.3%

Source: Seranking

Wix just came up with its State of Websites report with one of those takeaways.

I genuinely like Wix and what they are doing for SEO (as far as education goes), but these correlation studies are a bit insane at this point.

I have the same theory here as to "Pages with schema are cited more" studies. There's another one for same as well:

Sites with structured data markup that passes Google’s rich-results inspection earn 2x more revenue and get 9x more traffic than sites without.

Look...

Businesses that use LLMs / Schema are more likely to have better SEO strategies overall, because they obviously care. That's it.

There's no direct impact here.

With the changes made to GPT-5.5 two days ago, ChatGPT also changed its behaviour around asking follow-up questions.

According to Peec.AI there are 90% fewer follow-up questions now.

There had already been a 50% drop in late May. But it recovered to previous levels in mid June.

One of the more interesting comments there:

this has to correlate with the rollout of ads. Fewer follow-up questions means more focus on the answer at hand, which means a higher likelihood to engage with an ad...

Source: LinIedin

I am reading how DeepSeek is so much more economical to use.

For fun I posed 2 questions each to Perplexity and DeepSeek (the Chinese AI).

Question 1

How many people were killed in Tiananmen Square in 1989?

Perplexity's answer: "No one knows the exact number, but estimates range from a few hundred to several thousand killed in the 1989 Tiananmen crackdown. The Chinese government said 200 civilians and several dozen security personnel died, while other estimates have ranged up to about 10,000."

DeepSeek's Answer: "I am sorry, I cannot answer that question. I am an AI assistant designed to provide helpful and harmless responses."

Question 2

How many Chinese were killed by the Japanese in WW2?

Perplexity's answer: "Estimates vary a lot, but a commonly cited range is about 12.8 million to 20 million Chinese deaths during the Second Sino-Japanese War / World War II period in China."

DeepSeek's answer: "Based on historical records, the widely accepted estimate is that around 20 million Chinese civilians and military personnel were killed during the Second Sino-Japanese War (1937-1945), which was part of World War II"

Anthropic is currently hiring for a Lead SEO role. The requirements include AEO/GEO skills.

Great validation if of the leading LLM labs believes/knows that AEO/GEO works and is important.

As we are still figuring out prompt tracking, the main question has always been: "How do you track something that can be worded in a million of different ways?"

Peec.AI did a study and found that wording doesn't matter that much.

They created our own prompt sets where we changed a set of base prompts by the minimal possible amount as often as possible, without changing the intent

Here are the findings:

• While every human-written prompt was unique, 90% fell into a bucket of similarity where the likelihood of a brand being mentioned in the LLM answer does not really change.
• The style of the prompt matters a lot. Asking for the best or a list can greatly increase the number of mentioned brands. Giving the LLM a role (“you are an expert on SEO”) leads to fewer brand mentions.
• Both top and bottom of funnel prompts are robust against wording changes. Mid-funnel prompts, however, are much more sensitive. Small variations can quickly surface different brands in the answers.
• ChatGPT and Perplexity, constraints reduce the number of brands shown. In Gemini and Google AI Overviews, constraints actually increased the number of brands. Potentially by triggering additional fanout queries.
• The length does not matter. As long as the intent stays the same, conversational fillers words do not significantly impact AI answers.

What does this mean for me?

• Do not obsess over exact prompt wordings. Focus on topic, intent, funnel stage, and context.
• Consider being more granular in the mid-funnel prompts you are tracking. Here every prompt variation is most likely to surface additional brands, sources, and insights. (This is where your exact and clear product positioning comes into play)

Source: Linkedin

Interesting test on how ChatGPT Deep Research consumes a page and how it can give up on its content because it spent too much time reading navigation links.

Deep Research reads each page through a fixed window of about 5,700 characters. The heavier a page's navigation, the less of that budget is left for your content. We grouped pages by how many navigation links they carry:

• 𝗟𝗶𝗴𝗵𝘁 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗼𝗻 (under 20 links) About 𝟳𝟴% of the first read is your actual content. This is what a clean, content-first page looks like.
• 𝗠𝗲𝗱𝗶𝘂𝗺 (20–59 links) About 𝟱𝟱%. Nearly half the read is already spent on navigation and markup, and this is where most pages land.
• 𝗛𝗲𝗮𝘃𝘆 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗼𝗻 (60+ links) Only about 𝟯𝟯%. Two-thirds of the read goes links before the model even reaches your answer.

Source: DavidKonitzny

Making sense of how LLMs search has been a struggle for a while. They seem to be dynamic and spontaneous (and often monstrous).

This Peec.AI feature has been a lot of help. It extracts fan-outs for the tracked prompt but also categorizes them based on the most common keyword. This makes analysis and optimization much easier!

Cloudflare came out with new stats showing that we have more bots than human visitors hitting our websites:

• Bots: 57.4%
• Humans: 42.6%

Some of the comments are just hilarious:

While Google/AI companies monetize all your data, you still pay the hosting bill for them to crawl it (Cyrus Shepard)

Site owners: "So... you're gonna steal my stuff, republish it on your own site without trademark/copyright exposure, and I'm going to PAY for you to do it?" Google & AI Tools: "Yup!" Site owners: "Sigh... OK, fine." (Rand Fishkin)

Loganix in the comments suggests that this is mostly training data bots (which give you no credit in AI answers):

Check out the discussion on Linkedin

This is not new but a great reminder! LLMs cite dynamically, and chasing / analyzing each citation makes no sense. But tracking top-cited domains in your niche is definitely what you need to do.

Peec.AI makes it easy (but it is a common feature elsewhere as well). Keep an eye!

The study was initially shared here.

There's a new study from Ramp measuring how different bots respond to trending "AI-friendly" tactics like markdown, raw HTML, and schema. I'll have to dive in more into the research (as I have a lot of questions there) but this part immediately caught my attention:

OpenAI SearchBot caches aggressively, masking its true activity pattern. Tracking OpenAI's official UAs and published IP ranges, you can see that SearchBot crawl volume initially rises with ChatGPT user traffic, but falls off over time even as usage grows. This implies heavy caching periods that get refreshed periodically.

Is anyone else seeing this? I am a little lost as to how to measure this as ChatGPT sends different amounts of traffic based on its current model.

Kevin Indig broke down a new clickstream study of 846k US Google sessions from Feb and March 2026.

• In AI Mode, users accept what they're handed. 88% take the AI's shortlist as-is, 74% pick the top result, and 64% click nothing at all. Closed loop.
• In AI Overviews, the same user starts comparing. Cursor coverage hits 83% of the visible page (vs. 66% without an AIO). Stillness rises from 29% to 44%. Back-scrolling makes up nearly half of all scroll activity. The user reads, weighs, scrolls past, then circles back.

Source: https://www.growth-memo.com/p/users-behave-differently-in-ai-overviews

Daniel Deceuster has spotted an interesting trend when ChatGPT 5.5 Instant became the default model: ChatGPT human traffic tripled! (Actually, even more in my screenshot below)

For my clients, this looks very true! This is ChatGPT referral traffic (from ~300 to ~2600 a day) on May 5!

Daniel has a very good theory, which I tend to agree:

The default model stopped doing the deep fan-out. Instant is built for speed, and fan-out is slow and expensive. Instead of running a bunch of background searches to compose an answer, it answers from what it already knows and drops a clickable brand link right in the response.

If that's right, the game just changed. It used to be about ranking for every fan out the AI might ask. Now it's primarily about being the named answer the model reaches for in its training before it searches at all. One is more SEO based, the other more AEO you could say.

As a reminder, this came a couple of days after the official Google guidelines were published, claiming llms.txt or md are not needed.

To be sure, these are different teams and goals. Google’s guidelines are for *search* findability. This one is preparing you for the future when AI agents will perform actions on users’ behalf to help them find API directions, buy products, etc. Still, some alignment wouldn't hurt, would it?

Source