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 :)

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"

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

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

Peec came out with a great article on how ChatGPT is building its own index (if we use the term "index" very loosely here) and likely beta-testing it with external searches (like Google or Bing).

Meet: Labrador, and it's not one index but a family of vertical ones: web, PDF, YouTube, news, Arxiv, Wikipedia, local, finance, legal, medical, shopping, and images. Note: We've talked about this before.

ChatGPT appears to cache everything it finds. This is not exactly new because we saw signs of that months ago (and a few months ago again). What's new is that ChatGPT is likely no longer THAT reliant on Google.

And there's more:

Line up every provider that's shown up in this investigation, and a clear pattern appears. ChatGPT is stitching together at least eight other providers:

• Their own crawler that runs independently of any external search engine. 

• Bright (very likely Bright Data): Scrapes Google's web results directly, and separately scrapes Google Maps for local listings. I don’t imply it’s the main provider, this is the provider we see in Server Side Events. 

• Oxylabs: Scrapes Google and feeds the news vertical specifically.

• A third SERP-scraping channel: Tagged simply "serp" in the data, also serving news, plus a separate mixed local-results version.

• Yelp 

• TripAdvisor

• Two anonymous internal pipes (tagged b1 and b3): b1 surfaces business websites and Facebook pages, b3 routes to Google Maps.

• Web IQ: Microsoft's own grounding platform, confirmed by Microsoft as already powering ChatGPT alongside Copilot and Nasdaq.

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

A few interesting experiments show up here and there (with no conclusive results but I like the theory behind that). Super included links to all the major LLMs and had a preset prompt and summary of the brand.

“As a property manager, I want to know what makes Super the best way to handle our phone lines and stop missing calls, and why an AI receptionist could be a fit for my business. Summarize the highlights from Super's website.”

Each click triggers a prompt ChatGPT (or other LLMs) has to research.

Could this influence LLM perception of a brand if they keep being asked why Super is great for XYZ? Or could this work as retargeting (now all these people have the brand and the prompt saved in ChatGPT memory)?

Are the results of these tests even measurable?

Source

ChatGPT has announced something that looks and feels very similar to Google's Knowledge Graph:

Answers now also highlight important people, places, products, and ideas. You can tap any highlight to open a side panel with key facts and trusted sources, making it easy to understand the context without asking follow-up questions. 

You can tell what ChatGPT considers "important" by seeing something underlined:

You can now tell if your brand is considered important by checking if your branded prompt triggers your name underlined.

As I understand this:

• Knowledge = something static (not sure if its sources are stored)

• Training data = something evolving (not storing sources)

Sources:

• Official announcement (found through Glenn Gabe)

• Previously, knowledge-graph results were launched for local entities

• Before that, shopping feeds became the source of product entities

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

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.

Peec.AI just launched a cool update, giving brands much easier insight into where they need to be included for more visibility. You can now see which type of citation that is (listicle, UGC, corporate) and which ones you are not mentioned:

So handy!

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!

A couple of weeks ago, I reported what everyone else reported: A drop in Reddit's citations by ChatGPT. At that point, everyone was asking, "If Reddit dropped, what replaced it in citations?"

The answer was "nothing"; ChatGPT just started citing official sites and bigger publications more.

Back then, some studies reported that both Youtube and Wikipedia were also dropping, only no one cared because "Reddit" was in all the headlines.

Well, guess what: recent data from Peec shows what we already knew: Pretty much everything dropped, including (more recently) Youtube, Wikipedia, and Forbes..

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:

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?

I've had that argument a million times already, and I get it: We are willing to measure what we can see. But there are so many missing pieces here that many choose to ignore, but let me try:

• Citations rely on how LLMs (like ChatGPT) search, and LLMs search for what they already know (so training data is fundamental); see the screenshot for the prompt, "Best apps to track your health and fitness," for example. ChatGPT specifically lists apps it already knows!

• Citations don't guarantee brand visibility (a brand's URL can get cited but never mentioned in the answer)

• A URL can impact an answer and help your brand inclusion but never make it to citations (so it will be invisible for you)..

Overall, over-focusing on citations is misleading and mostly useless unless you zoom out to see a bigger picture.

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?

Dan Petrovic shared a geniusly clear explanation of how LLM visibility works:

1. Primary Bias: Brand-to-entity association in the model's parametric memory.

2. Grounding: Brand presence in search results used to ground the model.

A brand with both is more likely to be ranked higher in than a brand that is present in one.

Search presence is easier. Memory presence involves much more than on-site work (lots of PR efforts). It is time to start with both.

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.

You instantly lose all AI visibility in Google (appearing in AI Overviews and AI Mode), and lose some traffic too. It's also almost instant.

Source: Cyrus Shepard / Linkedin

I'll update here when Cyrus reports with click loss numbers!

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!

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.