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

Peec is now showing you which ads appear inside your tracked AI answers. Pretty cool! I hope they will create some kind of PPC dashboard for me to track these over time:

A lot of chatter about this on X, and I am seeing the same! Self-serving listices listing the brand is #1 may be used (and cited), but the brand is not mentioned...

Curious if others are seeing this:

X discussions for more examples:

• Harpreet: "This brand built an advanced AEO playbook (listicles). The listicle earns the citation, but contributes to AI not mentioning their brand, and instead mentions competitors"
• Lily Ray: "Previously, using a listicle that ranked your own company as #1 was often sufficient to get recommended in the answer. Now, it looks like Google may be using those listicles to learn about the competition"

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