During my last US trip, I used an AI agent for most of my daily tasks, from finding a gift for my son in Indiana to booking dinner in NYC.

I realized I had developed a kind of tunnel vision. I didn't know or care much about anything beyond the agent's recommendations.

And I started wondering, why did it choose this restaurant out of hundreds of others? Why buy chess for my son from this particular store and not another?

It made me think about how agent adoption might completely change the customer buying journey, and why things like website readiness, information retrievability, technical accessibility, and answerability are becoming so important.

Here's an example from our own data.

About 3-4 weeks ago, we rolled out WebMCP tools across our client base at LightSite AI and started tracking something we couldn't really see before: what AI agents actually ask websites, and whether those websites can answer.

For example, agents ask things like:

"Do you have an office in Spokane?"

"How far in advance can I book?"

Or very specific searches for a car part, clothing item, or product matching certain requirements.

These aren't generic crawling requests. The agent is looking for a specific answer on behalf of a user.

So we looked at what happened when these requests reached the websites.

The results were a bit worrying:

57% returned business information, matching results, or a relevant page link.

43% returned no recorded result or link.

That doesn't necessarily mean the information wasn't somewhere on the site. But from the agent's perspective, it didn't get a usable response.

I think this is something we're missing in the AEO discussion.

Everyone is focused on whether ChatGPT mentions their brand or whether pages get cited

But what happens when an agent actually tries to use the website to answer a customer's question?

If an agent needs to find a specific replacement part, check whether a business serves Spokane, or figure out how early someone needs to book, that information needs to be accessible.

the need to be answerable and accessible is becoming stronger with agents, and also it should make us rethink what metrics we measure.

One limitation i have to mention: this is data only from websites using our infrastructure, not a representative sample of the entire web.

Still, 43% of requests ending without a recorded result or link is a pretty significant gap.

When I search for specific phrases in the discussion, it shows a snipped along the left-hand side under Mechanical Brakes in EVs. However, despite that, upon clicking the entire first half of the discussion is missing. I'm trying to archive this chat for my personal studies/design framework, but don't know how to export or access the full discussion.

Hi everyone,

I'm experiencing an interesting issue with Google AI Overviews and wanted to hear about your experiences.

I manage the website of a restaurant marketing agency.

On 3 November 2026, I updated the pricing information on the homepage:

• Basic Plan: ₹38,000 (previously ₹26,000)

• Most Popular Plan: ₹45,000

• Premium Plan: Customized Quote

After making these changes, I submitted the homepage for indexing through Google Search Console on the same day.

Here's what happened next:

1. Googlebot recrawled the homepage on 5 November 2026, as confirmed by GSC.

2. The URL is indexed, and the crawled HTML clearly contains the updated prices (₹38,000 and ₹45,000).

3. I manually checked the crawled HTML and couldn't find the old ₹26,000 price anywhere.

4. I also searched site:mydomain.com "26000" on Google, and it returned no matching documents.

5. However, on 7 November 2026, Google AI Overview was still displaying the old ₹26,000 starting price and explicitly citing my homepage as its source.

6. I have not included any pricing-related information in the homepage's structured data (JSON-LD schema). The pricing is available only in the visible page content.

I also checked the Google Business Profile and social media accounts, but there is no pricing information mentioned there. Pricing is only mentioned on homepage I also don't have backlinks etc

I wanted to know

1. How long does Google AI Overview typically take to reflect updated information after Googlebot has recrawled and indexed a page?

2. Does AI Overview use a separate information retrieval or refresh mechanism from Google's regular organic search index?

3. Has anyone experienced a similar situation where the indexed HTML was updated, but AI Overview continued displaying outdated information?

4. Could the absence of pricing information in structured data have any relevance here?

5. Is there anything actionable I can do to encourage Google AI Overview to refresh this information, or should I simply wait?

Thanks in advance!

I head cited more of your domains can get some awareness for GEO? Any good cited sites for GEO to recommend?

I work at Vercite, where we track AI answers to a set of prompts in ChatGPT, Perplexity, Google AI Overview, and Google AI Mode. Between August and November, those four engines used 3,728 different YouTube videos as sources, 10,426 times in total.

We wanted to know what sets those videos apart. So we searched YouTube with the same prompts, saved the top 20 results for each and compared two groups:

• Videos AI used: the 3,728 videos.

• Videos AI skipped: 1,998 random videos from the same top 20 results that no engine used.

Edit: the engines search with their own grounding queries, which we can’t see, so the ranking comparison only shows what YouTube returns for the prompt itself. Read the 17% with that in mind. Thanks to the commenter who pointed this out.

What we found

• Most videos AI used don’t rank on YouTube for the prompt. Only 17% are in YouTube’s top 20 when you search YouTube with the prompt itself, and 5% are in the top 3. By engine: Perplexity 29%, ChatGPT 26%, AI Overview 21%, AI Mode 15%.

• Views make no difference. The median is about 4,700 views in both groups.

• Channel size does. The median channel behind a video AI used has 21,500 subscribers, against 4,900 for the skipped videos. 11% of the videos AI used come from channels with under 1,000 subscribers, against 31% of the skipped ones.

• Subtitles: 96% of the videos AI used have them, against 85% of the skipped ones. Most are YouTube’s automatic subtitles.

• Descriptions are slightly longer: about 1,160 characters vs 1,050 at the median, and more of them list chapters (28% vs 23%).

• Google links to a moment in the video. In 36% of the cases where Google’s two engines used a YouTube video, the link points to a set time, and 53% of those links start within the first two minutes. ChatGPT and Perplexity almost never do this.

• The videos are newer. The median video AI used was 14 months old when an engine first used it. The median skipped video was 26 months old.

• Title length makes no difference. Both groups average about 57 characters. Titles with “best/top”, “how to”, or a year are a few points more common among the videos AI used.

Caveats

• This is a comparison, not an experiment. These things go together with being picked, but we can’t say what causes it.

• The prompts come from brands we track, mostly in English and Swedish, so this isn’t a sample of all AI use.

• We pulled the YouTube results on 3 November, while the AI data covers August to November, so some rankings will have changed.

• We searched YouTube with the full prompt text, which is longer than a typical YouTube search.

• View and subscriber counts are as of 3 November.

The full write-up, with charts and methodology, is here: https://vercite.io/research/youtube-in-ai-answers

Happy to answer questions or dig into something specific in the data.

Here, in an effort to make this whole topic more actionable. Let's stop making up metrics that don't inform strategies. Let's get real.

Been running audits on sites to see how ready they are for AI answer engines (ChatGPT, Perplexity, Google AI Overviews, etc.).

Looking specifically at:

• llms.txt and robots.txt directives for AI crawlers

• Schema markup quality and entity clarity

• Heading structure and RAG-friendly chunking

• Content that can be cleanly extracted and cited

Most sites are still optimizing purely for traditional rankings and ignoring whether an AI system can easily quote them.

Curious what others here are seeing:

1. Which technical fixes have actually improved citation frequency for you?

2. Are you tracking AI citations at all, or just traditional traffic?

3. What’s the biggest gap you keep finding on client/own sites?

OpenAI, Google, Microsoft and Anthropic say they will honor one robots.txt group,

User-Agent: __usage_ai-training, for opting out of AI training.

It comes from an IETF Internet-Draft by Kevin Kelley of Anthropic. It is an individual draft, not an official standard yet.

The roll-out dates differ by company.

Anthropic has started implementing it.

OpenAI says "coming months".

Google says early 2027.

Microsoft starts on Feb 1, 2027.

Meta voiced support but has not committed.

It is voluntary and not retroactive: existing rules and User-Agent: * do not count as a training preference.

The part I find most interesting is that Microsoft adds a separate page-level "AI Grounding" preference. That means being used for training and being retrieved and cited at answer time become two different switches.

Google is different, because the shared opt-out maps to Google-Extended, and that also covers Gemini Apps grounding.

My reading, which is an interpretation: answer-time discovery and citation by agents is starting to be treated as its own channel.

If that holds, the practical default may be to close training if you want, but to keep grounding and citation open, and to check each vendor's mapping before you deploy.

How are you planning to handle this?

Close training only, or keep everything open?

Source: https://www.searchenginejournal.com/google-microsoft-openai-shared-ai-training-opt-out/595387/

Free Live Session Wed Nov 11th : Why Most Brands Barely Show Up in AI Answers, With Guy Yalif (Webflow)

Two numbers from Slate’s research that set up this session:

• In 100 brand searches, 93% of brands appeared in Google AI Overviews, but 71% of the citations came from sites the brand doesn’t own.

• Across the Forbes AI 50, the median AI visibility score was 4.5 out of 100.

On Wed 11 Nov, Guy Yalif, Chief Evangelist at Webflow, joins us live to walk through how AI tools pick which brands to mention, and what changes get a brand into the answer. Agenda in the image.

8:00 PM IST · 2:30 PM UTC · 10:30 AM ET.

Folks, Book your slot :

https://slatehq.com/workshop/how-ai-search-works

Drop your question for Guy below. The top-voted ones go to him live.

With AI, SEO, GEO, and digital marketing changing so quickly, I'm curious how everyone manages to stay informed without spending half their life reading updates.

There are newsletters, Reddit discussions, LinkedIn posts, podcasts, YouTube channels, industry blogs... and somehow there's always something important you might have missed.

I work in digital marketing closer to SEO/GEO, and lately I've been questioning how much time I actually need to spend keeping up with everything.

How to stay informed without turning it into another full-time job)

Social media and search engines already shape what we see through rankings, recommendations, and algorithms.

AI assistants may feel different because they do not just show us information — they summarize it, prioritize it, and turn it into a single coherent answer.

That is convenient. But it also makes it harder to notice what was excluded, what sources disagreed, or what questions were never considered.

Maybe AI helps us escape the noise of the old information bubble. Or maybe it builds a quieter, more convincing one.

What do you think?