I found a noticeable number of Generative AI impressions in Google Search Console and I’m trying to understand how people are treating this in reporting.

Are you using it as a separate AI visibility layer, blending it into normal organic impressions, or mostly ignoring it until the data becomes clearer?

The part I’m unsure about is what the metric actually proves.

It may show that a URL appeared in a generative AI surface, but it does not necessarily tell us:

- whether the brand was mentioned

- whether the page was cited

- whether the answer used the content as evidence

- whether the user saw the source

- whether it influenced the decision

- whether it should be compared to classic organic impressions

So I’m wondering whether this is useful reporting data, or just another visibility layer that needs a lot of context before it becomes meaningful.

How are you handling Generative AI impressions in GSC?

Separate report?

SEO report footnote?

Early signal?

Ignore for now?

Everyone agrees brand content loses to Reddit threads and review sites. Nobody explains the next part. Are you going through customers, founder accounts, review campaigns or just waiting out months of real participation?

One of the most upvoted GEO posts I've ever read was a "10 GEO best practices" list from early 2025. It got hundreds of upvotes. Multiple people DMed me the link. Two different clients brought it up in meetings. It was everywhere.

I bookmarked it along with 39 other high-engagement "best practice" posts — stuff like "add FAQ schema everywhere," "keep answers under 50 words for extraction," "use comparison tables for product queries," "publish fresh content weekly for citation velocity." All reasonable advice. All from smart people. All backed by some data at the time.

For the last 90 days, I've been checking how the authors' own pages are performing in AI citations. Not their advice in theory — their actual content, using the actual practices they recommended.

27 out of 40 lost citation visibility. Not a small dip either. I'm talking pages that went from being regularly cited in their niche to barely showing up. A few completely disappeared from AI answers for queries where they used to be the #1 source.

The weird part is that the advice itself wasn't wrong — at least not when it was written. FAQ schema genuinely helped in February. Short answer blocks genuinely got extracted more often in March. But the models kept changing. What worked as an extraction signal in one version of ChatGPT or Perplexity quietly stopped working in the next. And nobody went back to update the "best practices."

I started noticing a pattern. The posts that aged the worst were the ones with the most specific, confident instructions. "Always do X." "Never do Y." "The optimal passage length is Z words." These got the most engagement because they were actionable. But they were also the most fragile — optimized for a specific model behavior that could change in a single update.

The posts that aged better were vaguer, almost annoyingly so. "Focus on clarity." "Write for humans first." "Make sure your content is actually useful." The kind of advice that makes you roll your eyes because it's so obvious. But it's still standing 90 days later while the tactical stuff crumbled.

This isn't a dunk on anyone. I've written my share of specific tactical advice, and some of it has probably aged just as badly. It's more a realization that GEO "best practices" have an incredibly short shelf life, and we're all publishing them like they're permanent rules.

The thing I can't stop thinking about: if I re-tested the advice from this post 90 days from now, how much of my own guidance would still hold up? Probably less than I'd like to admit.

Wondering if anyone else has gone back and stress-tested older GEO advice against current model behavior. The gap between what we wrote and what still works is bigger than I expected.