This is exactly what set me off in trying to figure out the visibility gap.

What’s strange is that we’re moving into a world where recommendations matter more than a click, but attribution still assumes a traditional search funnel. By the time someone lands on your site, the most important decision may have already happened upstream and you have no idea.

The UTM case you mentioned is a good example: it only captures direct "AI to site" clicks, but misses scenarios where AI influences the decision indirectly (brand mention to later search to visit). From the site’s perspective tho... yeah it looks indistinguishable from organic search. It makes me wonder whether we’ll need a completely new mental model for attribution here. Perhaps less about “what query drove this visit” and more about “where did trust originate.”

Not sure what the right solution is yet, but it feels like we’re flying blind during a pretty major shift in how people discover things.

This matches a lot of what I’ve been seeing too.

What stood out to me is that AI seems far less concerned with domain age than Google is. If there’s enough contextual discussion around a product (ie. Reddit threads, blog posts, docs, comparisons) then AI models seem willing to surface it surprisingly early.

That said, what I’m still trying to understand is consistency. I’ve seen cases where a product gets recommended heavily for a week, then effectively disappears unless that external context keeps getting reinforced.

So it feels less like “rank once and you’re good” (SEO) and more like “stay present in the conversation.” Almost closer to reputation management than classic content marketing.

Curious if you’ve seen the same thing, especially around how long external mentions keep influencing AI recommendations before they decay.

You bring up something I've been trying to figure out as well. It feels like AI favors pages that give a direct, honest answer and then makes that answer immediately available. Seemingly plain, readable HTML with no friction preforms well. If the intent is obvious at fetch time, it seems to matter more than how “optimized” the page is. It feels less like SEO and more like “can this page be understood immediately.” I feel as if the initial response is basically <div id="root"></div> + a big JS bundle, it feels like you’re betting the crawler will execute it and I’m not convinced they consistently do. Curious if you’ve run into that too? Have you seen AI recommendations skew toward SSR/static pages vs client-rendered apps, even when the content is technically “there” once the JS runs?

Right there with you because SEO has evolved to a place that incentivizes a lot of bad behavior, and the end result made search worse for everyone. I’m personally less interested in “gaming” LLMs than understanding what they already do. From my side, this feels closer to observability than optimization when trying to see whether AI systems are even reading or understanding a site, not how to trick them into ranking something low quality.

The Raymond Chen analogy brings up something interesting. If everyone forces themselves on top, the signal collapses. My hope is that AI systems end up rewarding genuinely useful, well explained things rather than creating another arms race...but I’m not naive about how incentives tend to play out.

A huge concern of mine has been the introduction of ads. Once ads enter LLM responses, it’s hard not to ask whether we’re just rebuilding the same incentive structure that broke search in the first place.