The attribution point is huge: the “decision” can happen in the model’s answer, and your analytics only see the last hop.
A practical mental model for recommendations is less “ranking” and more confidence:
Does the model have enough context to map your product to a problem?
Are there independent mentions (docs, comparisons, forum threads) that look earned vs manufactured?
Is there procedural detail that makes it easy to justify recommending you (“here’s the workflow / constraints / outcomes”)?
For builders, a good AEO baseline is:
Publish a strong docs/use-case page that answers “when should I use this vs alternatives?”
Seed real-world context by participating in existing discussions (HN/Reddit/etc.) with genuine problem-solving and specifics.
Track influence with repeatable prompt tests + lightweight surveys (“how did you hear about us?”) since last-click won’t capture it.
It feels like early SEO again: less perfect instrumentation, more building the clearest and most defensible reference for your category.
However, there is a lack of information when a user opens your website after interacting with AI.
Google Search Console shows the user's query if the query is popular enough and your website is in the search results.
Bing shows all queries, even if they are not popular, and if your website is in the search results.
But if AI recommends your website when answering people's questions, you cannot find out what questions the user discussed, how many times your website was shown, and in what position. You can see the UTM tag in your website analytics (for example, GPT adds utm source), but that is the maximum amount of information that will be available to you. But if a user discussed a question with AI and only got your brand name, and then found your site in a search engine, you won't be able to tell that they found you with the help of AI advice.
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 is why most of these AI search visibility tools focus on tracking many possible prompts at once. LLMs give 0 insight into what users are actually asking, so the only thing you can do is put yourself in the user’s shoes and try to guess what they might prompt.
Disclaimer: I've built a tool in this space (Cartesiano.ai), and this view mostly comes from seeing how noisy product mentions are in practice. Even for market-leading brands, a single prompt can produce different recommendations day to day, which makes me suspect LLMs are also introducing some amount of entropy into product recommendations (?)
I don’t think there’s a clean solution yet but I’m not convinced brute force prompt enumeration scales either, given how much randomness is baked in. I guess that’s why I’ve started thinking about this less as prompt tracking and more as signal aggregation over time. Looking at repeat fetches, recurring mentions, and which pages/models seem to converge on the same sources. It doesn’t tell you what the user asked, but it can hint at whether your product is becoming a defensible reference versus a lucky mention.
From someone who's built a tool in this space, curious if you’ve seen any patterns that cut through the noise? Or if entropy is just something we have to design around.
Disclaimer: I've built a tool in this space as well (llmsignal.app)
This is especially important when launching new SaaS projects. Google does not trust new domains for the first 6-12 months. But if you publish information about your project on other sites, the AI will recommend your site in its responses. Just post a few times on Reddit, and in a week, GPT will be giving out links to your SaaS product. AI doesn't need exact low-frequency or high-frequency keywords like SEO does. AI is good at understanding user queries and giving out the right SaaS that solves the user's problem. You don't need to create a blog on your website and try to rank it in search engines. It is enough to post articles on other websites with information about your project.
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.
I have noticed something similar while building small tools — AI recommendations seem to favor clarity and can this answer a real task fast over classic SEO signals. Pages that explain what they do plainly and work without friction show up more often than heavily optimized ones.
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?
Even if I did know, the last thing the world needs is for SEO folks to figure out how to game LLMs if they haven’t already.
SEO has made web search unusable and practitioners are the scum of the earth.
But more practically like Raymond Chen said, if every app could figure out how to keep their windows always on top, what good would it do? The same with SEO.
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.
The attribution point is huge: the “decision” can happen in the model’s answer, and your analytics only see the last hop.
A practical mental model for recommendations is less “ranking” and more confidence:
Does the model have enough context to map your product to a problem? Are there independent mentions (docs, comparisons, forum threads) that look earned vs manufactured? Is there procedural detail that makes it easy to justify recommending you (“here’s the workflow / constraints / outcomes”)? For builders, a good AEO baseline is: Publish a strong docs/use-case page that answers “when should I use this vs alternatives?” Seed real-world context by participating in existing discussions (HN/Reddit/etc.) with genuine problem-solving and specifics. Track influence with repeatable prompt tests + lightweight surveys (“how did you hear about us?”) since last-click won’t capture it.
It feels like early SEO again: less perfect instrumentation, more building the clearest and most defensible reference for your category.
However, there is a lack of information when a user opens your website after interacting with AI.
Google Search Console shows the user's query if the query is popular enough and your website is in the search results. Bing shows all queries, even if they are not popular, and if your website is in the search results.
But if AI recommends your website when answering people's questions, you cannot find out what questions the user discussed, how many times your website was shown, and in what position. You can see the UTM tag in your website analytics (for example, GPT adds utm source), but that is the maximum amount of information that will be available to you. But if a user discussed a question with AI and only got your brand name, and then found your site in a search engine, you won't be able to tell that they found you with the help of AI advice.
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 is why most of these AI search visibility tools focus on tracking many possible prompts at once. LLMs give 0 insight into what users are actually asking, so the only thing you can do is put yourself in the user’s shoes and try to guess what they might prompt.
Disclaimer: I've built a tool in this space (Cartesiano.ai), and this view mostly comes from seeing how noisy product mentions are in practice. Even for market-leading brands, a single prompt can produce different recommendations day to day, which makes me suspect LLMs are also introducing some amount of entropy into product recommendations (?)
I don’t think there’s a clean solution yet but I’m not convinced brute force prompt enumeration scales either, given how much randomness is baked in. I guess that’s why I’ve started thinking about this less as prompt tracking and more as signal aggregation over time. Looking at repeat fetches, recurring mentions, and which pages/models seem to converge on the same sources. It doesn’t tell you what the user asked, but it can hint at whether your product is becoming a defensible reference versus a lucky mention.
From someone who's built a tool in this space, curious if you’ve seen any patterns that cut through the noise? Or if entropy is just something we have to design around.
Disclaimer: I've built a tool in this space as well (llmsignal.app)
This is especially important when launching new SaaS projects. Google does not trust new domains for the first 6-12 months. But if you publish information about your project on other sites, the AI will recommend your site in its responses. Just post a few times on Reddit, and in a week, GPT will be giving out links to your SaaS product. AI doesn't need exact low-frequency or high-frequency keywords like SEO does. AI is good at understanding user queries and giving out the right SaaS that solves the user's problem. You don't need to create a blog on your website and try to rank it in search engines. It is enough to post articles on other websites with information about your project.
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.
I have noticed something similar while building small tools — AI recommendations seem to favor clarity and can this answer a real task fast over classic SEO signals. Pages that explain what they do plainly and work without friction show up more often than heavily optimized ones.
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?
Even if I did know, the last thing the world needs is for SEO folks to figure out how to game LLMs if they haven’t already.
SEO has made web search unusable and practitioners are the scum of the earth.
But more practically like Raymond Chen said, if every app could figure out how to keep their windows always on top, what good would it do? The same with SEO.
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.