We get some version of the same question on a lot of client calls this year. "Are we showing up in ChatGPT? Are we in AI Overviews? What about Perplexity?" And every time, it's tempting to just repeat
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This is interesting because a lot of the “AI-readable” advice seems to turn into another checklist of magic ingredients, when the fundamentals still matter more.
As someone who builds AI systems, the part that stood out to me was the emphasis on what the system can actually access and understand, rather than adding more metadata and hoping an AI decides to cite you.
I also like the distinction around schema. It can make the structure of a page clearer without necessarily being a direct citation lever.
Feels like the broader lesson is similar to building AI applications: don't optimize for what you assume the model wants. Make the underlying information accessible, structured, and genuinely useful first.
Jonas
Sharing various dev's thoughts.
This is pretty close to how I look at it too. First: can the crawler access the page? Second: what does it actually get back? Then I start looking at mentions, citations, competitors and the content itself. A lot of “AI visibility” problems I’ve checked still turn out to be much simpler things like blocked access or thin HTML on the first response.