I recently spoke with Jesse Dwyer of Perplexity about SEO and AI search about what SEOs should be focusing on in terms of optimizing for AI search. His answers offered useful feedback about what publishers and SEOs should be focusing on right now.
AI Search Today
An important takeaway that Jesse shared is that personalization is completely changing
“I’d have to say the biggest/simplest thing to remember about AEO vs SEO is it’s no longer a zero sum game. Two people with the same query can get a different answer on commercial search, if the AI tool they’re using loads personal memory into the context window (Perplexity, ChatGPT).
A lot of this comes down to the technology of the index (why there actually is a difference between GEO and AEO). But yes, it is currently accurate to say (most) traditional SEO best practices still apply.”
The takeaway from Dwyer’s response is that search visibility is no longer about a single consistent search result. Personal context as a role in AI answers means that two users can receive significantly different answers to the same query with possibly different underlying content sources.
While the underlying infrastructure is still a classic search index, SEO still plays a role in determining whether content is eligible to be retrieved at all. Perplexity AI is said to use a form of PageRank, which is a link-based method of determining the popularity and relevance of websites, so that provides a hint about some of what SEOs should be focusing on.
However, as you’ll see, what is retrieved is vastly different than in classic search.
I followed up with the following question:
So what you’re saying (and correct me if I’m wrong or slightly off) is that Classic Search tends to reliably show the same ten sites for a given query. But for AI search, because of the contextual nature of AI conversations, they’re more likely to provide a different answer for each user.
Jesse answered:
“That’s accurate yes.”
Sub-document Processing: Why AI Search Is Different
Jesse continued his answer by talking about what goes on behind the scenes to generate an answer in AI search.
He continued:
“As for the index technology, the biggest difference in AI search right now comes down to whole-document vs. “sub-document” processing.
Traditional search engines index at the whole document level. They look at a webpage, score it, and file it.
When you use an AI tool built on this architecture (like ChatGPT web search), it essentially performs a classic search, grabs the top 10–50 documents, then asks the LLM to generate a summary. That’s why GPT search gets described as “4 Bing searches in a trenchcoat” —the joke is directionally accurate, because the model is generating an output based on standard search results.
This is why we call the optimization strategy for this GEO (Generative Engine Optimization). That whole-document search is essentially still algorithmic search, not AI, since the data in the…
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