One key justification for SEO and GEO/AEO being distinct is (to paraphrase) because “the URLs returned by AI “searching” only overlap with Google/Bing ~10% of the time, therefore they’re totally different.”
The data used to justify this changes, but the shape is the same. I’m not sure this is significant for the reasons people most often believe.
“Search” is Well Established for a Reason
This raises a fundamental question of “quality” in search. LLMs are not great at reliability or predictability, but the new entrants into the AI search world don’t have a lot of experiences in this area either.
Google and Bing haven’t perfected search - they have room to improve, clearly - but they ARE some of the most experienced in the game.
People focus on the difference between SERPs and AI results, but relatively few ask “why” they differ.
A lot of people assume that maybe AI search is doing something better… but what if the opposite is true?
The URLs returned by search tools are governed by the ‘grounding’ queries that have been used.
Then the extra layer on top - that checks for quality, accuracy and basics like whether the page is working or not - is hugely important and makes a load of mistakes.
Put another way, I think it’s a bit of a crap-shoot (technical term).
“AI is Better Than Traditional Search” is Starting to be Questioned
Those people seeing the difference/gap between AI results and tradition SERPs as an “advantage” and that may be true in the short-term, but I think it’s as likely that a lot of this gap is a bug (or at least something that is lacking), not a feature.
An article that recently released that helped summarise this even more effectively is from Duane, and that argues that AI Search hasn’t made life easier for the searcher, it’s just caused them to have to work more after they have results (to make sure they’re reliable), rather than when they’re searching.
Well worth a read!
So in some senses the “progress” that AI search has brought (so far) is likely an illusion, or part of the hype curve.
Where would you rather spend the effort, researching or verifying returned results?
For those people who don’t want to research and don’t apparently care about verification, AI search may be a net gain, but this immediately brings us back around to the question of quality.
Search is Fragmented, Unlikely to Converge Again.
I don’t think we’ll see AI/Search sources converge to close this gap, the opposite is likely to be true.
Google have made it harder and harder (or at least more expensive) to scrape search results, the most result being “goto” redirect links, in search results. This in addition to other means.
Search/tech providers are building their own sources. ChatGPT, Apple, Brave (etc) third-party data providers like SerpAPI and DataForSEO are building indexes and Google/Bing are providing their own data to assist in grounding for RAG processes.
The retrieval pipeline is getting more complex, the queries that trigger search results, when they’re used - how much the model reasons and questions before it searches all impact the results that are returned EVEN if the search sources are the same.
This is totally a recognition that the status quo WILL change fast. Google/Bing will have a key part as a grounding search source, but this ecosystem will become more complex.
OpenAI, Anthropic etc will get better at this too.
Aren’t AI Search Gaps Points of Weakness today?
Think about the countermeasures we need to assist AI search currently, pre-emptive (or reactive) 301 redirects to stop chatbots sending users to broken pages or fixing code that might be problematic for training pipelines. These aren’t because AI search is better, it’s because the fundamental process has significant weaknesses in it.
AI search IS different from traditional search. Understanding this difference and knowing how to handle it is hugely important. Patching these (relatively) short term failings and calling it “the future” may be naive and misguided.
Those who see this frankly basic/inconsistent nature of AI Chatbots as competitive advantage may be surprised when the quality problem is addressed more fundamentally and the gold rush is over.
This maps almost exactly onto what we see in commercial/retail queries specifically: products surfaced in AI Overviews often diverge meaningfully from what ranks in the Shopping tab, because one leans on a much looser retrieval layer while the other is grounded in the structured product graph. For merchants that means optimizing for two systems that don't share a quality bar, let alone a ranking logic. The gap you're describing isn't something retailers can just wait out either, since Google has every incentive to keep monetizing the SERP long after the AI answer box is "good enough."