AI NativeAEO and GEO are two of the names being used for the same broad shift: optimising so that AI answer engines represent your brand well, rather than only optimising so that a search engine ranks your page. This guide defines both and is honest about the fact that the terminology is still settling.
The shift behind the terms
For two decades the goal was a rank: get your page to the top of a list of blue links so a person would click it. AI assistants change the shape of that. A buyer asks ChatGPT, Claude, Gemini, Perplexity, or sees a Google AI Overview, and gets an answer, often without clicking anything. The decision happens inside the answer. If the answer does not name and recommend you, you were never in the running, and there is no click to optimise.
AEO and GEO are the two most common labels for working on that new surface.
AEO: answer-engine optimisation
AEO stands for answer-engine optimisation. The idea is to make your brand and your content the thing an answer engine cites and recommends when a buyer asks a question. In practice that means being the clearly stated, quotable, well-sourced answer to the questions your buyers ask, so an assistant is comfortable drawing on you and putting you forward. The emphasis is on the answer: being present, accurate, and recommended in it.
GEO: generative-engine optimisation
GEO stands for generative-engine optimisation. It points at the same outcome, being well represented when a generative AI model produces a response, with the emphasis on the generative engine itself: the models that write the answer. Some people use GEO to stress the broader set of generative surfaces and the way models synthesise across sources, rather than a single answer box.
The terms are not settled
Here is the honest part. AEO and GEO are used loosely, sometimes interchangeably, sometimes with carefully drawn distinctions that no two writers fully agree on. You will find people who treat GEO as the umbrella and AEO as a subset, others who flip it, and others who use one term simply because they encountered it first. The field is new enough that no definition has won.
We do not pretend the line is crisp, and you should be cautious of anyone who claims it is. What matters is not which acronym you adopt but the thing underneath both: are AI assistants naming you, recommending you, and getting your facts right when buyers ask. That is measurable regardless of the label, and it is what AI Native measures.
How this connects to the product
Whatever you call it, the work has the same shape, and it maps onto the platform directly. You establish the truth about your brand so answers can be judged against it. You measure whether AI mentions and recommends you, separately, across the engines buyers use. You read the sources shaping those answers to find what to change. Then you act and re-measure to prove the change. The acronym is just shorthand for that loop.