What is answer engine optimization vs GEO?
Answer engine optimisation (AEO) and generative engine optimisation (GEO) describe substantially the same discipline: getting your brand accurately represented and cited in AI-generated answers. AEO grew out of featured-snippet and voice-search work, while GEO was coined for generative engines such as ChatGPT and Perplexity. In everyday use the two terms overlap almost completely.
AEO is the older label. An "answer engine" originally meant any system that returned a direct answer instead of a list of links: featured snippets, knowledge panels and voice assistants such as Alexa. Practitioners doing AEO concentrated on question-shaped content, schema markup and concise passages that could be lifted verbatim into an answer box. GEO arrived later; the term was popularised by a 2023 academic paper of the same name and refers specifically to engines that generate answers with large language models.
Where people draw a distinction, it usually runs like this: AEO covers structured answer surfaces such as snippets and voice results, while GEO covers LLM-driven engines such as ChatGPT, Claude, Perplexity and Google's AI Overviews. The distinction rarely survives contact with real work. The underlying job is identical in both cases: publish clear, current, well-structured content that machines can retrieve, trust and quote, and make sure the facts about your brand are consistent wherever an engine might read them.
Our honest view is that the label matters far less than the work. GEO appears to be winning the naming contest at the moment, with AEO, LLMO and AI SEO circulating as alternatives, but adoption varies by region and audience, and the winner could change. Pick whichever term your team and clients recognise, and judge any agency or tool on what it actually measures rather than the acronym on the tin.