GEO vs AEO vs SEO: What Each One Actually Means

If you have been pitched GEO or AEO recently, you have probably noticed the definitions shift depending on who is selling. That is not entirely cynical. Two of these three terms are genuinely young, and the industry has not fully settled on where the lines sit.

But one of them has a precise academic origin and a body of controlled research behind it, which gives us something firmer than opinion to work from. This post defines each term, shows what the research actually found, and gives you a straight answer on whether you need all three. It expands on one section of our complete guide to AI search visibility.

SEO: the parent discipline

Search engine optimisation is the practice of improving a page so it ranks higher in a list of search results. It is roughly thirty years old, well documented, and its success metric is unambiguous: what position do you occupy for a given query.

SEO covers technical health, on-page relevance, content quality and off-page authority. Nothing about AI search has made any of that obsolete. Google still handles the large majority of global search, and, as covered in our post on why ranking first no longer guarantees an AI citation, ranking position remains one of the strongest predictors of whether an AI engine cites you at all.

AEO: optimising to be the answer

Answer engine optimisation predates the current AI wave. It emerged during the featured snippet and voice search era, when Google began extracting a single direct answer and displaying it above the links, and when smart speakers started reading one result aloud rather than offering ten.

The shift AEO responds to is that being on the page stopped being enough. You had to be the passage the engine chose to extract. That put the emphasis on content structure: clear question-led headings, direct answers placed immediately after them, concise definitions, tables, lists and FAQ markup.

AEO is now largely absorbed into AI search work, because the same structural qualities that won featured snippets also make a passage easy for a language model to lift cleanly.

GEO: optimising to be cited in a generated answer

Unlike the other two, generative engine optimisation has a precise origin. The term was coined in a 2023 paper formally published at KDD 2024, the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, held in Barcelona in August 2024. The authors were Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande.

The paper defines a “generative engine” as a system that uses generative models to gather and summarise information in order to answer a query, synthesising across multiple retrieved sources rather than returning them as a list. ChatGPT, Google AI Overviews and AI Mode, Gemini and Perplexity all fit that definition.

The researchers built GEO-bench, a benchmark of roughly 10,000 diverse user queries across multiple domains, then tested nine different content optimisation methods to see which ones increased the likelihood of a source being cited. That controlled testing is what separates GEO from the other two terms: there is actual evidence, not just practitioner consensus.

What the GEO research actually found

This is the most useful part of the paper for anyone making practical decisions, and it is rarely quoted accurately.

The researchers tested nine content optimisation methods against the GEO-bench benchmark and measured how each affected a source’s visibility in generated responses. The headline finding was that GEO methods could boost visibility by up to 40%. But the detail underneath matters more than the headline.

Three findings from that paper are worth internalising.

1. Classical keyword signals barely moved the needle

The researchers found that traditional SEO signals, keyword density in particular, showed minimal influence on citation probability within generated responses. What mattered instead were content-level characteristics associated with credibility.

2. Keyword stuffing made things worse

Loading content with repeated query terms scored 8% below the unmodified baseline, and 10% below in a separate validation run against Perplexity. This is the rare case of a tactic being not merely ineffective but actively counterproductive.

3. Clearer writing alone produced real gains

Fluency optimisation, which added no new information and simply improved the clarity of the prose, produced roughly 28% gains. Well constructed writing appears to be easier for a model to parse, summarise and attribute. Dense or convoluted text works against you even when the underlying information is strong.

The distinction that actually matters

The most common way these terms get misused is presenting them as alternatives, as though a business chooses SEO or GEO. They are layers.

The practical sequence follows from this. Fix crawlability, indexation and site health first. Restructure content so answers are extractable. Then build the third party citations and evidence density that decide whether a model names you. Skipping to the third step rarely works, because generative engines still retrieve from search indexes, and a page that cannot be found cannot be cited.

What Google says you do not need

Worth stating plainly, because there is a lot of product being sold around this. Google’s published guidance on AI features states there is no need for special file formats such as llms.txt, no need for unusual schema types, and no need to artificially chunk your content for AI systems. The stated requirements are unchanged: pages must be crawlable, indexable, eligible to appear in Search, and genuinely useful.

So do you need all three?

You need the outcomes. You almost certainly do not need three separate retainers.

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Colorado’s AI SEO agency · (800) 455-9670 Last updated September 2026