GEO vs AEO vs AIO: What the AI Search Terms Actually Mean
Key takeaway: GEO (Generative Engine Optimisation), AEO (Answer Engine Optimisation) and AIO (AI Optimisation) all describe the same goal: getting your business found, cited and recommended by AI systems like ChatGPT, Gemini, Claude and Perplexity. There is no agreed industry def
Key takeaway: GEO (Generative Engine Optimisation), AEO (Answer Engine Optimisation) and AIO (AI Optimisation) all describe the same goal: getting your business found, cited and recommended by AI systems like ChatGPT, Gemini, Claude and Perplexity. There is no agreed industry definition separating them, and they are used largely interchangeably. Google’s own guidance goes further and says optimising for its AI features is simply SEO. The label you choose matters far less than the work: crawler access, specific content, structured data and credible mentions.
If you have spent any time researching AI search recently, you have met the acronym soup. GEO. AEO. AIO. Sometimes LLMO. Different tools, different communities, and increasingly different commercial interests trying to own each label.
This guide cuts through it: what each term means, where it came from, where the definitions genuinely differ, and why the debate matters less than the practitioners pushing each label would like you to believe.
What is GEO?
GEO stands for Generative Engine Optimisation: the practice of structuring your website and online presence so AI systems understand who you are, what you offer, and whether you are worth recommending.
The term came out of academic research in late 2023, when researchers published the paper that named the field, and it spread through practitioner communities from there. It got a major push in May 2025 when Andreessen Horowitz used GEO as the name for the category in an influential investment thesis. Today GEO is the most widely used and most written-about of the terms, which is why it is the one we use, including in the GEO Score.
GEO covers everything an AI system might use to form a view of your business: technical accessibility, content quality, authority signals, structured data, and your presence across the wider web.
What is AEO?
AEO stands for Answer Engine Optimisation. It predates the current AI wave: the term originally described optimising content to be the direct answer in featured snippets, voice assistants and answer boxes. It has since been repurposed for AI search, and some practitioners and tools now use AEO as their preferred name for the whole discipline, arguing it is clearer and more ownable than GEO.
Where a distinction is drawn at all, AEO tends to emphasise structuring content so systems can extract a direct answer: clear headings, question-led sections, concise definitions. But in practice most people using AEO and most people using GEO are describing the same work.
What is AIO?
AIO is the muddiest of the three, because it is used to mean two different things:
AI Optimisation, a catch-all for making your business visible and trusted across AI systems, often with an emphasis on off-site reputation signals like reviews, mentions and consistent presence.
AI Overviews, Google’s AI-generated answers at the top of search results. Plenty of articles use AIO as shorthand for the Google feature itself, not a discipline.
If someone uses AIO, it is worth checking which they mean. Either way, the underlying recommendations end up in the same place as GEO and AEO.
And where does SEO fit?
Everywhere. Google’s official guide to optimising for its generative AI features is blunt about it: AI Overviews and AI Mode run on the same core ranking and quality systems as ordinary search, and from Google’s perspective, optimising for generative AI is still SEO. The technical foundations, crawlability, page experience, structured data and content quality, are shared.
Where GEO genuinely extends beyond classic SEO is in everything Google does not cover: how ChatGPT, Claude, Perplexity, Grok and DeepSeek find and cite you, how your brand is described across the sources AI systems trust, and whether AI assistants actually recommend you when customers ask.
Why so many terms?
Three honest reasons. First, the field is new and moved faster than its vocabulary; the research label (GEO), the repurposed label (AEO) and the catch-all label (AIO) all emerged within about two years. Second, commercial interest: tools and agencies each want a term they can own, build courses around and rank for. Third, genuine ambiguity: there is still no consensus definition separating these terms, and they are used interchangeably across trade and practitioner writing.
None of them is wrong. None is dramatically more correct. If you research any of them, you will find the same core recommendations.
What actually matters
Rather than picking a side in the terminology debate, look at what the businesses being recommended by AI systems have in common:
They are technically accessible. AI crawlers are allowed in, the site is crawlable, and the content renders without a fight. Non-negotiable, whatever you call the discipline.
They are specific. Their pages say exactly what they do, for whom, and where, in language a machine can match to a real question. Vague copy that could describe any business in the sector gets nobody recommended.
They are credibly mentioned. AI systems lean on sources they trust. A trade publication mention, a substantive podcast appearance or a respected directory listing outweighs a pile of thin links.
They are consistently described. The same business, described the same way, across their site, their profiles and their coverage. Contradictions and gaps make machines hesitate.
They measure it. They know whether AI assistants actually cite them, on which questions, against which competitors, and whether that is improving.
These are not GEO concerns or AEO concerns or AIO concerns. They are the concerns of any business that wants to be recommended by the machines millions of people now ask “who should I use?”
Quick comparison
| GEO | AEO | AIO |
|---|
| Stands for | Generative Engine Optimisation | Answer Engine Optimisation | AI Optimisation (also used for Google’s AI Overviews) |
| Origin | Academic research, late 2023 | Pre-AI search term, repurposed | Commercial usage, 2024 onwards |
| Typical emphasis | Citations and visibility across AI systems | Extractable, answer-shaped content | Cross-platform trust and reputation signals |
| In practice | Largely the same work, described from different angles | | |
| Result for your business | The same either way: be accessible, specific, credible and measured | | |
Frequently asked questions
Are GEO, AEO and AIO different disciplines?
Not meaningfully. There is no agreed definition separating them, and they are used largely interchangeably. Where differences are claimed, they are differences of emphasis: GEO leans toward citations and AI visibility, AEO toward answer-shaped content, AIO toward cross-platform trust signals. The underlying work is the same.
Which term should I use?
GEO is the most established and most widely searched, which is why SearchScore uses it. But the term is a label, not a strategy. Use whichever your team understands, and spend the energy on implementation.
Is GEO just SEO?
For Google’s AI features, Google itself says yes: its generative AI runs on the same ranking systems as ordinary search. But Google is one engine among several. GEO also covers how ChatGPT, Claude, Perplexity, Grok and DeepSeek find, understand and cite you, which classic SEO tooling does not measure.
What is LLMO?
Large Language Model Optimisation, another label for the technical end of the same field: how language models retrieve and cite content. If you are doing GEO properly, you are already doing LLMO.