# AEO vs GEO vs SEO vs AISO: What's Different, Which You Need > Answer engine optimization (AEO) vs generative engine optimization (GEO), from Cited (citedintel.com): where the real split sits (live retrieval vs model memory), a side-by-side table, and a decision guide for where to start. Source: https://www.citedintel.com/seo-vs-geo-vs-aeo-vs-aiso By Cited (citedintel.com) — the generative engine optimization (GEO) platform. --- Strategy guide · Reviewed August 2026 # SEO vs GEO vs AEO vs AISO: How the Four Differ and Which One You Need SEO wins ranked links, answer engine optimization (AEO) earns citations in live-retrieved AI answers, generative engine optimization (GEO) earns the recommendation itself across retrieval and model memory, and AISO (AI search optimization) is the umbrella label for the same AI-side practice. For most brands, GEO comes first. ## The short version The one-line answer SEO ends at a ranked link. AEO optimizes for surfaces that retrieve the live web. GEO covers those plus what models already believe from training. AISO, like AI SEO and LLMO, is another name for that same AI-side work. The cost of choosing wrong A program that only runs SEO or AEO leaves model memory unmanaged, and memory is what answers the buyer when the engine does not search. The source Written by the team behind Cited (citedintel.com), the GEO and AEO platform that closes the loop across buyer intent and funnel stages. This page is the map we run these disciplines with. ## SEO vs GEO vs AEO vs AISO at a glance Four labels, three actual jobs. The acronym matters less than which failure it fixes. | Term | Optimizes for | Finish line | Reaches model memory? | | --- | --- | --- | --- | | SEO (search engine optimization) | Ranked links on classic search results; the substrate every other discipline stands on | The click from a results page | No, but rankings feed the retrieval layer | | AEO (answer engine optimization) | Being selected and quoted by surfaces that retrieve the live web: Perplexity, AI Overviews, AI Mode, browsing modes | The citation inside a composed answer | No, retrieval only | | GEO (generative engine optimization) | Being recommended and cited when buyers ask AI what to buy: live retrieval plus what models already believe from training | The recommendation itself | Yes, via durable third-party evidence and entity clarity | | AISO (AI search optimization) | Umbrella label for the AI-side practice; used interchangeably with GEO, AI SEO and LLMO | Same as GEO | Yes, same scope as GEO under a different name | ## The four terms, defined ### Search Engine Optimization (SEO) SEO is the practice of earning ranked positions on classic search results so buyers click through to your site. It optimizes for the results page: crawlability, content quality, links and intent match. Every AI-side discipline stands on it — engines retrieve from the same indexed web — but its finish line is the click, not the answer. The oldest of the four by two decades. It is not replaced by the AI-side disciplines; it becomes their substrate, and its rankings feed the retrieval layer the others compete in. ### Generative Engine Optimization (GEO) GEO is the practice of earning recommendations and citations from AI engines, ChatGPT, Claude, Gemini, Perplexity, Google AI Mode, when buyers ask what to buy or use. It covers everything that shapes an AI answer: what the model already believes about your brand from training (model memory) and what it retrieves from the live web at answer time. The term traces to a 2023 academic paper (Aggarwal et al., "GEO: Generative Engine Optimization") and became the working industry label as AI assistants started forming buyers' shortlists. ### Answer Engine Optimization (AEO) AEO is the practice of optimizing content to be selected by answer engines, surfaces that retrieve live web content and compose it into a direct answer, like Perplexity, Google AI Overviews and AI Mode, and ChatGPT when it browses. The center of gravity is retrieval: structuring pages so they win the fetch and get quoted. The term predates the LLM era, it grew out of featured-snippet and voice-search optimization, and was re-adopted for AI answer surfaces as they replaced snippet boxes. ### AI Search Optimization (AISO) AISO is the umbrella label for the AI-side practice: earning visibility, citations and recommendations from AI search surfaces. In practice it names the same scope as GEO — retrieval plus model memory — and the market uses AISO, AI SEO and LLMO interchangeably with it. The label matters less than the scope: a program (or a vendor) that cannot diagnose and fix a model-memory gap is doing AEO work, whatever its name says. The newest label of the four, coined as tool vendors and job postings needed a term buyers would recognize without knowing the GEO acronym. It has no distinct methodology of its own. ## The split that actually matters: memory vs retrieval Arguing about which engines belong to GEO and which to AEO misses how the systems work. Every AI answer draws on up to two sources, and each source fails differently: Model memory What the model learned in training: your brand's reputation as it existed in the corpus. If memory skips you, the fix is durable, third-party evidence and entity clarity, published in the venues models train on. This is slow to change and slow to lose. AEO tactics do not reach it. Live retrieval What the engine fetches at answer time. If retrieval skips you, the fix is pages structured to win the fetch for a specific question: liftable answers, schema, crawler access. This can move in weeks. This is the layer AEO describes well. The clean model: AEO is the retrieval slice; GEO is the whole problem. Run AEO alone and memory goes unmanaged, and memory answers when the engine does not search. ## GEO vs AEO, side by side | | GEO | AEO | | --- | --- | --- | | Goal | Be the brand AI engines recommend and cite when buyers ask | Be the content answer engines select and quote for a query | | Covers | Model memory and live retrieval, both | Live retrieval surfaces | | Primary surfaces | ChatGPT, Claude, Gemini, Perplexity, AI Overviews, AI Mode | Perplexity, AI Overviews, AI Mode, browsing modes | | Core tactics | Entity clarity, third-party evidence, comparison content, venue presence, plus every AEO tactic | Answer-shaped pages, schema, crawler access, question-matched content | | Time to move | Retrieval in weeks; memory over months of durable evidence | Weeks, tied to crawl and index cycles | | Measured by | AI share of voice, endorsement, sentiment and citations on buyer prompts | Citation frequency and referral traffic from answer surfaces | | Fails silently when | You treat it as a content project and skip the technical and evidence layers | The buyer's engine answers from memory and never retrieves at all | ## What the numbers say in 2026 Four verifiable data points frame the GEO vs AEO decision, including one that is widely misquoted. ~900M weekly ChatGPT users The buyer population asking AI engines what to buy is no longer a niche. This is the audience both disciplines compete for. [OpenAI figures reported by TechCrunch, February 2026](https://techcrunch.com/2026/02/27/chatgpt-reaches-900m-weekly-active-users/) 35-51% of Google queries now trigger AI Overviews The retrieval-heavy surface AEO targets is now the default experience for a large share of searches, not an edge case. Range across 2026 query-set audits; highest on question-shaped queries ~40% fewer outbound organic clicks when an AI Overview is present The click you used to win at position one increasingly does not exist. Being inside the answer is the replacement for being under it. [Randomized field experiment, 2026 (Agarwal et al.)](https://www.tse-fr.eu/sites/default/files/TSE/documents/sem2026/eco_platforms/agarwal_google_search_aio.pdf) 7.1% conversion rate measured on ChatGPT referral traffic AI-referred visits are still small in volume and unusually high in intent. Losing them costs disproportionately more than the traffic numbers suggest. [Similarweb, ChatGPT referral analysis](https://www.similarweb.com/blog/insights/ai-news/chatgpt-referral-traffic-triples/) The number everyone quotes wrong The famous 40% GEO lift is real, published and almost always misread. It measures answer composition in a testbed, not organic discovery. The difference decides where your budget goes. What the study measured The foundational GEO research (Aggarwal et al., KDD 2024) found up to a 40% relative visibility lift, with credible citations, quotations and statistics as the strongest tactics. [Read the paper on arXiv](https://arxiv.org/abs/2311.09735) The part vendors skip The lift was measured in a fixed testbed where the engine had already retrieved the documents. It is a ceiling on content already in the answer's context, not a promise of discovery. What it means for GEO vs AEO Answer-shaped content (the AEO layer) only pays after you are retrieved or remembered at all. Getting retrieved and remembered is the layer most programs never measure. What Cited unlocks Every Cited (citedintel.com) audit classifies each loss as retrieval or memory, per prompt, engine and funnel stage, so the 40%-class content work lands where it can cash in. ## Which one do you need? Almost every team asking this question needs GEO with AEO inside it. The useful version of the question is where to start, and that depends on where you lose today. Engines mention competitors from memory, not you This is a memory gap and a GEO problem. More retrieval-optimized pages on your own site will not fix it: you need third-party evidence, entity clarity and presence in the venues your category's answers are trained and grounded on. You are absent from Perplexity and AI Overviews specifically Retrieval-heavy surfaces skipping you is the AEO slice: check crawler access first, then build pages that answer the retrieved question directly, with structure engines can lift. You rank well in classic search but AI answers skip you Rankings prove retrieval can find you; being skipped in answers means the content is not answer-shaped or the evidence is not citable. Start with the AEO layer, then audit what engines say from memory, most teams find both need work. You do not know where you lose Measure before choosing: run buyer prompts across engines and split the losses by source. Memory losses route to evidence work, retrieval losses route to page work. Guessing the split wrong wastes a quarter. Your team only has classic SEO skills today Keep the SEO program running — it feeds the retrieval layer everything else competes in — and add the AI-side loop on top: a monthly read of what engines say about you, evidence work for memory gaps, answer-shaped pages for retrieval gaps. The skills transfer; the finish line moves from the click to the citation. A vendor or job posting says AISO, AI SEO or LLMO Translate the label to scope before comparing. If the work covers model memory and live retrieval with recommendations as the finish line, it is GEO under another name. If it stops at schema, crawlability and answer-shaped pages, it is the AEO slice — useful, but it leaves memory unmanaged. ## Where SEO fits in this picture SEO is not replaced by either term: crawlability, structured data and content quality remain the substrate both practices stand on, and classic rankings still feed the retrieval layer. What changes is the finish line. SEO ends at a ranked link; GEO and AEO end inside the answer itself, where two or three brands get named and the rest get silence. For the deeper treatment of what carries over from the SEO playbook and what quietly fails, read GEO vs SEO: What Changes, What Stays in the library. [GEO vs SEO: What Changes, What Stays](https://www.citedintel.com/answer-engine-optimization/geo-vs-seo-what-changes-what-stays) [The GEO Hub: frameworks and the 90-day plan](https://www.citedintel.com/generative-engine-optimization) [The 10 best GEO tools in 2026, ranked](https://www.citedintel.com/best-geo-tools) ## GEO vs AEO: the questions teams actually ask ### AISO stands for AI search optimization, and in practice it names the same discipline as GEO: earning recommendations and citations from AI engines by managing both live retrieval and model memory. The market uses AISO, AI SEO, LLMO and GEO interchangeably; GEO is the most established label. When evaluating a vendor or a job description, ignore the acronym and check the scope: if it cannot fix a model-memory gap, it is AEO work wearing a broader name. ### SEO optimizes for ranked links on a results page; the other three optimize for the answer itself, where two or three brands get named and the rest get silence. SEO is not replaced, crawlability, structured data and content quality remain the substrate the AI-side disciplines stand on, but the finish line moves from the click to the citation or the recommendation. The deep tactical comparison lives in our GEO vs SEO guide. ### Treat AEO as the retrieval-focused slice of GEO. AEO's tactics, answer-shaped pages, schema, crawler access, are necessary and reach the surfaces that fetch live content. GEO adds the layer AEO cannot touch: what models already believe from training, which is managed with durable third-party evidence and entity clarity. Separate budgets for them usually produce two half-programs. ### Both labels get applied, and the distinction is not the point: AI Overviews are a retrieval-heavy surface, so AEO tactics carry most of the weight there, while the brand-level trust signals GEO builds influence which sources the overview leans on. Optimize the surface with AEO mechanics, and the brand with GEO evidence. ### GEO has become the broader industry label and covers the full scope, so it is the safer umbrella term; AEO remains precise when you specifically mean retrieval surfaces. What matters in a scope of work is not the acronym but whether it covers both sources: ask any vendor or hire how they would fix a model-memory gap, and the answer tells you which discipline they actually practice. ### Yes, if it measures answers across both memory-heavy and retrieval-heavy engines and diagnoses which source cost you the recommendation. That split is the feature to check for: a tool that only tracks citations on retrieval surfaces is an AEO tool regardless of its label. Cited runs buyer prompts across both kinds of surfaces and classifies each loss by cause, which is what makes the memory-vs-retrieval split actionable. 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