SEO optimizes for ranking in traditional search results. AEO optimizes for being the direct answer to a question. GEO optimizes for being cited, summarized, or referenced by generative AI systems. They overlap heavily in practice but target different outcomes.
The three terms, defined
SEO (Search Engine Optimization) is the original discipline: getting a page to rank highly in a search engine's list of blue links, for a query, so a human clicks through. Its core levers are backlinks, keyword relevance, page experience, and technical crawlability.
AEO (Answer Engine Optimization) shifted the target from "rank highly" to "be the answer" — optimizing content to be pulled directly into a featured snippet, a voice assistant's spoken response, or a knowledge panel, often without the user ever clicking through to the source page.
GEO (Generative Engine Optimization) is the newest term, referring specifically to optimizing for generative AI systems — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — that synthesize an answer from multiple sources and may or may not cite them. GEO covers new technical surfaces AEO and SEO never had to consider: llms.txt, AI crawler access control, and how content gets extracted and summarized (rather than just ranked or snippet-matched) by a language model.
What actually changed with GEO
Three things are genuinely new, not just rebranded SEO advice:
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A new technical layer to manage. robots.txt only used to matter for search-engine crawlers; now it needs explicit rules for a dozen-plus distinct AI bots with different purposes (training vs. search), a distinction that didn't exist in the SEO era. llms.txt is an entirely new file type with no SEO-era equivalent.
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Citation, not ranking, is the unit of success. In traditional search, you either rank or you don't, and position (#1 vs #10) is measurable. In generative answers, a page may be one of several sources synthesized into a single response, may be paraphrased rather than linked, and may not be individually attributed at all depending on the platform — success metrics are murkier and still being defined.
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Extraction, not just relevance, determines inclusion. A page can be topically perfect and still get skipped by an AI system if its actual answer is buried in unstructured prose the model has to work harder to parse (see our structure checklist for AI Overviews). This wasn't a ranking factor in traditional SEO the way it functions as an inclusion factor in GEO.
What didn't change
Most SEO fundamentals still matter and aren't being replaced:
- Content quality and accuracy — AI systems are, if anything, more sensitive to citing wrong or unclear information than traditional search ranking algorithms were, since a bad citation is directly visible in the generated answer.
- Technical crawlability — a page that's slow, blocked, or JavaScript-gated from crawlers is invisible to AI systems for the same reasons it was invisible to Googlebot.
- Topical authority and backlinks — still meaningful signals for how much a page or domain is trusted, likely feeding into which sources generative systems weight more heavily.
- Clear, well-organized writing — good writing was already easier to skim and understand; GEO just adds a second audience (models) that benefits from the same clarity.
Do you need a separate "GEO strategy"?
Practically: extend your existing SEO/content process to cover the genuinely new surfaces (llms.txt, AI-bot robots.txt rules, structured data, answer-first paragraph structure), rather than treating GEO as a wholesale replacement discipline. Most of the underlying skill — writing clearly, structuring content logically, backing claims with specifics — was never SEO-specific in the first place; it was just good writing that happened to also rank well.
Where to start
Run your site through our AI-Readiness Audit to see where you stand on the genuinely new GEO-specific checks, then work through the full audit guide for a step-by-step process.