Three acronyms, one shared foundation, and meaningfully different technical requirements. If you're already doing SEO, you're partly doing AEO and GEO — but the gaps matter enough to audit explicitly.
What each term actually means
SEO (Search Engine Optimization) is the original discipline: structure pages so Google and Bing can crawl, index, and rank them. It covers meta tags, canonical URLs, structured data, backlink signals, page speed, and Core Web Vitals. The pipeline is crawl → index → rank → serve link.
AEO (Answer Engine Optimization) targets the step where a search engine decides to surface a direct answer rather than a list of links — featured snippets, People Also Ask boxes, and voice results from Siri, Alexa, and Google Assistant. The page still goes through the SEO pipeline; AEO adds a layer that shapes what gets extracted as the answer.
GEO (Generative Engine Optimization) was named in the 2024 paper "GEO: Generative Engine Optimization" by Aggarwal et al. (Princeton, Georgia Tech, IIT Delhi). It targets AI systems — ChatGPT, Perplexity, Google AI Overviews, Claude — that synthesize answers from multiple sources instead of returning a ranked list. GEO isn't about ranking above competitors; it's about being usable source material at all.
Where they diverge technically
The crawlability baseline is shared. All three require a working robots.txt, a valid sitemap, HTTPS, fast TTFB, and stable URLs. Past that, the requirements branch:
SEO depends on PageRank-style signals: inbound links, domain authority, click-through rates, and dwell time. These are hard to manufacture — they accumulate through genuine usefulness and discoverability.
AEO requires structured Q&A patterns: FAQ schema (
FAQPageJSON-LD), clear definition paragraphs at the top of sections, and concise one-to-two sentence answers before elaboration. Voice results particularly punish pages where the answer is buried in the third paragraph.GEO requires entity clarity and machine-readable context:
OrganizationandSoftwareApplicationJSON-LD so the AI knows who publishes the content, an/llms.txtfile that surfaces the canonical pages worth citing, explicit AI crawler allowlisting inrobots.txt(GPTBot, ClaudeBot, Google-Extended), and authoritative direct-prose writing with sourced claims. AI systems can't infer entity identity the way a human reader does — you have to state it explicitly.
What overlaps (and why that's useful)
The same Organization JSON-LD block in your page <head> that
helps SEO also helps GEO by unambiguously naming your entity. The
same FAQPage schema that triggers a featured snippet for AEO also
makes FAQ content extractable for AI Overviews. Fast TTFB, stable
canonical URLs, and clean HTML structure improve all three.
The practical implication: don't treat these as three separate work streams. Start with the SEO foundation (correct canonicals, valid structured data, working sitemap). Then layer AEO signals (FAQ schema, answer-first paragraph structure). Then layer GEO additions (llms.txt, AI crawler allowlist, entity disambiguation in JSON-LD).
A quick comparison
| Signal | SEO | AEO | GEO |
| Canonical URLs | Required | Required | Required |
| Structured data (JSON-LD) | Helpful | Required for snippets | Required for entity clarity |
| Backlink signals | High weight | Indirect | Low weight |
| FAQ schema | Optional | High weight | Moderate |
/llms.txt | Not applicable | Not applicable | Recommended |
| AI crawler allowlist | Not applicable | Not applicable | Required |
| Answer-first prose | Optional | High weight | High weight |
Verify before shipping
The isitready.dev audit scores your site across all three surfaces in one pass — it flags missing structured data, AI crawler policy ambiguity, llms.txt gaps, and AEO-relevant schema issues together, with per-finding remediation notes. Run it against your canonical origin to see which of the three you're actually failing on.