Ten years ago, “search” meant one thing: get your page onto page one of Google. That’s no longer true. Today, people find businesses through three distinct paths — traditional search engines, AI answer engines like ChatGPT and Perplexity, and generative engines like Gemini and Copilot that write original answers instead of listing links. Each one has its own optimization discipline, and confusing them is why a lot of “AI SEO” advice being sold right now doesn’t work.
SEO is the discipline you already know — ranking a page in Google’s (or Bing’s) organic results. It’s built on crawlability, keyword relevance, backlinks, page speed, and technical health (valid HTML, mobile-friendliness, structured data). SEO still matters — Google remains the largest single source of buyer intent traffic on the internet — but it’s no longer the whole picture.
AEO is about winning the answer, not the click. When someone asks Google’s AI Overview, Perplexity, or a voice assistant a direct question, the engine picks one source (or a short list) to summarize and cite. AEO content is written to be extractable: it states the answer plainly near the top, uses clear headings that mirror real questions, and is marked up with FAQ or HowTo schema so machines can parse the structure, not just the words. The goal isn’t a top-10 ranking — it’s being the paragraph the engine quotes.
GEO is the newest and least understood of the three. It’s about how large language models like ChatGPT, Claude, and Gemini decide which brands to mention when they generate an original answer — not from a live search, but from a mix of retrieval and what the model has learned about your brand’s authority, consistency, and reputation across the web. GEO leans on entity clarity (does the model know who you are and what you do, consistently, everywhere), citations and mentions from sources the model already trusts, and structured technical signals like llm.txt and schema.org markup that make it easy for a model to retrieve accurate facts about you at inference time.
All three reward the same foundation: clear, accurate, well-structured content and a technically healthy site. Where they diverge is in the finish line. SEO optimizes for a ranking position. AEO optimizes for a direct citation inside an AI-generated answer box. GEO optimizes for being mentioned or recommended by a model that isn’t even actively searching the live web at that moment — it’s drawing on what it already “knows” about you.
They’re related disciplines, not the same discipline with a different name.
Start with the technical foundation all three depend on: structured data, fast and crawlable pages, and content that answers real questions directly instead of burying the point. Then layer in AEO-specific work (FAQ schema, direct-answer formatting) and GEO-specific work (consistent entity information, authoritative mentions, llm.txt) on top. Trying to do all three at once with no foundation is how most “AI SEO” engagements fail — they chase citations without ever fixing the crawlability and structure that make citations possible in the first place.
This is the exact framework we use when we build content hubs for clients — structured from the ground up for SEO, AEO, and GEO together, not bolted on after the fact. You can see it in practice on the St. Pete Marathon case study.
Structured for SEO, AEO, and GEO from day one — not bolted on after the fact.