SEO vs GEO: what's the difference?

What does each one optimize for?

SEO optimizes for position: when someone types a query into Google, your page should appear as high as possible in the list of results, so the searcher clicks through to your site. The unit of success is a ranked link and the click it attracts.

GEO optimizes for presence in the answer itself. When someone asks an AI assistant "which tool should I use for X?", the AI replies in a few sentences and names a handful of businesses. Often nobody clicks anything — the answer is the destination. GEO's unit of success is being one of the names in that answer, ideally with a citation pointing back to you.

How do the machines behind them differ?

A classic search engine works in three stages: a crawler fetches your pages, an index stores and organizes them, and ranking algorithms sort candidates for each query using hundreds of signals — relevance, links, page experience.

An AI engine works differently. It retrieves content it can access (from its training data, a live search step, or both), then a large language model synthesizes a written answer from those sources and decides which ones to name or cite. There is no fixed ranking to climb — the model composes a fresh answer each time, favoring sources it can read cleanly, understand unambiguously, and trust.

That difference matters in practice: pages built to satisfy a ranking algorithm can still be useless to a language model — for example, if the key facts only appear after JavaScript runs, or if the page never states plainly what the business does.

Where do SEO and GEO overlap?

The good news: a lot of the foundation is shared. Work you have already done for SEO often carries over directly:

• Content quality — genuinely useful, specific content wins in both systems.
• Technical health — a fast, crawlable, well-structured site helps both Googlebot and AI crawlers.
• Trust signals — demonstrated experience, expertise and authority (what SEO calls E-E-A-T) make both ranking algorithms and language models more willing to surface you.

Where do they diverge?

Beyond the shared foundation, the day-to-day work targets different signals:

• Keywords vs questions: SEO targets keyword phrases people type; GEO targets full questions people ask, answered directly near the top of the page.
• Backlinks vs citations and consistency: SEO builds link volume; GEO builds being cited by trusted sources and keeping your facts (name, offer, location) identical everywhere AI might read them.
• Position tracking vs mention tracking: SEO measures success as SERP rank for a keyword; GEO measures whether the AI's answer to a real buyer question names you at all.

How do you do both without doubling the work?

You don't need two content teams. A practical workflow: start every page from a real buyer question, answer it directly in the first paragraph, then expand — that structure serves Google featured snippets and AI answers alike. Add structured data that states your business facts once, machine-readably. Check that your robots rules welcome AI crawlers, not just Googlebot. Keep facts consistent across your site, directories and profiles.

Then measure both sides separately: keep your rank tracker for SEO, and periodically check whether AI engines actually mention you when asked the questions your buyers ask. That second check is exactly what a GEO scan does — it tests the technical conditions and asks real questions to ChatGPT and Perplexity to see whether you appear in the answer.