In plain terms
For twenty years the goal was to rank on a results page and win the click. Now a growing share of questions is answered by an AI that reads the sources for the user and writes one reply. GEO is the work of being among the sources it reads and names. If you are not in the answer, the user never learns you exist.
Why it matters
Buyers increasingly research suppliers by asking an AI. The answer names a handful of companies, and that shortlist is formed before anyone visits a website. GEO is about being on it, and about what the AI says when you are. It builds on SEO and does not replace it: most AI systems find their sources through search.
Example
A logistics software company notices that when prospects ask an assistant for “the best fleet-management tools for mid-sized carriers”, three competitors are named and it is not. It publishes clear comparison pages, original benchmark data and plain definitions, gets covered by two industry publications, and tracks monthly how often it appears in answers to forty test questions.
Most often confused with
GEO vs. SEO
SEO optimises for a ranking algorithm and a human who chooses among links. GEO optimises for a model that reads, selects passages and writes the answer itself. Much of the groundwork is shared, such as crawlable pages, authority and clear structure, but success is measured in mentions and citations, where SEO counts positions and clicks.
Origin: The term comes from a 2023 research paper by Aggarwal and colleagues at Princeton, Georgia Tech, the Allen Institute for AI and IIT Delhi.
Under the hood
Answer engines typically rewrite the question into several searches, retrieve passages, and synthesise a reply with citations, so content is judged passage by passage. What helps, according to the original study and later practice: statements that stand alone when quoted, statistics, quotations and cited sources, clear headings and definitions, up-to-date pages, and consistent facts about the brand across the web, since models also draw on third-party mentions. Technical prerequisites: AI crawlers allowed in robots.txt, server-rendered content, structured data. Measurement is still immature: results vary between runs, models and phrasings, so tracking relies on sampling a fixed set of prompts over time.