What is GEO? Generative engine optimization, from the paper that named it

GEO stands for generative engine optimization: adapting your content so that generative search engines are more likely to surface and cite it when they compose an answer. Unlike most marketing acronyms it has a precise origin. It was coined in a 2023 paper by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande, published at ACM SIGKDD in 2024, which introduced a benchmark called GEO-bench and showed that targeted content changes could boost visibility in generative engine responses by up to 40 percent.

Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande, in the paper 'GEO: Generative Engine Optimization', arXiv 2311.09735, first submitted in November 2023 and accepted to ACM SIGKDD 2024. The authors are associated with Princeton University and IIT Delhi. This matters because GEO is one of the very few terms in this field with a peer-reviewed origin rather than a vendor blog post behind it, and the paper is short enough to read yourself.

In practice, almost nothing, and the tactics are the same. AEO, answer engine optimization, is the older and broader term, covering featured snippets and voice answers as well as AI chat. GEO is the newer term and points specifically at generative engines. The honest distinction is one of provenance rather than method: GEO has an academic paper behind it, AEO grew out of practitioner writing. Both describe getting named and cited inside a generated answer. We use them interchangeably and so does most of the industry.

Treat it as a demonstration that optimization is possible rather than a number you should expect. The paper itself states that the efficacy of its strategies varies across domains and explicitly calls for domain-specific optimization methods, which is a caveat most people quoting the headline leave out. It was also measured on generative engine responses to a broad benchmark of user queries, not on B2B vendor-recommendation questions where a small number of company names compete for three to five slots. That is a different problem and nothing in the paper claims to have tested it.

The paper's broad finding is that content-level changes, rather than link-level or authority-level ones, moved visibility in generated answers, and that adding citations, quotations and statistics to source content was among the more effective directions. The important structural insight is that generative engines assemble an answer from passages, so optimization operates at the level of the citable claim rather than the ranked page. If you want the specific per-strategy numbers, read the paper directly rather than any summary of it, including this one.

No, and our own measurement shows the gap. SEO earns a ranked link; GEO earns a mention inside a composed answer. In a 15-region study of managed IT providers we checked engine answers against each region's own directory: those directories listed 228 firms, engines named 89 and passed over 139. Firms that rank organically routinely fail to appear in the AI answer for the same question. SEO is the floor, because being crawlable and indexable is a precondition for being retrieved at all. It is not the same game, because a generated answer is decided on corroboration rather than on rank.

The cheap parts are worth doing now and the expensive parts deserve evidence first. Making sure AI crawlers can actually fetch your site, describing yourself consistently across third-party sources, and putting the facts an engine needs into plain readable text are all low cost and useful regardless. Large content programmes justified purely by a 40 percent figure from a benchmark that did not test your query type deserve more scepticism. Measure your own baseline across engines first, because it is free, and let the gap decide the spend.