Search moved from a list of ranked links to a written answer, and the industry produced three overlapping acronyms to describe optimizing for that shift: SEO, AEO and GEO. There is no single body that governs these definitions, so different vendors draw the lines in different places. What follows is what each term is most commonly used to mean, and — for one of them — an actual paper you can go read rather than a marketing definition.
SEO: optimizing for a ranked list
Search engine optimization is the oldest and best-defined of the three. It is the practice of improving a page's position on a traditional search engine results page — Google or Bing — through technical crawlability, keyword relevance, backlinks and page experience. SEO assumes the output is a ranked list of ten or so blue links, and the goal is a higher position on that list. Nothing about SEO has become obsolete; AI systems still lean on the same underlying indexes. Google's own guidance is direct on this point: the standard SEO fundamentals "remain relevant for AI features in Google Search," per Google Search Central.
AEO: optimizing for the direct answer
Answer engine optimization is the practice of structuring content so it can be extracted and returned as a direct answer, rather than just linked to. It covers featured snippets, Google's "People also ask" boxes, voice assistant answers and AI Overviews — anywhere a system lifts a specific sentence or fact out of a page instead of sending a visitor to read the whole thing. AEO overlaps heavily with good technical writing: a clear question posed as a heading, followed immediately by a direct, complete-sentence answer, is the shape that answer engines extract most easily. There is no single official body that defines AEO's boundaries, and industry write-ups disagree on exactly where it ends and GEO begins.
GEO: the one with an actual paper
Generative Engine Optimization is the most precisely defined of the three, because it originates in a specific piece of academic research rather than industry usage. "GEO: Generative Engine Optimization" by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande was accepted to the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), held in Barcelona in August 2024, and is available on arXiv as paper 2311.09735. The authors are affiliated with Princeton University and IIT Delhi.
The paper frames "generative engines" — search systems that synthesize an answer from multiple sources using a large language model rather than returning a ranked list — as a new category the authors argue disadvantages content creators, who have "little to no control over when and how their content is displayed." To study the problem, the authors built GEO-bench, a benchmark of roughly 10,000 real user queries paired with the web sources used to answer them, and tested a set of content interventions inside a black-box optimization framework:
- Adding direct quotations from cited sources
- Citing statistics with a named source
- Improving writing fluency and clarity
- Adding relevant technical or domain terminology
According to the paper's own reported results, combining these interventions could improve a page's visibility inside generative-engine answers by up to 40% on their benchmark — the authors' own figure, from their own optimization framework, not an outside audit. They also found the effective strategy varied by domain, meaning what helped a historical-topic query did not necessarily help a technical one — which is itself an argument against a single universal "GEO checklist."
Why the three terms keep overlapping
In practice, industry usage blurs these categories constantly, and there is no dictionary anyone is required to follow. Some writers use AEO and GEO interchangeably; others draw a line where AEO means "structuring for extraction" and GEO means "influencing what a generative model says about your brand across the wider web, not just your own site" — a distinction closer to reputation management than on-page optimization. We are not going to pretend there is a settled taxonomy here, because there isn't one yet. What is consistent across every definition, including the KDD paper's, is the underlying mechanism: content that states a clear, quotable, sourced answer gets extracted more easily than content that doesn't, regardless of which of the three letters you put in front of "EO."
Why the benchmark result comes with an asterisk
The 40% figure gets repeated often, so it is worth being precise about what it describes. It is the improvement GEO-bench measured on the authors' own visibility metric, inside their own optimization framework, across their own set of roughly 10,000 queries — a research result demonstrating that a black-box optimization approach can work, not a benchmark result reproduced across ChatGPT, Perplexity and Gemini in production by an outside party. The paper itself flags that effectiveness "varies across domains," meaning a strategy that worked well on history or science queries did not transfer cleanly to other domains in their own testing. Treat it the way you would treat any single academic paper's headline number: a real, published, peer-reviewed finding, and also a starting point rather than a settled industry benchmark.
What this means for the work itself
You do not need to pick one label and optimize for it exclusively. A page with clean technical SEO, a direct answer near the top, and cited, specific facts is doing all three at once — because AEO and GEO are mostly asking the same underlying question in different vocabulary: can a machine easily identify what this page claims, and why it should trust the claim. That is also, not coincidentally, what our six checks measure: whether your site can be found, whether it answers questions directly, whether machines can parse its structure, whether it carries verifiable trust signals, and whether an AI agent could act on it.