> ## Documentation Index
> Fetch the complete documentation index at: https://docs.peec.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# What is AEO — Answer Engine Optimization, and how it differs from SEO

> A 2026 Stanford GSB paper defines AEO (also GEO, LLMO) as ensuring brands are included and favorably positioned in AI answers; AI-mediated search hit ~30% of search interactions by early 2026.

AEO (Answer Engine Optimization, in the paper "Agentic Engine Optimization") is the discipline of ensuring brands are included and favorably positioned within AI-generated responses. A Stanford GSB paper by Bliey & Chatwin ([SSRN](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7108218), Jul 2026) frames the field; the terms GEO (Generative Engine Optimization), AEO, GAIO, and LLMO are used largely interchangeably in the industry.

Key arguments from the paper (Jul 2026):

* **AI-mediated search grew from under 10% of search interactions in 2023 to roughly 30% by early 2026** (citing First Page Sage), fragmenting search across general-purpose platforms and specialized assistants.
* **The unit of value is shifting from the click to the synthesis.** In agentic search, inclusion depends less on domain authority or paid placement and more on whether a brand survives retrieval, grounding, and cross-source validation inside LLM systems.
* **The "Perfect Click":** a click arriving from an AI recommendation is confirmatory, not exploratory — the decision is substantially made — so one AI-mediated recommendation can be worth more in conversion terms than hundreds of unqualified impressions.
* **Expected rank across the distribution of users becomes the relevant metric**, not static rank on a common results page, because outputs are non-deterministic and personalized.

The mechanics the paper builds on — passage-level retrieval, query fan-out, reranking — are covered with measured data in [How AI search chooses passages](/research/how-ai-search-chooses-passages) and [What is a query fan-out](/research/what-is-a-query-fanout).

**Source:** [Answer Engine Optimization: How Agentic AI Reshapes SEO](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7108218), Miles Bliey & Keira Chatwin, Stanford GSB (SSRN), Jul 2026.