Generative Engine Optimization: How to Dominate AI Search

TL;DR

This paper analyzes the emerging landscape of AI-powered search engines, revealing their biases and differences from traditional search, and proposes strategic optimization practices for content creators to succeed in this new environment.

Contribution

It provides the first large-scale empirical comparison of AI Search and traditional search, highlighting biases and differences, and introduces a strategic framework for Generative Engine Optimization (GEO).

Findings

AI Search favors authoritative third-party sources over brand-owned content.

Significant differences exist among AI Search services in domain diversity and freshness.

Practitioners should engineer content for AI scannability and authority to improve visibility.

Abstract

The rapid adoption of generative AI-powered search engines like ChatGPT, Perplexity, and Gemini is fundamentally reshaping information retrieval, moving from traditional ranked lists to synthesized, citation-backed answers. This shift challenges established Search Engine Optimization (SEO) practices and necessitates a new paradigm, which we term Generative Engine Optimization (GEO). This paper presents a comprehensive comparative analysis of AI Search and traditional web search (Google). Through a series of large-scale, controlled experiments across multiple verticals, languages, and query paraphrases, we quantify critical differences in how these systems source information. Our key findings reveal that AI Search exhibit a systematic and overwhelming bias towards Earned media (third-party, authoritative sources) over Brand-owned and Social content, a stark contrast to Google's more balanced mix. We further demonstrate that AI Search services differ significantly from each other in their domain diversity, freshness, cross-language stability, and sensitivity to phrasing. Based on these empirical results, we formulate a strategic GEO agenda. We provide actionable guidance for practitioners, emphasizing the critical need to: (1) engineer content for machine scannability and justification, (2) dominate earned media to build AI-perceived authority, (3) adopt engine-specific and language-aware strategies, and (4) overcome the inherent "big brand bias" for niche players. Our work provides the foundational empirical analysis and a strategic framework for achieving visibility in the new generative search landscape.