# Generative Engine Optimization: How to Dominate AI Search

arXiv: https://arxiv.org/abs/2509.08919 · HTML: https://mlsanity.com/paper/2509.08919/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [What Evidence Do Language Models Find Convincing?](https://mlsanity.com/paper/2402.11782/) (2024)
- [Manipulating Large Language Models to Increase Product Visibility](https://mlsanity.com/paper/2404.07981/) (2024)

## Built on by (most cited first, 18 of 18)

- [AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization](https://mlsanity.com/paper/2603.20213/) (2026)
- [SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization](https://mlsanity.com/paper/2602.12187/) (2026)
- [AgentWebBench: Benchmarking Multi-Agent Coordination in Agentic Web](https://mlsanity.com/paper/2604.10938/) (2026)
- [Deep-Research Agents Can Be Poisoned via User-Generated Content](https://mlsanity.com/paper/2605.24245/) (2026)
- [MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution](https://mlsanity.com/paper/2512.05958/) (2025)
- [IF-GEO: Conflict-Aware Instruction Fusion for Multi-Query Generative Engine Optimization](https://mlsanity.com/paper/2601.13938/) (2026)
- [Diagnosing and Repairing Citation Failures in Generative Engine Optimization](https://mlsanity.com/paper/2603.09296/) (2026)
- [EcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agents](https://mlsanity.com/paper/2605.12887/) (2026)
- [How Large Language Models Source Brand Reputation Across Languages and Markets](https://mlsanity.com/paper/2606.25787/) (2026)
- [Don't Measure Once: Measuring Visibility in AI Search (GEO)](https://mlsanity.com/paper/2604.07585/) (2026)
- [How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews](https://mlsanity.com/paper/2604.27790/) (2026)
- [AI Answer Engine Citation Behavior An Empirical Analysis of the GEO16 Framework](https://mlsanity.com/paper/2509.10762/) (2025)
- [The End of Rented Discovery: How AI Search Redistributes Power Between Hotels and Intermediaries](https://mlsanity.com/paper/2603.20062/) (2026)
- [Prominence-Stratified Failure Modes in Retrieval-Augmented Commercial Recommendation: A 37,000-Run Audit](https://mlsanity.com/paper/2605.27439/) (2026)
- [Exploring LLM biases to manipulate AI search overview](https://mlsanity.com/paper/2605.00012/) (2026)
- [SlopShape: Identifying AI-Generated Commercial Web Content](https://mlsanity.com/paper/2609.15369/) (2026)
- [Disentangling Models from Personas in Heterogeneous LLM Simulations](https://mlsanity.com/paper/2610.07535/) (2026)
- [What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO)](https://mlsanity.com/paper/2610.15033/) (2026)

## Abstract (arXiv)

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.