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Evidence record 4165 · automatically gathered

Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots

Large language model (LLM) answer engines are increasingly used for information seeking, shifting visibility from ranked lists to synthesized answers. This enables Generative Engine Optimization (GEO), which targets LLM answer engines' evidence pool and generation. We analyze the search engine optimization (SEO) to GEO transition to identify two risks: (i) concentrated influence from low contestability and system sensitivity, and (ii) undisclosed commercial influence embedded in evidence and rea

Record details

Published: 18 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Transparency
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Regulation · Shared topic: Transparency

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Shared topic: Regulation · Shared topic: Transparency

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Shared topic: Regulation · Shared topic: Transparency

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How to cite this record

ethics.ai (18 May 2026), “Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots,” evidence record 4165, https://ethics.ai/record/4165 (originally published by arXiv).

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