Generative Engine Optimization
Generative Engine Optimization (GEO) is the academic name for AEO, formalised in a 2024 KDD paper by researchers at Princeton and IIT Delhi who tested which content modifications improve visibility in AI-generated answers.
Why it matters
GEO is the peer-reviewed foundation that justifies AEO as a discipline. The findings (statistics +41%, citations +115% for lower-ranked content) give marketing teams an evidence base for what actually moves AI visibility — distinct from vendor-driven 'AI SEO' guides.
The Princeton paper
Aggarwal et al. published GEO: Generative Engine Optimization at ACM KDD 2024. They built GEO-Bench — a benchmark of user queries across multiple domains — and ran controlled experiments on nine content-modification strategies to measure which reliably improves visibility in generative engine responses.
Key findings
- Citation insertion drove a 115% visibility increase for content ranked fifth or lower in classical search.
- Statistics addition drove a 41% improvement on Position-Adjusted Word Count.
- Quotation addition and technical-term richness also drove measurable gains.
- Keyword stuffing actively reduced visibility — the opposite of how it interacts with classical search engines.
Why "GEO" vs "AEO"
The names describe the same practice. GEO is the term you'll see in academic papers; AEO is the term industry adopted. Some practitioners also use AI SEO or LLMO. Picking one for internal use beats switching between four interchangeably.
Related terms
