- GEO (Generative Engine Optimization) means increasing how often a source gets cited or drawn on inside an AI-generated answer (the output of ChatGPT, Perplexity, or Google's AI Overviews) rather than ranking in a list of links.
- Unlike AEO, GEO has a clean, verifiable academic origin: "GEO: Generative Engine Optimization" by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, and Deshpande, first posted to arXiv in November 2023 and presented at KDD 2024.
- That paper tested nine specific, narrow content-editing methods against a purpose-built benchmark. It did not test schema markup, and it is frequently misquoted to justify tactics it never touched. See the dedicated paper explainer for the actual findings.
- The marketing industry's current use of "GEO" (as an umbrella term for the entire practice of AI-search visibility) is a much broader claim than what the original 2023 paper set out to prove. Both uses are real; they're just not interchangeable in scope, and confusing them is how a narrow, domain-dependent research finding gets oversold as a universal law.
"GEO" gets used two different ways in the wild, at two very different scales of claim, and most explanations blur them together. This piece keeps them separate: the specific academic term, and the much larger industry umbrella that borrowed its name.
What is GEO as an academic term?
Generative Engine Optimization began as the title of a single research paper, "GEO: Generative Engine Optimization," by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, and Ameet Deshpande, first posted to arXiv in November 2023 (arXiv 2311.09735) and presented at KDD 2024, the ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
The paper's actual contribution is narrower and more specific than the term's current popularity suggests: it built a benchmark (GEO-bench) and a testing framework to measure which of nine specific content-editing methods (adding citations, statistics, quotations, and others) actually improved how often a source got pulled into a generative AI answer. It found real, measurable effects, but with an important condition attached: the effect is domain-dependent, meaning what works for factual content underperforms for opinion-driven content, and there's no single tactic that works uniformly everywhere. The full breakdown of what was tested, what worked, and what the paper explicitly did not test (schema markup among the notable omissions) is covered in the GEO paper, explained.
That's the entire scope of GEO-as-academic-term: a specific paper, a specific benchmark, nine specific methods, tested under specific, disclosed conditions.
What is GEO as a marketing term?
Somewhere between the paper's 2023 publication and 2025, "GEO" stopped being shorthand for that one paper's findings and became an umbrella label for the entire practice of optimizing for AI-search visibility: everything from crawler access to entity consistency to citation tracking, most of which the original paper never tested at all. Andreessen Horowitz's May 2025 newsletter piece, "How Generative Engine Optimization (GEO) Rewrites the Rules of Search," is widely credited with pushing this broader usage into mainstream marketing and venture-capital conversation, framing GEO as "Act II of search," a wholesale paradigm shift from ranking-based to reference-based visibility, backed by observations like generative queries averaging roughly 23 words against roughly 4 for traditional search, and session times running around 6 minutes.
That's a legitimate and useful way to talk about the broader shift. It is not, however, the same claim as "the GEO paper proved this works," a conflation that shows up constantly, including on this site's own earlier posts, which is exactly why the GEO paper, explained exists as a corrective: to separate what the specific 2023 study actually demonstrated from the much larger industry narrative that now borrows its name.
How does GEO differ from AEO and SEO mechanically?
The clearest way to separate the three isn't by vibe, it's by which retrieval system each one is actually optimizing for:
| Term | What it optimizes for | Underlying system |
|---|---|---|
| SEO | Rank position in a list of links | Search engine's ranking algorithm (backlinks, keywords, page authority) |
| AEO | Selection as a direct answer (snippet, knowledge panel, voice response) | Still Google's own crawling/ranking infrastructure, applied to a narrower answer-box target |
| GEO | Inclusion or citation inside a generative, synthesized answer | A language model's retrieval-and-generation pipeline, a mechanically different process that doesn't rank pages at all |
For the full mechanical comparison, including where these systems' evaluation metrics genuinely diverge (click-through rate vs. citation/reference rate), see AEO vs GEO vs SEO: what's actually different.
Why does the distinction between the two "GEOs" matter?
If a vendor cites "the GEO paper" to justify a tactic, it's worth asking which claim they're actually making: something the 2023 paper specifically tested (in which case, ask which of the nine methods, and note the domain-dependent caveat), or something that falls under the broader industry umbrella but was never in that paper's scope at all. The gap between those two claims is exactly where an unverified tactic gets a false stamp of academic legitimacy.
Frequently asked questions
What does GEO stand for?
Generative Engine Optimization: the practice of increasing how often a source gets cited or drawn on inside an AI-generated answer, such as a response from ChatGPT, Perplexity, or Google's AI Overviews.
Where did the term GEO come from?
A specific academic paper, 'GEO: Generative Engine Optimization' by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, and Deshpande, first posted to arXiv in November 2023 (arXiv 2311.09735) and presented at KDD 2024. The paper built a benchmark and tested nine specific content-editing methods for their effect on citation likelihood in generative answers.
Is the marketing industry's 'GEO' the same as the academic paper's GEO?
Not exactly. The paper tested nine narrow, specific editing methods (adding citations, statistics, quotations, and others) against a fixed benchmark. The marketing industry now uses 'GEO' as an umbrella term for the entire discipline of optimizing for generative AI visibility, a much broader claim than what the original paper actually tested or proved.
How is GEO different from AEO and SEO?
SEO optimizes for rank position in a list of links. AEO optimizes for being selected as a direct answer on Google's own surfaces (snippets, knowledge panels). GEO specifically targets generative chat interfaces (ChatGPT, Perplexity, Claude, Gemini) that retrieve and synthesize an answer rather than ranking pages at all, a mechanically different retrieval pipeline than either of the other two.
Sources referenced
- Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan & Deshpande, "GEO: Generative Engine Optimization" (arXiv 2311.09735, presented at KDD 2024)
- Andreessen Horowitz: "How Generative Engine Optimization (GEO) Rewrites the Rules of Search" (May 2025)
For what the 2023 paper actually tested and found (including what it explicitly did not test), see the GEO paper, explained. For the AEO side of this comparison, see what is AEO?
