Generative engine optimization (GEO) is the practice of adapting digital content and online presence management to improve visibility in results produced by generative artificial intelligence (GenAI). [1] Six researchers led by a team at Princeton University (Dr. Vishvak Murahari, Dr. Karthik Narasimhan, and Dr. Ameet Deshpande) invented and introduced GEO in an academic paper published in November 2023. [2] GEO describes strategies intended to influence the way large language models, such as ChatGPT, Google Gemini, Claude, and Perplexity, retrieve, summarize, and present information in response to user queries. [3]
GEO exists within a broader ecosystem of content optimization strategies. While traditional Search Engine Optimization (SEO) focuses on improving rankings in conventional search engines, and Answer Engine Optimization (AEO) targets platforms that provide direct answers through voice assistants and featured snippets, GEO specifically addresses optimization for generative AI platforms that synthesize responses using large language models. Recent data indicate that approximately 53% of website traffic continues to originate from traditional organic search, yet an estimated 58% of queries are now conversational in nature, demonstrating the growing importance of GEO and AEO alongside traditional SEO methods. Industry practitioners increasingly recognize that SEO, AEO, and GEO represent complementary aspects of a unified content strategy rather than competing approaches. [4] [5]
Unlike traditional search engine optimization (SEO), which focuses on improving rankings in conventional search engines such as Google or Bing, [6] GEO specifically targets generative engines — AI-driven systems that produce direct, summarized answers rather than lists of external links. [7] The approach aims to ensure that brands and publishers are cited or represented on such platforms. Other terms used to describe the same concept include AI SEO (artificial intelligence search engine optimization) and LLMO (large language model optimization). [8] [9]
The development of GEO is rooted in fundamental shifts in user behavior, technology, and business analytics that accelerated in the early 2020s. [10]
Shift in User Behavior: The Principle of Least Cognitive Load [11]
Generative engines provide direct, narrative-style answers, which reduces the mental effort (cognitive load) required from the user compared to manually visiting, evaluating, and synthesizing information from multiple websites. This alignment with user psychology drove the rapid adoption of "answer engines" and accelerated the trend of zero-click searches , where a user's query is resolved without leaving the search results page. [12]
Economic Drivers: The 'AI Dark Funnel'
The migration of the initial stages of the user journey (awareness, research, comparison) into AI conversations creates an analytics blind spot for businesses. This unmeasurable space has been termed the 'AI Dark Funnel', as traditional web analytics cannot track interactions within these closed AI systems. GEO developed as a strategic response to this challenge, providing a methodology to influence this new, invisible top of the funnel. [11] [13] [ better source needed ]
The concept of GEO developed in parallel with the rise of generative AI technologies that became integrated into mainstream search and information retrieval systems. [14] In November 2023, six researchers introduced the term "generative engine optimization" in their paper GEO: Generative Engine Optimization. [15] [2] They described GEO as "a new paradigm that helps content creators improve the visibility of their content in answers generated by generative engines," stressing the need to adapt existing optimization strategies to the AI-driven search environment. [2]
In the same study, the researchers introduced GEO-Bench, a benchmark dataset of 10,000 queries designed to evaluate GEO techniques empirically. The results showed that certain optimization practices significantly increased the likelihood of a source being cited or included in generative engine answers, recognizing GEO as a distinct though related field to SEO. [2]
Following publication of the paper, the term GEO gained traction among digital marketing firms, SEO consultancies, and technology companies. By early 2024, marketing outlets such as Search Engine Land began covering the concept, identifying GEO as a strategic necessity for visibility in AI-generated content. [8] [16]
The growing interest led to the development of dedicated GEO tools and services, including getSAO; Geometrika; [17] KIME [18] ; Cognizo AI; Hall; buzzsense.ai; [19] Scrunch AI; Am I On AI; Otterly AI; Peec AI; Otterly AI; Writesonic; Rankshift; Senso; Whitebox; Profound; and Wilgot. [20] [ needs independent confirmation ] By 2025, generative engine optimization had become a standard part of digital marketing strategies, with many firms incorporating GEO into their SEO workflows. [21] Some industry analyses have also discussed the emergence of specialised file formats—such as the proposed llms.txt—as part of ongoing experiments to make web content more interpretable to generative engines. [22] [23] [ better source needed ] Tools such as Semrush's "AI Visibility Toolkit" and "Enterprise AIO" reflect this trend by tracking how entities are referenced and presented within responses produced by large-language-model-based answer engines. [24]
Traditional SEO emphasizes keyword density, backlinks, and page rankings within link-based indices. By contrast, generative engines such as ChatGPT, Perplexity, and Google’s Search Generative Experience (SGE) generate direct, context-driven answers to user queries. [2]
A 2025 study by Apoorav Sharma and Prabhjot Dhiman, titled The Impact of AI-Powered Search on SEO: The Emergence of Answer Engine Optimization [25] , argued that generative AI is transforming the logic of search engines — from a link-based model to a context-based model that delivers immediate, click-free responses. This shift, the authors claimed, alters how digital visibility is measured and achieved. [26]
Two main categories of AI-powered platforms are identified:
GEO primarily focuses on the latter category, with the aim of improving the chances that brands and content sources appear in AI-generated answers. [27] [ better source needed ] [28] [ better source needed ]
The move to AI-driven platforms changes both optimization methods and the benchmarks for digital marketing success. Traditional measures such as click-through rate (CTR) and first-page ranking are being replaced by new indicators, including:
Marketing companies have developed dashboards to measure GEO outcomes, adapting key performance indicators (KPIs) to a search environment where visibility begins within an AI-generated conversation rather than on a search results page. [27]