Navigating the Shift: A Comparative Analysis of Web Search and Generative AI Response Generation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916017236606976 |
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| author | Chen, Mahe Wang, Xiaoxuan Chen, Kaiwen Koudas, Nick |
| author_facet | Chen, Mahe Wang, Xiaoxuan Chen, Kaiwen Koudas, Nick |
| contents | The rise of generative AI as a primary information source presents a paradigm shift from traditional web search. This paper presents a large-scale empirical study quantifying the fundamental differences between the results returned by Google Search and leading generative AI services. We analyze multiple dimensions, demonstrating that AI-generated answers and web search results diverge significantly in their consulted source domains, the typology of these domains (e.g., earned media vs. owned, social), query intent, and the freshness of the information provided. We then investigate the role of LLM pre-training as a key factor shaping these differences, analyzing how this intrinsic knowledge base interacts with and influences real-time web search when enabled. Our findings reveal the distinct mechanics of these two information ecosystems, leading to critical observations on the emergent field of Answer Engine Optimization (AEO) and its contrast with traditional Search Engine Optimization (SEO). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16858 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Navigating the Shift: A Comparative Analysis of Web Search and Generative AI Response Generation Chen, Mahe Wang, Xiaoxuan Chen, Kaiwen Koudas, Nick Information Retrieval The rise of generative AI as a primary information source presents a paradigm shift from traditional web search. This paper presents a large-scale empirical study quantifying the fundamental differences between the results returned by Google Search and leading generative AI services. We analyze multiple dimensions, demonstrating that AI-generated answers and web search results diverge significantly in their consulted source domains, the typology of these domains (e.g., earned media vs. owned, social), query intent, and the freshness of the information provided. We then investigate the role of LLM pre-training as a key factor shaping these differences, analyzing how this intrinsic knowledge base interacts with and influences real-time web search when enabled. Our findings reveal the distinct mechanics of these two information ecosystems, leading to critical observations on the emergent field of Answer Engine Optimization (AEO) and its contrast with traditional Search Engine Optimization (SEO). |
| title | Navigating the Shift: A Comparative Analysis of Web Search and Generative AI Response Generation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2601.16858 |