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).

翻译：生成式人工智能作为主要信息源的兴起，标志着传统网络搜索范式的转变。本文通过大规模实证研究，量化了Google搜索与主流生成式AI服务返回结果之间的根本差异。我们从多个维度进行分析，证明AI生成的答案与网络搜索结果在所咨询的源领域、这些领域的类型（如付费媒体与自有媒体、社交媒体）、查询意图以及所提供信息的新鲜度方面均存在显著差异。随后，我们探究了LLM预训练作为塑造这些差异的关键因素，分析了当启用实时网络搜索时，这一内在知识库如何与外部搜索交互并产生影响。我们的研究揭示了这两种信息生态系统的不同运行机制，从而对新兴的答案引擎优化领域及其与传统搜索引擎优化的差异提出了重要见解。