Core Insights - The article emphasizes the fundamental differences between human search behavior and AI search requirements, highlighting that AI searches are dynamic, iterative, and often involve multiple queries to address complex tasks [1][6][9]. Group 1: AI Search vs. Traditional Search - AI search is characterized by its need for multi-turn, iterative queries, contrasting with the static, one-time queries typical of human searches [1][6]. - The accuracy of AI search results is prioritized over speed, with a focus on comprehensive information coverage rather than just the top results [8][9]. - AI agents require longer, more detailed content to understand context, differing from traditional search engines that provide short summaries [7][8]. Group 2: Challenges in AI Search Integration - Different AI applications face unique challenges when integrating search capabilities, such as the need for task decomposition in office applications and ensuring low-latency responses in AI hardware [10][15][28]. - The importance of authoritative content has increased significantly, as AI agents generate answers directly from search results, necessitating strict standards for content quality [7][24]. Group 3: Search Infrastructure and Technology - The search infrastructure provided by companies like Xiaosu Technology includes intelligent search and content reading capabilities, essential for AI agents to access reliable information [10][11]. - The article discusses the need for a large-scale data index and advanced algorithms to ensure timely and accurate search results, addressing the limitations of traditional search methods [29][31]. Group 4: Future of AI Search - The future of search is expected to be closely tied to AI agents, with a projected exponential increase in token consumption as AI applications become more prevalent [41]. - Companies are focusing on enhancing search quality to reduce the reliance on costly AI models, suggesting that effective search can significantly lower operational costs [35][36].