Z Potentials·2025-12-06 05:27Core Insights - The article discusses the transformation of AI search from traditional search engines to native AI search, highlighting the competitive landscape among various startups and the need for a new search architecture focused on AI [1][3][5]. Group 1: Historical Context - In the 1990s, various startups explored different methods of internet search, with Yahoo using a directory approach and Google later revolutionizing the field with its PageRank algorithm [1][2]. - The emergence of Google in 1998 marked a significant shift, as its algorithm quickly became the preferred method for navigating the internet, effectively solving the search problem for users [2]. Group 2: Current Landscape - The current search environment is undergoing a major shift, with numerous startups competing to create AI-native search systems that can index the web for AI applications [3][6]. - Traditional web search is primarily optimized for human users, often resulting in cluttered results filled with ads and redundant information, which can hinder the effectiveness of AI models [3][5]. Group 3: Emerging Trends - The article posits that deep research will become a dominant and monetizable form of agent-based search, as clients are willing to pay for high-quality research outputs [5][17]. - Many companies are opting to outsource their search capabilities to specialized service providers due to the high costs and complexities associated with maintaining search infrastructure [7][15]. Group 4: Technological Innovations - New search architectures are being developed to support AI agents, focusing on real-time data access and dynamic information retrieval, which enhances the capabilities of AI models [11][12]. - The introduction of Retrieval-Augmented Generation (RAG) and Test-Time Computation (TTC) allows models to access real-time information and improve their reasoning capabilities, transforming static models into dynamic reasoning systems [11][12]. Group 5: Use Cases - Deep research has emerged as a prominent use case for AI search APIs, enabling agents to conduct extensive research tasks that would take humans significantly longer to complete [17][19]. - AI search is also being utilized for CRM lead enrichment, automating the process of gathering and updating relevant information from various sources [19]. - Real-time access to technical documentation and code examples is crucial for coding agents, ensuring they reference the most current and relevant information [20]. Group 6: Competitive Dynamics - The competitive landscape is shifting towards API platforms, where user-facing products can leverage various search functionalities through single integrations [15][22]. - Companies are increasingly evaluating search providers based on the quality of results, API performance, and cost, leading to a diverse range of offerings in the market [22][23].