90%被大模型吃掉，AI Agent的困局

Core Viewpoint - The article discusses the challenges faced by general-purpose AI agents, particularly in the context of market competition and user engagement, suggesting that many agents may be overshadowed by large models and specialized agents [4][6][12]. Group 1: Market Dynamics - General-purpose agents like Manus and Genspark are experiencing declining revenue and user engagement, indicating a lack of compelling applications that drive user loyalty and payment [6][20][23]. - Manus reported an annual recurring revenue (ARR) of $9.36 million in May, while Genspark reached $36 million ARR within 45 days of launch, showcasing the initial market potential [20]. - However, both products have seen significant drops in monthly recurring revenue (MRR) and user traffic, with Manus experiencing a 50% decline in MRR to $2.54 million in June [22][23]. Group 2: Competitive Landscape - The article highlights that general-purpose agents are struggling to compete with specialized agents that are tailored for specific tasks, leading to a loss of market share [15][17]. - The high subscription costs of general-purpose agents, combined with the increasing capabilities of foundational models, make them less attractive to users who can access similar functionalities at lower costs [12][28]. - Companies like Alibaba and ByteDance are focusing on developing their own agent platforms while promoting developer ecosystems, indicating a strategic shift towards enhancing their competitive edge [26][29]. Group 3: User Experience and Application - General-purpose agents have not yet identified "killer" applications that would encourage users to pay for their services, often focusing on tasks like PPT creation and report writing, which do not sufficiently engage users [24][32]. - The lack of integration with internal knowledge bases and business processes limits the effectiveness of general-purpose agents in enterprise settings, where accuracy and cost control are paramount [15][16]. - Current agents often struggle with complex tasks due to their reliance on multiple steps, leading to inconsistent output quality, which further diminishes user trust and engagement [33][34]. Group 4: Technological Innovations - Some developers are exploring innovations like reinforcement learning (RL) to enhance the capabilities of agents, aiming to transition from simple tools to more autonomous and adaptable systems [36][40]. - The article notes that advancements in model architecture, such as the introduction of linear attention mechanisms, are being leveraged to improve the performance of agents in handling large volumes of text [35][36]. - The potential for RL to significantly improve agent performance is highlighted, with recent tests showing substantial improvements in task handling capabilities [38][40].