%% %% %% %% research.95579.com 1 丨证券研究报告丨 报告要点 行业研究丨深度报告丨软件与服务 [Table_Title] 阿里云（2）：Token 爆发在即，看好全栈玩家突 围 %% %% %% %% research.95579.com 2 [Table_Summary] 观察海外 AI 产业的发展路径，我们发现从大厂密集投入 Capex（2023 年）到 Token 的爆发 （2025 年）并非一蹴而就，中间存在两年的滞后期。国内大厂投入滞后一年，实际是 2024 年 下半年开启正式的 AI 资本开支周期。参照海外的回报周期，国内 token 数量有望在今年迎来真 正的爆发。在 token 爆发的背景下，云厂商作为应用的血液将率先受益。横向对比所有 AI 时代 大厂，我们发现具备全栈式 AI 布局的企业有望率先形成 AI 投资回报的正循环。因为在丰富的 既有场景下，全栈式 AI 玩家更容易将模型的每一次进步转化为业务收入或者业务壁垒。 分析师及联系人 [Table_Author] 宗建树 卢之晗 SAC：S0490520030004 SAC：S0490525120001 S ...

Core Insights - The article discusses the evolving landscape of Artificial General Intelligence (AGI) and highlights three key trends shaping its future development in China and the U.S. [10] Group 1: Trends in AGI Development - Trend One: Beyond Scaling, a New Paradigm is Emerging - The discussion around Scaling has shifted from whether to continue expanding model sizes to questioning the value of such investments. Efficiency has become a critical concern as the marginal returns on increased computational power diminish [14][15]. - Trend Two: Token Efficiency is Becoming a Decisive Factor - Token efficiency has emerged as a crucial variable in determining the potential of large models. The ability to utilize tokens effectively is now seen as essential for achieving higher intelligence levels and completing complex tasks [20][22][24]. - Trend Three: Diverging Evolution Paths for Chinese and American Models - The development of large models in the U.S. is increasingly focused on productivity and enterprise applications, while in China, the emphasis is on cost sensitivity and stability. This divergence reflects different market demands and cultural approaches to research and development [26][28][29]. Group 2: Key Discussions and Insights - The AGI-Next summit gathered leading figures in AI to discuss the future of AGI, emphasizing a shift from application-level discussions to foundational questions about the direction of next-generation AGI [6][10]. - The consensus among researchers indicates that the next phase of AGI development will require a reevaluation of existing paradigms, with a focus on efficiency and the role of token utilization in model performance [10][11][20]. - The cultural differences between U.S. and Chinese AI research environments contribute to the distinct paths taken by their respective large model developments, with U.S. labs often pursuing high-risk, high-reward projects, while Chinese labs focus on practical applications and efficiency [29].

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