1月10日，在由清华大学基础模型北京市重点实验室、智谱AI发起的AGI-Next前沿峰会上， 中国"基模四杰"罕见集齐： 腾讯"CEO/总裁办公室"首席AI科学家姚顺雨、Kimi创始人杨植 麟、智谱创始人唐杰、阿里巴巴Qwen技术负责人林俊旸， 以及加拿大皇家学院院士、香港科 技大学荣休教授杨强等AI界知名大咖，围绕AI新范式、Agent、中国大模型公司的挑战及机会 等话题展开了讨论。 其中， 杨植麟首次深度分享了Kimi的技术重点 ，他透露，2025年，月之暗面的两个技术进化 主线是提升"TokenEfficiency"，以在有限的数据下冲击更高的智能上限；以及扩展"长上下 文"能力，以满足Agentic时代越来越长程的任务对模型的记忆能力需求。 履新腾讯后，姚顺雨首次公开亮相 值得注意的是， 此次峰会是姚顺雨加入腾讯后，首次对外界分享其对AI产业的观察。 现年27 岁的姚顺雨毕业于清华大学姚班和普林斯顿大学。他在2024年加入OpenAI后，迅速成为团队 核心研究者之一，参与推动 AI Agent和任务执行系统方向的开发。 2025年12月17日，腾讯宣布升级大模型研发架构，新成立AI Infra部、AI ...

Core Insights - The AGI-Next conference highlighted the competitive landscape of AI in China, focusing on the importance of foundational models and their impact on future business strategies [4][5] - Key players in the AI industry, including Zhiyuan, Tencent, and Alibaba, are exploring different paradigms for AGI, emphasizing the need for new metrics to evaluate model intelligence [6][7] - The discussion revealed a consensus on the increasing differentiation between consumer (ToC) and business (ToB) applications of AI, with distinct strategies for each segment [11][12] Group 1 - The AGI-Next conference featured prominent figures in China's AI sector, including Zhiyuan's founder Tang Jie and Tencent's newly appointed chief scientist Yao Shunyu, indicating a significant gathering of industry leaders [4][5] - The conference underscored the belief that the capabilities of foundational models will determine the success of future AI ventures, with a focus on maintaining a leading position in model development [5] - Tang Jie expressed concerns that the gap between Chinese and American models may not be closing, as many American models remain closed-source [5][6] Group 2 - The participants discussed the evolution of AI paradigms, with Tang Jie suggesting that the exploration of conversational models has reached its peak, and future efforts should focus on coding and reasoning capabilities [6][7] - Yao Shunyu emphasized the importance of scaling not just in computational power but also in architecture and data optimization to enhance model performance [6][7] - The need for new standards to measure AI intelligence was highlighted, with concepts like Token Efficiency and Intelligence Efficiency being proposed as metrics [7][41] Group 3 - The differentiation between ToC and ToB applications was a key theme, with Yao Shunyu noting that while ToC requires strong integration of models and products, ToB focuses on enhancing productivity through the best models available [11][12] - Lin Junyang pointed out that the success of AI applications depends on understanding real user needs, suggesting that effective communication with enterprise clients is crucial for developing successful AI solutions [8][12] - The conversation also touched on the potential for AI to automate significant portions of human work, particularly in the ToB sector, where higher model intelligence correlates with increased revenue [43][44] Group 4 - The participants acknowledged the challenges of deploying AI models effectively, with a focus on the need for better education and training to maximize the benefits of AI tools [44][57] - The discussion included insights on the importance of collaboration between academia and industry to address unresolved questions in AI research, such as the limits of intelligence and resource allocation [20][21] - The potential for new paradigms in AI, such as continuous learning and memory integration, was identified as a critical area for future exploration [38][40]