腾讯AI战略再提速。 12月17日，证券时报记者获悉，腾讯升级大模型研发架构，新成立AI Infra部、AI Data部、数据计算平 台部，全面强化其大模型的研发体系与核心能力。Vinces yao出任"CEO/总裁办公室"首席AI科学家，向 腾讯总裁刘炽平汇报；同时兼任AI Infra部、大语言模型部负责人，向技术工程事业群总裁卢山汇报。 对此，尽管内部信息并未公布其中文姓名，证券时报记者了解到，Vinces yao正是今年9月传言加入腾讯 的前OpenAI研究员、AI领域顶尖人才姚顺雨。 "AI下半场"理论，出圈的姚顺雨 公开资料显示，姚顺雨现年27岁，是AI领域的顶尖新锐人才。 姚顺雨本科毕业于清华大学姚班，博士就读于普林斯顿大学，2024年8月加入OpenAI后，成为团队核心 成员之一，参与了OpenAI 2025年首批智能体产品operator与deep research相关的研发工作。2025年5月， 27岁的他成为中国区最年轻的《麻省理工科技评论》TR35入选者之一，这份荣誉足以证明其在全球AI 领域的顶尖潜力与行业认可度。 真正让姚顺雨出圈的，是他提出的"AI下半场"理论。在其相关研究论述《T ...

腾讯AI战略再提速。 腾讯AI战略的关键一跃 12月17日，证券时报记者获悉，腾讯升级大模型研发架构，新成立AI Infra部、AI Data部、数据计算平 台部，全面强化其大模型的研发体系与核心能力。Vinces yao出任"CEO/总裁办公室"首席AI科学家，向 腾讯总裁刘炽平汇报；同时兼任AI Infra部、大语言模型部负责人，向技术工程事业群总裁卢山汇报。 对此，尽管内部信息并未公布其中文姓名，证券时报记者了解到，Vinces yao正是今年9月传言加入腾讯 的前OpenAI研究员、AI领域顶尖人才姚顺雨。 "AI下半场"理论，出圈的姚顺雨 公开资料显示，姚顺雨现年27岁，是AI领域的顶尖新锐人才。 姚顺雨本科毕业于清华大学姚班，博士就读于普林斯顿大学，2024年8月加入OpenAI后，成为团队核心 成员之一，参与了OpenAI 2025年首批智能体产品operator与deep research相关的研发工作。2025年5月， 27岁的他成为中国区最年轻的《麻省理工科技评论》TR35入选者之一，这份荣誉足以证明其在全球AI 领域的顶尖潜力与行业认可度。 真正让姚顺雨出圈的，是他提出的"AI下半场"理 ...

腾讯AI战略再提速。 12月17日，证券时报记者获悉，腾讯升级大模型研发架构，新成立AI Infra部、AI Data部、数据计算平台部， 全面强化其大模型的研发体系与核心能力。Vinces yao出任"CEO/总裁办公室"首席AI科学家，向腾讯总裁刘炽 平汇报；同时兼任AI Infra部、大语言模型部负责人，向技术工程事业群总裁卢山汇报。 对此，尽管内部信息并未公布其中文姓名，证券时报记者了解到，Vinces yao正是今年9月传言加入腾讯的前 OpenAI研究员、AI领域顶尖人才姚顺雨。 "AI下半场"理论，出圈的姚顺雨 公开资料显示，姚顺雨现年27岁，是AI领域的顶尖新锐人才。 姚顺雨本科毕业于清华大学姚班，博士就读于普林斯顿大学，2024年8月加入OpenAI后，成为团队核心成员之 一，参与了OpenAI 2025年首批智能体产品operator与deep research相关的研发工作。2025年5月，27岁的他成为 中国区最年轻的《麻省理工科技评论》TR35入选者之一，这份荣誉足以证明其在全球AI领域的顶尖潜力与行 业认可度。 腾讯AI战略的关键一跃 有分析人士认为，腾讯本次大模型研发架构的升级或 ...

此前有消息称，OpenAI著名研究者姚顺雨已经加入了腾讯混元大模型团队，他将在混元组建一支自己 领导的研究团队，年薪达上亿元。当时腾讯在其官方公众号辟谣称该消息为假消息。 作为AI界的顶尖人才，姚顺雨身上有不少"天才"叙事。资料显示，姚顺雨毕业于著名的"清华姚班"，之 后在普林斯顿大学进修计算机科学博士。其间，"27岁入选MIT TR35"的光环，以及他作为清华大学学 生说唱社联合创始人的经历，常被外界津津乐道。 2024年8月，姚顺雨加入了OpenAI。在OpenAI期间，他担任研究科学家，专注于将大型语言模型从理论 研究推向实际应用，特别是AI Agent的开发。据悉，其主导开发了OpenAI 首个发布的智能体模型及产 品，同时参与了Deep Research项目。 腾讯混元是腾讯AI研发的核心团队，任何顶尖人才的加入都可能被视为其加强AI实力的信号。此前， 姚顺雨曾提出"AI下半场" 概念，即从追求模型规模到定义有用任务，引发业界共鸣。他的职业选择无 疑也被视为对AI未来发展方向的一种预示。 今年 5月姚顺雨在一次对谈中被问到，如果自己主管微信，会如何在微信中做agent。姚顺雨表示自己会 先观望和探索 ...

Core Viewpoint - Tencent has announced a significant organizational restructuring in its AI division, with the notable addition of Yao Shunyu, a prominent figure in the AI research community, as the Chief AI Scientist [1][4][11]. Group 1: Yao Shunyu's Background and Role - Yao Shunyu, a former OpenAI researcher and a distinguished academic, has joined Tencent as the Chief AI Scientist in the CEO's office, reporting directly to Tencent's president, Liu Chiping [2][4]. - At only 28 years old, Yao has made substantial contributions to the field of AI, particularly in the area of large models and agent-based research, with notable works including Tree of Thoughts and ReAct [3][19]. - His recent departure from OpenAI and subsequent move to Tencent has garnered significant attention, highlighting his status as a leading talent in the AI sector [3][11]. Group 2: Organizational Changes at Tencent - Tencent has restructured its AI organization, establishing new departments such as AI Infra, AI Data, and Data Computing Platform to enhance its large model development capabilities [6][8]. - The AI Infra department, led by Yao, will focus on building the technical capabilities for large model training and inference, aiming to create a competitive edge in AI infrastructure [8][10]. - The restructuring aims to strengthen Tencent's engineering advantages and improve the efficiency of AI large model research, aligning with the company's strategic goals in AI [8][12]. Group 3: Tencent's AI Product Development - Over the past year, Tencent has launched more than 30 new models under its Mix Yuan series, with Mix Yuan 2.0 showing significant improvements in pre-training data and reinforcement learning strategies [9]. - Tencent's AI product, Yuanbao, has rapidly gained user acceptance, becoming one of the top AI applications in China, and is integrated into major platforms like WeChat and QQ [10]. - The company is undergoing a comprehensive AI-driven efficiency transformation, with over 900 applications utilizing its Mix Yuan models across various internal services [10][12]. Group 4: Strategic Importance of AI for Tencent - Tencent's advancements in AI are closely tied to its extensive resources, including rich scenarios, vast data, and a strategic approach, positioning the company favorably in the AI landscape [14][15]. - The recruitment of top talent like Yao Shunyu signifies Tencent's commitment to accelerating its AI initiatives and enhancing its capabilities in the competitive AI market [11][12].

Core Viewpoint - Alibaba's CEO, Wu Yongming, emphasizes that achieving Artificial General Intelligence (AGI) is just the beginning, with the ultimate goal being the development of Artificial Superintelligence (ASI) that can self-iterate and surpass human capabilities [2] Group 1: Market Reaction - Following Wu's announcement, Alibaba's stock price surged by 9% on September 24, reaching a four-year high [5] - The market's positive response indicates strong investor confidence in Alibaba's future prospects in the AI sector [5] Group 2: Business Strategy - Wu highlights that the AI business in China has entered a new phase, characterized by emerging commercial opportunities [6] - The focus is on transforming intelligence into useful products, potentially creating multi-billion dollar companies [6] - Alibaba Cloud aims to capture as many of these emerging companies as possible as potential clients [6] Group 3: Financial Performance - Alibaba Cloud reported a revenue of 33.398 billion yuan for Q2 2025, marking a 26% year-on-year increase, the highest growth rate in three years [8] - AI revenue now constitutes over 20% of Alibaba Cloud's external commercialization income [8] Group 4: Product Development - Wu identifies two key products: 1. Large models as the next-generation operating system, with Tongyi Qianwen open-sourcing over 300 models [11] 2. AI cloud as the next-generation computer [12] - The strategy involves using the free large models to establish market presence and developer ecosystems, followed by monetization through cloud services [13] Group 5: Investment Plans - Alibaba plans to invest 380 billion yuan over the next three years in AI and cloud computing infrastructure, averaging over 10 billion yuan per month [13] - This significant investment underscores the company's commitment to building a robust AI ecosystem [13] Group 6: Competitive Advantage - The company's competitive edge may also stem from Jack Ma's determination and the resulting market confidence [14]

Core Viewpoint - The article discusses the transition to the "second half" of AI, emphasizing the need for reliability and engineering frameworks to ensure AI applications are trustworthy and effective [1][4][57]. Group 1: Importance of Data and Reliability - Data is crucial for AI application capabilities, but it does not automatically create value without a reliable processing engine [3][4]. - Reliability encompasses various metrics, including accuracy, speed, and the ability to avoid "hallucinations," which are misleading outputs generated by AI models [4][8]. Group 2: Transition from Model Competition to Engineering Competition - The shift in focus from "what AI can do" to "how to make AI do it correctly" marks a significant change in the industry [4][5]. - Various frameworks, such as LangChain and DSPy, are emerging to address these challenges, but they often lack robust reliability guarantees [4][9]. Group 3: High-Order Programs (HOP) - HOP is introduced as a new paradigm that integrates engineering principles into AI applications, aiming to mitigate hallucinations and enhance reliability [6][20]. - HOP is not a new programming language but a framework that combines symbolic logic with neural networks to create a reliable control system for AI [22][25]. Group 4: Mechanisms of HOP - HOP utilizes a structured approach to express business logic in programming languages, ensuring clarity and reducing ambiguity [23]. - The HopLogic execution framework within HOP allows for the breakdown of complex tasks into verifiable steps, enhancing reliability to over 99% in professional applications [28][37]. Group 5: Practical Applications and Industry Impact - HOP has demonstrated its potential in sectors like finance and healthcare, significantly improving reliability and reducing development time [39][43]. - The framework allows for agile iterations without the need for extensive retraining of models, making it a cost-effective solution for businesses [52][53]. Group 6: Future of AI Engineering - The article concludes that the future of AI will depend on high-quality data and reliable engineering frameworks, with HOP serving as a key driver for scalable professional productivity [54][64]. - The establishment of a reliable framework and the development of high-quality data will enable AI to evolve from a supportive role to a core driver of industry transformation [64][65].

Group 1 - Tencent officially refuted rumors regarding former OpenAI researcher Yao Shunyu joining the company with a salary of "over 100 million" [1] - The clarification was made through Tencent's official WeChat account "Goose Factory Blackboard" [1] Group 2 - Yao Shunyu graduated from Tsinghua University and obtained a PhD in Computer Science from Princeton University [3] - He joined OpenAI in 2024, contributing to the development of intelligent agent products and deep research [3] - Yao proposed the Tree of Thoughts framework to improve decision-making models during his doctoral studies [3] - He led the ReAct method, which introduced the "reasoning-action" interaction paradigm for language agents [3] - In 2025, he spearheaded the Computer-Using Agent project, integrating a new paradigm of reinforcement learning and shifting AI technology focus from training-oriented to evaluation-oriented, introducing the concept of "AI's second half" [3]

Core Viewpoint - The article discusses the evolving job market for AI graduates, emphasizing the shift from model parameters and training techniques to defining valuable problems and creating evaluation systems that fit real-world scenarios [2][6][11]. Group 1: Industry Trends - The AI job market is rapidly changing, with companies of all sizes actively recruiting AI talent [2]. - The focus of AI competition is shifting from merely improving model performance to understanding how to apply AI effectively in real-world contexts [6][11]. - The saturation of benchmark tests is occurring faster, indicating diminishing returns from traditional model development approaches [6][11]. Group 2: Company Selection Criteria - Graduates should consider companies that can sustain AI development, focusing on user engagement and the ability to create a complete cycle from technology development to commercial application [11][12]. - The strength of the coupling between technology and business is crucial; AI should be a core driver rather than a supplementary feature [12]. - Companies must demonstrate commercial validation of AI capabilities, such as having revenue-generating AI applications and clients willing to pay for AI features [13][14]. Group 3: Tencent as a Case Study - Tencent exemplifies a company with a broad and deep engagement in various fields, providing a rich environment for AI development [15][16]. - Tencent's AI technologies are integrated into its core business operations, enhancing user engagement and driving revenue growth [17][18]. - The company has clear AI monetization cases, with significant revenue contributions from AI-driven advertising and gaming sectors [18][19]. Group 4: Talent Development Programs - Tencent's "Qingyun Plan" is a high-priority initiative aimed at nurturing top technical talent, offering competitive compensation and a supportive environment for innovation [21][22]. - Participants in the Qingyun Plan have opportunities for significant contributions to AI projects and can publish research in top conferences [23][24]. - The program emphasizes a non-traditional management culture, allowing for exploration and creativity in research [24][25].

Core Insights - The focus of AI is shifting from problem-solving to problem-definition, emphasizing the importance of evaluation over training [1][4] - The evaluation process is a continuous practice that drives development and requires a scientific approach [7][10] Evaluation Framework - Building a product evaluation system is fundamentally about applying the scientific method, involving a cycle of questioning, experimentation, and analysis [8] - Initial steps include observing data, examining inputs, outputs, and user interactions to identify operational strengths and weaknesses [8] - Data labeling is crucial, prioritizing problematic outputs to create a balanced and representative dataset for targeted evaluation [8] Hypothesis and Experimentation - Formulating hypotheses about errors is essential, which may involve analyzing retrieval documents and reasoning paths [9] - Designing experiments to validate these hypotheses is necessary, including rewriting prompts or updating retrieval components [9] - Measuring results quantitatively is critical to determine the effectiveness of changes made during experiments [9] Evaluation-Driven Development (EDD) - EDD helps create better AI products by defining success criteria through product evaluation before development begins [12] - The process involves establishing baseline evaluations and continuously assessing each adjustment to ensure measurable progress [12] - EDD fosters a feedback loop that is rooted in software engineering practices, ensuring that improvements are based on objective data [12] Automation and Human Oversight - Automated evaluation tools enhance monitoring but cannot replace human oversight; regular sampling and analysis of user feedback are still necessary [14][15] - High-quality labeled data is essential for calibrating automated tools to align with human judgment [14] - Maintaining a feedback loop of data sampling, output labeling, and tool optimization is crucial for effective evaluation [14][15]