今天热度最高的是字节的Seedance 2.0，各大券商都在陆续组织Seedance的路演，网友们也都对 Seedance 2.0进行了实测，效果确实非常好。 3、字节算力链，除了大家熟知的几家算力公司，我们之前就在星球中提到过ZJ的液冷的采购情况， 有兴趣的读者可以到星球查看。 另一个让行业感到振奋的原因是它极其清晰的商业化取向 ，它在模型设计之初就主动去收敛那些不 确定的生成路径，把推理成本和延迟控制在了一个可以规模化使用的区间 。 Token 消耗也做了极大 的优化，根据业内的一个数据， 5 分钟视频的制作成本拉到了几千元甚至未来的几百元量级，让电 商、短剧和广告行业看到了真实降本增效的希望 。 最后，它不要求用户成为"提示词大师"，而是允许通过 9 种不同的模态输入来锚定创作意图 。 字节链 今天分析师们的观点都陆续出来了，目前大家比较一致认为利好比较多是这三类： 1、AI内容制作与分发，比如短剧和漫剧 2、AI Infra，这个就不多说了，就那么几家公司 这次有什么不一样？ Seedance 2.0能引起这么大的讨论最根本的原因在于它完成了从"生成一段画面"到"完成一个作品"的 逻辑跨越 。 以前的 ...

1、 李强主持召开国常会 研究促进有效投资政策措施 重要程度：★★★★★ 以下为晚报正文： 国务院总理李强2月6日主持召开国务院常务会议，听取2025年国务院部门办理全国人大代表建议和全国政协提案工作情况汇报，研究促进有效投资政策措 施，部署修订《环境空气质量标准》，讨论《中华人民共和国招标投标法(修订草案)》。 会议指出，促进有效投资对于稳定经济增长、增强发展后劲具有重要作用。要创新完善政策措施，加力提效用好中央预算内投资、超长期特别国债、地方政 府专项债券等资金和新型政策性金融工具。要结合制定实施"十五五"规划，着眼于长远发展需要和构筑未来竞争优势，在基础设施、城市更新、公共服务、 新兴产业和未来产业等重点领域，深入谋划推动一批重大项目、重大工程。 此外，李强主持召开国务院第十次全体会议，讨论拟提请十四届全国人大四次会议审议的政府工作报告稿和"十五五"规划纲要草案稿。 李强指出，宏观政策要靠前发力，财政资金尽可能提前安排，加强资金下达和项目建设的协同配合，使政策尽快落地见效。各项重点工作要抓紧推进，条件 成熟的及早组织实施。坚持政策支持和改革创新并举，更好激发市场活力，挖掘内需新增长点。要密切跟踪形势变化 ...

元宝、千问引发春节红包大战，豆包则将在春晚亮相，在烧钱抢市场的背后，公司同样在砸钱 抢人。 互联网职场社区脉脉2月5日发布数据显示，互联网大厂在脉脉发布的AI岗位类型覆盖产品、运 营、增长、研发、算法等多个领域，各家纷纷以高薪吸引相关人才，其中，元宝用户运营、活 动运营岗位年薪超过75万元，"豆包AI产品经理"岗位年薪达60万元，"豆包AI应用工程师"岗 位年薪接近100万元。此外， "千问App用户增长算法工程师"岗位年薪最高可达128万元，"千 问App用户增长"岗位年薪最高可达112万元。 近日，腾讯正式举行"青云奖学金"首期颁奖礼， 宣布为15位在校硕博生开出每人总价值50万 元的高额激励， 其中包括20万元现金及价值30万元的云异构算力资源。这批获奖者均来自计 算机科学、人工智能及其交叉领域。例如，清华大学的白雨石在NeurIPS、ICML、ICLR、 ACL等国际顶会发表10篇一作论文，在Hugging Face 上开源的数据集和模型共被下载超过200 万次。 据"青云奖学金"项目工作人员介绍，项目自去年10月启动后，共收到来自全国近400名学生的 申请，经过资深专家组成的评审团的严格评审和多轮答 ...

田晏林 发自 凹非寺 量子位 | 公众号 QbitAI "Your brain is for having ideas, not holding them. " ——Tiago Forte《Building a Second Brain》 LLM是AI的"第一大脑"，记忆平台是AI的"第二大脑"。 畅销书作者Tiago Forte在《构建第二大脑》中曾分享核心观点： "生物大脑只用于思考创造，而外部系统用于信息的可靠存储。" ——这对我们理解AI的"双脑"分工极富启示。 事实上，LLM就如同AI的"第一大脑（生物脑）"，它擅长思考、推理与即时生成，而不擅长长期、精确地存储海量事实。 而记忆平台是AI的"第二大脑"，它主要按需为LLM提供准确的"记忆"支撑，让LLM从记忆负担中解放，专注于更高层次的推理与创造，从而协 同产生更精准、个性化且可行动的价值。 两者结合，记忆平台负责"记住一切"，LLM负责"思考一切"。 3.0 生产力时代（2025年至今）：萃取"隐性知识"，固化核心资产 行业焦点转向直接提升生产效率。关键一跃在于能否将员工的决策逻辑、经验权衡等隐性知识数字化、轨迹化。 这不再是简单问答，而是通过记 ...

❑ 2026 年 1 月 5 日，NVIDIA 宣布，NVIDIA BlueField-4 数据处理器（NVIDIA BlueField 全栈平台的一部分）为 NVIDIA 推理上下文内存存储平台提供支持， 该平台是面向下一代 AI 前沿的新一代 AI 原生存储基础设施。1 月 20 日，在 2026 阿里云 PolarDB 开发者大会上，阿里云旗下云原生数据库 PolarDB 正式 发布 AI 数据湖库（Lakebase）等系列全新产品能力。 评论： ❑ 我们认为：大模型记忆和硬件，将成为模型发展核心叙事，助力 AIDB 与向量 数据库规模化进程： 证 券 研 究 报 告 计算机行业重大事项点评 AI infra：算力系统化升级 DB for AI 进程加速 事项： 行业研究 计算机 2026 年 01 月 27 日 推荐（维持） 华创证券研究所 证券分析师：吴鸣远 邮箱：wumingyuan@hcyjs.com 执业编号：S0360523040001 联系人：周楚薇 邮箱：zhouchuwei@hcyjs.com 行业基本数据 | | | 占比% | | --- | --- | --- | | 股票家数( ...

" 「选择悖论」正在AI模型与算力世界里上演。 " 作者丨 赵之齐 编辑丨 包永刚 北京一月的初雪落下前，我们在清华见到了翟季冬教授。他手持保温杯，说话很利落，即便一边思考一边 叙述，言辞间也几乎没有停顿、没有模糊地带。 这位曾带队拿下15次世界超算冠军的清华计算机系长聘教授，此刻正在拆解一个行业怪象： 为什么在大 模型参数狂飙、算力价格下探的当下，用户的AI落地负担却越来越重？ 他指出，如今Benchmark （基准测试） 上的高分，在比对用户真实需求时不一定管用，有时，同个模型 在不同MaaS平台上跑出来的效果可能差异巨大，因为部分服务商为了降低成本，会对模型进行"阉割 级"量化。而面对眼花缭乱的MaaS供应商，用户要在性能、价格与稳定性之间做取舍，往往光调研一轮市 场报价，就已耗尽精力。 "把选型的主动权完全交给用户，其实是很大的挑战" ， 翟季冬直言。这种"选择悖论"不仅折磨着开发 者，更在吞噬企业的利润——对于企业来说， 降本增效的核心可能并非追求最顶尖的模型，而是如何调度 能力恰当的模型，让昂贵的大模型处理复杂指令，让轻量的小模型应付日常任务 。 洞察到这一痛点后，由翟季冬的几位毕业学生发起的AI ...

Core Viewpoint - PPIO is strategically betting on underappreciated directions in the tech landscape, transitioning from edge cloud services to GPU inference platforms and now to Agent sandboxes, showcasing its adaptability and foresight in a rapidly evolving market [2][6]. Group 1: Company Background and Growth - PPIO was founded in 2018 amidst fierce competition in the edge computing and CDN market, with a vision to integrate idle computing resources into a distributed platform [3]. - The company initially struggled to find a balance between supply and demand until the pandemic-induced surge in online traffic helped it establish a growth trajectory [4]. - By 2024, PPIO's revenue is projected to reach 558 million, reflecting exponential growth in a short period [4]. Group 2: Technological Development and Market Position - PPIO has developed a unique Agent sandbox, which provides a secure environment for AI agents to operate, preventing unauthorized access to external resources [4][19]. - The company has focused on building a comprehensive AI cloud service capability, moving from edge cloud to GPU inference and now to PaaS solutions [6][11]. - PPIO's strategy emphasizes creating technology for unseen demands, positioning itself ahead of industry trends [12][14]. Group 3: Market Strategy and Differentiation - PPIO aims to avoid competing in the saturated GPU trading market, instead opting for a model that integrates idle distributed computing resources into cloud services [15][17]. - The company has identified a significant opportunity in the AI developer market, which is expected to grow rapidly, with new applications consuming resources at a much higher rate than traditional internet giants [25]. - PPIO's approach to open-source and non-binding API capabilities caters to the evolving needs of AI developers, contrasting with traditional cloud service models that often lock users into proprietary systems [22][24]. Group 4: Future Outlook and Challenges - PPIO is currently preparing for an IPO in Hong Kong, indicating confidence in its growth trajectory and market position [6]. - The company recognizes that the primary challenge lies in demand-side growth, particularly in latency-sensitive applications [32]. - PPIO's unique distributed cloud model, built on fragmented and heterogeneous infrastructure, sets it apart from traditional cloud providers that rely on centralized data centers [27].

Investment Rating - The report maintains a "Recommended" rating for the industry, indicating a positive outlook for the sector's fundamentals and expected performance relative to the benchmark index [2][23]. Core Insights - The report emphasizes that 2026 is the inaugural year for AI applications, suggesting that the market is just beginning to experience significant growth in this area [1]. - The computer sector has shown strong performance, with a notable increase in stock prices, indicating robust investor interest and market activity [4][17]. - Key developments in AI technology are highlighted, including Alibaba's significant upgrade to its Qianwen App, which integrates various services and enhances its AI capabilities [9][11]. Industry Overview - The industry comprises 286 stocks, with a total market capitalization of approximately 4,800.7 billion and a circulating market value of about 4,256.1 billion [2]. - The computer sector's absolute performance over the past 1 month, 6 months, and 12 months has been 20.2%, 24.2%, and 53.2%, respectively, showcasing strong growth [4]. - The report notes that the competition for AI application and infrastructure is intensifying, with a focus on major players like Alibaba and various vertical AI application companies [16]. Market Performance Review - In the second week of 2026, the computer sector rose by 3.82%, with notable stock performances from companies such as Jiechuang Intelligent and Shiji Information, which saw increases of 28.95% and 28.69%, respectively [17][18]. - The report provides a detailed ranking of stocks based on their weekly performance, highlighting both the top gainers and losers in the sector [17].

Core Insights - The article discusses the challenges and potential of Vibe Coding in AI infrastructure development, highlighting its limitations in complex systems and proposing a document-driven approach to enhance its effectiveness [3][5][20]. Group 1: Challenges of Vibe Coding - Vibe Coding faces three main issues: context loss, decision deviation, and quality instability, primarily due to the lack of a structured decision management mechanism [4][5]. - The complexity of AI infrastructure, characterized by thousands of lines of code and numerous interrelated decision points, exacerbates these challenges [4][5]. Group 2: Document-Driven Vibe Coding Methodology - The document-driven approach aims to systematize key decisions during the design phase, significantly reducing complexity and improving code quality [6][20]. - By focusing on high-level design decisions, developers can leverage AI for detailed code implementation, achieving complex functionalities with minimal coding [7][20]. Group 3: Implementation in Agentic RL - The article presents a case study on optimizing GPU utilization in Agentic Reinforcement Learning (RL) systems, which face significant resource scheduling challenges [11][12]. - A proposed time-sharing reuse scheme dynamically allocates GPU resources, addressing the inefficiencies of existing solutions and improving overall system performance [14][15]. Group 4: Performance Validation - Experiments on a large-scale GPU cluster demonstrated that the time-sharing reuse scheme increased rollout throughput by 3.5 times compared to traditional methods, significantly enhancing task completion rates and reducing timeout occurrences [46][50]. - The analysis indicates that the additional system overhead introduced by the new scheme is minimal, validating its practical value in large-scale Agentic RL training [53][55]. Group 5: Team and Future Directions - The article concludes with an introduction to the ROCK & ROLL team, which focuses on advancing RL technologies and enhancing the practical application of large language models [57]. - The team emphasizes collaboration and open-source contributions to foster innovation in the RL community [58].

Core Insights - The main challenge for companies in adopting AI is not the technology itself but the speed of decision-making by leaders, with only 1% of companies achieving "mature deployment" of AI despite 92% planning to invest more in it [2][3][32] - AI's integration into businesses requires a transformation in internal capabilities, including strategic choices, organizational collaboration, data and processes, governance, and risk control [4][32] Group 1: AI Infrastructure and Deployment - The future of AI opportunities lies in two layers of infrastructure: AI Infra (computational power) and Agent Infra (intelligent agent infrastructure), which are essential for scaling AI applications [8][9] - Companies need to connect models, computational power, data, tools, and processes to succeed in the AI landscape [9] - AI deployment in enterprises requires building a knowledge base, creating digital employees, and optimizing workflows to fundamentally reshape work processes [13][28] Group 2: AI as a Collaborator - The perception of AI as a collaborator rather than just a tool is crucial for its effective use, as it combines the advantages of both human and programmatic capabilities [14] - Understanding AI's role and capabilities can help organizations leverage its strengths while managing its limitations [14] Group 3: Real-World Applications and Case Studies - Companies like Meitu and DJI exemplify a growth strategy focused on leveraging core technological capabilities rather than merely expanding product lines [15][16] - AI's true value in industries lies in its ability to eliminate uncertainties in production and R&D processes, enhancing efficiency and quality [28] - The shift from general models to specific intelligent agents tailored to business needs is essential for practical AI applications in enterprises [22][24] Group 4: Organizational Capability and Transformation - Successful AI integration requires organizations to develop the ability to manage data and operate intelligent agents, rather than relying solely on AI experts [24][25] - The focus should be on embedding AI into the organizational framework to ensure it becomes a part of the operational capabilities [32][34] - The current period presents an optimal opportunity for companies to transform AI into a growth logic and organizational productivity [35]