支撑这一野心的是一份新提交的"Tan PSU Award"高管激励计划。该方案的核心条款是，若博通在2030财年 （FY2030）前实现特定的AI收入门槛，CEO陈福阳将获得相应奖励。 这也意味着陈福阳的薪酬与AI收入强绑 定。 芯片巨头博通正将人工智能置于其战略版图的核心。 博通CEO陈福阳预计，公司AI相关收入预计在两年内将超过软件和非AI业务收入的总和。同时，其设定了到 2030财年AI收入最高达到1200亿美元的目标，并与CEO薪酬直接挂钩。他指出，未来AI芯片市场将出现分化 ——大型云服务商将主导定制化ASIC芯片的应用，而广大企业级客户将继续依赖通用GPU。 据高盛9月9日发布的会议纪要报告，公司总裁兼CEO陈福阳（Hock E. Tan）在近日举行的高盛Communacopia + 科技大会上明确表示，满足特定客户的AI计算需求是公司的首要任务，并预计在未来两年内，公司的AI相关收 入将超过软件及其他非AI业务收入的总和。 AI收入激增：两年内或成绝对支柱，2030剑指1200亿 报告中最引人注目的信息，莫过于陈福阳对AI收入的激进预测。他明确表示， 满足少数核心客户的AI计算需求 是公司的首要 ...

虽然错过了科创的大肉，但今天也算是有点补偿，那就是布局的白酒终于开始走强了，上午已经收获一个涨停。上周五尾盘冲进去的群核科技概念股，早 盘也冲了一个涨停，只是没有封住，所以高抛一些，还留了一些。 只要牛市确认来了，那么无非就是风水轮流转，明天到我家。都能吃到几口肉的，现 在还在明显低位的票，其实就是那几个板块，迟早会水漫金山。 上午领涨板块，主要是稀土方向，周末有些消息主要是把进口稀土矿的部分也进入纳入管控，因为中国强的不只是稀土的矿场，尽管我们确实占有优势， 更重要的还是稀土的冶炼本领。之前我也看过分析，主要是中国的有色行业太强大，因为稀土的含量很低，通常是副产品出来，如果你没有其他有色的高 产量，就不会有稀土的商业化路径。 周末消息面继续利好稀土涨价，所以我们看到镨钕、稀土磁材、有色钨等继续大涨，再就是液态金属和有色铜走强，部分黄金概念表现也还不错，这里主 要是美联储降息，那么全球货币宽松那自然对于大宗商品是利好。再就是芯片产业链，特别是英伟达板块依然走强。周末因为他要推出以太网，所以叠加 了B30取代H20的消息，产业链还是相当不错，cpo方向、光通信都涨起来，液冷服务器需要制冷剂，于是制冷剂也起来了。 ...

Core Viewpoint - The article discusses the competition between InfiniBand and Ethernet in AI networking, highlighting the advantages of Ethernet in terms of cost, scalability, and compatibility with existing infrastructure [6][8][22]. Group 1: AI Networking Overview - AI networks are primarily based on InfiniBand due to NVIDIA's dominance in the AI server market, but Ethernet is gaining traction due to its cost-effectiveness and established deployment in large-scale data centers [8][20]. - The establishment of the "Ultra Ethernet Consortium" (UEC) aims to enhance Ethernet's capabilities for high-performance computing and AI, directly competing with InfiniBand [8][9]. Group 2: Deployment Considerations - Teams face four key questions when deploying AI networks: whether to use existing TCP/IP networks or build dedicated high-performance networks, whether to choose InfiniBand or Ethernet-based RoCE, how to manage and maintain the network, and whether it can support multi-tenant isolation [9][10]. - The increasing size of AI models, often reaching hundreds of billions of parameters, necessitates distributed training, which relies heavily on network performance for communication efficiency [10][20]. Group 3: Technical Comparison - InfiniBand offers advantages in bandwidth and latency, with capabilities such as high-speed data transfer and low end-to-end communication delays, making it suitable for high-performance computing [20][21]. - Ethernet, particularly RoCE v2, provides flexibility and cost advantages, allowing for the integration of traditional Ethernet services while supporting high-performance RDMA [18][22]. Group 4: Future Trends - In AI inference scenarios, Ethernet is expected to demonstrate greater applicability and advantages due to its compatibility with existing infrastructure and cost-effectiveness, leading to more high-performance clusters being deployed on Ethernet [22][23].

Core Insights - The article discusses the rise of AI Networking, particularly focusing on the "Scale Up" segment, highlighting its technological trends, vendor dynamics, and future outlook [1] Group 1: Market Dynamics - The accelerator market is divided into "commercial market" led by NVIDIA and "custom market" represented by Google TPU and Amazon Tranium, with the custom accelerator market expected to gradually match the GPU market in size [3] - Scale Up networking is transitioning from a niche market to mainstream, with revenue projected to exceed $1 billion by Q2 2025 [3] - The total addressable market (TAM) for AI Network Scale Up is estimated at $60-70 billion, with potential upward revisions to $100 billion [12] Group 2: Technological Evolution - AI networking has evolved from "single network" to "dual network," currently existing in a phase of "multiple network topologies," with Ethernet expected to dominate in the long term [4] - The competition between Ethernet and NVLink is intensifying, with NVLink currently leading due to its maturity, but Ethernet is expected to gain market share over the decade [5] - Scale Up is defined as a "cache coherent GPU to GPU network," providing significantly higher bandwidth compared to Scale Out, with expectations of market size surpassing Scale Out by 2035 [8] Group 3: Performance and Cost Analysis - Scale Up technology shows a significant performance advantage, with latency for Scale Up products like Broadcom's Tomahawk Ultra at approximately 250ns, compared to 600-700ns for Scale Out [9] - Cost-wise, Scale Up Ethernet products are projected to be 2-2.5 times more expensive than Scale Out products, indicating a higher investment requirement for Scale Up solutions [9] Group 4: Vendor Strategies - Different vendors are adopting varied strategies in the Scale Up domain, with NVIDIA focusing on NVLink, AMD betting on UA Link, and major cloud providers like Google and Amazon transitioning towards Ethernet solutions [13] - The hardware landscape is shifting towards embedded designs in racks, with a potential increase in the importance of software for network management and congestion control as Scale Up matures [13]

Core Viewpoint - The rise of humanoid robots has gained significant attention following a performance at a Spring Festival gala, highlighting advancements in embodied intelligence and the need for improved hardware, particularly chips, to overcome existing challenges [1] Group 1: Humanoid Robot Development - Humanoid robots are increasingly compared to upright vehicles, requiring perception systems, high-performance chips, and effective power management for extended operation [2] - The execution capabilities of humanoid robots differ from cars, as they must also manipulate objects with dexterity, particularly through their hands [2] Group 2: ADI's Role in Humanoid Robotics - ADI has been involved in the robotics market for years and is now accelerating its offerings, including traditional chips and subsystems to facilitate product design and implementation [4] - ADI provides a range of products for humanoid robots, including sensors, internal connection systems, motor control modules, and power management solutions [5] Group 3: Connection Technologies - GMSL (Gigabit Multimedia Serial Link) is highlighted as a key technology for internal connections in humanoid robots, offering efficient data transmission and improved performance [9] - ADI's GMSL solution supports real-time transmission of video, sensor data, and power, making it suitable for the complex requirements of humanoid robots [10] Group 4: Isolation and Control Solutions - ADI offers isolation devices to protect sensitive electronics in humanoid robots from electrical interference, ensuring reliable operation in challenging environments [10] - The ADMT4000 solution provides precise joint control for robotic arms, enabling memory of positions even after power loss, thus enhancing operational reliability [12][14] Group 5: Challenges in Dexterous Manipulation - The development of dexterous hands, referred to as "smart hands," is a critical challenge in the humanoid robotics industry, requiring advanced sensors and AI algorithms [15] - Simplifying internal connections within these dexterous hands is also a significant focus for developers [15]