昨夜今晨 【官方通报绍兴地铁深夜撞人事件：致3死1伤】 大家早上好！这里是今天的早报，每天早上，我都会在这里跟你聊聊昨夜今晨发生了哪些大事儿。 绍兴市轨道交通集团有限公司9月25日发布关于绍兴轨道交通2号线一期存车线事故的公告： 9月13日23时40分许，在车辆非运营期间，绍兴轨道交通2号线一期存车线保洁单位4名保洁人员穿越轨道时， 被一辆返场检修的电客车碰撞，经紧急送医救治，3人抢救无效死亡，1名伤者经治疗正在康复之中。 事故发生后，公司第一时间开展人员救治、家属安抚等工作，遇难人员善后事宜已于9月17日完成。 经初步调查，本次事故系保洁人员未按规定路径进入行车正线区域，被返场检修电客车碰撞而导致的生产安全 事故，暴露出公司安全生产管理责任落实不到位。 目前，公司正积极配合进一步调查，调查结果将及时向社会公布。 【特朗普：不会允许以色列吞并约旦河西岸】 当地时间25日，美国总统特朗普表示，无论是否与以色列总理内塔尼亚胡进行了交谈，他都不会允许以色列吞 并约旦河西岸。 特朗普表示，当日与以色列总理内塔尼亚胡进行了交谈，也与所有中东领导人进行了交谈，他们即将就加沙问 题达成协议，甚至可能实现和平。 【英特尔大涨 ...

大约在100万年前，随着现代人类大脑的出现，人类长达40亿年的进化故事终于画上了句号。回首过 去，我们可以开始描绘一幅图景或一个框架，来展现人类大脑和智能的形成过程。我们可以将这些故事 整合到我们前五次突破的框架中。 第一次突破是转向：通过区分外界刺激的好坏，从而趋利避害地进行导航。 迄今为止，人类的历史可以分为两大篇章。第一篇是进化的篇章，讲述现代人类如何从宇宙中的原始无 生命物质中演化而来。第二篇是文化的篇章，描述大约10万年前，社会性的现代人类如何从生物学上大 体相同但文化上尚处于蒙昧状态的祖先中逐步崛起、发展。 尽管进化篇跨越了数十亿年的漫长岁月，但我们在历史课上学习的绝大部分内容，却是在文化篇中相对 较短的时间内展开的——所有的文明、技术、战争、发现、戏剧、神话、英雄与反派，都在这段与进化 篇相比犹如一眨眼的时间里一一上演。 大约6亿年前，原本具有径向对称神经元的类珊瑚动物逐渐演化成两侧对称动物。这种两侧对称的身体 结构将导航决策简化为二元的转向选择，神经网络被整合成第一个大脑，使具有相反效价的信号能够被 整合成单一的转向决策。多巴胺和血清素等神经调质使持续的状态能够更有效地重新定位并局部搜索特 定 ...

Core Insights - The article outlines five major breakthroughs in the evolution of intelligence, from the development of basic navigation in early organisms to the potential emergence of superintelligence in artificial entities [2][3][5][11]. Breakthroughs in Intelligence - **First Breakthrough: Turning** - Approximately 600 million years ago, early bilateral animals evolved a simple nervous system that allowed for basic navigation by distinguishing between positive and negative stimuli [2]. - **Second Breakthrough: Reinforcement** - Around 500 million years ago, the first vertebrates developed a brain structure that enabled learning from past experiences, establishing a foundation for emotional and cognitive traits [3]. - **Third Breakthrough: Simulation** - About 100 million years ago, early mammals developed the ability to mentally simulate actions and events, leading to advanced planning and fine motor skills [4]. - **Fourth Breakthrough: Mentalization** - Between 10 to 30 million years ago, early primates evolved the capacity to understand their own and others' mental states, enhancing social interactions and learning [5]. - **Fifth Breakthrough: Language** - Language emerged as a means to connect internal simulations, allowing for the accumulation of knowledge across generations [5]. Evolutionary Context - Human history can be divided into two main chapters: the evolutionary chapter, detailing the biological development of modern humans, and the cultural chapter, which encompasses the rapid advancements in civilization over the last 100,000 years [6][7]. - The article emphasizes the significance of the last 100,000 years in shaping human civilization, contrasting it with the extensive evolutionary timeline [6]. Future of Intelligence - The article posits that the next breakthrough may involve the emergence of superintelligence, where artificial entities surpass biological limitations, leading to unprecedented cognitive capabilities [9][10]. - It discusses the implications of this potential shift, including the redefinition of individuality and the evolution of intelligence beyond biological constraints [10][11]. Philosophical Considerations - The article raises critical questions about the goals of humanity as it approaches the sixth breakthrough, emphasizing the importance of values and choices in shaping the future of intelligence [11][12].

Core Viewpoint - The core viewpoint of the article emphasizes that deep application of artificial intelligence (AI) is essential for businesses to transition from basic AI tools to fully integrated business processes, which is crucial for achieving superintelligence [1] Group 1: AI Application - Current AI applications are no longer limited to pilot projects but are expanding into comprehensive and deep applications across various sectors [1] - Intelligent agents are driving the transition of AI from mere tools to integral components of business processes [1] Group 2: Business Integration - Deep application involves not building a new application system from scratch but rather integrating AI deeply with existing business processes [1] - This integration is identified as a key factor for companies to achieve superintelligence [1]

Core Insights - Nvidia has announced a strategic partnership with OpenAI, committing to invest up to $100 billion in infrastructure, including the deployment of at least 10GW of Nvidia systems, which will consist of millions of GPUs [1][2][3] Investment Details - OpenAI will deploy a minimum of 10GW of Nvidia systems, which translates to approximately 10 million kilowatts, and Nvidia will continuously invest as the computing power comes online [1][2] - The total investment from Nvidia could reach $100 billion (approximately 711.47 billion RMB) [1][2] Infrastructure Development - The first GW-level data center based on Nvidia's Vera Rubin platform is set to be deployed in the second half of 2026 [1][2] - The estimated cost to build 1GW of computing power is around $50-60 billion, with Nvidia's chips and systems accounting for about $35 billion of that cost [3] Market Implications - The investment scale for the 10GW data center project is estimated to be between $500 billion and $600 billion, similar to Nvidia's previously announced Stargate project [3] - OpenAI's CEO, Sam Altman, emphasized that this infrastructure is crucial for achieving OpenAI's goals and delivering services to users [3] AI Industry Trends - The AI landscape is shifting from model competition to a focus on infrastructure expansion, with significant investments being made to overcome computing power bottlenecks [4] - OpenAI has signed a $300 billion five-year contract with Oracle and plans to spend $100 billion on backup servers from cloud service providers over the next five years, in addition to Nvidia's investment [4] Investment Recommendations - The current focus in AI infrastructure is shifting from cloud vendor spending to AIDC cloud computing centers, with companies like Oracle, Coreweave, and others being highlighted for their GPU card advantages [4] - In the domestic market, attention remains on AI infrastructure, with recommendations for companies like Runze Technology and Aofei Data [5]

Investment Rating - The report maintains a "Recommended" rating for several key companies in the AI infrastructure sector, including 德科立, 仕佳光子, 中际旭创, 新易盛, 天孚通信, and 润泽科技 [5]. Core Insights - The report highlights a significant investment by NVIDIA in OpenAI, amounting to up to $100 billion (approximately 711.47 billion RMB), aimed at deploying at least 10GW of NVIDIA systems, which includes millions of GPUs [1][2]. - The transition from "selling shovels" to "delivering shovels" is noted, with the estimated cost of building 1GW of computing power being around $50-60 billion, where NVIDIA's chips and systems account for approximately $35 billion [2]. - The AI sector is entering a phase focused on infrastructure expansion rather than just model competition, with OpenAI's mission to drive "superintelligence" being heavily reliant on computational power [3]. - The report suggests that the current focus in AI infrastructure has shifted from cloud vendor spending to AIDC cloud computing centers, recommending companies with a first-mover advantage in GPU cards [4]. Summary by Sections Investment Opportunities - Key companies to watch include Oracle, Coreweave, Nebius, AppliedGigital, and Iris Energy for their GPU card advantages [4]. - In the domestic market, companies like 润泽科技, 奥飞数据, and 润建股份 are highlighted for their potential in AI infrastructure [4]. Financial Projections - The report provides earnings per share (EPS) and price-to-earnings (PE) ratios for several companies, indicating strong growth potential for 2024-2026 [5]. - For example, 中际旭创 is projected to have an EPS of 12.94 RMB by 2026 with a PE ratio of 34, while 新易盛 is expected to reach an EPS of 13.10 RMB with a PE of 27 [5]. Market Trends - The report emphasizes the increasing demand for AI infrastructure, with OpenAI's rapid growth and significant contracts with cloud service providers indicating a robust market environment [3]. - The establishment of a 1GW data center by OpenAI in 2026 is anticipated to mark a milestone in the infrastructure arms race, paving the way for large-scale application deployment [3].

Core Insights - Nvidia and OpenAI have announced a strategic partnership and a significant investment of up to $100 billion to support OpenAI's AI data center plans [1][20]. Group 1: Partnership Details - OpenAI plans to build and deploy a system requiring 10 gigawatts (GW) of Nvidia systems [2]. - The first phase of the data center will consume 1 GW and is set to be deployed in the second half of 2026, with Nvidia's initial $10 billion investment occurring upon completion of the first 1 GW system [6][15]. - The partnership is structured to ensure that funding aligns with the deployment of GPU usage, providing a gradual payment to OpenAI as the project progresses [7]. Group 2: Economic Rationale - Building a 1 GW data center is estimated to cost between $50 billion and $60 billion, with approximately $35 billion allocated specifically for Nvidia's chips and systems [15][16]. - This investment highlights Nvidia's significant role in OpenAI's infrastructure development, ensuring mutual benefits for both companies [17]. Group 3: Market Impact - Following the announcement, Nvidia's stock price surged, with a market capitalization of approximately $4.46 trillion [10]. - OpenAI's anticipated revenue for this year is projected to reach $13 billion, a more than threefold increase from last year's $4 billion [28]. Group 4: Future Projections - OpenAI's revenue forecast for 2030 has been raised by about 15%, potentially exceeding $200 billion, surpassing the recent revenue performances of Nvidia and Meta [29]. - The collaboration is expected to significantly impact the AI infrastructure landscape, marking the beginning of a new era in AI investment [32].

Core Viewpoint - Nvidia and OpenAI have announced a strategic partnership and a significant investment of up to $100 billion to support OpenAI's AI data center plans [2]. Group 1: Investment Details - The investment will be paid to OpenAI gradually as the deployment of GPUs increases [4]. - This investment represents approximately 2.2% of Nvidia's current market capitalization of about $4.46 trillion [5]. Group 2: Infrastructure and Energy Consumption - OpenAI plans to utilize millions of Nvidia GPU systems to deploy AI data centers, with an energy consumption target of at least 10 gigawatts [7]. - The first phase of the data center will have an energy consumption of 1 gigawatt and is expected to be deployed in the second half of 2026 [9]. Group 3: Strategic Partnership - OpenAI will be the first "beta user" of Nvidia's DGX supercomputer system, highlighting a decade-long collaboration between the two companies [10]. - OpenAI will receive priority in GPU delivery, while Nvidia will benefit from OpenAI's highest priority in external collaborations [11]. - OpenAI will become the preferred "computing" and "network" strategic partner in Nvidia's AI factory growth plan, ensuring optimal performance for OpenAI's models and infrastructure software [12]. - Both companies plan to finalize the specific details of their strategic partnership in the coming weeks [13].

Core Insights - OpenAI plans to build and deploy a 10 GW Nvidia system, equivalent to approximately 4 to 5 million GPUs, matching Nvidia's total shipments for the year and doubling last year's output [3][5][12] - Nvidia and OpenAI announced a strategic partnership with an investment of up to $100 billion to support OpenAI's AI data center plans [5][12] Investment and Financials - The partnership will be implemented in phases, starting with a 1 GW data center to be deployed in the second half of 2026, with an initial investment of $10 billion from Nvidia upon completion of the first system [7][14] - Building a 1 GW data center is estimated to cost between $50 billion and $60 billion, with about $35 billion allocated for Nvidia's chips and systems, indicating a significant share for Nvidia in OpenAI's infrastructure [14][17] - OpenAI's revenue is projected to reach $13 billion this year, a more than threefold increase from $4 billion last year, with a revised forecast for 2030 revenue exceeding $200 billion [17][18] Market Position and Competition - The collaboration secures Nvidia a strategic customer in OpenAI, enhancing its position in the AI chip market amid competition from AMD and cloud service providers [14][18] - OpenAI's R&D costs are expected to account for nearly 50% of its total revenue by 2030, significantly higher than the 10% to 20% range typical for other tech giants [17][18] Future Outlook - The partnership aims to accelerate the development of OpenAI's next-generation AI infrastructure, with expectations for significant outcomes in the coming months [12][18] - The AI infrastructure arms race has entered a new phase with investments reaching the billion-dollar mark, indicating a growing demand for computational resources [18]

Core Insights - Meta is betting on smart glasses as a significant entry point into the future of consumer electronics, with the launch of three new models, including the "Meta Ray-Ban Display" featuring a micro-screen and AI assistant [1][4][6] - The company is shifting its focus from a "metaverse experience" to practical daily use cases for smart glasses, aiming to integrate technology seamlessly into everyday life [2][5] - Despite the excitement around the product, there were technical issues during the live demonstration, highlighting potential challenges in the AI assistant's functionality [3][5] Product Launch - Meta introduced the "Meta Ray-Ban Display" at a price of $799, nearly double the cost of its predecessor, indicating a shift towards positioning the product as a high-end consumer electronic device [1][5] - The glasses are designed to allow users to interact with applications, play music, and share content on social media without needing to pull out their phones [1][2] Market Positioning - The new smart glasses are aimed at everyday users rather than gamers, reflecting a strategic adjustment in Meta's approach to market adoption [2][5] - The design of the glasses is intended to be discreet, resembling regular sunglasses, which may enhance user acceptance compared to bulkier VR devices [2][4] AI Integration - Meta has invested heavily in AI, with billions spent on data centers and restructuring its AI department, viewing smart glasses as an ideal platform for advanced AI capabilities [4][5] - The company envisions smart glasses as the "second pair of eyes" in the AI era, emphasizing the need for daily wear to fully integrate AI into users' lives [5][6] Challenges Ahead - The high price point of $799 raises questions about whether the features justify the cost for consumers [5][6] - Privacy concerns are significant, as the glasses contain cameras and microphones, potentially leading to societal debates about surveillance and data privacy [5][6] - The success of the smart glasses will depend on building a robust application ecosystem, requiring third-party developers to create diverse use cases beyond Meta's own platforms [5][6] Strategic Implications - Meta's attempt to transition smart glasses from a novelty item to an essential tool reflects a broader ambition to define the next computing platform as smartphone growth slows [6] - The company's future in this space hinges on overcoming technical, privacy, pricing, and ecosystem challenges, which will determine whether it can replicate the success of the iPhone [6]