在近日的一场播客中，被问及在筹备的AI硬件时，OpenAI CEO奥尔特曼(Sam Altman)透露，未来不会 推出单一的一个设备，而是"一系列的小设备"。根据供应链的爆料信息来看，其AI硬件没有屏幕且体积 为口袋大小，设备外形类似苹果早期的iPod Shuffle，采用可夹式设计，可挂于颈部，外观和手机、智 能眼镜都有很大的不同。奥尔特曼预测，人们使用计算系统的方式会发生转变，它会从一种略显笨拙的 被动响应式状态，转变为极具智能的主动预判式。 除了AI硬件，在播客中奥尔特曼还被问及上市，他直言"对成为一家上市公司的CEO一点也不感兴趣"。 奥尔特曼表示，OpenAI即使要上市，也会比历史上任何一家伟大公司都晚得多，保持私有状态的灵活 性对于冲刺"超级智能"至关重要，他更愿意把精力放在改变世界上。 ...

作者 | 第一财经 刘晓洁 OpenAI的AI硬件计划备受关注，就在近日的一场播客中，被问及在筹备的AI硬件时，OpenAI CEO 奥尔特曼（Sam Altman）透露，未来不会推出单一的一个设备，而是"一系列的小设备"。 目前OpenAI并未公布硬件的具体形态，但根据供应链的爆料信息来看，其AI硬件没有屏幕、且体积 为口袋大小，设备外形类似苹果早期的iPod Shuffle，采用可夹式设计，可挂于颈部，外观和手 机、智能眼镜都有很大的不同。 2025.12. 20 本文字数：1822，阅读时长大约3分钟 主持人也抛出了外界关注的一系列尖锐问题，例如"OpenAI是否已失去领先优势"，如何看待竞争对 手谷歌等。 奥尔特曼辩护称，OpenAI拉响的 "红色警报"并不是恐惧，而是一种极度敏锐的防御机制。当潜在 的竞争威胁出现时，保持警惕并迅速行动是件好事。他提到，今年早些时候遇到DeepSeek的挑战 时，内部也启动了"红色警报"。 "流行病学领域有个说法：疫情之初你采取的任何行动，其价值都远超后期行动，但大多数人早期做 得不够，事后才陷入恐慌。我基本上是秉持这种哲学来应对竞争威胁的。"奥尔特曼说。 当被问及为 ...

Core Viewpoint - OpenAI is developing a series of small AI hardware devices rather than a single device, aiming to transform user interaction with technology into a more proactive and intelligent experience [1][3]. Group 1: AI Hardware Development - OpenAI's upcoming AI hardware is expected to be pocket-sized, screenless, and designed for neck-wearing, resembling early iPod Shuffle models [3]. - The CEO, Sam Altman, believes that traditional screen-based interfaces are outdated and that new device forms are necessary to fully leverage AI capabilities [3]. - The hardware is still in the prototype stage, with expectations for a public reveal around late 2026 or 2027, following significant recruitment from Apple's hardware team [4]. Group 2: Market Position and Competition - Altman expressed that OpenAI's "red alert" system is a proactive defense mechanism against competitive threats, emphasizing the importance of early action in response to challenges [6]. - Despite concerns about competitors like Google, Altman believes that OpenAI has not yet faced significant impacts from new models like Gemini 3, although they reveal some weaknesses in product strategy [6]. - Altman views Google as a formidable competitor but suggests that their integration of AI into existing business models may hinder innovation [6][7]. Group 3: Future Outlook and Company Philosophy - OpenAI's leadership is not currently focused on going public, as maintaining private status is seen as crucial for pursuing "superintelligence" [5]. - Altman prefers to concentrate on transformative goals rather than being distracted by quarterly earnings and stock prices [5].

在本期内容你会看到： 9 条新鲜资讯 3 个鲜明观点 Hunt for News｜先进头条 斯坦福计算机学位含金量暴跌，初级工程师就业市场萎缩 20% 曾经被视为硅谷就业「金饭碗（golden ticket）」的斯坦福大学软件工程学位，如今被应届毕业生形容为已贬值至「青铜」水平。 欢迎收看最新一期的 Hunt Good周报！ 3 个有用工具 1 个有趣案例 斯坦福生物工程副教授 Jan Liphardt 指出，就连斯坦福的计算机科学毕业生现在也难以在知名科技公司找到入门级工作，这种情况简直疯狂。 学生们描述校园内的就业氛围非常沉闷，就业市场呈现出极端的两极分化：只有极少数拥有丰富产品构建经验的顶级工程师，能获得优质 offer，而其他 人只能争夺剩余的残羹冷炙。 造成这一局面的核心原因在于 AI 编程能力的飞速进化。Palo Alto 的 AI 初创公司 Vectara CEO Amr Awadallah直言，「我们不再需要初级开发人员了，现 在的 AI 编程能力比名校毕业的平均水平还要好。」 数据佐证了这一残酷趋势。据斯坦福大学的一项研究显示，自 2022 年底达到顶峰以来，22 至 25 岁的早期职业软件 ...

Core Viewpoint - The article discusses the limitations of achieving Artificial General Intelligence (AGI) due to physical and resource constraints, emphasizing that scaling alone is not sufficient for significant advancements in AI [3][20][32]. Group 1: Limitations of AGI - Tim Dettmers argues that AGI will not happen because computation is fundamentally physical, and there are inherent limitations in hardware improvements and scaling laws [8][10][12]. - The article highlights that as transistor sizes shrink, while computation becomes cheaper, memory access becomes increasingly expensive, leading to inefficiencies in processing power [11][17]. - The concept of "superintelligence" is critiqued as a flawed notion, suggesting that improvements in intelligence require substantial resources, and thus, any advancements will be gradual rather than explosive [28][29][30]. Group 2: Hardware and Scaling Challenges - The article points out that GPU advancements have plateaued, with significant improvements in performance per cost ceasing around 2018, leading to diminishing returns on hardware investments [16][17]. - Scaling AI models has become increasingly costly, with the need for linear improvements requiring exponential resource investments, indicating a nearing physical limit to scaling benefits [20][22]. - The efficiency of current AI infrastructure is heavily reliant on large user bases to justify the costs of deployment, which poses risks for smaller players in the market [21][22]. Group 3: Divergent Approaches in AI Development - The article contrasts the U.S. approach of "winner-takes-all" in AI development with China's focus on practical applications and productivity enhancements, suggesting that the latter may be more sustainable in the long run [23][24]. - It emphasizes that the core value of AI lies in its utility and productivity enhancement rather than merely achieving higher model capabilities [24][25]. Group 4: Future Directions and Opportunities - Despite the challenges, the article suggests that there are still significant opportunities for improvement in AI systems through better hardware utilization and innovative model designs [39][45][67]. - It highlights the potential for advancements in training efficiency and inference optimization, indicating that current models are not yet fully optimized for existing hardware capabilities [41][43][46]. - The article concludes that the path to more capable AI systems is not singular, and multiple avenues exist for achieving substantial improvements in performance and utility [66][69].

衡宇 发自 凹非寺 量子位 | 公众号 QbitAI 庞若鸣被扎克伯格天价挖去Meta后，谁在执掌苹果大模型团队？ 团队的权力交接，其实比外界想象中要快，也要安静得多。 答案很快浮出水面。接手这支团队的人，是庞若鸣的老搭档：Zhifeng Chen。 对Zhifeng Chen来说，这算赶上了最好的时候，也算赶上了最坏的时候。 一方面，庞若鸣离开之后，苹果内部一边重组AI权责，一边启动了针对核心研究人员的留人方案，其中最直接的动作就是涨薪。 另一方面，如大家所知，在这一波AI浪潮里，苹果的动作无论从成效还是速度来说，都太过不尽如人意。 Zhifeng Chen接过的这根交接棒，不可谓不烫手。 Zhifeng Chen是谁？ 今年，Zhifeng Chen离开待了19年又7个月的谷歌，加入苹果。 这和庞若鸣的职业路径高度相似，都是在谷歌待了十几年，然后转投苹果麾下。 庞若鸣今年夏天离开苹果后，Zhifeng Chen开始直接领导苹果基础模型团队， 直接管理二十多名下属。 但Chen和庞若鸣两人世界线的最初交集，早在两人加入谷歌之前。 2000年，Chen从复旦大学计算机科学专业毕业，开启了在普林斯顿大学的硕士研 ...

闻乐 发自 凹非寺 量子位 | 公众号 QbitAI "不是AGI还没到，而是永远到不了。" CMU（卡内基梅隆大学）教授、艾伦人工智能实验室研究员 Tim Dettmers 从硬件瓶颈、资源成本、现实应用三重维度论证： 第一个是信息移动成本 。 比如有效计算需要平衡 全局信息传到局部 和 局部信息整合 ，可信息移动的成本会随距离呈平方级上升；芯片缓存也能说明问题，L2、L3缓 存比L1 大，但却因物理位置更远而速度更慢。 为什么AGI从一开始，就是个违背物理规律的幻想工程？ 一篇长文，指出 GPU性能峰值停在2018年，机架级优化2027年耗尽潜力，AI每提升1%的能力，资源消耗要翻好几倍 …… 核心观点 AGI的讨论都在回避"计算的物理枷锁" 智能不是飘在天上的想法，而是得靠电脑、芯片这些实实在在的东西算出来，而这些东西都得遵守物理规律。 计算从不是抽象概念，所有智能都要扎根物理现实。 这也是Dettmers反驳AGI的核心，很多人在聊到AGI时总把它当成抽象的哲学概念，但很多人忽略了硬件实现，而硬件必然受到物理规律限 制。 现在芯片里的晶体管越做越小，虽然能降低计算成本，但内存反而越来越贵，现在芯片上几 ...

Core Insights - OpenAI's CEO Sam Altman expresses concerns about competition, particularly from Google, which he views as a significant threat to OpenAI's market position [2][11] - Altman emphasizes the importance of user retention and the development of "AI-native software" rather than merely integrating AI into existing products [2][12] - OpenAI is focusing on creating a comprehensive product ecosystem that enhances user experience through personalization and memory capabilities [9][10] Group 1: Competition and Market Position - Altman acknowledges that OpenAI is in a "red alert" state due to increasing competition, particularly after the release of Google's Gemini 3, but believes the impact has not been as severe as initially feared [5][6] - He notes that while Google has a strong distribution advantage, OpenAI's user base has grown significantly, reaching nearly 9 million users, which provides a competitive edge [3][8] - Altman believes that maintaining a slight paranoia about competition is beneficial for OpenAI's strategy and product development [6][7] Group 2: Product Development and Strategy - OpenAI is not rushing to release GPT-6; instead, it plans to focus on customized upgrades that cater to specific user needs, with significant improvements expected in early 2024 [36][37] - The company aims to build the best models and products while ensuring sufficient infrastructure to support large-scale services [8][9] - Altman highlights the importance of creating a cohesive product ecosystem that integrates various functionalities, making it easier for users to adopt and rely on OpenAI's offerings [10][24] Group 3: Enterprise Market Focus - OpenAI's strategy has shifted towards prioritizing enterprise solutions, as the technology has matured enough to meet business needs [27][28] - The company has seen rapid growth in its enterprise segment, with increasing demand for AI platforms from businesses [28][29] - Altman emphasizes that the enterprise market is ready for AI integration, particularly in areas like finance and customer support [29][30] Group 4: Infrastructure and Financial Outlook - OpenAI has committed approximately $1.4 trillion to build its infrastructure, which is essential for supporting its AI capabilities and future growth [39][48] - The company anticipates that as revenue grows, the cost of inference will eventually surpass training costs, leading to profitability [48][49] - Altman acknowledges that while current spending is high, the long-term vision is to create a sustainable business model that leverages AI advancements [50][51]

Core Insights - OpenAI is focusing on long-term strategies rather than immediate competition metrics, emphasizing organizational resilience and adaptability in response to market threats [1][3] - Altman highlights the importance of user retention through personalized experiences and memory, which can create significant switching costs for users [6][10] - The company is witnessing a rapid increase in enterprise users, reaching 1 million, indicating a shift towards a unified AI platform for businesses [9][10] Group 1: Competitive Strategy - OpenAI's "red code" response to competition is a tactical maneuver rather than a sign of panic, allowing the company to quickly address weaknesses in its product strategy [3][4] - Altman rejects the notion of model commoditization, arguing that while general use cases may see many options, high-value applications will still require superior models [5][6] - The company aims to redefine competition by focusing on user experience and retention rather than just technical specifications [5][6] Group 2: User Engagement and Retention - Altman identifies three key "stickiness mechanisms": personalization and memory, magical experiences, and platform inertia, which can lock users into the OpenAI ecosystem [6][10] - The potential for AI to remember user interactions and preferences could transform user relationships from mere tool usage to deeper, personalized engagements [6][13] - Altman emphasizes that once AI can provide personalized long-term context, the cost of switching to another service will increase significantly [6][10] Group 3: Market Dynamics and Growth - OpenAI's enterprise market is rapidly expanding, with significant growth in sectors like coding, finance, and customer support, suggesting a strategic approach to market education and habit formation [10][11] - The company is positioning itself as a foundational player in AI infrastructure, with a focus on meeting the increasing demand for computational power [14][15] - Altman discusses the potential for AI to replace certain jobs while also creating new ones, highlighting the need for careful management of this transition [12][19] Group 4: Future Outlook and Challenges - Altman expresses uncertainty about the timeline for achieving AGI and superintelligence, indicating that while progress may be rapid, there are also potential unknown challenges [16][17] - The discussion around IPOs suggests that OpenAI is considering public financing as a necessary step for its future growth and infrastructure investments [17][18] - The interview raises critical questions about the future of AI in the workplace, the ethical implications of AI companionship, and the concentration of power within the industry [19][20]

【环球网科技综合报道】12月18日消息，据businessinsider报道称，多位知情人士透露，埃隆·马斯克上周在xAI位于旧金山的总部召开的全体员工大会上表 示，如果公司能够成功度过未来两到三年的关键发展期，xAI将最终在人工智能竞赛中击败所有竞争对手。 在这场内部会议上，马斯克强调了xAI在算力扩张、数据基础设施建设和融资能力方面的显著优势。他指出，公司快速扩展其数据中心规模和处理海量数据 的能力，是实现"超级智能"（即超越人类智能的通用人工智能，AGI）并成为全球最强大AI企业的关键。 马斯克重申了他对AGI时间表的乐观预期，称xAI有望在未来几年内、甚至最早于2026年实现与人类智能相当或更高级别的通用人工智能。他此前曾在11月 表示，即将于2026年初发布的Grok 5模型有约10%的可能性达成AGI。 据与会员工透露，马斯克还向团队展示了xAI当前的资金实力：公司每年可获得约200亿至300亿美元的资金支持，并受益于与特斯拉等关联企业在地理和工 程资源上的协同效应。今年早些时候，特斯拉已将Grok AI集成至其车载系统，进一步拓展了xAI技术的实际应用场景。 xAI正在加速推进其名为"巨像"（C ...