2026年美国拉斯维加斯消费电子展（CES）正在举行。作为国际消费电子领域的"风向标"，CES历来是 观察前沿技术走向的重要窗口。今年，一个新词被频频提及——"物理人工智能"（物理AI，Physical AI）。 在展会一系列主题演讲和行业讨论中，"物理AI"成为出现频率最高的关键词之一。AI芯片行业领军企业 英伟达首席执行官黄仁勋在主旨演讲中17次提及这一概念。与2025年CES上热议的"AI智能体"相比，这 一新的热词折射出AI技术发展路径和产业关注重点的变化。 "物理AI"究竟是指什么？按照英伟达的解释，"物理AI"是指让摄像头、机器人和自动驾驶汽车等自主系 统能够在物理世界中完成感知、理解、推理，并执行或协调复杂动作。业界普遍认为，其核心并非停留 在算法和算力层面，而是让AI真正具备在现实世界中"看、想、动"的能力。换句话说，AI正在嵌入机器 人、自动驾驶汽车、智能终端甚至工业设备，使"机器智能"不再只是提供信息或建议，而是直接参与并 改变现实环境。 除了居家生活应用，还有一些企业带来"物理AI"在公共生活场景的落地方案。深圳道通科技展示的"未 来城市全景沙盘"覆盖高速公路、光储充电站、桥梁、码头等典 ...

以下文章来源于日经中文网 ，作者日经中文网 日经中文网 . 编制日经指数的《日本经济新闻》的中文版。提供日本、中国、欧美财经金融信息、商务、企业、高科技报道、评论和专栏。 将在美国开幕的全球最大规模的科技展会"CES"将率先呈现2026年的科技趋势。对于科技行业，备受关注的是如何把生成式AI实际应用到终端和社会中。有望在 2026年问世的是面向"后智能手机"时代的新终端…… 来源｜ 日经中文网（ID：rijingzhongwenwang） 封面来源 ｜ 视觉中国 OpenAI提出"后智能手机"产品 有望在2026年问世的是面向"后智能手机"时代的新终端。美国OpenAI预告称，将在2026年内发布"AI终端"。估计将是一种没有显示屏、仅通过语音来操 作的小型终端。 自2007年美国苹果推出"iPhone"以来，已经过去约20年，智能手机已深度融入人们的生活。但设计iPhone的著名设计师乔尼·艾夫（Jony Ive）对由此催生 出智能手机依赖问题感到懊悔，并正在协助OpenAI开发新终端。 美国Meta正在开发搭载AI的眼镜型终端。其构想是用户可透过透明镜片观看现实世界，同时显示数字画面。2025年12月，M ...

近日，工业和信息化部、中央网信办、国家发展改革委等八部门联合印发《"人工智能+制 造"专项行动实施意见》（下称《实施意见》），提出 到2027年，我国人工智能关键核心技术 实现安全可靠供给，产业规模和赋能水平稳居世界前列。 《实施意见》旨在加快推进人工智 能技术在制造业融合应用,打造新质生产力，全方位、深层次、高水平赋能新型工业化。 新年伊始，多部门就联合印发人工智能相关文件，这也进一步向外界传递出一个清晰信号： 人工智能正加速走出实验室和屏幕的限制，走进工厂车间、走进我们的生活场景 ，成为重塑 真实物理世界的核心驱动力。 当前，能够明显看到的趋势是，我们正处于一个由"数字AI"向"物理AI"跃迁的关键节点。过去 几年里，大众对人工智能的认知，大多还是停留在大语言模型、创意生成等纯数字化的软件领 域，但随着技术的深度演进，AI的触角已经深刻延伸至制造业的肌理。 今天的人工智能，已经不再仅是数据，也不是简单用来"闲聊"的数字玩具，而是一种生产工 具。展望未来，AI对经济的影响，有望出现从"局部盆景"到"全面森林"的跨越。在这个进程 中，中国制造有先发优势，也应当加速起跑。 出品丨21财经客户端 21世纪经济报道 ...

星海情报局 . 以下文章来源于星海情报局 ，作者星海老局 关注国产替代和中国品牌出海，每年写100个中国品牌案例，见证中国产业崛起！ 本文来自微信公众号： 星海情报局 ，作者：星海老局 这种危机感具体来说就是： 以英伟达为代表，美国资本正在大力推动AI往现实世界的生产中渗透，这种渗透不再是单点式的孤 立行动，如今已然变成了成体系的战略布局——美国正试图用AI来复兴其制造业。 而一旦这样的尝试成功，结果对我们来说将会是非常严峻的：美国将会重新获得制造业优势，我们的 工程师、高级技工优势则会被稀释——美国制造业会复兴，而我们则将面临订单减少和岗位收缩。 黄仁勋到底要干啥？ 黄仁勋昨天在CES的演讲，浓缩成一句话就是：如何降低物理AI的开发成本。而物理AI，是AI工厂 的前置条件。 先来说说所谓的"物理AI（Physical AI）"。 关于"物理AI"，英伟达的官网上是这样介绍的： 昨天在美国拉斯维加斯举办的CES展览上，英伟达的黄仁勋发表了长达90分钟演讲。 ...... 抛开这些花花绿绿的溢美之词，老局看完之后，第一反应其实是一种危机感。 Physical AI lets autonomous systems ...

第一财经每日早间精选热点新闻，点击「听新闻」，一键收听。 【今日推荐】 降准降息可期！央行2026年政策定调 1月5日至6日，2026年中国人民银行工作会议召开，部署全年七大重点工作，围绕货币政策实施、金融 服务实体经济、风险防控、金融改革开放等核心领域，明确行动路径。其中，在货币政策方面，会议强 调，把促进经济高质量发展、物价合理回升作为货币政策的重要考量，灵活高效运用降准降息等多种货 币政策工具。 特朗普称委内瑞拉将向美国移交3000万至5000万桶石油 当地时间1月6日，美国总统特朗普宣布，委内瑞拉临时政府将向美国移交3000万至5000万桶石油。这些 石油将按市场价格出售，而所得资金将由特朗普进行监管，以确保这些资金用于"造福委内瑞拉人民和 美国人民"。特朗普称，已要求能源部长克里斯·赖特立即执行这一计划。石油将由储油船装载，并直接 运送至美国的卸货码头。目前委内瑞拉方面对此暂未做出官方表态。 北欧五国外长就格陵兰岛问题发表联合声明 当地时间1月6日，丹麦、芬兰、冰岛、挪威和瑞典外长发表联合声明。声明称，作为北欧、北极国家及 北约盟国，五国共同致力于维护北极地区安全、稳定与合作，已采取一切措施加强该地 ...

证券日报网12月17日讯索辰科技在12月16日回答调研者提问时表示，物理AI的独特价值，在于它不是 对传统技术的简单升级，而是推动工业制造从"有人操作"向"无人自主"变革的核心驱动力。相比传统仿 真软件或通用AI，它具备三大不可替代的优势：一是极致的真实性。传统仿真需要对物理世界进行简 化和假设，结果与现实总有偏差。而物理AI通过学习海量真实物理数据，能够直接高精度还原复杂的 物理规律。这对于航空航天、无人驾驶等需要"零误差"的无人装备领域至关重要。二是颠覆性的实时 性。传统对复杂场景的仿真计算，可能需要数小时甚至数天。而物理AI依托GPU的强大算力，能实现 毫秒级的数据处理和决策输出。比如在无人机自主避障场景中，它能瞬间分析环境数据、生成避障路 径，这是实现真正自主智能的前提。三是强大的自适应能力。传统仿真模型是固定的，无法应对动态变 化的真实工业场景。物理AI则具备自主学习能力，可以根据实时采集的数据，动态优化自身算法，适 应场景变化。例如，它可以实时监测工业机器人状态，预测故障并自动调整参数，实现真正的无人化运 维。这三点决定了物理AI是未来装备全面无人化发展的核心技术，它让工业软件从一个辅助设计的"工 ...

Core Insights - AI large model technology is rapidly entering everyday life, accompanied by potential threats, as highlighted by Gartner's report on the top ten strategic technology trends for 2026, with over half related to AI [1][12] - AI plays a dual role as both a foundation for innovation and a source of new security risks, necessitating a balance between value creation and threat prevention in corporate strategies [1][12] Group 1: AI Technology Trends - Gartner emphasizes four key technologies: AI-Native Development Platforms, Domain-Specific Language Models, Multiagent Systems, and Physical AI [2] - By 2030, it is predicted that 80% of enterprises will transform large software engineering teams into smaller, agile teams empowered by AI-native development platforms [3] Group 2: Domain-Specific Language Models - Domain-Specific Language Models, particularly those trained on proprietary enterprise data, can provide significant operational value, shifting AI from general capabilities to specialized applications [6] - For instance, using internal data for AI training can enhance efficiency in manufacturing by providing quick solutions to machine faults through natural language queries [6] Group 3: Multiagent Systems - The development of multiagent systems is moving from isolated AI agents to collaborative teamwork, improving task success rates and adaptability to changing enterprise needs [6] Group 4: Physical AI - Physical AI is currently focused on fully autonomous vehicles and robotics, with two main implementation approaches: Visual Language Models and World Models [7][8] - By 2028, it is expected that 80% of warehouses will utilize robotic technology or automation [8] Group 5: AI Supercomputing Platforms - The foundation for these applications is the construction of AI supercomputing platforms, integrating various computing chips to handle complex data processing tasks [8] - Enhancing computational efficiency and connectivity is crucial, as demonstrated by NVIDIA's recent technologies that link quantum computing with traditional supercomputers [9] Group 6: Transition from Possibility to Value - The period from 2023 to 2024 is identified as the "technology explosion" phase for AI, while 2025 to 2026 will focus on delivering tangible value [10] - Companies will shift from seeking universal models to more cost-effective, domain-specific models, emphasizing practicality over model worship [10] Group 7: AI Integration Challenges - Integrating AI capabilities into existing workflows requires significant organizational changes, including software restructuring, team reorganization, and employee retraining [11] - The main challenges for AI deployment will transition from technical issues to engineering and business problems, focusing on reliable, compliant, and profitable operations [11] Group 8: AI Security Threats - The rapid advancement of AI presents significant security threats, including AI-driven attacks that can lead to identity fraud and phishing [12] - Proactive network security, utilizing AI for predictive threat intelligence and automated defenses, is projected to become a critical technology by 2026 [12] Group 9: Future of AI Security Solutions - By 2030, proactive defense solutions are expected to account for half of enterprise security spending, with AI security platforms providing unified protection mechanisms [12] - The future AI landscape will be characterized by innovation and risk, necessitating robust security measures to ensure AI serves as a catalyst for business growth [12]

不再是"AI+"的修修补补，而是AI原生重构系统底层逻辑；不再局限于数字世界的生成与理解，而是物理AI打通虚拟与现实的行动闭环；不再是单一模态 的孤军奋战，而是多模态技术融合万象；更有世界模型让AI从"数据应答"走向"规律预判"。 这场关乎技术架构、应用形态与认知高度的变革已然来临，谁将成为重塑产业、定义未来的最强风口？ AI原生引发系统应用底层革命 当算法模型的迭代速度超越行业想象边界，当AI从屏幕后的工具跃变为渗透现实的"参与者"，2026年将成为人工智能发展的关键分水岭。 如果说"AI+"是在现有系统上"打补丁"或"外挂"AI功能，那么AI原生则意味着以AI为系统设计的底层逻辑与能力中枢，这套系统为AI而生、因AI而长，驱 动从技术架构、业务流程、组织角色到价值创造方式的全方位重塑。 这种变革并非简单的功能叠加，而是以生成式AI为核心重构开发范式，让智能成为应用的原生属性而非附加能力。从"AI+"走向"AI原生"，正成为AI未来 发展的关键方向。 | 维度 | 传统"Al+"架构 | AI原生架构 | | --- | --- | --- | | 设计起点 | 现有业务流程 | Al能力边界 | | 数据 ...

Group 1 - The core investment themes for the next five years include technology, metals and mining, passion investments, ASEAN and Chinese markets, and biotechnology [9][30][40] - The technology sector is expected to continue its growth, but current valuations are high [9] - The metals and mining industry may experience explosive growth due to potential metal shortages, particularly in uranium, silver, and platinum [30] - Passion investments, which are assets with limited supply that do not involve technological iteration, are likely to see increased demand during periods of innovation and wealth growth [33] - The ASEAN and Chinese markets are projected to prosper, with Chinese innovation capabilities rapidly advancing [36][38] Group 2 - Generative AI is anticipated to be a major source of profit in future society, with its effective compute power growing exponentially [10][19] - The effective compute power of AI has increased by 100,000 times from 2019 to 2023, and this growth is expected to continue [13] - The application of generative AI in various industries can create strong business moats, unlike large language models which lack brand loyalty and key technological barriers [20] Group 3 - Approximately 80% of jobs are expected to be completed by intelligent robots by 2050, with significant implications for labor markets [22][29] - The rise of reasoning AI and physical AI is expected to transform industries, with robots and intelligent systems taking over both physical and cognitive tasks [24][25] - The cost of producing robots is significantly lower than the cost of training human labor, leading to a potential shift in workforce dynamics [28] Group 4 - The biotechnology sector is currently undervalued compared to the AI sector, with a price-to-earnings ratio of around 10-11 times for international biotech ETFs [40] - AI is significantly reducing research and development costs in biotechnology, leading to a rapid increase in the number of new products [42] - The potential for breakthroughs in personalized medicine and advanced health monitoring technologies is high, making biotechnology a promising investment area [42]

Core Insights - Jeff Bezos is returning to an operational role as co-CEO of the AI startup "Project Prometheus," which has secured $6.2 billion in funding, partly from Bezos himself, making it one of the most well-funded early-stage startups globally [1][3]. Group 1: Company Overview - Project Prometheus focuses on "Physical AI," which refers to intelligent systems that can perceive, understand, and interact with the physical world, differentiating it from traditional language models [3]. - The company aims to provide AI solutions for engineering and manufacturing in sectors such as computing, aerospace, and automotive [3][5]. - A team of nearly 100 people has been assembled, including researchers from top AI firms like OpenAI, DeepMind, and Meta, showcasing Bezos's influence and the ambitious technological vision of the project [5]. Group 2: Leadership and Management Style - Bezos is applying his management philosophy from Amazon, known for its detail-oriented approach and data-driven decision-making, to Project Prometheus [5][6]. - The company is still in stealth mode, but its focus is becoming clearer: achieving significant advancements in engineering and manufacturing through AI [5]. Group 3: Investment Strategy - Bezos's involvement in the Physical AI sector is not spontaneous; he previously participated in a $400 million funding round for the robotics AI company Physical Intelligence and invested in Periodic Labs, which raised $300 million to build robotic laboratories [6][7]. - Through Bezos Expeditions, he has invested in several robotics and AI startups, indicating a systematic bet on the Physical AI sector [7]. Group 4: Competitive Landscape - The announcement of Bezos's return to a CEO role has drawn comments from Elon Musk, highlighting the competitive dynamics between the two billionaires in AI, space exploration, and electric vehicles [8].