Core Viewpoint - The article discusses the evolution of policy from "Internet Plus" to "Artificial Intelligence Plus," highlighting the differences in focus and implications for the future of technology integration in China [2][5][18]. Policy Evolution - The "Artificial Intelligence Plus" action plan was released by the State Council on August 26, 2025, aiming to deeply integrate AI with various sectors of the economy and society, outlining six key actions and corresponding developmental goals [2][3]. - In contrast, the "Internet Plus" action plan was issued on July 4, 2015, emphasizing the role of the internet as a foundational infrastructure for resource integration and innovation [3][7]. Terminology and Focus - A notable difference between the two documents is the frequency of the term "platform," which appears 62 times in the "Internet Plus" document but only 4 times in the "Artificial Intelligence Plus" document, indicating a shift in focus from platform-centric models to a broader integration of AI [4][5]. Technological and Economic Differences - The article argues that the fundamental nature of the internet as a "connector" contrasts with AI as a "cognitive" technology, leading to different policy approaches. The internet's value lies in connecting dispersed entities, while AI enhances the intelligence of individual nodes [9][10]. Industry Practices - The concept of "industrial internet" emerged from the "Internet Plus" initiative, where platform companies sought to extend their influence from consumer to enterprise sectors. However, the reality of business needs has led to a decline in enthusiasm for this strategy [11][12]. AI Integration and Future Prospects - The "Artificial Intelligence Plus" initiative aims to transition service industries from digital empowerment to intelligent-driven services, suggesting a shift in how businesses will adopt AI technologies [13][14]. - The article expresses optimism about achieving the 2027 goal of widespread AI integration across six key areas, citing lower resistance compared to the "Internet Plus" initiative [13][14]. Long-term Goals and Network Strategies - The long-term objectives for 2030 and 2035 require a network strategy to support the comprehensive development of the intelligent economy, emphasizing the need for interaction between supply and demand [16][18]. - The article notes a lack of emphasis on the internet within the "Artificial Intelligence Plus" policy, which may overlook the potential benefits of network effects in facilitating AI's growth [17][18]. Intelligent Internet Concept - The concept of "intelligent internet" is introduced as a potential framework for integrating AI and internet technologies, suggesting that the evolution from platform models to intelligent agents could enhance the effectiveness of AI applications [19][20]. - The emergence of open-source protocols for intelligent agents may facilitate a transition to a more decentralized and efficient model, breaking the dominance of traditional platform companies [19][20]. Conclusion - The article concludes that both "Internet Plus" and "Artificial Intelligence Plus" are not mutually exclusive but rather interdependent, with the potential to jointly drive China towards a new stage of intelligent economic and social development [22].

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The emergence of high-performance open-source large models has significantly lowered the barriers and costs for AI application innovation, driving the development of intelligent computing applications across various sectors such as finance, government, education, healthcare, and industry [7][14] - The report emphasizes the differentiated network connection requirements arising from the rapid growth of intelligent computing applications, highlighting the need for high bandwidth, low latency, and high reliability to support AI model training and inference [7][15] - The report proposes five key features for high-quality computing dedicated lines tailored for intelligent computing applications: intelligent perception, business certainty experience, elastic network on demand, intelligent operation and maintenance, and optical computing collaboration [7][15] Summary by Sections Overview - The proliferation of open-source large models since 2023 has disrupted the previous monopoly in the field, enabling rapid innovation in intelligent computing applications across various industries [14] - The report identifies the need for networks to perceive business types and provide differentiated connection capabilities to ensure optimal service experiences [14] Differentiated Dedicated Line Service Requirements for Intelligent Computing Applications Financial Intelligent Computing Applications - Financial institutions are leveraging AI for customer service, risk management, and operational efficiency, requiring high bandwidth and low latency for various applications [17][22] - Specific network requirements include: - AI service assistants: 5 Mbps bandwidth, latency < 5 ms, availability ≥ 99.99% [27] - Digital lobby managers: 200 Mbps bandwidth, latency < 2.5 ms, availability ≥ 99.99% [27] - AI financial compliance checks: 150 Mbps bandwidth, latency < 5 ms, availability ≥ 99.99% [27] - AI fraud detection systems: 5 Mbps bandwidth, latency < 5 ms, availability ≥ 99.99% [27] Government Intelligent Computing Applications - The report discusses the transition from basic digitalization to comprehensive intelligent governance, emphasizing the need for flexible network services to handle varying demands [29][33] - Network requirements include: - Intelligent government customer service: < 5 Mbps bandwidth, latency < 500 ms, availability ≥ 99.99% [38] - Intelligent traffic management: < 200 Mbps bandwidth, latency < 20 ms, availability ≥ 99.99% [38] - Intelligent environmental monitoring: 200 Kbps to 20 Mbps bandwidth, latency < 500 ms, availability ≥ 99.99% [38] Educational Intelligent Computing Applications - The report highlights the transformation in education through intelligent computing, with applications in personalized learning and automated assessment [39][43] - Network requirements include: - Smart classrooms: 100-500 Mbps bandwidth, latency < 25 ms, availability ≥ 99.99% [45] - Intelligent monitoring systems: ~4 Gbps bandwidth, latency < 5 ms, availability ≥ 99.99% [45] Healthcare Intelligent Computing Applications - The healthcare sector is increasingly adopting intelligent computing to enhance diagnostic accuracy and operational efficiency [46][49] - Network requirements include: - AI-assisted imaging: 10 Gbps bandwidth, latency < 10 ms, availability ≥ 99.9% [52] - AI-assisted diagnosis: 500 Mbps to 1 Gbps bandwidth, latency < 5 ms, availability ≥ 99.9% [52] Public Security Intelligent Computing Applications - AI is being integrated into public security to enhance risk identification and response capabilities [54][58] - Network requirements include: - AI video monitoring: 200 Mbps bandwidth, latency < 5 ms, availability ≥ 99.99% [60] - AI policing services: 20 Mbps bandwidth, latency < 50 ms, availability ≥ 99.99% [60] Entertainment Intelligent Computing Applications - The report discusses the digital transformation of the entertainment industry, particularly in cloud gaming and media production [66][67] - Network requirements include: - Cloud gaming: 120 Mbps bandwidth per user, latency < 1 ms [66] - 3D scene reconstruction: 1 Gbps bandwidth, latency < 1 ms [67]

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。 昨夜今晨 【宝马因部件安全隐患召回超过33万辆汽车】 据新华社，综合媒体和相关监管机构日前公布的信息，德国宝马集团将召回超过33万辆汽车，原因是起动机及 相关部件可能存在短路隐患。 据德新社报道，此次宝马召回计划中，在德国有超过13万辆汽车受影响。宝马尚未公布全球召回数量，但考虑 到这次召回涉及的是2015年9月至2021年9月生产的多款车型，影响的范围可能覆盖更多国家，实际召回规模可 能超过已公布的数字。 另据美国国家公路交通安全管理局日前发布的召回公告，宝马在当地将召回近20万辆汽车，是2019年至2022年 生产的车型。受影响车辆的"起动机继电器可能发生腐蚀，从而导致继电器过热并短路"，存在引发火灾风险。 该机构提醒车主，在问题解决前，应将车辆停放在室外并远离建筑物。 据悉，由于此次召回车辆将更换起动机，部分车型还需安装功率更大的电池，累计成本可能较高。 【广汽菲克核心资产即将开始第6次拍卖】 据第一财经，9月27日晚间，广汽菲克管理人发布公司破产清算案资产处置方案重要通报。关键信息有两点， 其一，拟决定将广汽菲克公 ...

【钛媒体综合】日前，中国人民银行货币政策委员会2025年第三季度例会内容公布，释放出一系列关键 政策信号。 在分析国内经济形势时，三季度例会表示，中国经济运行稳中有进，社会信心持续提振，高质量发展取 得新成效，但仍面临国内需求不足、物价低位运行等困难和挑战。和二季度例会对比，此次经济运行的 定调由"呈现向好态势"调整为"稳中有进"，面临问题方面删去了"风险隐患较多"的表述，整体评价仍较 为积极。 在研究下阶段货币政策思路时，三季度例会建议，加强货币政策调控，提高前瞻性、针对性、有效性， 根据国内外经济金融形势和金融市场运行情况，把握好政策实施的力度和节奏，抓好各项货币政策措施 执行，充分释放政策效应。这一定调和二季度例会基本一致，但新增"抓好各项货币政策措施执行，充 分释放政策效应"的表述，意味着未来一段时间货币政策的重点仍是在落实已出台的政策。 三季度例会还颇为关注结构性货币政策工具的使用，在二季度例会"扎实做好金融'五篇大文章'，加力 支持科技创新、提振消费，做好'两重''两新'等重点领域的融资支持"的基础上，新提了加大对小微企 业、稳定外贸等金融支持力度。 三季度例会对如何平衡支持实体经济与保持银行体系 ...

游戏ETF（516010）跟踪的是动漫游戏指数（930901），该指数从沪深市场中选取涉及动漫制作、 游戏开发、发行运营等业务的上市公司证券作为指数样本，以反映动漫游戏产业相关上市公司证券的整 体表现。动漫游戏指数聚焦于内容创作、技术研发及产业链上下游企业，能够较好地体现中国动漫游戏 行业的发展动态和市场趋势，具有较高的成长潜力和投资价值。 （责任编辑：张晓波 ） 没有股票账户的投资者可关注国泰中证动漫游戏ETF联接A（012728），国泰中证动漫游戏ETF联 接C（012729）。 【免责声明】本文仅代表作者本人观点，与和讯网无关。和讯网站对文中陈述、观点判断保持中立，不对所包含内容 的准确性、可靠性或完整性提供任何明示或暗示的保证。请读者仅作参考，并请自行承担全部责任。邮箱： news_center@staff.hexun.com 9月26日午后，游戏ETF（516010）回调超3%。 注：如提及个股仅供参考，不代表投资建议。指数/基金短期涨跌幅及历史表现仅供分析参考，不 预示未来表现。市场观点随市场环境变化而变动，不构成任何投资建议或承诺。文中提及指数仅供参 考，不构成任何投资建议，也不构成对基金业绩的 ...

Core Insights - The K2 Think model, developed by MBZUAI and G42 AI, is touted as the fastest open-source AI model, achieving a speed of over 2000 tokens per second, specifically 2730.4 tokens per second in tests [1][3][9] - K2 Think is claimed to be the most advanced open-source AI inference system to date, with a focus on mathematical reasoning [2][9] - The model is based on Qwen 2.5-32B and has been designed to excel in complex problem-solving through innovative training techniques [1][12] Performance Metrics - K2 Think has demonstrated consistent performance, maintaining speeds above 2000 tokens per second across various tests, including mathematical problems [3][7] - The model achieved notable scores in multiple mathematical benchmarks, such as 90.83 in AIME'24 and 81.24 in AIME'25 [9] Technical Innovations - The K2 Think team implemented six key innovations to enhance the model's capabilities: - Supervised Fine-Tuning (SFT) for structured reasoning [12] - Reinforcement Learning with Verifiable Rewards (RLVR) to improve performance in logic and mathematics [12] - Planning before reasoning to outline problem-solving strategies [12] - Best-of-N sampling to generate multiple answers and select the best [12] - Speculative Decoding for parallel answer generation and validation [12] - Hardware acceleration using Cerebras WSE for high-speed token generation [12] Safety and Security - The K2 Think team conducted comprehensive safety testing, ensuring robustness against harmful requests and information leaks [12]

Group 1 - The core viewpoint is that the media industry is experiencing a surge in AI applications, with a cultural confidence boost from content output, making this year pivotal for the explosion and reshaping of China's open-source large model applications [1] - The box office for the summer of 2025 is projected to reach 110.02 billion yuan, with an annual total box office of 383.28 billion yuan and 8.87 billion moviegoers, indicating a recovery of approximately 85% in box office and 76% in audience numbers [1] - AI technologies, such as Google's nano banana model, are showing impressive results in image editing and are expected to accelerate applications in film and gaming sectors [1] Group 2 - The gaming sector is anticipated to show strong performance in Q3, with a record high in the number of licenses issued and an upward trend in multi-platform products [1] - The long-term outlook for the IP sector is positive, particularly for 2B licensing and 2C channel-driven growth [1] - The Film ETF (516620) tracks the CSI Film Index (930781), which includes listed companies involved in film content provision, distribution, and channels, reflecting the overall performance of China's film industry [1]

Group 1 - The core viewpoint of the articles highlights the rising influence of AI applications in the media industry and the cultural confidence driven by content output, with expectations for a significant year for China's open-source large models and a reshaping of application patterns [1] - The gaming industry is showing strong mid-year performance, with continued optimism for the sector's prosperity and an anticipated high growth inflection point in Q3 [1] - Google's launch of the Nano Banana image model is expected to accelerate applications in e-commerce, advertising, design, film, and gaming, with its core advantages being strong image consistency, high generation efficiency, and reduced usage barriers [1] Group 2 - The gaming ETF (516010) tracks the anime and gaming index (930901), which selects listed companies involved in game development, publishing, anime production, and derivatives from the Shanghai and Shenzhen markets to reflect the overall performance of related securities [1] - The anime and gaming index focuses on the cultural entertainment and digital creative industries, reflecting the development trends and market dynamics of China's anime and gaming industry [1] - Investors without stock accounts can consider the Guotai CSI Anime and Gaming ETF Connect A (012728) and Connect C (012729) [1]

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The Chinese enterprise-level GenAI market is experiencing explosive growth, with daily model invocation reaching 101,865 billion tokens in the first half of 2025, a 363% increase from 21,999 billion tokens in the second half of 2024 [8][18][11] - The market is transitioning towards a dual-track development of open-source and closed-source models, with open-source models gaining traction due to their cost-effectiveness and flexibility [13][16] - The focus of enterprise-level model application is shifting from seeking a single powerful model to finding optimal solutions tailored for specific business scenarios, emphasizing cost-performance ratio, system flexibility, and security [6][20] Summary by Sections Introduction - The report, published by Frost & Sullivan in collaboration with the Head Leopard Research Institute, surveys 700 IT department heads, technical directors/managers, and AI project leaders across various industries including finance, manufacturing, internet, consumer electronics, and automotive [4][28] - The study aims to assess the deployment of open-source and closed-source models in the enterprise-level GenAI market and to provide structured insights into the current application status and trends [4] Section 1: Overview of Enterprise-Level GenAI Development - The development of enterprise-level GenAI is characterized by the parallel growth of open-source and closed-source models, with open-source models becoming the preferred choice for low-cost implementation and autonomy [13][16] - Open-source models are increasingly recognized for their adaptability and long-term value, while closed-source models are favored for their reliability and performance [13][16] Section 2: Current Status and Trends of Model Invocation - The daily invocation of enterprise-level models has surged, indicating a shift from pilot testing to large-scale implementation, with significant implications for resource consumption and industry restructuring [18][19] - Key drivers of this growth include the expansion of model and computing power supply, accelerated deployment in various sectors, and the emergence of ecosystem effects that enhance efficiency [19][20] Section 3: Analysis of Model Invocation Behavior - The choice between open-source and closed-source models is primarily driven by business value, with open-source models offering greater flexibility and control, while closed-source models provide reliability and ease of use [24][26] - The top factors influencing the selection of open-source models include performance, customization ease, and knowledge ownership, whereas closed-source models are chosen for their reliability and brand reputation [25][26][27]

Core Insights - The Frost & Sullivan report highlights the rapid growth and adoption of generative AI in the Chinese enterprise market, with an astonishing daily consumption of 10.2 trillion tokens expected by the first half of 2025 [1] - The report indicates a significant increase of 363% in daily model invocation compared to the second half of 2024, surpassing the 10 trillion tokens mark [1] - Leading platforms in this market include Alibaba Tongyi, ByteDance Doubao, and DeepSeek, which collectively hold over 40% market share [1] Industry Trends - Public cloud has emerged as the preferred method for Chinese enterprises to deploy and invoke large models, with 70% of companies favoring this approach [3] - A notable 71% of enterprises plan to further increase their use of generative AI services in public cloud environments, indicating a shift towards seeking optimal solutions for specific business scenarios [3] - The rise of open-source models is becoming a key driver for market growth, with a significant reduction in performance gaps between domestic open-source models and top international closed-source models [3] Future Projections - It is predicted that over 80% of enterprises will adopt open-source large models, suggesting that open-source solutions will dominate enterprise-level applications in the future [3]