智能超参数今天发布《中国大模型中标项目监测与洞察报告 (2025) 》系列文章的第二篇，我们将对大模型厂商表现 进行集中盘点。 2025年全年，智能超参数统计到了7539个大模型相关中标项目，其中1760个项目未披露金额（为便于统计，中标金 额标注为0元），其余5779个中标项目披露的中标金额达到295.2亿元。与2024年全年数据相比，2025年的大模型中 标项目数量增长了396%，披露中标金额增长了356%。 如此多的中标项目，都被哪些厂商拿走了？ 同样以中标项目数量来计算，三大运营商凭借广泛的渠道网络（总公司、分公司、子公司等）也是拿到大模型中标 项目的重要阵营。 依据智能超参数的统计数据，中标数量排名前30名的厂商中，有10家是运营商背景的机构。政务、医疗等行业客户 倾向选择这类运营商背景的机构作为技术服务商，因为可以规避很多数据合规风险。不过，需要指出的是，运营商 中标的大模型项目中，很多采购方都来自体系内的公司。 云厂商也可以看作是一个明显的拿标主力阵营。虽然这个阵营的成员跟我们统计的通用大模型厂商有很多重复，但 是凭借在算力、模型、AI平台、AI应用等全栈或者某一层的优势，这些云厂商成为大模型项 ...

但有趣的是，就在市场一片欢腾之际，我发现一个奇怪现象：同样是AI概念股，有的已经翻倍，有的却纹丝不动。这让我想起18年前刚接触量化交易时的 一个顿悟时刻——股市炒的从来不是现实差，而是预期差。 二、预期差：机构与散户的最大鸿沟 一、AI独角兽上市背后的投资密码 最近资本市场最劲爆的消息莫过于国产AI企业MiniMax即将上市。这家成立仅4年的公司，凭借大模型技术已经服务全球200多个国家的2.12亿用户。消息一 出，相关概念股应声而动。 翠微股份翻倍 东软集团不涨反跌 中科江南后发制人 润和软件高开低走 记得2007年那轮大牛市，当时所有人都知道银行股业绩好得不得了。但诡异的是，等财报真正公布时，股价反而开始下跌。后来我才明白，市场早就price in了这些利好。 这就是典型的"预期差"现象。机构凭借信息优势和数据工具，往往能提前布局；而散户只能后知后觉地追涨杀跌。就像这次MiniMax上市，你以为的新闻可 能早就是机构的旧闻。 三、稳定币概念的冰火两重天 去年4月那波行情特别能说明问题。当时上证指数三个月涨了500点，稳定币概念风头无两。但同样是这个概念： 关键差别在哪？看看翠微股份的数据就明白了。早在概念 ...

Core Viewpoint - The event highlighted the role of "large models + intelligent agents" in leading the digital transformation of remote banking, with iFlytek as a key technical contributor [1] Group 1: Technological Advancements - iFlytek has made significant breakthroughs in large model technology, successfully applying it to remote banking scenarios [1] - The company emphasizes the importance of a "good technology, safe and controllable" approach, establishing a comprehensive security governance system for the financial industry's intelligent transformation [1] Group 2: Industry Collaboration - iFlytek collaborates with partners such as Huawei and Huishang Bank to create an open and shared communication platform [1] - The deep sharing by iFlytek's research institute vice president identified industry pain points, providing valuable references for peers [1] Group 3: Future Directions - iFlytek aims to continue developing large model and intelligent agent technologies, offering more mature solutions to empower the digital transformation of remote banking [1] - The company's technological practices and transformation strategies provide diverse reference paths for banking institutions [1]

Group 1 - The core point of the article highlights that the acceleration of AI integration, weak consumer spending, and rising costs are driving companies to cut expenditures and adjust their workforce structures, leading to significant layoffs in the U.S. job market [1][4][5] - In October, U.S. companies announced layoffs of 153,000 employees, a staggering increase of 183% month-over-month, marking the highest monthly total since 2003 and a 175% increase compared to the same month last year [1][3] - Year-to-date, approximately 1.1 million layoffs have been announced, representing a 65% increase from the previous year, making it the largest year for layoffs since the pandemic began [1][3] Group 2 - The technology sector is identified as the most affected industry, with 33,300 layoffs in October, nearly six times the number in September, primarily due to the impact of AI integration and automation [3][4] - Other sectors experiencing layoffs include consumer goods, with 3,400 layoffs, and non-profit organizations, which have seen a staggering 419% increase in layoffs this year due to government shutdowns [3] - The five industries with the highest cumulative layoffs this year are government, technology, warehousing, retail, and services, collectively accounting for over 70% of total layoffs [3] Group 3 - The report indicates that the current wave of layoffs is closely linked to the accelerated application of AI technology, which is reshaping workforce demand, particularly in the technology and media sectors [4][5] - The labor market is experiencing a longer re-employment cycle for laid-off workers, with reduced job supply and extended job search periods, indicating a weakening momentum for job growth [3][5] - Analysts suggest that the combination of AI penetration, cooling consumer demand, and fiscal uncertainties is prompting companies to adopt defensive measures, potentially delaying economic recovery [5]

Core Insights - The integration of artificial intelligence (AI) into the media industry is a key driver for systemic transformation and innovation in content production and dissemination [1][3] - The 2025 Academic Annual Conference of the China News Technology Workers Association served as a platform for discussing the deep integration of AI with mainstream media [1][3] Group 1: AI and Media Transformation - AI-generated content (AIGC) is recognized as one of the most mature applications of technology in the media sector, facilitating significant changes across various media processes [1] - The conference highlighted the need for mainstream media to evolve from mere tool innovation to a comprehensive ecological reconstruction [3] Group 2: Technological Contributions - Satellite communication is identified as a core enabler for new and intelligent media, enhancing capabilities in news collection, transmission, and distribution [6] - The development of large model technologies is transitioning from technical exploration to engineering innovation, focusing on practical applications in media [7] Group 3: Industry Standards and Recognition - The conference introduced five new group standards, including "Intelligent Entities in the News Industry," aimed at filling gaps in standardization and fostering innovative business models [8] - The 2025 Wang Xuan News Science and Technology Award recognized 181 outstanding projects, reflecting a significant increase in submissions and showcasing high standards in technological innovation within the industry [8][10] Group 4: Practical Applications and Future Directions - The integration of AI in media practices is being explored through specific applications, such as engaging in the sports economy to enhance media value and data accumulation [7] - The Chengdu Media Group's intelligent media asset library project, which utilizes big data, blockchain, and AI, exemplifies the industry's move towards intelligent content management systems [10]

Core Insights - The 2025 World Manufacturing Conference was successfully held in Hefei, Anhui, from September 20 to 23, focusing on the theme "Intelligent Manufacturing, Creating a Better Future" [1] - The conference attracted participants from over 40 countries and regions, featuring 4 major events, 6 project matchmaking activities, and 23 specialized activities [1] Industry Developments - The conference showcased 10 comprehensive exhibition areas and an interactive exhibition area for unmanned systems, covering a total area of 30,000 square meters, highlighting achievements in China's new industrialization since the 14th Five-Year Plan [1] - A special focus was placed on the development of the artificial intelligence industry, with a dedicated robot section and the release of several large model technologies and application scenarios [1] Investment and Cooperation - The conference facilitated the establishment of 735 cooperation projects with a total investment of 380.2 billion yuan, representing a 2.4% increase in project numbers and a 3% increase in investment compared to the previous year [1] - Manufacturing projects accounted for over 90% of the total number and investment, while projects and investments from the Yangtze River Delta region also exceeded 50% [1]

Core Insights - The insurance industry is undergoing a profound transformation through deep integration with cutting-edge technology [1] - The collaboration between Huawei and China Pacific Insurance aims to enhance operational efficiency and decision-making accuracy in key areas such as sales and claims [1] - This partnership represents not only a technological upgrade but also a practical implementation of strategic pathways for digital transformation in the financial insurance sector [1] Group 1 - The "Pacific Insurance Financial Industry Lighthouse" initiative brings multiple positive effects, providing high-performance computing support for China Pacific Insurance's business system [1] - The use of large model technology significantly improves operational efficiency and decision-making accuracy in critical processes [1] - The successful collaboration between Pacific Insurance Technology and Huawei validates the feasible model of "independent innovation + scenario application" [1] Group 2 - The partnership offers advanced concepts and practical frameworks for the digital transformation of the domestic financial insurance industry [1]

Group 1: Overall Market Performance - In Q2 2025, the Hang Seng Technology Index showed the highest revenue and net profit growth rates among major Hong Kong indices, with revenue growth at 14.43% and net profit growth at 16.18% [1][2] - Excluding Alibaba, JD Group, and Meituan, the net profit growth rates for the Hang Seng Index, Hang Seng Composite Index, and Hang Seng Technology Index were -1.04%, 3.88%, and 25.34% respectively [2] Group 2: Industry Insights - The materials, healthcare, and information technology sectors led in net profit growth rates, with the information technology sector showing a Q2 net profit growth of 29.67% [3][4] - The ROE (TTM) for the information technology sector increased by 2.44 percentage points to 13.18% compared to the same period last year [3] Group 3: Consumer Sector Performance - Non-essential consumer sector net profit growth significantly declined to 3.10% in Q2 2025 from 44.64% in Q1, with AI-driven companies performing well [4][5] - The media and entertainment sector saw a net profit growth of 32.27%, driven by AI business, with advertising and publishing sectors showing substantial increases [5] Group 4: Financial Sector Performance - The financial sector's net profit growth was 5.02% in Q2 2025, recovering from a -2.56% decline in Q1, with securities and brokerage net profit growth at 73.80% [7] - The banking sector's net profit growth was -0.11%, indicating continued pressure on traditional banking profitability [7] Group 5: Healthcare Sector Performance - The healthcare sector's net profit growth reached 42.50% in Q2 2025, up from 26.47% in Q1, with significant improvements in ROE [6] Group 6: Energy and Materials Sector Performance - The energy sector experienced a net profit decline of 19.36% in Q2 2025, worsening from -12.63% in Q1 [8] - The materials sector showed strong performance with a net profit growth of 50.78%, supported by high ROE levels [8]

Core Insights - The 7th China FinTech Forum held on September 10-11 in Beijing focused on "Technology Empowerment - Digital Transformation and Application in the Financial Industry" [1] - The forum gathered experts from regulatory, financial, technological, and academic fields to discuss key issues such as the implementation of large model technology, data security, and the transformation of traditional institutions [1] - Digital transformation in the banking sector is now considered a necessity rather than an option, driven by advancements in AI, big data, and cloud computing [1] Group 1: Digital Transformation - Digital transformation is essential for the sustainable development of the banking industry, as stated by the Secretary of the China Banking Association [1] - AI technologies, particularly large models, are opening new pathways for technological empowerment in banking, enhancing asset organization efficiency and reducing operational costs [1] - The transformation of financial data into a core productivity resource is a primary goal of digital transformation in the banking sector [1] Group 2: Risks and Challenges - The application of AI technologies introduces new risks for commercial banks, notably data risks and model risks, which significantly impact model application, business security, and operational resilience [2] - Addressing the existing digital gaps is crucial for the efficiency and effectiveness of AI applications in banking [2] - The rapid evolution of customer behavior poses a significant challenge for commercial banks, necessitating a better alignment between banking services and customer needs [3] Group 3: Solutions and Strategies - To tackle the challenges posed by AI, banks need to enhance their understanding, develop strategies, implement tactical measures, and strengthen process control and result orientation [3] - The emergence of large models has optimized methods for processing unstructured data, addressing the issue of information asymmetry in financial technology [3] - A systematic approach to deepening the application of AI technologies is essential for banks to meet new operational challenges and achieve refined management and high-quality development [3]

Core Viewpoint - The rise of AI technology is creating both risks and opportunities in the job market, particularly in the backend development sector, where traditional roles are declining while AI-related positions are surging [1][2]. Group 1: Job Market Trends - Traditional CRUD development positions have decreased by 30%, while 80% of new technical roles require AI capabilities such as large model development and RAG architecture [2]. - The average annual salary for AI developers exceeds 400,000, compared to 200,000 for traditional frontend and backend roles [2]. - AI can now independently perform tasks such as code generation and debugging, leading to a significant shift in job demand [2]. Group 2: AI Job Opportunities - The AI competition has entered a phase of "hundreds of billions in investment," resulting in a substantial increase in AI and algorithm positions, with salaries rising by 50% compared to previous years [2]. - Many algorithm positions have starting salaries reaching 25,000 to 30,000, which is already the ceiling for many frontend and backend roles [2]. Group 3: Training and Education - A recommended "Algorithm Engineer Training Program" has been developed by top technical experts, focusing on recommendation algorithms and large model technologies, with a job guarantee of at least 290,000; otherwise, a full refund is provided [3][22]. - The curriculum covers a comprehensive range of big data algorithms and practical applications, including environment setup, data mining algorithms, machine learning, and deep learning [5][26]. Group 4: Project-Based Learning - The training program emphasizes real-world projects, allowing students to learn through practical application, covering various big data components and advanced algorithms [8][26]. - Projects include building user profiles, recall systems, and recommendation systems, utilizing tools like Hadoop, Hive, and Spark [12][14][24]. Group 5: Employment Success Stories - The program has seen a 90% success rate in job placements, with the highest reported salary reaching 75,000 [31]. - Alumni have successfully transitioned to algorithm roles with significant salary increases, such as a 68% rise from 250,000 to 420,000 [34].