2025年中国大模型应用市场洞察白皮书 模型驱动应用创新，撬动市场千亿增量 （精华版） China Large Model Application Industry 中国大型モデル応用産業 概览标签：大模型、消费端应用、企业端应用 1 报告提供的任何内容（包括但不限于数据、文字、图表、图像等）均 系头豹研究院独有的高度机密性文件（在报告中另行标明出处者除外 ）。 ，任何人不得以任何方式擅自复制 、再造、传播、出版、引用、改编、汇编本报告内容，若有违反上述 约定的行为发生，头豹研究院保留采取法律措施，追究相关人员责任 的权利。头豹研究院开展的所有商业活动均使用"头豹研究院"或"头豹 "的商号、商标，头豹研究院无任何前述名称之外的其他分支机构， 也未授权或聘用其他任何第三方代表头豹研究院开展商业活动。 研究目的 深入辨析消费端与企业端两大市场的核心应 用场景、流量格局及用户需求差异；同时， 通过分析不同参与者的竞争优势与发展策略 ， 揭示当前应用落地面临的关键挑战，研判不 同应用赛道的发展成熟度与未来商业价值演 进方向。 研究区域范围：中国及全球 研究对象：大模型应用层 本报告的关键问题： 1）当前大模型在消费端 ...

日前，山东省人民政府办公厅印发《关于加快释放数据价值加力推进数字经济高质量发展的实施意见》 (以下简称《实施意见》)，提出以实体经济和数字经济融合发展为重点，全面推动数字经济核心产业提 质、产业数字化转型提级。到2027年，数据要素市场化价值化路径更加成熟，实体经济和数字经济融合 效能全面释放，数字经济核心产业增加值年均增速超过10%，数字经济核心产业增加值占GDP比重稳步 提升。 根据《实施意见》，山东将优化数字基础设施建设布局，筑牢数字经济发展底座。持续推进网络设施建 设。高标准建设青岛国际通信业务出入口局。加快高速数据传输网建设，部署200G/400G超大容量光传 输系统。推动重点场所、行政村、近海重点航路实现5G覆盖。前瞻布局6G、卫星互联网、量子信息等 新型网络设施，推动低空互联网建设。科学布局算力设施建设。加快建设全省一体化算力网络，重点打 造济南、青岛、枣庄3个算力枢纽，推动70%以上新增算力向枢纽集聚。 此外，《实施意见》提出，推进实体经济数智变革，激发传统产业高质量发展活力。加速农业数字化转 型，2025年底前，打造100个以上智慧农场、智慧牧场、智慧渔场等应用场景。加速工业数字化转型。 深 ...

策 略 研 究 投资要点： 风险提示：增长政策落地进度不及预期，国内经济修复不及预期。 年内继续看好港股的三大理由 [Table_Authors] 本报告导读： ①近期 AH 溢价创近六年新低反映港股市场流动性无虞，近期股指走弱主要受互联 网权重板块结构性拖累。②展望未来，我们认为年内需重视港股三大因素：AI 领域 技术突破催化科技成长、美联储降息背景下外资可能超预期，南下增配力量仍有较 大空间。③受益于资产稀缺性优势，港股市场望持续吸引增量资金入市助推行情向 上，结构上重视本轮产业周期中弹性更大的恒生科技。 [Table_Report] 策略研究 /[Table_Date] 2025.08.23 2025-08-24 请务必阅读正文之后的免责条款部分 策 略 研 究 海 外 证 券 研 究 报 告 [Table_Summary] 6 月以来港股指数表现弱于 A 股，但 AH 溢价指数创近六年新低。6 月中旬以来在 A 股指数不断向上创新高之际，港股行情表现却偏震 荡。尽管港股指数表现弱于 A 股，但我们观察到恒生 AH 股溢价指 数较 6/19 的 131.54 进一步下行至 8/15 的低点 122.6， ...

本周A股开启狂飙态势，科创50一周暴涨超13%。电子板块市值首超银行，问鼎A股"第一板块"。 寒武纪单月猛涨75%，剑指"A股第一高价股"宝座，机构高喊：对标茅台，冲击万亿！ 资金正以前所未有的速度涌入市场，"存款搬家"规模或超10万亿。 但狂欢中依旧分化十分明显：选对主线，你就是牛市；选错板块，可能满仓踏空。 下一站，国产算力与AI应用能否接棒领跑？这场史诗级人工智能大行情，谁将率先撞线万亿市值？ 一周交易数据：寒武纪冲"王" 本周上证指数+3.49%，深证成指+4.57%，创业板指+5.85%，科创50指数+13.31%，北证50指数 +8.40%。 本周分段市值涨幅排行榜 1、500亿市值以上公司本周涨幅TOP5 | 排序 | 证券代码 | 证券名称 | 周涨跌幅 (%) | 收盘价 | 总市值 | | --- | --- | --- | --- | --- | --- | | | | | | (元) | (亿元) | | 1 | 688702. SH | 盛科通信 | 43. 95 | 130.00 | 533.00 | | 2 | 688521. SH | 芯原股份 | 41. 79 | 149.8 ...

近期，关于生成式AI投资回报的讨论已成为产业界的核心议题 。当以大模型为代表的AI走向产业腹 地，一个关键挑战也随之浮现： 如何跨越其巨大的技术潜力与真实的商业价值之间的鸿沟？ 金融业是数字化转型的先锋。在人工智能+浪潮中，金融机构如何走好大模型落地的最后一公里？ 带着这一关切，腾讯研究院与毕马威企业咨询基于对金融机构的深度调研与前沿实践分析，联合撰写了 《2025金融业大模型应用报告》。报告的核心观点是，当前AI应用的关键，并非陷入"为了AI而AI"的技 术竞赛，而是要回归技术服务商业的本质——以投入产出比 （ ROI ） 为标尺，校准应用范式，优化落地 路径。 事实上，穿透喧嚣，一场由大模型驱动的、真正以ROI为导向的生产力革命，早已在金融业的头部机构 中悄然发生： 这远非零散试点或工具集成所能企及，它要求我们像建设工业时代的电网、信息时代的光缆一样，进行 系统性的规划与投入。这不仅是一场技术革命，更是一场涵盖数据基建、组织形态、信任机制乃至社会 伦理的全维度重构。 信审效率变革：一家领先大行将过去需要数小时甚至数天完成的复杂信贷审批报告分析压缩至3分 钟，准确率提升超15%； 投研能力破壁：一家头部券商 ...

Group 1 - The article announces the launch of the 2026 global campus recruitment for the Shanghai Artificial Intelligence Laboratory, offering over 100 positions [1] - The laboratory seeks individuals who are not only skilled in algorithms but also excel in complex engineering and are eager to validate technology in real-world scenarios [3] - Candidates are encouraged to pursue challenging and innovative research, focusing on fundamental issues rather than settling for easy achievements [3] Group 2 - The recruitment is targeted at graduates from January 2025 to October 2026, with specific categories for "Dream New Stars," "Academic New Stars," "Engineering New Stars," and "Competition New Stars" [4] - There are six categories of positions available, including algorithm, research and development, product, operations, solutions, and functional/support roles [6][7] - The application process includes online submissions starting from August 20, 2025, followed by a series of written tests and interviews [10][11] Group 3 - The laboratory provides a top-tier research platform with extensive computational resources and data support, encouraging candidates to engage in scalable and impactful projects [12][13] - Candidates can apply by scanning a QR code or contacting the provided assistant for any issues during the application process [14]

总量双周报：慢牛行情逐步强化 2025 年 8 月 21 日 总量双周报 专题报告 | 分析师 | 林阳 电话：021-65465572 邮箱：linyang@dxzq.net.cn | 执业证书编号：S1480524080001 | | --- | --- | --- | | 分析师 | 康明怡 电话：021-25102911 邮箱：kangmy@dxzq.net.cn | 执业证书编号：S1480519090001 | | 分析师 | 林瑾璐 电话：021-25102905 邮箱：linjl@dxzq.net.cn | 执业证书编号：S1480519070002 | | 分析师 | 田馨宇 电话：010-66554013 邮箱：tianxy@dxzq.net.cn | 执业证书编号：S1480521070003 | | 分析师 | 刘嘉玮 电话：010-66554043 邮箱：liujw_yjs@dxzq.net.cn | 执业证书编号：S1480519050001 | 主要观点： 宏观：7 月 CPI 数据进一步印证前期消费数据企稳回升的有效性。自今年 2 月以来，7 月 CPI 同比（0.1%，前值 ...

Core Viewpoint - The main challenge for AI applications in the financial industry is not the technology itself, but the internal digital transformation readiness of institutions, particularly in data governance and the deep integration of business processes [1][10]. Group 1: Current State of AI Applications and Industry Opportunities - AI is penetrating the entire process of wealth management, from customer acquisition to post-investment services, with many institutions focusing on internal efficiency improvements [9]. - AI technologies are enhancing content creation and user experience in the financial sector, with tools like digital humans and large models optimizing content consumption [9]. - The transformation driven by AI is evident in the creation of personalized recommendations based on deep analysis of customer behavior data [9][10]. Group 2: Overcoming Native Barriers in AI Applications in Finance - The low tolerance for error in the financial industry necessitates a human-in-the-loop approach, where AI serves as a tool to enhance efficiency but human oversight remains essential [13]. - Key challenges include ensuring data security, achieving high accuracy in wealth management queries, and leveraging the data-intensive nature of the financial sector for AI applications [13]. Group 3: Enhancing Investor Experience through AI - AI can effectively correct irrational investment behaviors and provide continuous support to clients, enhancing trust and emotional reassurance [15]. - As AI models evolve, they are expected to take on more tasks traditionally performed by humans, although the journey towards full automation remains lengthy [15].

Core Insights - Financial institutions are actively embracing large models to drive business model innovation and efficiency improvements in the context of the accelerating digital economy [1] - The 2025 Government Work Report supports the widespread application of large models, indicating a favorable policy environment for regional financial institutions to leverage this technological transformation for high-quality development [1] Transformation Drivers - The digital transformation of regional banks is driven by three main factors: changing customer demands for personalized and scenario-based financial services, the rapid expansion of fintech companies increasing market competition, and the necessity for commercial banks to reduce costs and enhance risk management [2] Application of Large Models - Several financial institutions are exploring the application of large models, which deepen the integration of business and technology. Departments with stronger technical foundations are proactively exploring intelligent applications, while those with weaker capabilities seek technological empowerment [3] Investment in Technology - Chengdu Rural Commercial Bank is committed to advancing large model applications, investing over 1.1 billion yuan in a new generation of information technology projects, which has led to an asset scale exceeding 480 billion yuan and annual revenue and profit growth rates of over 30% and 50%, respectively [4] Challenges in Digital Transformation - Regional banks face multiple challenges in their digital transformation, including limited development space due to competition from state-owned banks, high operational costs, weaker risk control capabilities, and a shortage of composite talents. Their average technology investment is only 2.1% of revenue, significantly lower than the 4.3% for state-owned banks [5] Future Innovation Paths - The application of AI in regional banks should focus on three main strategies: integrating large models with data analysis to reduce the workload of customer managers, developing lightweight models to lower resource consumption, and enhancing IT application efficiency through AI [8] - The regional economic environment significantly impacts financial institutions, and the ability of new large model technologies to reshape competitive dynamics remains to be validated [8]

Core Viewpoints - The rapid evolution of large models and intelligent agents is ushering in a new phase of intelligent upgrades across various aspects of the financial industry, including marketing, risk control, operations, compliance, and system support [2][3] - The upcoming AICon Global Artificial Intelligence Development and Application Conference will focus on innovative practices of large models in the financial sector, particularly in investment research, intelligent risk control, and compliance review [3] - The integration of large and small models is currently the main solution in the financial industry, as small models still play a crucial role in execution efficiency and problem-solving [3][10] Summary by Sections AI Project Evaluation - When evaluating an AI project, key considerations include identifying suitable application scenarios, verifying technical paths and implementation forms, and assessing ROI throughout the development and deployment process [5][6] - The focus should be on finding pain points in small scenarios and ensuring that the necessary conditions for end-to-end implementation are met [5] Application of Intelligent Agents - Intelligent agents are being utilized in various financial business scenarios, such as data insights, due diligence, and investment advisory, but face challenges due to the immaturity of foundational models and tools [3][7] - The combination of agents and large models is seen as beneficial, particularly in internal services, while external services require careful evaluation of compliance and ROI [6][7] Challenges in Implementation - Major challenges include the performance drop of large models when deployed locally, the high hardware costs associated with private deployment, and the difficulty for business personnel to accurately express requirements for workflow construction [26][27] - The sensitivity of large models to their operating environment poses significant challenges, as even minor changes can lead to inconsistent outputs [27][28] Future Directions - The future of intelligent agents in finance may involve the development of dynamic defense capabilities against AI-driven attacks and the establishment of an intelligent agent alliance for risk control across the industry [32][34] - There is a need for collaboration between traditional AI and large models to address specific financial scenarios, ensuring compliance and data quality while managing computational resources effectively [35][36]