杜晓宇 陈楚仪 腾讯金融研究院 在实践推进过程中，金融机构普遍遵循三项原则，目标指向提质增效。一是风险可控优先。聚焦幻觉风险 可控、信息边界清晰的场景开展应用，强化风险前置识别与防护。二是内部提效优先。率先面向技术研 发、运营管理等中后台流程落地应用，便于快速验证技术成效。三是辅助决策优先。强调赋能员工而非岗 位替代，通过工具化手段提升分析判断效率。 银行业发挥头雁效应，在应用深度与广度上保持领先。银行机构正在加大资本支出和研发投入力度，发挥 其场景广泛覆盖的优势，持续夯实技术底座、保障落地成效。从短期来看，代码助手、智能问答等成熟场 景快速释放效率红利，部分机构已有超过30%的代码由AI生成；从长期来看，AI应用正向智能投顾、一线 营销赋能等核心创收领域拓展。 技术普惠正在重塑行业竞争格局，为中小金融机构提供了换道发展的窗口。随着DeepSeek、腾讯混元等高 性能模型的开源与普及，AI应用的资金和技术门槛显著降低。中小机构可以将资源聚焦于特定业务场景与 私域数据价值挖掘，形成差异化优势，其关键在于战略聚焦与组织灵活性。例如，依托决策链条短等特 点，深耕供应链金融、特定客群财富管理等垂直领域，并与领先大模型 ...

智通财经APP获悉，东方财富证券发布研报称，看好互联网科技龙头快速发展，看好传媒板块受政策边 际向好驱动的影视公司以及有丰富储备的游戏公司。互联网板块有望在AI浪潮中持续受到资金青睐。 传媒分板块来看：1)游戏板块建议关注后续产品储备丰富的公司;2)影视板块建议关注直接受益于政策驱 动和AI的相关公司;3)广告营销板块建议关注互动新场景以及程序化广告布局的公司。 东方财富证券主要观点如下： 云计算市场快速发展，国内外AI能力差距缩小 随着AI技术持续发展，云计算行业快速增长;同时，2025年AIagent开始兴起，云计算的弹性算力可支撑 复杂场景下的协同需求。规模方面，据中国信息通信研究院统计，2024年中国云计算市场未来五年仍将 保持每年20%以上增长，至2030年可达3万亿+。大模型方面，国内外模型差距逐渐缩小。国外，Google 和XAI等模型在视频生成、代码能力等多个领域能力迅速提升。国内，头部模型平台如Deepseek、阿里 Qwen、腾讯混元等均保持技术迭代，在推理能力、模型架构、上下文理解能力、AIagent等多方面实现 提升，缩小与海外公司差距。 风险提示 宏观经济增长不及预期;技术升级迭代及 ...

Group 1 - The report highlights the sustained demand for computing power, with significant growth expected in the AI infrastructure sector driven by domestic advancements in computing chips and increased capital expenditure from cloud service providers [2][3]. - The domestic AI infrastructure is anticipated to experience rapid growth by 2026, following a slight dip in expectations due to external factors such as the ban on NVIDIA chips [2]. - The report emphasizes the structural alpha opportunities within the industry, particularly in segments like optical modules, liquid cooling, switches, and power supplies, as the demand for AI computing continues to rise [2]. Group 2 - The report indicates that the terminal AI market is on the verge of significant expansion, with policy support and ecosystem development expected to drive growth in 2026 [3]. - Innovations in products, such as Meta's AI glasses, are likely to accelerate the market penetration of terminal AI applications [3]. - The report suggests that the industry is transitioning from a phase of thematic catalysts to one of performance realization, with the emergence of "hit products" expected to further boost the sector [3]. Group 3 - The telecommunications sector is currently experiencing a phase of capital expenditure reduction, business restructuring, and increasing dividend payouts, which positions it favorably for investors [4]. - The report notes that the telecommunications sector has shown resilience, with profit growth outpacing revenue growth, and a stable or increasing dividend yield in a low-interest-rate environment [4]. - Emerging business areas, particularly in AI and satellite communications, are expected to contribute to a second growth curve for telecommunications companies [4]. Group 4 - The North American AI sector has seen a remarkable increase in capital expenditure, with projections indicating that spending could exceed $600 billion by 2026, driven by robust demand for AI services [12][13]. - The report outlines that the AI computing market is characterized by a dual demand for training and inference, with inference demand expected to surpass training demand in the near future [35][36]. - The report highlights the importance of energy management solutions, such as 800 VDC systems, in addressing the rising power consumption associated with AI data centers [69][70].

Core Viewpoint - The article highlights the increasing trend of major tech companies, particularly Tencent, actively recruiting talent at academic conferences like ICCV 2025, showcasing their technological advancements while simultaneously seeking to attract top talent in the AI field [6][30][36]. Group 1: Recruitment Strategies - Major tech companies are shifting from merely showcasing research achievements at conferences to directly recruiting talent, as evidenced by Tencent's significant presence at ICCV 2025 [6][30]. - Tencent's exhibition at ICCV was the second largest, featuring numerous core business leaders engaging with students and discussing technical routes and job opportunities [8][11]. - The event allowed students to interact directly with business leaders, providing a unique opportunity to discuss job openings and gain insights into Tencent's various AI initiatives [34][36]. Group 2: Technological Showcase - Tencent showcased a wide array of AI technologies across its business units, including advancements in 3D generation and video synthesis, with multiple academic papers accepted at the conference [13][21]. - The company presented its latest innovations, such as the Hunyuan 3D generation project and real-time digital human solutions, attracting significant attention from attendees [15][20]. - The presence of Tencent's top researchers at the conference facilitated engaging discussions, enhancing the visibility of their technological capabilities [27][29]. Group 3: Market Position and Future Outlook - Tencent's investment in AI research is substantial, with R&D spending projected to reach 39.16 billion RMB in the first half of 2025, reflecting a year-on-year growth of 21% and 17% in the first two quarters [43]. - The company is positioned to leverage its extensive user base and diverse business applications to convert technological advancements into market opportunities [45][46]. - The recruitment efforts at conferences like ICCV are part of a broader strategy to secure top talent, which is essential for maintaining a competitive edge in the rapidly evolving AI landscape [39][40].

鹭羽 发自 凹非寺 量子位 | 公众号 QbitAI 从5秒到 4分钟 ，Sora2也做不到的分钟级长视频生成，字节做到了！ 先来看一个前方潜水员拍摄的"真实"海底世界Vlog： 华生，有发现么？不同于一般的AI生成视频，只有短短几秒钟……这个片子全程1分40秒， 都是"水分"、都是AI 。 这就是字节和UCLA联合提出的新方法—— Self-Forcing++ ，无需更换模型架构或重新收集长视频数据集，就能轻松生成分钟级长视频，也 不会后期画质突然变糊或卡住。 通过利用教师知识和自生成视频片段指导自回归生成，最长生成视频可达 4分15秒 ，而且高质量、还开源。 话不多说，再看几个视频效果尝尝鲜。 长达3分钟的无人机视角下的海岸线，be like： 时长拉到极致，4分15秒跟随大象的脚步纵览草原美景。 而相同时长下，此前的长视频生成SOTA SkyReels 做出的效果是酱紫的： （重生之我成为一只蚂蚁） Self-Forcing++在短时长上继承了 Self-Forcing 的高质量画面效果，长时长生成也能达成性能指标All kill，视觉稳定性大幅领先 CausVid 等方法。 或许，AI电影时代离我们已 ...

Core Insights - The rapid integration of artificial intelligence (AI) into various industries is transforming China's economic landscape, with AI becoming a key driver of growth and efficiency [4][22] - The core AI industry in China surpassed 700 billion RMB in 2024, maintaining over 20% annual growth, significantly outpacing overall economic growth [4][22] - AI is being recognized as a strategic tool for enhancing productivity and facilitating deep structural transformation across traditional sectors [4][22] Group 1: AI's Impact on Industries - AI has demonstrated its effectiveness in retail, with a sales competition showing AI-driven sales outperforming human efforts by over three times [1] - The AI-driven transformation is evident in various trillion-yuan markets, including retail, finance, and logistics, reshaping efficiency and growth [8] - AI is not merely a replacement for traditional methods but acts as a catalyst for innovation and productivity in established industries [20] Group 2: Infrastructure and Technological Advancements - The foundation of AI's success lies in robust cloud computing and computational power, which are essential for its widespread application [9][10] - Major Chinese tech companies like Tencent, Alibaba, and Huawei are competing to enhance their cloud services to support AI operations [9][10] - The development of domestic large models and stable computational power is crucial for the advancement of AI applications across the country [12][22] Group 3: New Business Models and Opportunities - AI is creating new business models and industries, significantly lowering the barriers to creativity and production, as seen in the 3D printing sector [14][18] - The integration of AI in 3D printing allows users to generate high-quality models easily, marking a shift towards an AI-driven era in consumer-grade 3D printing [18] - AI's capabilities in cross-cultural understanding and content generation are opening new markets for Chinese enterprises, enhancing their global competitiveness [19] Group 4: Traditional Industries and AI Integration - AI is enhancing traditional industries by improving productivity and addressing challenges such as rising labor costs and declining capital returns [20] - Collaborations between tech firms and traditional manufacturers, such as the partnership between GAC Group and Tencent, are leading to advancements in smart manufacturing and global expansion [20][21] - In sectors like healthcare, AI is streamlining processes and improving decision-making, as demonstrated by the applications in hospitals and medical institutions [22]

Core Insights - The focus of the industry is on how companies can implement cutting-edge AI technologies into practical business scenarios for sustainable growth as the AI model technology hype returns to rationality [2] - Tencent's Senior Executive Vice President emphasized that "driving industrial efficiency through intelligence and revenue scale through globalization" are the two core drivers of corporate growth [2] Group 1: AI Infrastructure and Investment - Tencent is significantly investing in AI infrastructure, with a strong emphasis on providing comprehensive support from infrastructure to model training and inference acceleration tools [4] - The shift in the big model industry focus from training to inference has become an industry consensus, leading to a surge in inference demand [4] - Tencent has established 11 regional offices globally and deployed 9 global technical support centers, enhancing its international infrastructure investment [5] Group 2: AI Strategy and Development - Tencent aims to create "human-centered AI," with a clear positioning that embraces AI across all business sectors [5] - The company has released over 30 models in the past year, focusing on achieving stronger model performance at lower deployment and inference costs [6] - Tencent's intelligent agent strategy was officially launched, providing a comprehensive open development platform and support for various application scenarios [6] Group 3: Market Dynamics and User Engagement - The demand for intelligent agents is diverse, with small and medium enterprises seeking more commercial support products from Tencent's intelligent agent development platform [6] - Tencent's AI applications are still in the investment phase, with a focus on enhancing product and service experiences rather than immediate commercialization [7] - User inquiries to Tencent's AI applications have reached the total monthly inquiries from earlier this year, indicating growing engagement [7]

Core Insights - Tencent's AI strategy has become a fundamental part of its business model, with AI capabilities integrated across various sectors [2][10] - The company emphasizes a smarter investment approach in AI, focusing on efficiency and cost-effectiveness while serving over 1 billion users [5][6] - Tencent's AI application, Yuanbao, has gained significant traction, becoming one of the top AI native applications in China, with daily user interactions surpassing previous monthly totals [10] AI Investment Strategy - Tencent aims to balance AI investment and output, recognizing the need for substantial ongoing investment while optimizing efficiency [6] - The company has released over 30 AI models in the past year, focusing on reducing deployment and inference costs while enhancing model performance [7][8] - Tencent's historical focus on core products has led to a competitive position in the cloud market, emphasizing the importance of sustainable revenue streams [9] Yuanbao Development - Yuanbao has rapidly evolved, integrating its capabilities into various Tencent products, enhancing user interaction across platforms like WeChat [10][12] - The future development of Yuanbao is seen as a continuous process, with ongoing discussions about user experience and potential feature testing [12] Hardware and Software Integration - Tencent has fully adapted to mainstream domestic chips and is committed to optimizing its software capabilities in conjunction with hardware [14] - The company collaborates with multiple chip manufacturers to ensure the best hardware configurations for different AI models and scenarios [14]

Group 1: Google Gemini and AI Tools - Google Gemini has topped the App Store free chart, surpassing ChatGPT, due to its popular Nano Banana image editing feature [1] - Gemini is a comprehensive AI toolkit that includes Canvas, Veo3 video generation, Storybook, and Deep Research among other functionalities [1] - The Google AI suite also features NotebookLM knowledge base (allowing up to 300 file uploads), Flow video generation (supporting 1080p HD), AI Mode search, and Gemini CLI local assistant [1] Group 2: xAI's Grok 4 Fast Model - xAI has launched the Grok 4 Fast model, achieving a generation speed of 75 tokens per second, which is ten times faster than the standard version [2] - User tests indicate that the new model excels in programming and middle school math tasks, solving LeetCode problems in under 2 seconds [2] - Despite its speed advantage, Grok 4 Fast compromises on accuracy, making it suitable for simple queries or tool usage, reflecting xAI's recent focus on speed [2] Group 3: Keling AI's Digital Human - Keling AI has introduced an upgraded digital human feature that supports up to 60 seconds of output at 1080P/48fps, significantly enhancing facial recognition and lip-sync accuracy [3] - The new feature allows for prompt-based control of character emotions and actions, enabling digital humans to display richer expressions and body language [3] - Keling's digital human service is priced at 0.12 yuan per second at 720P, approximately one-third the cost of similar products from Heygen, nearing the industry's lowest price [3] Group 4: Tencent's AI Painting Upgrade - Tencent's Mix Yuan has proposed a new method to optimize AI painting, improving diffusion model training through Direct-Align and Semantic Relative Preference Optimization (SRPO) techniques [4] - Direct-Align optimizes the entire diffusion trajectory, addressing the "reward hacking" issue seen in traditional methods that only optimize later stages [4] - The FLUX1.dev model trained with SRPO has seen a threefold increase in realism and aesthetic scores, requiring only 32 H20 blocks for 10 minutes of training [4] Group 5: Albania's AI Minister - Albania has become the first country to appoint an "AI Minister," named Diella, which will oversee public procurement projects [5] - Diella aims to serve as a benchmark for government transparency reforms, responsible for evaluating tenders and selecting personnel to achieve 100% integrity in public bidding [5] - This initiative seeks to address long-standing issues of corruption in public procurement in Albania while promoting the country's digital government transformation [5] Group 6: xAI's Workforce Changes - xAI has reportedly laid off about 500 employees from its data labeling team, accounting for one-third of that team, with affected employees receiving salary payments until the end of November [6] - The company announced a strategic shift to reduce general AI mentors while expanding the professional AI mentor team by tenfold, focusing on recruiting talent from STEM, finance, and medicine [7] - Prior to the layoffs, xAI required employees to participate in tests determining their job security, leading to concerns about the fairness of the process among some employees [7] Group 7: UCLA's Energy-Efficient Imaging - A research team from UCLA has published a paper in Nature on a nearly zero-energy optical image generation model, with Shiqi Chen, a Zhejiang University alumnus, as the first author [8] - The system generates static noise using digital encoders, imprinting noise patterns onto laser beams via spatial light modulators, and then converting the noise into images with a second device [8] - This system can produce images of handwritten digits, fashion items, and Van Gogh-style artworks, making it suitable for VR, AR displays, and wearable devices due to its ultra-fast and low-energy characteristics [8] Group 8: AI Programming Challenges - A senior developer, Carla Rover, experienced significant issues with "vibe coding," leading to a project overhaul and emotional distress [9] - A report from Fastly indicates that 95% of developers require additional time to fix AI-generated code, leading to the emergence of "vibe coding cleanup specialists" with salaries reaching $100,000 [9] - Many experienced developers express that AI programming resembles "caring for a 6-year-old," lacking systematic thinking and often introducing security vulnerabilities, with 50% of their time spent on requirements and 30-40% on fixing AI code [9] Group 9: Anthropic's AI Economic Index - Anthropic has released its first comprehensive AI economic index report, revealing that the proportion of users assigning complete tasks to Claude has increased from 27% to 39% [10] - The report highlights a close correlation between AI usage and regional economic characteristics, with Washington D.C. and Utah showing the highest per capita usage, while Hawaii focuses on travel planning and Massachusetts on scientific research [10] - Data indicates that regions with higher GDP exhibit greater AI usage rates, with wealthier countries showcasing more diverse use cases, while enterprise users have an automation rate of 77%, significantly higher than that of individual users [10]

Core Viewpoint - The article discusses advancements in AI image generation, specifically focusing on the introduction of two key methods, Direct-Align and Semantic Relative Preference Optimization (SRPO), which significantly enhance the quality and aesthetic appeal of generated images [5][14]. Group 1: Current Challenges in Diffusion Models - Existing diffusion models face two main issues: limited optimization steps leading to "reward hacking," and the need for offline adjustments to the reward model for achieving good aesthetic results [4][8]. - The optimization process is constrained to the last few steps of the diffusion process due to high gradient computation costs [8]. Group 2: Direct-Align Method - Direct-Align method allows for the recovery of original images from any time step by pre-injecting noise, thus avoiding the limitations of optimizing only in later steps [5][10]. - This method enables the model to recover clear images from high noise states, addressing the gradient explosion problem during early time step backpropagation [11]. - Experiments show that even at just 5% denoising progress, Direct-Align can recover a rough structure of the image [11][19]. Group 3: Semantic Relative Preference Optimization (SRPO) - SRPO redefines rewards as text-conditioned signals, allowing for online adjustments without additional data by using positive and negative prompt words [14][16]. - The method enhances the model's ability to generate images with improved realism and aesthetic quality, achieving approximately 3.7 times and 3.1 times improvements, respectively [16]. - SRPO allows for flexible style adjustments, such as brightness and cartoon style conversion, based on the frequency of control words in the training set [16]. Group 4: Experimental Results - Comprehensive experiments on the FLUX.1-dev model demonstrate that SRPO outperforms other methods like ReFL, DRaFT, and DanceGRPO across multiple evaluation metrics [17]. - In human evaluations, the excellent rate for realism increased from 8.2% to 38.9% and for aesthetic quality from 9.8% to 40.5% after SRPO training [17][18]. - Notably, a mere 10 minutes of SRPO training allowed FLUX.1-dev to surpass the latest open-source version FLUX.1.Krea on the HPDv2 benchmark [19].