证券研究报告|行业研究周报 [Table_Date] 2026 年 1 月 11 日 [Table_Title] 腾讯推出 AI 小程序成长计划，监管启动外卖 反垄断调查 [Table_Title2] 传媒行业周报系列 2026 年第 2 周 [Table_Summary] ►市场行情回顾 2026 年第2周（实际交易日为 2026.1.5-2026.1.9）上证指 数上涨 3.82%，沪深 300 指数上涨 2.79%，创业板指数上涨 3.89%。恒生指数下跌0.27%，恒生互联网指数下跌0.41%， 行业落后恒生指数 0.13pct。SW 传媒指数上涨 13.1%，领先 创业板指数 9.21pct，在申万一级 31 个行业涨跌幅排名中 位列第 3 位。子行业中出版、广电和互联网服务位列前三 位，分别为上涨 19.4%、上涨 14.8%和上涨 14.05%。 ►核心观点&投资建议 腾讯推出 AI 小程序成长计划，通过平台资源整合加速 AI 应用生态落地。据微信官网，微信于 1 月 5 日正式推出 AI 应用及线上工具小程序成长计划，为开发者提供为期一年 的云开发资源、免费 AI 算力、数据分析、流量激励及更 ...

核心观点： 机器人：景气度加速向上，马斯克宣布第三代机器人设计定型，发布在即。 全球首款全身力控小尺寸人形机器人"上纬启元 Q1"正式亮相。上纬新材发布全球首款全身力控小尺寸人形机器人 "上纬启元 Q1"，采用轻量化路线，通过工程化压缩体型与重量，实现"可打包携带"的产品形态。公司公告显示， 该业务专注于个人与家庭场景，未面向工商业领域，目前仍处于产品开发阶段；对于极客和创作者群体，Q1 因体型小、 结构开放，可适配智元灵创平台及开发接口，成为可反复调试改造的载体。 腾讯 Robotics X 实验室与宇树科技达成战略协作。前者通过 Tairos（钛螺丝）具身智能大模型，为宇树机器人本体 提供人机交互友好的导游导览导购服务支持，共同打造行业应用标杆。Robotics X 实验室以 SDK 和云服务形式输出 规划大模型与多模态感知模型，由宇树科技及生态企业完成系统集成与多场景落地交付。 征和工业发布链式灵巧手"臻手・CHOHO Hand"。征和工业在 "具身智能灵巧手发展开年论坛暨全球首创链式灵巧 手发布会"上，发布链式灵巧手"臻手・CHOHO Hand"，现场展示其单手 40kg 的承重能力。该产品兼具高承重 ...

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The coffee industry remains highly prosperous with brands actively opening new stores, although there is a caution regarding short-term data volatility due to the seasonal downturn [3] - The tea beverage sector is under slight pressure as it enters the off-season, with a trend of subsidy reductions expected despite the government's investigation into delivery platforms [3] - The e-commerce sector continues to face challenges, showing lackluster performance due to the domestic consumption environment [3] - Music streaming platforms are highlighted as valuable internet assets driven by domestic demand, with a recommendation to focus on music subscription platforms [3] - The virtual asset and trading platform market is experiencing limited catalysts, with cryptocurrency prices remaining volatile [3] - The automotive service sector is seeing expansion, with new 4S stores being opened by Zhongsheng Group in various cities [3] - The internet healthcare sector is gaining attention with OpenAI's launch of "ChatGPT Health," suggesting a focus on this area [3] - The AI and cloud sectors are viewed positively, with recommendations to monitor leading tech companies with strong cash flows [3] - The media sector is showing signs of recovery, with new games performing well and user growth in the gaming segment [3] Summary by Sections 1.1 Consumer & Internet - The Hang Seng non-essential consumer index decreased by 0.98%, underperforming the Hang Seng index by 0.57 percentage points [8] - Notable stock performances include: Gu Ming (+8.72%), Ba Wang Tea (+6.99%), and Luckin Coffee (-6.47%) [8][10] 1.2 Platform & Technology 1.2.1 Streaming Platforms - The Hang Seng media index increased by 3.22%, outperforming both the Hang Seng index and the technology index [15] - Key stock performances include: iQIYI (+0.99%) and Tencent Music (-2.86%) [15] 1.2.2 Virtual Assets & Internet Brokers - As of January 9, the global cryptocurrency market cap reached $319.54 billion, up 3.40% [22] - Bitcoin and Ethereum prices were $90,505 and $3,083.14, reflecting changes of +0.6% and -1.2% respectively [22] 1.2.3 Automotive Services - The Hang Seng composite index rose by 0.38%, with notable stock performances including Advance Auto Parts (+12.73%) [31] 1.2.4 O2O - The Hang Seng internet technology index decreased by 0.27%, with key stock performances such as JD Health (+13.31%) and Didi Global (-7.19%) [37] 1.2.5 AI & Cloud - The Nasdaq internet index increased by 1.59%, with Amazon (+9.22%) and Google (+4.26%) showing strong performances [39] 1.3 Media - The Shenwan一级传媒 index increased by 13.14%, with the advertising and marketing sector showing the largest gains [46] - Key stock performances include: Xindong Company (+18.50%) and Tencent Holdings (-1.93%) [46]

Investment Rating - The industry investment rating is "Positive" and maintained [7] Core Insights - The new game cycle in January continues with several products like "Duck Duck Goose" and "Heart Town" launching successfully. The trend of Chinese games going overseas remains strong, with leading companies like Dd and others showing impressive performance. The industry is expected to see a series of new game launches, leading to continuous catalysts [2][4] - The gaming sector's product cycle in 2026 shows strong sustainability and performance certainty, indicating room for valuation improvement. It is recommended to continue focusing on investment opportunities in the gaming sector, with relevant companies including Giant Network, Kaiying Network, Perfect World, 37 Interactive, G-bits, Yaoji Technology, Shengtian Network, Tencent Holdings, and Xindong Company [2][4] Summary by Sections New Game Launches - The January new game cycle has seen successful launches, including "Duck Duck Goose," which has gained significant popularity, and "Heart Town," which topped the global free charts in over 50 regions during its pre-download phase [2][4] - The performance of these new games validates the importance of global expansion and social interaction as growth engines in the gaming industry, with a strategy of "evergreen games + globalization" becoming key for leading companies [10] Overseas Market Performance - The overseas gaming market continues to thrive, with Dd's "Whiteout Survival" achieving over $4 billion in global revenue by December 2025. Other games like "Tasty Travels: Merge Game" and "Truck Star" have also shown strong performance in the overseas market [10] Future Game Releases - Upcoming game releases include Tencent's "Counter-Strike: Future" on January 13, "Rock Kingdom: World" on March 26, and several others from various companies, indicating a busy launch schedule that may catalyze further industry growth [10]

Core Insights - The competition in large models has shifted from "Chat" to "Agent," focusing on executing complex tasks in real environments rather than just scoring on leaderboards. The industry anticipates 2026 as the year when commercial value will be realized, with a technological evolution towards verifiable reinforcement learning (RLVR) [2][4][5]. Group 1: Competition Landscape - The engineering challenges of the Chat era have largely been resolved, and future success will depend on the ability to complete complex, long-chain real tasks. The core value of AI is transitioning from "providing information" to "delivering productivity" [4]. - The bottleneck for Agents lies not in cognitive depth but in environmental feedback. Future training paradigms will shift from manual labeling to RLVR, enabling models to self-iterate in systems with clear right or wrong judgments [5][6]. - The industry consensus suggests that while China has a high chance of catching up in the old paradigm (engineering replication, local optimization, toC applications), its probability of leading in new paradigms (underlying architecture innovation, long-term memory) is likely below 20% due to significant differences in computational resource allocation [5][11]. Group 2: Strategic Opportunities - Opportunities for catching up lie in two variables: the global shift towards "intelligent efficiency" as scaling laws encounter diminishing returns, and the potential paradigm shift driven by academia around 2026 as computational conditions improve [5][19]. - The ultimate variable for success is not leaderboard scores but the tolerance for uncertainty. True advancement depends on the willingness to invest resources in uncertain but potentially transformative new paradigms rather than merely chasing scores in the old paradigm [5][10]. Group 3: Perspectives from Industry Leaders - Industry leaders express cautious optimism regarding China's potential to lead, with probabilities of success varying. For instance, Lin Junyang estimates a 20% chance of leading due to structural differences in computational resource allocation and usage [11][12]. - Tang Jie acknowledges the existing gap in enterprise AI lab research but bets on a paradigm shift occurring around 2026, driven by improved academic participation and the emergence of new algorithms and training paradigms [15][19]. - Yang Qiang believes that China may excel in toC applications first, drawing parallels to the internet history, while emphasizing the need for China to develop its own toB solutions to bridge existing gaps [20][24]. Group 4: Technological Innovations - The future of AI will require advancements in multi-modal capabilities, memory structures, and self-reflective abilities, which are essential for achieving higher levels of intelligence and functionality [68][70][73]. - The introduction of new optimization techniques, such as the MUON optimizer, aims to enhance token efficiency and long-context processing, which are critical for the performance of agent-based models [110][116]. - The development of linear attention mechanisms is expected to improve efficiency and performance in long-context tasks, addressing the limitations of traditional attention models [116]. Group 5: Future Directions - The industry is focused on distinguishing between scaling known paths through data and computational increases and exploring unknown paths to discover new paradigms [98][99]. - The potential for AI to participate in scientific research is anticipated to expand significantly, opening new possibilities for innovation and application [101].

Core Viewpoint - The article discusses the challenges faced by input method applications in the era of AI, highlighting user frustrations with accuracy and excessive advertisements, despite significant investments from major tech companies in enhancing these tools [2][4][14]. Group 1: User Experience Issues - Users are increasingly dissatisfied with input methods, reporting issues such as inaccurate word predictions and excessive advertisements, which detract from the overall user experience [2][4]. - A specific case is mentioned where a long-time user of Sogou Input Method uninstalled the app due to frequent incorrect suggestions, indicating a decline in basic functionality despite advanced AI features [4]. - Complaints about the voice recognition capabilities of input methods have also surfaced, with users noting that corrections often take longer than typing the text directly [4]. Group 2: AI Integration and Competition - Major input method providers, including Sogou, Baidu, and iFlytek, are engaged in a competitive race to integrate advanced AI capabilities into their products, aiming to enhance user experience and functionality [2][8][9]. - The input method market is characterized by a concentrated structure, with leading companies holding a combined market share of 84.4% as of July 2025, indicating a competitive landscape [8]. - Input methods are evolving from simple typing tools to becoming the primary interface for AI interactions, with companies aiming to position their products as essential gateways to AI capabilities [9][10]. Group 3: Commercialization and Privacy Concerns - Input methods face challenges in monetization, struggling with a "high traffic, low value" dilemma, which complicates their ability to generate revenue despite having a large user base [15][16]. - Privacy concerns are paramount, as input methods have been criticized for collecting unnecessary personal information, leading to regulatory scrutiny and the need for companies to adapt their data collection practices [16]. - Companies are implementing features that allow users to choose between different modes of data collection, balancing functionality with privacy protection [16]. Group 4: Future Directions - The future of input methods is seen as a shift towards becoming intelligent agents that can understand user intent and context, moving beyond basic text input to more complex interactions [12]. - Companies are exploring multi-modal input methods that incorporate voice, text, and images, which require sophisticated algorithms and technology to manage effectively [17].

Core Viewpoint - The article discusses a newly highlighted feature of WeChat Pay that allows users to receive reminders about lost items from merchants, which has sparked significant online discussion and interest among users [1][4]. Group 1: WeChat Pay Lost Item Reminder Feature - Users have recently discovered that WeChat Pay has a hidden feature that sends reminders about lost items after transactions at certain merchants [1]. - Some users were already aware of this feature, while others expressed surprise at its existence [4]. - Tencent's customer service indicated that this feature is only supported by select merchants, and personal information is not visible to merchants; only transaction details are shared [6]. Group 2: Other WeChat Features - WeChat offers various practical features that users may not be aware of, such as editing Excel documents directly from images received on mobile devices [7][9]. - Users can create personal chat groups to organize files better, which can be done by initiating a group chat with a unique four-digit code [11][13]. - The "File Transfer Assistant" can be used for searching specific content by entering a "" followed by the search term [14]. - Users can now withdraw all messages sent in a single action, enhancing message management [15]. - In group chats, users can selectively receive important notifications even when "Do Not Disturb" mode is activated [17].

Core Insights - A high-profile dialogue on AI took place in Beijing, featuring leading figures in China's large model sector, indicating a significant moment for the industry [1][2][15] - The discussion focused on the evolution of AGI, with a consensus that the future lies in autonomous learning and problem-solving capabilities [3][4][17] Group 1: Key Figures and Their Contributions - Tang Jie, a professor at Tsinghua University and founder of Zhipu AI, recently led the company to become "China's first stock in foundational models" [1][15] - Yao Shunyu, a former OpenAI researcher and now Tencent's chief scientist, emphasized the importance of autonomous learning in AGI's future [4][18] - Lin Junyang, head of Alibaba's Tongyi Qianwen model, discussed the need for models to evolve beyond general-purpose tools to specialized applications [7][21] Group 2: Future Directions in AGI - The next "singularity" in large models is expected to focus on autonomous learning, moving beyond passive responses to proactive decision-making [3][17] - Yao Shunyu highlighted that autonomous learning is a gradual process driven by data and task evolution, with current models already showing signs of self-optimization [4][18] - Concerns about the risks of autonomous AI were raised, emphasizing the need for proper guidance in AI development [3][17] Group 3: Scaling Law and Efficiency - The Scaling Law, which posits that increasing data and computational power leads to better model performance, is facing diminishing returns, prompting a shift towards "Intelligence Efficiency" [5][19] - Tang Jie proposed that future advancements should focus on achieving higher intelligence with less computational investment [5][19] - Yao Shunyu noted that improvements in model architecture and optimization are crucial for enhancing model performance beyond mere scaling [6][20] Group 4: Model Differentiation - The conference highlighted the trend of model differentiation, where models are tailored to specific scenarios rather than being one-size-fits-all solutions [7][21] - Yao Shunyu pointed out that in B2B contexts, strong models can significantly reduce operational costs, while in B2C, the focus should be on contextual understanding [8][22] - Lin Junyang emphasized the importance of integrating models with real-time user environments for better performance in consumer applications [8][22] Group 5: The Future of AI Agents - There is widespread optimism about the potential of AI agents to automate tasks, particularly in B2B settings, though challenges remain in B2C applications [11][25] - The development of agents is seen as a multi-stage process, with current models still reliant on human-defined goals [12][26] - The future of agents may involve more interaction with the physical world, enhancing their utility and effectiveness [11][25] Group 6: Competitive Landscape and Innovation - The dialogue acknowledged the existing gap between Chinese and American AI capabilities, with a consensus on the need for innovation to bridge this divide [12][26][28] - Yao Shunyu emphasized the importance of breakthroughs in computational power and market maturity for China's AI future [13][27] - Tang Jie identified opportunities for China to excel in AI through a culture of risk-taking and innovation among younger generations [14][28]

Core Insights - The AGI-Next summit highlighted the challenges and opportunities for Chinese large model companies, featuring prominent figures in AI discussing new paradigms and advancements in technology [2][4]. Group 1: AI Market Dynamics - The Chinese large model market is showing significant differentiation between To C (consumer) and To B (business) segments, with distinct underlying logic for each [4]. - In the To C market, most users do not require high intelligence from models, leading to a trend of vertical integration where model and application layers are closely coupled for better user experience [4][5]. - Conversely, in the To B market, higher intelligence correlates with increased productivity and willingness to pay, creating a head effect where top models command higher subscription fees [5][6]. Group 2: Future AI Paradigms - The next generation of AI is expected to focus on context capture rather than just model parameter competition, emphasizing the importance of understanding user context for better responses [5]. - There is a belief that signals of autonomous learning will emerge by 2025, although current attempts lack the pre-training capabilities seen in leading companies like OpenAI [8]. - The potential for AI to evolve autonomously and act proactively is seen as a key feature of future paradigms, though it raises significant safety concerns [9]. Group 3: Technological Advancements - Memory technology is anticipated to develop linearly, with breakthroughs expected in the near future as algorithms and infrastructure improve [10]. - The gap between academia and industry in large model development is narrowing, with more academic institutions gaining access to computational resources, fostering innovation [11]. - The industry faces efficiency bottlenecks, with the need to achieve greater intelligence with less investment becoming a driving force for new paradigms [11]. Group 4: AI Agent Development - The evolution of AI Agents is seen as a critical change for the AI industry by 2026, moving from human-defined goals to AI autonomously defining objectives [13]. - The ability of AI Agents to address long-tail problems is highlighted as a significant value proposition for AGI [13]. - The commercialization of AI Agents faces challenges related to value, cost, and speed, necessitating a balance between solving real human issues and managing operational costs [14].

Core Insights - The AGI-Next summit gathered prominent figures in AI, discussing new paradigms, challenges, and opportunities for Chinese large model companies [1] - Yao Shunyu, Tencent's Chief AI Scientist, highlighted the distinct characteristics of the To C and To B markets in the AI landscape [5][6] Group 1: Market Dynamics - Yao Shunyu noted that the To C market does not require high intelligence most of the time, with applications like ChatGPT serving as enhanced search engines [5] - In contrast, the To B market shows a willingness to pay significantly for top-tier models, with companies willing to pay $200/month for premium models, while interest in lower-tier models is minimal [5] - The disparity in model performance is expected to widen, as weaker models incur hidden costs in enterprise settings due to the need for manual error checking [5] Group 2: Technological Evolution - Yao emphasized that future competitiveness will hinge on capturing context rather than merely increasing model parameters, as better responses depend on understanding user preferences and real-time data [6] - The development of autonomous learning is underway, with some teams using real-time user data for training, although significant breakthroughs are yet to be realized due to a lack of pre-training capabilities [7] - Lin Junyang pointed out that the potential of reinforcement learning (RL) remains untapped, and achieving AI's proactive capabilities poses safety risks that need careful management [9] Group 3: Future Paradigms - Tang Jie expressed optimism about the emergence of new paradigms driven by continuous learning and memory technologies, as the gap between academia and industry narrows [10][11] - The industry faces efficiency bottlenecks, with data scales increasing from 10TB to 30TB, yet the returns on investment are diminishing, necessitating a focus on "intelligence efficiency" [10] - The evolution of AI agents is seen as a critical change, with the potential for models to autonomously define goals and plans, moving beyond human-defined parameters [13] Group 4: Commercialization Challenges - The commercialization of AI agents faces challenges related to value, cost, and speed, with a need to ensure that agents address meaningful human tasks without incurring prohibitive costs [14]