以下文章来源于Counterpoint 咨询 ，作者Counterpoint Counterpoint 咨询 . Counterpoint Research 是一家专注于科技行业的全球性研究公司，在全球主要的市场有着强大的影响 力。我们致力于为合作伙伴提供准确、及时的市场数据，帮助他们做出明智的决策。 根据 Counterpoint Research 发布的《2026 年定位平台竞争力评估报告》 ，HERE 与 TomTom 在定 位平台竞争力评估指数中脱颖而出，双双获评"先锋"。该报告基于截至 2025 年 12 月收集并分析的 数据撰写。 研究副总裁 Peter Richardson 在评论定位平台发展态势时表示："定位平台正在从传统的基础地图解 决方案，转型为 AI 驱动的数据平台，通过实时定位智能、预测性洞察以及高度个性化的体验创造 价值。同时，生成式 AI 正在利用定位数据，提供更具前瞻性与上下文感知能力的主动式服务。在 这一背景下，'搜索'与'附近'等定位感知型 AI 功能正加速走向主流，显著提升用户体验。定位智能 也将成为未来代理式 AI 体验的关键基础。未来的智能出行体验，在于 AI 驱动的定 ...

港股科网股持续走强，快手(01024.HK)涨超4%，阿里巴巴(09988.HK)涨超2%，百度(09888.HK)、哔哩 哔哩(09626.HK)等跟涨。 本文源自：金融界AI电报 ...

生成引擎优化（GEO）是从传统搜索引擎优化（SEO）演变而来的新兴技术， 旨在通过 AI 工具提高内容在搜索引擎和社交媒体平台上的曝光率。它的核心逻 辑包括了解目标客户群体、精准营销和内容分发。百度在 GEO 方面的应用已经 占据了其广告搜索业务约 40%的份额，每年营收中的 2/3 来自广告搜索业务。 百度的 GEO 业务布局可以分为三大类：技术供给链、内容服务生态和营销解决 方案。在技术供给链方面，百度提供标准化的 SDK 包和平台工具，开发者可以 利用这些工具进行二次开发和定制。在内容服务生态方面，百度通过全国 20 多个数据众包基地获取大量数据标注素材，为 AI 训练提供支持。在营销解决方 案方面，百度提供端到端的策略服务，通过词包类服务帮助客户实现精准投放。 百度如何通过技术工具链实现 GEO？ 百度通过技术工具链实现 GEO 主要依赖于 RAG 优化、大模型基模和向量数据 库检索等技术。具体来说，开发者可以使用百度提供的 SDK 包或 API 接口，将 需要优化的内容导入系统，并选择投放的平台，如 Deepseek、豆包、百度文 心等。系统会根据用户需求生成策略，通过几分钟到几十分钟的时间完成优化 ...

Group 1 - The Hang Seng Index opened up by 0.55%, and the Hang Seng Tech Index rose by 0.88%, with notable gains in tech stocks like Meituan and Baidu, both increasing over 2% [1] - Lithium stocks showed strong performance, with Ganfeng Lithium and Tianqi Lithium both rising over 4%, while the precious metals sector also strengthened, with Zijin Mining up nearly 3% and China Aluminum increasing over 2% [1] - Citic Securities anticipates a second round of valuation recovery and performance resurgence in the Hong Kong stock market by 2026, driven by internal "15th Five-Year Plan" catalysts and external economic stimulus [1] Group 2 - Zheshang International views the fundamentals of the Hong Kong stock market as still weak, with a slight decline in the funding environment, but maintains a cautiously optimistic outlook for the mid-term market trends [2] - The firm highlights sectors that are relatively prosperous and benefit from policy support, including new energy, innovative pharmaceuticals, and AI technology, as well as low-valuation state-owned enterprises [2] - The expected performance of the Hong Kong stock market in spring 2026 is projected to be driven by "AI applications, PPI improvement, and expanded domestic demand," with a recommendation to focus on quality stocks in these areas [2]

Group 1 - Anta Sports-R (82020), Tencent Holdings-R (80700), and Geely Automobile-R (80175) have the highest short-selling ratios at 100.00%, 90.92%, and 80.03% respectively [1][2] - Meituan-W (03690), Alibaba-W (09988), and Tencent Holdings (00700) lead in short-selling amounts, with 1.554 billion, 1.440 billion, and 1.253 billion respectively [1][2] - Tencent Holdings-R (80700), China Wangwang (00151), and Country Garden (02007) have the highest deviation values at 45.18%, 36.17%, and 33.66% respectively [1][2] Group 2 - The top short-selling amounts are led by Meituan-W (03690) at 1.554 billion, followed by Alibaba-W (09988) at 1.440 billion, and Tencent Holdings (00700) at 1.253 billion [2] - The top short-selling ratios include Anta Sports-R (82020) at 100.00%, Tencent Holdings-R (80700) at 90.92%, and Geely Automobile-R (80175) at 80.03% [2] - The highest short-selling deviation values are observed in Tencent Holdings-R (80700) at 45.18%, China Wangwang (00151) at 36.17%, and Country Garden (02007) at 33.66% [2][3]

Core Viewpoint - The global shared mobility market is undergoing a critical transition from human-driven to automated services, exhibiting significant regional differentiation [4][9]. North America Market - The North American ride-hailing market is dominated by Uber and Lyft, creating a stable pricing power. In the Robotaxi sector, Waymo holds a monopoly while Tesla aggressively disrupts the market. Chinese Robotaxi companies face barriers due to a 2025 U.S. Department of Commerce ban on hardware and software, complicating their commercialization path [4][9][16]. European Market - The European regulatory environment is fragmented and stringent, with local automakers lagging in L4 algorithm development. This creates a unique "hybrid model" opportunity, where "U.S./local platforms + Chinese technology" could break through. Uber and Lyft's collaboration with Baidu Apollo indicates that de-branding technology output is a favorable solution for entering the European market [4][9][16]. Middle East Market - The Middle East presents a unique "three highs and one low" characteristic: high customer spending, high policy support, high infrastructure investment, and low energy costs. Gulf countries are eager to reduce oil dependency, viewing autonomous driving as a national strategy. Chinese companies like WeRide and Pony.ai benefit from dual advantages of road rights and licenses, making it an ideal training ground and commercialization area for overseas expansion [4][9][16]. Southeast Asia Market - The Southeast Asian ride-hailing market is large but has low customer spending. Low labor costs may lead to economic challenges for Robotaxi operations. In the short term, large-scale deployment of Robotaxis is not cost-effective, and two-wheeled vehicles remain mainstream. Singapore, with its high labor costs, may achieve Robotaxi commercialization [4][5][9]. Investment Focus - Focus on the L4 RoboX industry chain, prioritizing B-end software over C-end hardware. Recommended stocks include: - Hong Kong stocks: Xpeng Motors, Horizon Robotics, Pony.ai, WeRide, Cao Cao Mobility, and Black Sesame Technology - A-shares: Qianli Technology, Desay SV, and Jingwei Hirain - Downstream application-related stocks from the Robotaxi perspective include integrated models (Tesla, Xpeng Motors), technology providers with revenue-sharing models (Horizon, Baidu, Pony.ai, WeRide, Qianli Technology), and the transformation of ride-hailing/taxi services (Didi, Cao Cao Mobility, Ruqi Mobility, Dazhong Transportation, Jinjiang Online) [6][9]. Regulatory and Market Barriers - The regulatory landscape for Robotaxis abroad features a dual approach of support and regulation. Companies must assume clear accident liability and purchase sufficient liability insurance. Vehicles must have complete data recording capabilities and undergo third-party safety assessments. Operationally, there are restrictions on operational areas, fleet size, and speed [12][14]. Market Size and Growth - The North American shared mobility market is projected to grow significantly, with the total Gross Transaction Value (GTV) expected to reach billions by 2030. The European market also shows substantial potential, albeit with slower conversion rates. The Middle East is characterized by strong government support, while Southeast Asia presents a high-growth potential due to infrastructure gaps [21][22][27]. Pricing Dynamics - Pricing dynamics vary significantly across regions, influenced by local labor costs and regulatory environments. North America has high labor costs, allowing Robotaxis to survive without extreme price reductions. In contrast, Europe faces stringent labor protections that increase operational costs. The Middle East's pricing is shaped by government-led transportation strategies, while Southeast Asia's ultra-low fares are supported by low labor costs [33][34]. Profitability Disparities - Profitability varies significantly across countries, with developed regions showing higher absolute margins per Robotaxi. Revenue per vehicle in China, UAE, UK, and the US is estimated at approximately $40,000, $90,000, $250,000, and $250,000 respectively, with gross margins reflecting these disparities [34][35].

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The competition in AI entry points is intensifying, with major companies increasing their investments. China's AI presence globally has significantly improved, with domestic large models continuously iterating. Despite GPT-5.2 and Gemini 3 Pro leading, Chinese models have effectively altered the North American dominance in the competitive landscape. In the global Top 10, three positions are held by Chinese models, and in the Top 15, there are six Chinese companies. By 2025, China's open-source AI model usage is expected to account for over 70% of the global market [2][11][19] - The demand for inference has surged, with the emergence of o1 class inference models unlocking approximately 10 times the potential of traditional models in terms of inference-time compute. The demand for computing power has shifted from being solely "training-driven" to a dual focus on "training + inference" [2][5][37] - The battle for entry points has evolved beyond mobile devices to OS-level intelligent agents and super apps. By December 24, 2025, ByteDance's AI application Doubao announced daily active users (DAU) exceeding 100 million, while Qianwen App reached over 30 million monthly active users within 23 days of public testing, becoming the fastest-growing AI application globally. Doubao bypasses traditional interfaces, creating an "AI operating system" that directly interacts with super apps like WeChat and Alipay, challenging the rules of the traditional app era [2][44][45] Summary by Sections AI Entry Point Competition - China's AI global presence has significantly improved, with domestic large models continuously iterating. In the global Top 10, three positions are held by Chinese models, and in the Top 15, there are six Chinese companies. By 2025, China's open-source AI model usage is expected to account for over 70% of the global market [2][11][19] - The competition for entry points has evolved beyond mobile devices to OS-level intelligent agents and super apps, with significant user engagement reported for new AI applications [2][44][45] Domestic Chip Breakthroughs - The smart computing center in China is expanding, with a projected compound annual growth rate (CAGR) of 57% from 2020 to 2028, reaching 2,781.9 EFLOPS by 2028. Domestic chip technology is steadily improving, with local cloud service providers accelerating the construction of heterogeneous environments [5][50] - Domestic general-purpose GPUs are upgrading from "usable" to "good," with performance metrics approaching those of leading international models. The production capacity of domestic chip manufacturers like SMIC is continuously increasing, providing solid support for domestic AI chip production [5][53][54] Supply and Demand Dynamics - The demand side is characterized by a surge in inference demand as AI applications become more prevalent, while the supply side sees continuous improvements in domestic GPU performance and accelerated adaptation by cloud service providers [5][59] - The AI server market is expected to see a shift towards inference servers becoming the mainstream, with a projected market size of approximately $39.3 billion in 2024, reflecting a year-on-year growth of 49.7% [5][64]

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 Insights - The article discusses the challenges faced by input method applications, particularly focusing on Sogou Input Method, as it approaches its 20th anniversary in 2026. Despite advancements in AI capabilities, user experience has deteriorated, leading to complaints about inefficiency and inaccuracies in word prediction and voice recognition [2][3][5]. User Experience Issues - Users express frustration over the declining accuracy of input methods, with complaints about incorrect word suggestions and excessive advertisements disrupting their experience [3][5][6]. - A long-time user of Sogou Input Method reported issues with common character suggestions, indicating a failure to learn user habits despite repeated corrections [4][5]. AI Integration and Competition - Major input method companies, including Tencent's Sogou, Baidu, and iFlytek, are engaged in a competitive race to integrate advanced AI features into their products, aiming to enhance user experience and functionality [3][9][10]. - 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 fierce competition for user engagement and AI capabilities [9]. Future Development Goals - Sogou Input Method aims to evolve into a "super entrance" for AI, allowing users to interact with AI agents through the input method, which is seen as a key direction for future development [10][11]. - Baidu Input Method also seeks to position itself as a reliable smart entry point for user expression, leveraging AI to enhance communication and collaboration [11][12]. Commercialization Challenges - Input methods face a "high traffic, low value" monetization dilemma, struggling to effectively convert user engagement into revenue [16][17]. - Privacy concerns have led to increased scrutiny and regulatory challenges, prompting companies to adapt their data collection practices to ensure user safety and compliance [18][19]. Technological Limitations - Despite advancements in AI algorithms, challenges remain in accurately understanding user intent, particularly in word prediction, due to the variability in user language habits and the quality of training data [13][19]. - The complexity of multi-modal interactions, where users input data through various means, requires sophisticated algorithms and technology to ensure seamless processing [19].

二、政策与技术环境 一、背景与核心诉求 随着人工智能技术飞速发展，AI 基础设施成为产业智能化变革的核心支撑，在国家 "十四五" 数字经济发展规划与 "东数西算" 工程推动下，我国算力中 心建设呈现政策驱动显著、智能化需求爆发、应用场景深化三大特征。但与此同时，AI 基础设施面临合规要求收紧、云平台漏洞频发、大模型新型攻击 涌现等多重安全挑战，构建高效、安全、可靠的 AI 基础设施成为行业刚需。 （一）整体安全架构 百度构建了以合规为纲、技术为骨、管理为翼的全方位多层级安全防护体系，涵盖合规与标准规范、核心安全域分层防护、管理与运行体系三大维度，形 成 "边界 - 平台 - 租户 - 密码 - 模型 - 运营" 六层联动防护，适配 AI 基础设施特性。 （二）关键安全域防护 模型应用安全：从语料安全清洗、输入输出安全管控、数据安全保护、安全评测能力建设四方面入手，通过 prompt 审核、回复干预、数据加密、蓝军评 测等手段，覆盖大模型训练、部署、推理全生命周期。 云平台安全：以 "治理 - 防护 - 合规 - 运营" 为主线，涵盖安全治理（漏洞、基线）、计算 / 存储 / 网络安全、等保与密评合规、物理安全 ...