Core Insights - 360 Group has launched the 360 Smart Brain Visual Model, marking its entry into the SMB market and expanding its capabilities in the AI and IoT sectors [2][8] - The new visual model aims to enhance the value of IoT data through multi-modal integration, addressing limitations of traditional deep learning algorithms [3][5] - The introduction of the visual model is seen as a significant step towards achieving general artificial intelligence, enabling machines to understand images and videos beyond mere data collection [3][6] Group 1 - The 360 Smart Brain Visual Model is designed to integrate with smart hardware, enhancing their capabilities and providing deeper insights into data [2][5] - The model focuses on three main capabilities: Open Object Detection (OVD), Image Captioning, and Visual Question Answering (VQA), which can be applied in various scenarios such as store inspections and equipment checks [3][4] - The launch of the visual model is part of 360's strategy to leverage its existing technology and experience in AI to penetrate the SMB market, addressing challenges faced by small and medium enterprises in digital transformation [7][8] Group 2 - The visual model's multi-modal capabilities are expected to reduce costs and improve the efficiency of AI applications in vertical industries, particularly in security and surveillance [3][8] - 360 Group's founder emphasized that the emergence of large models signifies the arrival of a new era in artificial intelligence, capable of understanding complex visual data [3][6] - The company plans to continue exploring AI applications in security, aiming to empower SMBs with affordable and effective digital solutions [8]

题图｜视觉中国 根据汽车市场信息公司J.D.power今年7月底的最新调研，中国汽车尤其是自主品牌产品的智能化，已 经达到了一个全新的高度——即使是入门级的中国自主品牌新能源汽车，其智能化得分也已接近甚至 超过传统豪华燃油车型。 这一结果也符合大众对于中国汽车这些年智能化提升、产品崛起的直观体验。 但随着新能源汽车座舱智能化全面覆盖各类"控车"场景，新的"隐形天花板"已经出现。 以过去几年行业和大众衡量座舱智能化水平的关键指标——"车机流畅度"与"语音指令处理能力"为 例，多数产品已实现高度流畅的操作体验，并能连续处理十余条语音指令。但部分厂商受"AI功能竞 赛"影响，为追求"领先"盲目叠加功能——其中不乏消费者既不需要、也不会使用的功能。 智能座舱的创新是否已走到尽头？若未止步，未来应如何突破、向何处发展，已成为中国新能源汽车 产业亟待厘清的关键议题。 面向未来，智能座舱应该如何分 级？ 从需求角度出发，一套足够面向未来的智能座舱分级大致需要满足以下几个需求： 1. 覆盖智能座舱的全场景应用，避免局限于当前的功能逻辑； 2. 具备技术普适性，不依赖特定品牌或厂商体系； 3. 保持中长期有效性，体现行业技术发 ...

Investment Rating - The report maintains a "Strong Buy" rating for the industry [1] Core Insights - The evolution of intelligent driving has entered the AI 2.0 phase, focusing on scalable capabilities and the ability to autonomously handle extreme edge scenarios, which will further enhance the commercial viability of intelligent driving systems [1] - Major players in the high-level intelligent driving sector are accelerating their entry into the Robotaxi business, leveraging mass-produced vehicles to optimize model training and performance in extreme scenarios [1][79] - The report highlights the importance of diverse real-world data and robust R&D resources as key competitive advantages for players in the AI driving space [81] Summary by Sections 1. Tesla's Software and Hardware Iterations - Tesla's FSD (Full Self-Driving) software has achieved significant milestones, with over 60 billion miles driven cumulatively, showcasing its leading position in intelligent driving [7] - The next-generation AI5 chip is expected to greatly enhance the performance and energy efficiency of Tesla's driving systems [8][9] 2. Development Stages of High-Level Intelligent Driving in China - The industry has transitioned from a rule-based system to a fully data-driven approach, marking the arrival of the AI driving era [15][12] - The current phase emphasizes end-to-end models that utilize extensive data for improved driving performance and user experience [18][19] 3. Technical Architecture: Mainstream Player Directions - The VLA (Vision-Language-Action) model integrates visual, language, and action modalities, enhancing the system's ability to understand and interact with the physical world [27][28] - Huawei's ADS 4.0 emphasizes a scene-driven approach, utilizing cloud simulations to train AI drivers without relying on large language models [49][50] 4. Business Model: Acceleration of Robotaxi Initiatives - The Robotaxi business is seen as a critical avenue for data collection and model optimization, with major players planning to leverage mass-produced vehicles for this purpose [65][66] - The report outlines two main technological routes for Robotaxi operations: the "crossing route" represented by Waymo and the "gradual route" represented by Tesla, each with its own advantages and challenges [67][68] 5. Investment Recommendations - The report recommends investing in companies such as Seres, Horizon Robotics, Great Wall Motors, Li Auto, and Xpeng Motors, which are well-positioned to capitalize on the advancements in AI driving technology [81]

Group 1: U.S. Macro Strategy - The expectation for interest rate cuts fluctuated, with a significant drop in the probability of cuts due to hawkish signals from some Federal Reserve officials and the impact of government shutdowns on employment data [2] - The U.S. labor market shows signs of downward pressure, with the unemployment rate rising to 4.4%, despite a rebound in job creation [2] - The S&P 500 index fell by 2.9%, driven by concerns over the AI investment bubble, high valuations, and the shifting interest rate outlook [3] Group 2: U.S. Stock Market - The core contradiction in the U.S. stock market is between high valuations and the uncertain future of AI, with a recommendation to adjust annual return expectations to single-digit levels [3] - A 10%-20% market correction is anticipated, with a current 5% decline already observed, suggesting a continued wait for more favorable valuations [3] - Diversification is advised, with attention to sectors like industrials, utilities, energy, and healthcare beyond technology stocks [3] Group 3: U.S. Bond Market - The 10-year U.S. Treasury yield is expected to fluctuate around 4.1%, with a long-term downward trend anticipated due to the Fed's accommodative stance [4] - Investors are encouraged to maintain positions in 2-5 year bonds, with long-term bonds recommended for purchase when yields exceed 4.2% [4] Group 4: Currency and Commodities - The U.S. dollar may experience a slight rebound in the short term, but long-term pressures are expected due to a generally weak non-U.S. currency environment [5] - The Chinese yuan is projected to appreciate slightly, influenced by the narrowing interest rate differential and increasing market willingness to exchange [5] - Gold is entering a short-term adjustment phase, with a long-term bullish outlook, although significant price increases are not expected in 2026 [5] Group 5: Chinese Macro Strategy - Domestic demand remains weak, with significant declines in housing and land transactions, and a notable drop in car sales [7] - Export momentum is weakening, although overall data for November shows some resilience, particularly in container throughput [7] - Fiscal revenue showed mixed results, with tax revenues increasing by 8.6%, while non-tax revenues fell sharply [8] Group 6: Policy and Market Outlook - The "anti-involution" policy aims to standardize pricing in the lithium iron phosphate industry, which may impact pricing strategies [9] - The LPR remains unchanged, indicating a stable monetary policy environment with limited expectations for further cuts [9] - The bond market sentiment is weak, with long-term bonds underperforming short-term ones, and a cautious approach is advised for long-term bond investments [10] Group 7: A-Share Market - The A-share market experienced a decline, with the Shanghai Composite Index down 3.9% and the ChiNext Index down 6.1%, influenced by external market conditions and high valuations [10] - The outlook for A-shares remains positive for the next year, driven by expected liquidity easing and potential earnings recovery [10] - High-valuation technology stocks are sensitive to liquidity changes, while dividend-paying sectors may provide stability [11]

Core Insights - The current investment in AI infrastructure in the U.S. is seen as a proactive measure in anticipation of the advancements in general artificial intelligence, although there are signs of a potential bubble in the market [1][3][27] - Major data center projects in the U.S. have surpassed a total installed capacity of 45 GW, with an expected investment exceeding $2.5 trillion, raising concerns about a possible systemic downturn if these investments do not yield expected returns [2][4][5] - Companies like OpenAI and Anthropic are experiencing significant revenue growth, with OpenAI projected to exceed $20 billion in annual revenue by the end of the year, a fivefold increase from the previous year [3][8][10] Investment Trends - In Q3, cloud computing revenues for Amazon, Microsoft, and Google reached $33 billion, $30.9 billion, and $15.2 billion respectively, driven by AI, with year-on-year growth rates of 20%, 28%, and 34% [4][11] - OpenAI plans to invest approximately $1.4 trillion in building its computing infrastructure over the next eight years, indicating a strong demand for computational power [8][18] - The total cash and equivalents of major tech companies involved in AI infrastructure exceed $200 billion, providing a solid financial foundation for these investments [19] Market Dynamics - The demand for AI capabilities is expected to grow, with the number of global AI users reaching around 1 billion, indicating significant potential for further expansion [7] - The AI sector is facing scrutiny regarding the sustainability of its growth and the sources of its funding, with concerns about reliance on debt financing [5][20][21] - Historical comparisons suggest that while there are signs of a bubble, the current valuations are still within a reasonable range supported by strong performance metrics [23][25] Future Outlook - The AI investment wave may experience short-term valuation corrections, but the long-term direction is deemed valid, as technological advancements often come with cycles of overheating and correction [27] - The construction of data centers is aligned with the U.S. reindustrialization strategy, which aims to bolster domestic manufacturing and infrastructure [17] - Analysts predict that the total spending on AI data centers and chips could reach $2.9 trillion by 2028, with a significant portion expected to be financed through debt [20]

尊敬的各位嘉宾、各位网友： 大家好！ 欢迎大家参加摩尔线程智能科技（北京）股份有限公司首次公开发行股票并在科创板上市的网上路演活 动。在此，我谨代表摩尔线程，向长期关心、支持公司发展的广大投资者表示热烈欢迎！向一直以来关 注我国GPU技术突破、智算产业发展的社会各界朋友表示衷心的感谢！很高兴能借助今天上证路演中 心、上海证券报及中国证券网的互动交流平台，与大家真诚沟通、共同探讨摩尔线程的发展与未来。 当前，全球科技竞争格局正在深刻变革，GPU作为支撑通用人工智能、数字孪生、具身智能等前沿产业 的核心算力引擎，其战略地位已升至前所未有的高度。公司自2020年成立以来，始终专注于全功能GPU 的自主研发与设计，是国内高端AI芯片领域极具代表性的企业。公司的目标是成为具备国际竞争力的 GPU领军企业，为融合人工智能和数字孪生的数智世界打造先进的加速计算平台。 公司的发展路径与国家推动高水平科技自立自强的战略方向同频共振。基于自主研发的MUSA统一系统 架构，摩尔线程实现了单芯片同时支持AI计算加速、图形渲染、物理仿真和科学计算、超高清视频编 解码的技术突破，为构建自主可控的高性能算力底座奠定了关键的技术基础。 目前 ...

炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ （来源：每日经济新闻） 每经记者：可 杨 每经编辑：魏官红 大模型行业正站在一个拐点。过去数年由技术突破点燃的狂热，正在2025年遭遇一场商业化大考。随着 智谱等头部"独角兽"公司相继启动IPO（首次公开募股）流程，市场叙事正从模型能力的"军备竞赛"转 向落地应用的审视。 资本市场开始用更严苛的尺度检验大模型企业，是否具备可持续的商业模式和长期价值。这一冲突，在 开源与闭源的路线之争上尤为激烈。 图 受访者供 在行业普遍认知中，开源意味着共享，商业化则意味着独占。作为中国大模型赛道的关键玩家，智谱却 始终坚持一个反直觉的判断：开源与商业化长远来看并不冲突。这一判断的底层逻辑是什么？在技 术"摸高"与应用落地之间，企业应如何平衡资源？AI（人工智能）的"性价比"竞争是否已经到来？带着 这些问题，11月17日，《每日经济新闻》记者（以下简称NBD）与智谱董事长刘德兵进行了一场深度 对话。在他看来，基础模型的进步对应用落地有巨大的促进作用，但技术"摸高"绝不能做"空的、虚 的"东西，必须能转化成商业优势。 谈模型：大参数模型是技术锚点 NBD ...

Core Viewpoint - The article discusses the recent decline in U.S. stock markets, attributing it to three main pressures: the diminishing expectations of interest rate cuts by the Federal Reserve, rising concerns over an AI investment bubble, and historically high valuations in the stock market [1][2]. Group 1: Reasons for U.S. Market Adjustment - The Federal Reserve's expectations for interest rate cuts have significantly decreased, with a higher probability of pausing cuts in December due to hawkish signals from officials and the impact of government shutdowns on employment data [1][2]. - Concerns about an AI investment bubble are growing, driven by a mismatch between exponential growth in capital expenditure and linear growth in revenue from AI applications. Nvidia's strong earnings report did not alleviate market fears, as its revenue is tied to capital spending rather than actual market demand [2]. - U.S. stock valuations are at historical highs, with the Shiller P/E ratio exceeding levels seen in 2021 and 1929, only surpassed by the peak of the 2000 internet bubble. This suggests that the market has priced in overly optimistic growth expectations, limiting further valuation expansion [2]. Group 2: Outlook for U.S. Markets - The impact of interest rate expectations is likely to be short-term, with a higher probability of a dovish stance from the Federal Reserve in the future. Although December's rate cut is uncertain, rates may drop to around 3% by the end of 2026 [3]. - The core contradiction in the U.S. market lies between high valuations and the uncertain prospects of AI. While AI's potential remains, the timeline for its widespread productivity enhancement is uncertain, leading to justified concerns about an AI bubble [3]. - It is recommended to adjust annual return expectations to align with single-digit profit growth rates and to prepare for potential market corrections of 10%-20%. The current market has already seen a 5% correction, but valuations have not yet returned to reasonable levels, suggesting a continued wait for better entry points [4]. Group 3: Outlook for A-shares and H-shares - A-shares and H-shares experienced a synchronized adjustment due to external market declines and prior pressure releases, influenced by the drop in U.S. markets and changing expectations regarding the Federal Reserve's interest rate decisions [5]. - The core factors affecting A-share performance remain its fundamentals and liquidity. A dovish path from the Federal Reserve is expected to continue, with domestic asset allocation likely favoring equity markets in a low-interest environment [5]. - After the current adjustment phase, A-shares and H-shares are anticipated to continue rising in the following year, supported by improved performance in a recovering inflation environment [5]. Group 4: Sector Insights - High-valuation technology stocks are sensitive to liquidity changes and may face adjustment pressures, while dividend stocks and technology sectors exhibit a seesaw effect, with dividend stocks currently showing advantages [6]. - Consumer stocks have been less affected by liquidity expectations due to their adjusted valuations, presenting opportunities for left-side positioning despite limited fundamental improvement [6]. - The Hang Seng Technology Index has seen a 20% adjustment, with historical bull market corrections typically ranging from 20%-30%, indicating potential for increased focus once adjustments are complete [6].

Core Viewpoint - The rapid advancement of generative AI and large models contrasts with the slow commercial adoption, as evidenced by a recent decline in the percentage of U.S. companies using paid AI products [2][3]. Group 1: AI Project Challenges - Approximately 90% of vertical enterprises do not truly understand AI, leading to ineffective implementation without tailored models [3]. - The telecom industry has historically absorbed new technologies, and AI is seen as the next evolution, with significant advancements expected by 2025 [3]. Group 2: Future of AI and Agent Technology - The AI industry is at a crossroads, with a shift from foundational model development to large-scale application deployment, raising questions about the future of basic model research [6]. - There is a consensus that future AGI will rely on world models that integrate multiple modalities, although specific applications may require tailored models for efficiency [6][7]. - The development of specialized models for various industries is viewed as a practical approach to achieving commercial viability before moving towards universal models [7]. Group 3: Agent Technology Implementation - By 2025, agent technology is expected to become a core trend, with practical applications emerging across various industries, including healthcare and education [8]. - Current implementations of agent technology have demonstrated effectiveness, with plans for broader deployment in 2026 [8]. - Challenges remain in integrating agents into existing workflows, primarily due to limitations in multi-modal capabilities of large models [8][9]. Group 4: Computational Power and Industry Growth - The AI industry faces ongoing challenges related to computational power, with domestic GPU companies accelerating their development to address these needs [9]. - As computational issues are resolved, significant advancements in multi-modal models and agent technology are anticipated [9][10]. Group 5: Consumer Acceptance and Market Trends - Consumer acceptance of AI products is increasing, with a shift towards deploying AI capabilities from cloud to edge devices [9][10]. - The mobile AI sector is expected to see rapid growth, with small models achieving high accuracy in practical applications [11]. Group 6: Humanoid Robots and Industry Development - Humanoid robots are still in the exploratory phase, with significant technical challenges remaining before widespread commercial deployment [12][13]. - The manufacturing of humanoid robots involves complex components, with a focus on developing autonomous control capabilities as a critical bottleneck [13]. - The path to commercial viability for humanoid robots is expected to begin in industrial settings before expanding to consumer applications [14][15].

当人工智能的浪潮从虚拟世界涌向物理现实，汽车产业正站在这场变革的最前沿。自动驾驶领域技术路线纷繁复杂，"端到端"、"世界模型"、"VLA"等术语 层出不穷，业界对于何为"最优解"莫衷一是。 在由南方财经全媒体集团指导、21世纪经济报道主办的2025新汽车年度盛典上，南方财经全媒体集团编委会成员、集团客户端编委会执行总编辑、21世纪经 济报道编委会成员袁丁主持题为《汽车有"魂"，AI如何重塑用车体验？》的圆桌论坛，邀请到地平线副总裁吕鹏、千里智驾首席科学家秦海龙和场景实验室 创始人吴声三位业界专家，旨在穿透技术路线的迷雾，深入探讨AI如何超越工具属性，为汽车注入可进化、可感知的"灵魂"，从而彻底重塑人类的用车体 验。 场景实验室创始人吴声旗帜鲜明地指出，AI驾驶已超越"工具"范畴，正成为一种真实的生活方式，是通向AGI(通用人工智能)的最佳实践。 袁丁：当AI加速渗透物理世界，自动驾驶领域正处在充满分歧的路口。端到端、世界模型、VLA路线，哪条路线是自动驾驶的"最优解"？我们还要等待多久 才能迎来物理AI的曙光？吴声老师曾提出"AI场景革命元年"的概念，在汽车行业，AI场景应如何定义？目前是处于元年，还是已进入 ...