0 1 股价的分化表现 对AI行业的看法，有一大一小两个问题，大问题是AI行业是否有泡沫，有多大泡沫，小问题是各公司的竞争谁能胜出。如果观察2025年资本市场的表 现，大家投票的结果是对泡沫有一定的担忧，但更害怕错过，对竞争投票的结果是谷歌和博通在2025年表现更好。 截止到2025年11月25日，纳斯达克100指数累计上涨19.07%，谷歌和博通分别涨了70.49%和67.26%，AI时代最大的卖铲人英伟达涨了32.44%，而微 软、META、亚马逊都低于平均值。 谷歌的上涨得益于Gemini 3的发布，META的下跌是因为Llama4产品的不及预期，团队波动等问题。想回答整个AI领域是否可持续的问题，我们还是要 先搞清楚AI军备竞赛短期的波动原因在哪里。 | | | | 美股AI巨头2025年涨幅 (截至2025.11.25) | | | | | --- | --- | --- | --- | --- | --- | --- | | 谷歌 | 博通 | 英伟达 | 纳斯达克 100指数 | 微软 | META | 亚马逊 | | 70.49% | 67.26% | 32.44% | 19.07% | 14 ...

在技术早期，总有人试图寻找唯一正确的路线，希望通过一次性押注来穿越迷雾。但具身智能的复杂性正在提醒行业，具身智能不是从一条路 径长出来，而是从无数次试错、冲突与调和中被"雕刻"出来。模型不完美，数据不完整，架构不统一，这听上去像缺陷，却恰恰是具身智能最 真实的生命力所在。 意料之内的是具身智能在2025年末依然保持高昂姿态前行。 更在意料之内的是，具身智能依然没有共识。 2025智源具身OpenDay圆桌论坛上，国内最顶尖的具身从业者来了一场"各执一词的真心话"，无论是模型架构的选择，还是数据的使用都未能在圆桌对话 上找到统一的发展方向。一时间不少人对于具身智能仍无共识这事儿，抱有遗憾。 从产业视角来看，共识的缺失有三重利好意义： 其一，无共识本质上打破了单一技术路线的垄断性话语权，避免行业陷入"路径依赖"的创新陷阱。在具身智能领域，从"分层架构 vs 端到端"的技术路线 分歧，到"通用人形机器人vs场景化具身智能"的落地选择，无共识状态让不同技术理念、学科背景的团队获得平等试错空间； 其二，成熟行业的共识往往伴随着高准入壁垒，而具身智能的"无共识"状态，为中小企业、初创团队乃至跨界玩家提供了弯道超车的机会。 ...

中国AI企业月之暗面（Moonshot AI）再度成为业界焦点。 据多位知情人士透露，该公司正与IDG资本、腾讯等国际顶级投资机构洽谈新一轮美元融资，估值有望飙升至40亿美元。与此同时，市场传闻其计划在完 成本轮融资后，并于2026年下半年启动IPO。 知情人士透露，月之暗面此次融资规模预计达6亿美元，投前估值约40亿美元。若谈判顺利，这将是该公司继2024年8月3亿美元融资后的又一里程碑。 月之暗面官方否认了"明年下半年IPO"的具体时间表，但仍有知情人士表明其上市筹备已在进行中，公司正在与投行接洽，评估纽交所、港交所双重上市 的可能性。 站在40亿美元估值的新起点，月之暗面的IPO征程既是荣耀加冕，更是生死考验。在这个中美科技博弈的关键时刻，其每一步动向都将牵动全球AI产业的 神经。 不过，相较于OpenAI 5000亿美元的惊人估值，中国AI企业的估值普遍相去甚远。月之暗面40亿美元的估值上限，仅相当于其美国同行的1/125。 月之暗面目前的营收主要来自B端API调用与定制化解决方案，2023年营收约2.1亿元人民币。相较之下，OpenAI单季度营收已突破10亿美元。但纵向对比 国内同行，其38亿美元的 ...

Group 1 - The A-share market experienced a decline, with the ChiNext index dropping by 1% and sectors such as Hainan, gaming, and solar thermal power showing gains, while precious metals and battery sectors faced losses [1] - The AI ETF (515070) fell by 1.53%, with notable stock movements including 37 Interactive Entertainment hitting the daily limit, 360 Technology rising by 7.1%, and Stone Technology dropping by 5.2% [1] - The Kimi Linear architecture, which surpasses the Transformer architecture in various scenarios, introduces the "Kimi Delta Attention" mechanism, achieving a 75% reduction in KV cache usage and a 6-fold increase in decoding throughput [1] Group 2 - CITIC Securities analysis indicates a shift in AI large model development from a focus on parameter scale to achieving higher "capability density" and better architectural efficiency, driven by algorithmic innovations inspired by brain science [2] - This transition is expected to lower the computational threshold, enabling small and medium enterprises to access AI technology at reduced costs, thus creating broader industrial applications and investment opportunities [2] - The AI ETF (515070) tracks the CS AI Theme Index (930713), focusing on companies providing technology and resources for AI, with top-weighted stocks including major domestic tech leaders [2]

Core Insights - The collaboration between the University of California, San Francisco, and the Allen Institute has led to the development of an AI model named CellTransformer, which has created the most detailed mouse brain map to date, encompassing 1,300 brain regions and subregions [1][3] Group 1: AI Model and Technology - CellTransformer utilizes a Transformer architecture similar to that used in models like ChatGPT, which excels in understanding contextual relationships [3] - The model analyzes the relationships between adjacent cells in spatial contexts, predicting molecular characteristics based on a cell's "neighborhood structure" [3] Group 2: Brain Mapping Advancements - Unlike previous brain maps that primarily categorized based on cell types, this new model focuses on the brain's structural regions, automatically defining boundaries based on cellular and molecular features rather than human judgment [3][4] - The resulting brain map is one of the most precise and complex data-driven maps of an animal brain to date, accurately representing known regions like the hippocampus and discovering new subregions in less understood areas like the midbrain reticular formation [3][4] Group 3: Implications and Applications - The new brain region delineation is entirely data-driven, revealing numerous unknown areas that may correspond to unexplored brain functions [4] - The potential applications of the CellTransformer model extend beyond neuroscience, with the algorithm being applicable to other organ systems and cancer tissues, utilizing spatial transcriptomics data to uncover biological mechanisms in health and disease, thus providing new tools for drug development and disease treatment [4]

Group 1 - The AI startup landscape in 2025 is characterized by divergent paths, focusing on either B-end or C-end applications, and whether to concentrate on domestic or global markets [1] - B-end applications are seen as having a mature business model with clear payment logic, particularly in the "cost reduction and efficiency enhancement" sector, making it a preferred area for investment [1][2] - C-end markets, despite challenges like payment difficulties, hold potential opportunities through continuous observation and rapid iteration, leveraging domestic talent and evolving model technologies [1] Group 2 - The technical characteristics of AI determine the landing logic in different scenarios, with a focus on customized development for complex enterprise environments [2] - Globalization is viewed as a crucial strategy to break competitive deadlocks, with faster growth opportunities concentrated overseas, supported by the global capabilities of Chinese product managers [2] - Chinese companies possess unique advantages in going global, combining strong AI technology capabilities with a complete supply chain system to create high-cost performance smart devices [2] Group 3 - The emergence of institutional incubation models empowers startups, with organizations like Innovation Works significantly reducing risks by investing in scarce directions 1.5-2 years ahead [3] - The dual drivers of technological iteration and market evolution are clarifying the AI entrepreneurial landscape, emphasizing the importance of precise demand insights and flexible strategy adjustments [3]

Core Viewpoint - DeepSeek has released the experimental version DeepSeek-V3.2-Exp, which introduces a new sparse attention mechanism aimed at optimizing training and inference efficiency in long-context scenarios [3][5][10]. Summary by Sections Model Release - DeepSeek-V3.2-Exp has been open-sourced with a parameter count of 685 billion [3]. - The release includes a paper detailing the new sparse attention mechanism [5]. Sparse Attention Mechanism - The DeepSeek Sparse Attention (DSA) is the only architectural improvement in version 3.2, focusing on enhancing computational efficiency when processing extended text sequences [5][6][10]. - DSA achieves fine-grained sparse attention while maintaining nearly the same output quality as its predecessor, DeepSeek-V3.1-Terminus [9]. Performance Comparison - A comparison of benchmark results between DeepSeek-V3.1-Terminus and DeepSeek-V3.2-Exp shows that the new version performs comparably across various tasks [11]. - Specific benchmark results include: - MMLU-Pro: 85.0 (V3.1) vs. 85.0 (V3.2) - AIME 2025: 88.4 (V3.1) vs. 89.3 (V3.2) - Codeforces: 2046 (V3.1) vs. 2121 (V3.2) [11]. Future Developments - The upcoming release of Z.ai's GLM-4.6 model is noted, with GLM-4.5 being the previous flagship model [12].

Core Insights - The report focuses on the development and trends of the AI industry during China's 14th Five-Year Plan (2021-2025) and the outlook for the 15th Five-Year Plan (2026-2030), highlighting significant changes and advancements in technology, industry ecology, policy support, and application expansion [2][8]. Group 1: 14th Five-Year Plan Review - The AI industry has undergone five major qualitative changes, establishing a foundation for "factorization" [9]. - Technological transformation is marked by the dominance of the Transformer architecture, which has unified AIGC (AI-Generated Content) and completed the "engine convergence" [12][19]. - The computing power landscape has shifted, with domestic AI chips closing the efficiency gap with international counterparts, and the evolution from general IDC (Internet Data Center) to AIDC (AI Data Center) [25][26]. - Data has transitioned from governmental sharing to being recognized as a fiscal element, with mechanisms for asset inclusion and revenue sharing being established [33][34]. - Market dynamics have changed, with the end of the visual dividend leading to a downward shift in both supply and payment curves, allowing for a revaluation of AI [10][12]. Group 2: 15th Five-Year Plan Outlook - The AI factorization leap will be characterized by "price discovery, scale trading, and cross-border output," with Agents as the core vehicle [9]. - The product dimension will see a shift from passive execution to autonomous collaboration, with revenue models evolving from token-based to profit-sharing [9][10]. - The supply side will benefit from a complete domestic ecosystem, enabling the definition of "Agent instruction sets" and achieving pricing power [9][10]. - Demand will expand into global southern markets, with significant population potential and a projected compound annual growth rate of 9.2% for the digital economy [9][10]. - Five key application scenarios are expected to see iterative expansion, transitioning from project-based to subscription-based consumption [9][10]. Group 3: Investment Recommendations - Investment opportunities are identified in four main areas: computing power infrastructure, AI Agents and MaaS (Model as a Service) providers, intelligent terminals and embodied intelligent robots, and AI applications in green and low-carbon initiatives [9][10].

Core Insights - The demand for AI computing is driving attention towards AI chips beyond GPUs, with companies like Google and Groq leading the way in alternative technologies [1][3] - In the domestic market, ASIC custom chip manufacturers are rapidly developing, as the cost of specialized chips decreases, allowing more firms to explore personalized AI capabilities [2][4] AI Chip Market Trends - The trend of seeking development opportunities outside of GPU chips is becoming more pronounced, with companies recognizing that innovation is necessary to compete with NVIDIA [3][4] - The success of GPUs is largely attributed to NVIDIA's established engineering teams, which are not easily replicable by newcomers [3] Technological Advancements - The introduction of Tensor Cores in NVIDIA's Tesla V100 series has highlighted the efficiency of tensor processing units (TPUs) in handling large data volumes, offering significant computational advantages [4][5] - The scaling laws in AI models continue to demand higher performance from underlying AI computing clusters, presenting challenges for domestic XPU chips [5] Interconnectivity and Infrastructure - Companies are focusing on enhancing interconnectivity between chips, cabinets, and data centers to meet the demands of high-speed data transmission [5][6] - 中昊芯英 is exploring advanced interconnect technologies, such as OCS all-optical interconnects, to improve its capabilities [6] Competitive Landscape - NVIDIA's InfiniBand protocol is seen as a competitive advantage for large-scale data center deployments, while domestic firms are leaning towards Ethernet protocols for their flexibility and improved performance [6] - The development of software ecosystems is crucial for domestic AI chip platforms, as they need to build their own software stacks to compete with NVIDIA's established CUDA ecosystem [6][7] Future Directions - The evolution of AI models, particularly those based on the Transformer architecture, continues to shape the landscape, with ongoing optimizations and adaptations [7] - The compatibility and smooth operation of various platforms will be essential for the success of domestic AI chips, similar to the early days of the Android ecosystem [7]

Core Insights - The demand for AI computing is driving attention towards non-GPU AI chips, with companies like Google and Groq leading the way in alternative architectures [1][2] - The rise of custom ASIC chips is notable, as companies seek to develop personalized AI capabilities at lower costs [1][2] - The evolution of AI chips is marked by a shift towards architectures that prioritize performance and energy efficiency, moving away from traditional GPU models [2][3] Market Trends - New players in Silicon Valley, such as Groq and SambaNova, are focusing on architecture innovation rather than GPU-based designs [2] - The success of NVIDIA is attributed to its established engineering teams, making it challenging for new entrants to replicate its model [2][3] - The increasing focus on custom ASIC chips is evidenced by significant orders, such as Broadcom's recent billion-dollar contracts [1][2] Technological Developments - The introduction of Tensor Cores in NVIDIA's Tesla V100 series has enhanced performance without significant changes to CUDA Cores [3] - TPU chips are likened to innovations in the electric vehicle industry, offering better data migration and lower energy consumption [4] - The need for efficient data transmission in AI infrastructure is becoming a critical challenge, with companies exploring high-speed interconnect solutions [5][6] Competitive Landscape - NVIDIA's closed approach has prompted competitors to advance Ethernet protocols, which have become more competitive in recent years [6] - The development of software ecosystems is crucial for domestic AI chip manufacturers, as they need to build their own toolchains to compete with NVIDIA's CUDA [6] - The Transformer architecture remains foundational for most large language models, providing opportunities for AI chip manufacturers to align their products with ongoing model iterations [7]