从招股书看，智谱的技术底气和商业野望均不容小觑，其大模型基础能力，要去全球评测打榜；主攻B 端场景的变现策略，要与大厂分庭抗礼。 1月8日，智谱登陆港股，首日股价较发行价上涨超13%，成功拿下"大模型全球第一股"。从北京到香 港，从高校实验室到股票交易所，智谱在资本市场的一小步，也是大模型在公众视野的一大步。 尽管大模型成熟期言之尚早，其成长历程不可能只有上市这一次考试，但大考毕竟广受关注。 大考智谱走过的路，是中国大模型产业从技术探索走向商业验证的缩影。智谱董事长刘德兵说，IPO最 重要的是对"技术逻辑能否跑通商业逻辑"的实战检验。 智谱上市踩中了人工智能资本化的风口期。近段时间，国产GPU摩尔线程和沐曦股份上市接连引爆市 场。国产大模型赛道的智谱敲钟，再度推高市场对AI企业的商业化预期。 资本盛宴开启，但鸣锣开市仅仅是一个起点，当大模型的神秘面纱一次次被揭去，企业需要接受来自公 开市场最严苛、最持续的审视——技术和算力必须落地生根，数据和业绩才是最终的答卷。 资本市场既是最好的加速器，也是无情的试金石。它奖励前瞻性，但绝不会为纯粹的梦想长期买单。即 便没上市，曾经的行业领军者已分化出不同的路径选择，不断尝 ...

Core Insights - The "AI New Year First Meeting" was held in Beijing on January 5, focusing on the construction of the 2026 Beijing Artificial Intelligence Innovation High Ground [5][18] - Beijing has rapidly advanced its position in the global AI landscape, with numerous prominent AI models emerging from the city [4][8] - The city is recognized as a fertile ground for tech startups due to its talent pool, research resources, and supportive government policies [4][11][18] Group 1: AI Development and Innovation - The Beijing Academy of Artificial Intelligence officially released the "Zhongzhi FlagOS 1.6," a software stack aimed at solving compatibility issues for training large models across different AI chips [5] - Beijing's AI research output is significant, with 7,340.3 adjusted papers and an AI index of 402.59, placing it first globally [8] - The city has transformed from a "follower" to a core source of AI research and innovation [8] Group 2: Talent and Ecosystem - The concentration of high-end, interdisciplinary talent in Beijing is a key factor driving innovation in the AI sector [4][11] - The presence of major universities like Tsinghua University facilitates a strong academic atmosphere, fostering a culture of innovation among young researchers [6][11] - Companies in Beijing benefit from a well-established AI ecosystem that encourages collaboration and avoids isolated development [11][12] Group 3: Government Support and Policy - The Beijing government demonstrates a deep understanding of technological frontiers, providing strong support for long-term investments and early-stage startups [18][19] - The city is developing multiple innovation districts, including the Haidian Original Community, to enhance its AI industry landscape [18][20] - Beijing's development strategy emphasizes a "one committee, one industry, one area, one product" approach to foster AI integration across various sectors [19] Group 4: Industry Growth and Future Prospects - By 2025, Beijing's core AI industry is projected to reach a scale of 450 billion yuan, with over 2,500 companies established [16] - The city is expected to continue leading in AI innovation, contributing to various sectors such as healthcare, governance, and industry [22][23] - The narrative of Beijing's AI development reflects China's commitment to technological self-reliance and innovation [22][23]

北京发布首批人工智能创新街区 中新网北京1月5日电 (记者 吕少威)5日，北京市举办"2026北京人工智能创新高地建设推进会"。会上发 布首批人工智能创新街区，拟支持海淀原点社区、经开区模数世界、朝阳区光智空间、石景山文化智境 等4个创新街区建设，打造以海淀为核心的"一核多点"布局。力争在未来两年内，各创新街区核心区基 本形成示范引领带动作用，成为北京人工智能核心引擎、全国人工智能创新地标。 北京市发改委有关负责人表示，北京人工智能人才、企业、核心产业规模等均在全国明显领先，占全国 半壁江山，形成了抖音豆包、智谱GLM、月之暗面Kimi、百度文心等一批基础模型和生数、面壁、可 灵、深势等一批垂类模型，形成平台企业迭代、新锐企业竞逐的良好态势。 据介绍，本次发布的首批人工智能创新街区坚持"一区一品"差异化发展。 海淀区以创新策源、创业首选为特色定位，建设"原点社区"。打造"全球AI人才创新创业第一站"，突出 技术策源、人才集聚、生态赋能，建设成为AI原创策源技术的全球首发地、AI青年人才共创共燃的栖 息地、AI全要素精准对接的生态服务站。原点社区以五道口为核心，发挥高校院所、创新主体密集优 势，整合东升大厦、清 ...

Core Insights - The article discusses the decision-making process for individuals in the large model field regarding whether to pursue a PhD or engage in entrepreneurial ventures related to agents [1][2] Group 1: Importance of Foundation in Large Models - A solid foundation in large models is crucial, as the field encompasses various directions such as generative models, multi-modal models, fine-tuning, and reinforcement learning [1] - Many mentors lack sufficient expertise in large models, leading to a misconception among students about their readiness for related positions [1] Group 2: Role of a Pioneer in Research - The suitability of an individual to take on the role of a "pioneer" in research is essential, especially in a field with many unexplored directions [2] - The ability to independently explore and endure failures is emphasized as a key trait for those aiming to innovate from scratch [2] Group 3: Community and Learning Resources - The "Large Model Heart Tech Knowledge Planet" community offers a comprehensive platform for beginners and advanced learners, featuring videos, articles, learning paths, and Q&A sections [2] - The community aims to provide a space for technical exchange and collaboration among peers in the large model domain [4] Group 4: Learning Pathways - The community has compiled detailed learning pathways for various aspects of large models, including RAG, AI Agents, and multi-modal training [4][9] - Each learning pathway includes clear technical summaries, making it suitable for systematic learning [4] Group 5: Benefits of Joining the Community - Members gain access to the latest academic advancements and industrial applications related to large models [7] - The community facilitates networking with industry leaders and provides job recommendations in the large model sector [7][68] Group 6: Future Plans and Engagement - The community plans to host live sessions with industry experts, allowing for repeated viewing of valuable content [65] - A focus on building a professional exchange community with contributions from over 40 experts from renowned institutions and companies is highlighted [66]

Core Viewpoint - The article emphasizes the importance of utilizing open-source large language models (LLMs) and retrieval-augmented generation (RAG) for businesses, particularly for small teams, rather than fine-tuning models without sufficient original data [2][6]. Group 1: Model Utilization Strategies - For small teams, deploying open-source LLMs combined with RAG can cover 99% of needs without the necessity of fine-tuning [2]. - In cases where open-source models perform poorly in niche areas, businesses should first explore RAG and in-context learning before considering fine-tuning specialized models [3]. - The article suggests assigning more complex tasks to higher-tier models (e.g., o1 series for critical tasks and 4o series for moderately complex tasks) [3]. Group 2: Domestic and Cost-Effective Models - The article highlights the potential of domestic large models such as DeepSeek, Doubao, and Qwen as alternatives to paid models [4]. - It also encourages the consideration of open-source models or cost-effective closed-source models for general tasks [5]. Group 3: AI Agent and RAG Technologies - The article introduces the concept of Agentic AI, stating that if existing solutions do not work, training a model may not be effective [6]. - It notes the rising demand for talent skilled in RAG and AI Agent technologies, which are becoming core competencies for AI practitioners [8]. Group 4: Community and Learning Resources - The article promotes a community platform called "大模型之心Tech," which aims to provide a comprehensive space for learning and sharing knowledge about large models [10]. - It outlines various learning pathways for RAG, AI Agents, and multi-modal large model training, catering to different levels of expertise [10][14]. - The community also offers job recommendations and industry opportunities, facilitating connections between job seekers and companies [13][11].

Core Insights - The CEO of Nvidia, Jensen Huang, highlighted the rapid advancement of artificial intelligence in China, noting that the country is progressing at an incredible speed [1] Industry Developments - The model layer in China features outstanding technologies such as DeepSeek, Alibaba, and Kimi from the "Dark Side of the Moon," indicating a strong competitive landscape [1] - DeepSeek is recognized as the world's first open-source inference model, marking a significant breakthrough in AI technology [1] Competitive Landscape - The competition in China is described as intense, with many individuals striving to establish great companies or launch significant businesses, leading to the development of increasingly impressive functionalities [1]

Core Insights - The AI sector is showing positive momentum, with the CSI Artificial Intelligence Theme Index rising by 0.3% and the Shanghai Stock Exchange Sci-Tech Innovation Board AI Index increasing by 0.2% [1] - Six major AI language models, including DeepSeek and Baidu's Wenxin Yiyan, scored no less than 50 out of 60 on a national college entrance examination essay, demonstrating their strong capabilities in language understanding and creation [1] - CITIC Securities indicates that the tech sector has recently rebounded from its lows and remains in a high cost-performance range, with improving risk appetite leading to significant gains in overseas markets, particularly in the tech sector, which will reflect on A-shares [1] Investment Opportunities - The AI industry chain is highlighted as a key area for investment, focusing on upstream computing power autonomy and downstream application innovation [1] - The Artificial Intelligence ETF (159819) and the Sci-Tech Innovation AI ETF (588730) cover the entire AI industry chain, providing convenient tools for investors to capitalize on industry development opportunities [1] - The latest scale of the Artificial Intelligence ETF (159819) exceeds 16 billion yuan, making it the largest among similar products [1] Index Composition - The Sci-Tech Innovation AI Index tracks 30 large-cap stocks involved in AI foundational resources, technology, and application support, with over 85% of its composition in the electronics and computer sectors [4] - The index is designed to focus on computing power and application segments, reflecting the growth potential in these areas [4]

作者 | 第一财经 刘晓洁、郑栩彤 一年一度高考来临，考生之后，按惯例是各家AI的答题时间。这一年AI模型圈加速迭代进化，让AI写作文，水平会有提高吗？让名师来打分，各大模型的成绩排名有多大变化？ "相较去年，AI进步很快，不再机械单一，变得有思想了。"在看完几家模型的作文后，四川南充市嘉陵一中语文教师李东林对第一财经表示。 2024年，第一财经写了一期九大模型"决战"高考，当时李东林老师在看完AI作文后，认为它们"缺一点情感和灵气"，到今年，他觉得AI已经补上了这方面，但仍达不到细腻。 "人写作，可以刻意抒情和感性，即使是思辨类题目，也需要去契合某些人的感受。"李东林认为，这是AI仍然缺少的部分。 湖南省中学语文特级教师左建国有同样的看法，他觉得Al作文整体有提升，但提升幅度不大。因为有关时代、社会与生活方面的新素材并不多，明显储存不够，尤其是在抒发个人情感方面， 仍然是短板，缺少生命的温度。 左建国老师曾担任高考作文阅卷组的副组长，在高考阅卷方面有十几年的经验。"Al作文已经把考场作文变成一种可计算的拼图，形式上固然能逼近完美，但个性化的思考，以及拨动人心的 语言，几乎没有看到，这是AI与真人思维的真正差 ...

Group 1 - The core viewpoint of the articles is that AI writing models have shown significant improvement over the past year, becoming more sophisticated and capable of expressing ideas, although they still lack emotional depth and personal touch compared to human writing [1][2][3] - The ranking of AI models has changed, with Google's Gemini and DeepSeek emerging as top performers, while previous leaders like Yuanbao and Tongyi Qianwen have dropped in rankings [2][3] - The evaluation of AI-written essays indicates that while models can perform well on structured topics, they struggle with more abstract or nuanced prompts, highlighting the limitations of AI in understanding complex human emotions [5][6] Group 2 - The performance of various AI models in the essay evaluation shows a range of scores, with Gemini and DeepSeek achieving average scores of 61.5, while models like ChatGPT and Zhiyu performed poorly, indicating a disparity in capabilities [3][12][14] - The feedback from educators emphasizes that while AI can generate coherent essays, they often lack the depth and emotional resonance that human writers can provide, which is crucial for achieving high scores in subjective evaluations [2][5][20] - The assessment process involved a specific high school essay prompt, which was designed to test the models' ability to engage with contemporary cultural and philosophical themes, further illustrating the challenges AI faces in nuanced writing tasks [6][20]