点击图片查看完整路演回放↑↑↑ 6月4日，我们非常荣幸地邀请到了深圳市日斗投资管理有限公司的创始人、董事长王文先生做客私募排排网直播间， 王总深入剖析了当前资本 市场的关键趋势， 从人工智能产业的突破性进展，到港股市场的投资机会，再到消费行业的价值挖掘， 为我们带来了一场精彩纷呈的投资盛 宴。王总特别提到"期待中国股票市场的DeepSeek崛起时刻"，这一新颖比喻引发了广泛共鸣。以下小编整理出来的直播精华片段： （ 点击图片 查看完整直播回放 ） Q：王总，您近期提到"期待中国股票市场的DeepSeek崛起时刻"，这个比喻非常新颖，能否请您解释一下，什么是"DeepSeek崛起时刻"？ （ 点 击蓝字查看完整直播回放 ） 王文：今年春节期间，市场对贸易战的担忧情绪较为浓厚。与此同时，Deepseek公司的崛起改变了市场对中国人工智能产业的认知。此前普遍 认为中国人工智能技术与美国存在十年以上的差距，数字鸿沟难以逾越 。但Deepseek的出现证明中美技术差距远比预期要小，这显著增强了市 场对中国科技实力的信心。 从供给端观察，在A股IPO节奏调整的背景下，港股市场承接了大量内地优质企业的上市需求，为投资者提供 ...

Core Viewpoint - The article discusses the decline of Google in the AI sector, highlighting its transition from a dominant player to a follower in the face of competition from OpenAI and other emerging companies [1][6][21]. Group 1: Google's Historical Dominance - Google was once the absolute leader in AI, known for significant breakthroughs such as the invention of the Transformer architecture and the development of AlphaGo [3][12]. - The company was a hub for top AI talent, with its research leading to numerous milestones in the field [3][12]. Group 2: The Impact of ChatGPT - The launch of ChatGPT in late 2022 disrupted Google's position, showcasing superior conversational capabilities and rapidly gaining over 100 million monthly active users [6][12]. - Google's rushed response with its Bard application was met with criticism, leading to a significant drop in its stock price and market capitalization [6][12]. Group 3: Recent Developments and Challenges - At the 2025 developer conference, Google announced several AI products, but many were still in testing phases, lacking the innovative breakthroughs seen from competitors like OpenAI [3][8]. - Analysts noted that Google's efforts appeared more as a reaction to competition rather than proactive innovation, with many products resembling existing market offerings [8][11]. Group 4: Strategic and Organizational Issues - Google's reliance on advertising revenue has made it hesitant to fully embrace AI search capabilities, fearing a decline in ad revenue as AI-generated results reduce user clicks [12][13]. - Internal bureaucratic challenges and a lack of collaboration between its AI research teams have hindered effective innovation and product development [15][20]. Group 5: Market Position and Future Outlook - Google's market share in search has been declining, with figures showing a drop below 90% for the first time in years [17]. - The company faces significant challenges in regaining its competitive edge, as it struggles to attract and retain top talent while competing against more agile startups [20][21]. - Despite its current struggles, there remains potential for Google to leverage its technological foundation and resources to adapt and innovate in the AI landscape [21].

Core Viewpoint - Google's Gemini 2.5 Pro has been launched as an upgraded version of its flagship model, maintaining its top position in the LMArena rankings, but developer feedback indicates a divide in actual application experiences [1][6]. Performance Metrics - Gemini 2.5 Pro achieved higher scores in multiple AI performance benchmarks, with an Elo score increase of 24 points, reaching a total of 1470 [1][2]. - In specific tests, Gemini 2.5 Pro outperformed OpenAI's models in areas such as GPQA and the "Humanity's Last Exam," scoring 21.6%, which is 1.3 percentage points higher than OpenAI's o3 [2][3]. Competitive Landscape - Despite high scores, there are concerns about the practical utility of Gemini 2.5 Pro, with some developers favoring Anthropic's Claude series for programming tasks [4][5]. - The competition among models is shifting from mere scoring to performance in specific application scenarios, with developers increasingly valuing real-world effectiveness [6][7]. Cost Efficiency - Gemini 2.5 Pro offers a more cost-effective pricing structure compared to OpenAI's o3 and Claude 4 Opus, with input costs at $1.25 and output costs at $10 per million tokens, while OpenAI's prices are significantly higher [6][7]. Developer Feedback - Developer experiences vary, with some reporting superior performance from Gemini 2.5 Pro in coding tasks, while others find Claude models to be more effective in specific programming scenarios [5][6].

Investment Rating - The semiconductor production equipment industry is rated as Attractive [5][70]. Core Insights - The imminent launch of DeepSeek R2, which features 1.2 trillion parameters and significant cost efficiencies, is expected to positively impact the Japanese semiconductor production equipment (SPE) industry [3][7][11]. - The R2 model's capabilities include enhanced multilingual support, broader reinforcement learning, multi-modal functionalities, and improved inference-time scaling, which could democratize access to high-performance AI models [7][9][11]. - The development of efficient AI models like R2 is anticipated to increase demand for AI-related SPE, benefiting companies such as DISCO and Advantest [11]. Summary by Sections DeepSeek R2 Launch - DeepSeek's R2 model is reported to have 1.2 trillion parameters, a significant increase from R1's 671 billion parameters, and utilizes a hybrid Mixture-of-Experts architecture [3][7]. - The R2 model offers cost efficiencies with input costs at $0.07 per million tokens and output costs at $0.27 per million tokens, compared to R1's $0.15-0.16 and $2.19 respectively [3][7]. Industry Implications - The launch of R2 is expected to broaden the use of generative AI, leading to increased demand for AI-related SPE across the supply chain, including devices like dicers, grinders, and testers [11]. - The report reiterates an Overweight rating on DISCO and Advantest, which are positioned to benefit from the anticipated increase in demand for AI-related devices [11]. Company Ratings - DISCO (6146.T) is rated Overweight with a target P/E of 25.1x [12]. - Advantest (6857.T) is also rated Overweight, with a target P/E of 14.0x [15].

Investment Rating - The semiconductor production equipment industry is rated as Attractive [5] Core Insights - The imminent launch of DeepSeek R2, which features 1.2 trillion parameters and significant cost efficiencies, is expected to positively impact the Japanese semiconductor production equipment (SPE) industry [3][7] - The development of lightweight, high-performing AI models like DeepSeek R2 is anticipated to democratize access to generative AI, thereby expanding the market for AI-related SPE [11] Summary by Sections DeepSeek R2 Characteristics - DeepSeek R2 is reported to have 1.2 trillion parameters, with 78 billion active parameters and utilizes a hybrid Mixture-of-Experts architecture [3] - The input cost for R2 is $0.07 per million tokens, significantly lower than R1's $0.15-0.16, while the output cost is $0.27 compared to R1's $2.19 [3][7] - Enhanced multilingual capabilities and broader reinforcement learning are key upgrades in R2, allowing it to handle various data types including text, image, voice, and video [9][11] Market Implications - The anticipated launch of R2 is expected to boost demand for AI-related devices, including GPU and HBM, as well as custom chips and other AI devices [11] - The report reiterates an Overweight rating on DISCO and Advantest, which are expected to benefit from increased demand for AI-related devices [7][11] Company Ratings - Advantest (6857.T) is rated Overweight with a target price of ¥10,300 based on expected earnings peak [16] - DISCO (6146.T) is also rated Overweight with a target P/E of 25.1x based on earnings estimates [13]

朱嘉明 《杭州模式：DeepSeek与中国算谷》 刘典 胡宇东 著 中信出版集团 2025年新年和春节前后，在中国发生了一桩现象级的事件：一个成立于2023年7月，仅仅有一年半历史 的大语言模型研发公司"深度求索"（DeepSeek）于新年前发布通用的大型语言模型DeepSeek-V3，且于 1月20日发布了用于完成复杂逻辑任务的DeepSeek-R1，形成了人工智能理念和实用的普及化冲击波， DeepSeek这个英文名字几乎做到了大众皆可正确发音的地步。 实现宏观和微观的完美融合 人们很快意识到，这桩现象级的事件发源地是杭州，"杭州六小龙"的名字随即进入人们的视野：除了 DeepSeek之外，还有游戏科学（Game Science），其最为人知的产品是《黑神话：悟空》；宇树科技 （Unitree），专注人形机器人开发；强脑科技（Brain-Co），致力于非侵入式脑机接口技术；云深处科 技（DEEP Robotics），开发四足机器人和全地形越野机器人；群核科技（Manycore Tech），是以GPU 集群和人工智能技术为底座的空间智能企业。 于是，一个事实上早已形成和得到深度发育的杭州模式，很快成为各界关 ...

Core Insights - DeepSeek has released an upgraded version of its R1 model, which shows improved performance compared to its predecessor and surpasses OpenAI's o3 model, although it still lags behind o4-mini(high) and Google's Gemini 2.5 Pro Preview 05-06 [1][2] Model Performance - The new R1 model achieved a total score of 63.55, an increase of 1.61 points from the previous version, placing it fourth in the rankings [2] - The highest score was obtained by o4-mini(high) at 70.51, followed by Gemini 2.5 Pro preview 05-06 at 66.48 [2] Reasoning and Instruction Following - The instruction-following capability of the new R1 model improved significantly, scoring 48.46, which is 17.09 points higher than the old version, but still falls short of international top models like o3 (66.95) and o4-mini(high) (68.07) [4] - The reasoning task scores showed a decline of 1.7 points compared to the old R1 model, with the main differences observed in mathematical and scientific reasoning tasks, while performing better in coding tasks [4] Reduction in Hallucination Rate - The updated R1 model has optimized its performance regarding "hallucination" issues, with a reduction in hallucination rates by approximately 45%-50% in tasks such as rewriting, summarization, and reading comprehension [4] - The hallucination rate for the new R1 model is now at 13.86%, a decrease of 7.16 percentage points, although it still has a significant gap compared to the best-performing model, doubao-1.5-pro-32k, which has a hallucination rate of only 4.11% [5] - The most notable improvements in hallucination rates were observed in text summarization and reading comprehension tasks, with reductions of 9.27% and 14.49%, respectively [5]

今天大家都在谈MS的这篇DeepSeek R2分析的报告，提前曝光了R2的性能和参数，我们简单总结一 下这个报告的核心内容： DeepSeek R2 使用了多达 1.2 万亿个参数，采用了新颖的架构，实现了运行成本的显著降低。其采用 混合专家混合（MoE）架构，有 780 亿个活跃参数。 并且R2 使用华为的 Ascend 910B 芯片进行训练，而非 NVIDIA 的芯片。 R2 增强了多语言覆盖能 力，能流畅处理非英语语言；扩展了强化学习，利用更大的数据集，使模型能够进行更具逻辑性和 更像人类的推理；增加了多模态功能，能够处理文本、图像、语音和视频数据；实现了推理时的缩 放，通过采用通用奖励模型（GRM），在推理过程中增加计算资源，从而提高了输出质量。 R2 具有高成本效益，输入成本为每百万代币 0.07 美元，输出成本为每百万代币 0.27 美元，而 R1 的 输入成本为 0.15-0.16 美元，输出成本为 2.19 美元。 由于这篇报告讲的人已经很多了，我们就不赘述了，而且报告也放到了星球中，有兴趣的朋友可以 到星球中看原文。 今天这篇文章来看另一篇AI的分析，Artificial Analysis ...

Core Viewpoint - The article discusses the emergence of AI and its transformative impact on industries, highlighting the importance of cognitive innovation and the role of organizations that can adapt and thrive in this new landscape [2][3][23]. Group 1: AI Development Milestones - The introduction of the Transformer model by Google's Brain Team in June 2017 laid the foundation for subsequent language model advancements [1]. - The explosive growth of ChatGPT in 2023 marked the beginning of AI commercialization, while DeepSeek's emergence in 2025 demonstrated a significant shift in industry perception by achieving technological parity at a fraction of the cost [3][12]. Group 2: Cognitive Innovation - The article emphasizes that the evolution of AI is not merely a technical race but a revolution in the underlying logic of cognitive innovation [4]. - The course led by Professor Li Shanyou aims to dissect the methods of innovation in the AI era, revealing the cognitive leap from technological breakthroughs to commercial applications [4][20]. Group 3: Case Studies and Competitive Dynamics - The course will analyze the rise of OpenAI, detailing its journey from Musk's vision to the rapid user adoption of ChatGPT, which reached over one million users in just five days [10][12]. - It will also explore DeepSeek's strategy of achieving a 90% reduction in training costs through its unique architecture, showcasing how a small team can outperform larger organizations [11][13]. Group 4: Practical Tools and Frameworks - The course will introduce a practical framework for innovation, focusing on model building, single-point breakthroughs, and team organization, which are essential for navigating the AI landscape [11][25]. - Participants will learn how to identify their business's cognitive axes and value dimensions, as well as the management principles of emergent organizations [11][25]. Group 5: Target Audience - The course is designed for various innovators, including entrepreneurs, executives, product managers, investors, and technology enthusiasts, who seek to leverage cognitive advantages in the AI era [17][18].

研究报告 『亿欧智库 目录 CONTENTS 01 政策与技术双轮驱动中国大模型产业落地提速 1.1 政策与技术推动中国大模型产业加速发展 1.2 党政及国央企率先落地大模型应用试点 02 大模型应用落地挑战催生一体机需求爆发 2.1 大模型应用落地面临挑战，大模型一体机可有效解决 2.2 中国大模型一体机需求爆发，开启千亿级市场 2.3 中国大模型一体机厂商优秀案例分析 ０３ 大模型一体机未来趋势展望 政策与技术双轮驱动中国大模型 产业落地提速 当前，人工智能相关政策呈现行 业应用与地方落实并进趋势，引 导AI大模型向更多行业场景赋能。 从落地层面来看，2024年以来大 模型采购金额急速攀升，中国大 模型商业化落地加速。2024年第 一季度大模型处于刚获备案阶段， 下半年招投标市场活跃，项目数 量和披露金额增长显著。 作为数智化转型领域的前沿探索 者，三大运营商以及金融、能源 领域企业在大模型投入上突出， 此外政务与教科投入也在前列。 其中，DeepSeek作为中国开源大 模型的代表，模型性能直逼o3和 Gemini 2.5 Pro，凭借其低成本、 高效能的特点，引起广泛关注。 截至目前，已有45%的央企完 ...