新大陆 20250512 摘要 • 新大陆与中科软国际合作，推动开源鸿蒙在金融场景的适配，包括 POS 机 具等金融终端产品；与电科院北京分公司合作，推动开源鸿蒙在煤炭行业 的应用，实现设备和软件一体化应用。 • 新大陆自动识别完成自研解码芯片与开源鸿蒙系统的适配，实现国产化替 代；工业级读码器产品通过开源鸿蒙兼容性测试，实现从芯片、模组到设 备端的全面适配；与华为签署开源鸿蒙生态使能合作协议，开发自主可控 的工业领域解决方案。 • 新大陆在网证网号领域与公安部一所合作开发解密卡芯片及解密算法，是 目前唯一一家拥有公安部一所解密授权的加密芯片供应商，为其网络认证 号码业务提供坚实基础。 • 《国家网络身份认证公共服务管理办法（征求意见稿）》推动网证网号发 展，传统身份证识别设备替换为网证网号适配设备市场规模约 400 亿至 800 亿元，软件端市场规模达千亿元以上，新大陆作为核心受益公司，有 望占据 20%-30%的市场份额。 • 新大陆与公安部一所独家合作，为其提供二维码解决方案，已在景区、酒 店、机场等场景实现商业模式落地，但大规模推广仍需政策推动。 Q&A 新大陆公司在鸿蒙操作系统方面的布局和进展如何？ ...

[Table_IndNameRptType] 全球科技 行业周报 Google 发布 Gemini 2.5 Pro AI 模型，关注鸿蒙产业机会 [Table_IndRank] 行业评级：增持 报告日期： 2025-05-12 行业指数与沪深 300 走势比较 分析师：金荣 分析师：来祚豪 执业证书号：S0010524100001 邮箱：laizh@hazq.com 分析师：刘京松 执业证书号：S0010524090001 邮箱 ：liujs@hazq.com 相关报告 ·行业点评：海内外 AI 发展势头强 劲，关注港股互联网反弹机会 2024-05 -07 ·公司点评：特斯拉 25Q1 点评：利润 低于预期，新车将于上半年推出 2025- 04-30 执业证书号：S0010521080002 邮箱：jinrong@hazq.com ·公司点评：谷歌 25Q1 点评：利润超 预期，全年 CapEx 维持 750 亿美元 202 5-04-30 ·公司点评：台积电 25Q1 点评：AI 需 求驱动业绩增长，25Q2 指引超预期 Opt imus2025-04-28 主要观点： [⚫Table_Summary] ...

点评报告 软通动力(301236.SZ) 庞倩倩 计算机行业首席分析师 执业编号：S1500522110006 邮 箱： pangqianqian@cindasc.com 证券研究报告 公司研究 盈利预测：公司积极开拓新技术、新赛道、新客户，在享受企业数字化 请阅读最后一页免责声明及信息披露 http://www.cindasc.com 1 快速整合同方计算业务，围绕国产化算力底座打造软硬协同。2024 年 1 月，软通动力完成同方计算机有限公司（简称：同方计算机）的并购整 合，并于 2024 年 11 月将同方计算机正式更名为"软通计算机有限公 司"。目前公司在已有的清华同方和机械革命品牌基础上，推出北京信 创整机品牌——软通华方，并在软硬件协同、产品出货规模、市场占有 率等方面表现出色。 硬件销售领域：根据 Canalys 机构报告，2024 年第四季度公司 PC（含 台式机与笔记本）出货量同比增长 126%，市场份额稳居国内第二。2024 全年同比增长 106%，市场份额较上年翻倍，达 9%，位列第三。PC 终端 业务在党政教育行业稳居第一。公司推出信创 AIPC、开源鸿蒙 AIPC/智 能交互平板、机 ...

行业研究 传媒 2025 年 05 月 12 日 证 券 研 究 报 告 传媒行业周观察（20250505-20250509） 夸克升级"深度搜索"功能，AI 应用方向催 推荐（维持） 化丰富；关注影视、游戏景气度回暖 【市场观点】 【投资建议】 华创证券研究所 证券分析师：刘欣 电话：010-63214660 邮箱：liuxin3@hcyjs.com 执业编号：S0360521010001 证券分析师：赵海楠 邮箱：zhaohainan@hcyjs.com 执业编号：S0360524070016 行业基本数据 | | | 占比% | | --- | --- | --- | | 股票家数(只) | 140 | 0.02 | | 总市值(亿元) | 15,612.09 | 1.58 | | 流通市值(亿元) | 14,165.64 | 1.80 | 相对指数表现 | % | 1M | 6M | 12M | | --- | --- | --- | --- | | 绝对表现 | 9.4% | 3.0% | 18.2% | | 相对表现 | 5.1% | 9.3% | 13.2% | -18% 1% 19% 38% ...

Core Viewpoint - The domestic large model industry is attempting to break through its current challenges and reconstruct a new order, but the unstable market environment poses significant risks [1] Group 1: Open Source Trends - DeepSeek has disrupted the industry's perception of open-source models, prompting OpenAI's CEO to reconsider the validity of open-source strategies [1] - Domestic large model companies like Alibaba, Baidu, and SenseTime are accelerating their open-source initiatives [1] - Open-source is seen as a key strategy to reduce dependency on foreign software and hardware, but the commercial viability of open-source projects remains complex [2][5] Group 2: Challenges in Implementation - Developers face significant technical adaptation and maintenance costs, despite open-source models lowering the technical barrier [4] - The integration of large models into existing systems requires extensive customization, which can be resource-intensive for companies [4] - The complexity of data acquisition, cleaning, and labeling poses additional challenges for businesses, particularly small and medium-sized enterprises [4] Group 3: Investor Sentiment - Investors are cautious about the open-source model due to the unclear profitability and traditional software sales evaluation methods not being applicable [5] - The potential for significant financial loss if investments in proprietary models are undermined by open-source alternatives is a concern for investors [4][5] Group 4: Business Models - Chinese large model companies are adopting a "free-to-use plus value-added services" model to build a commercial framework around open-source models [6][8] - Companies like Baidu are leveraging their cloud services to monetize the usage of their open-source models, creating a win-win situation for developers and the company [8] - The success of open-source models may depend more on the quality of cloud services than on the models themselves, as seen in the strategies of Meta and Hugging Face [9][10] Group 5: Future Outlook - Open-source is viewed as a pathway for the Chinese large model industry to overcome technological barriers, but commercial sustainability is equally important [10] - The increasing tariff barriers from the U.S. add pressure to the large model industry, making the choice of cloud platforms more critical than the open-source models themselves [10]

整理 | 华卫、核子可乐 4 月 30 日凌晨，Meta 举行了其有史以来的首届 AI 开发者大会 LlamaCon。现场，该公司宣布推出一 款面向消费者的独立 Meta AI 聊天机器人应用程序，这款应用将与 ChatGPT 展开竞争，同时还发布 了一个面向开发者的 API，用于在云端访问 Llama 模型。 这两项产品发布都旨在扩大 Meta 开源 Llama AI 模型的应用范围，但这一目标或许只是次要的， Meta 的真正动机是：击败 OpenAI。从大致方向来看，Meta 的 AI 野心正推动形成一个蓬勃发展的开 源 AI 生态系统，而这将对像 OpenAI 这样将其模型封闭在服务背后的"封闭式" AI 供应商构成挑战。 首先，Meta AI 应用几乎像是对 OpenAI 传闻中要推出的社交网络的先发制人之举。作为一款智能助 手，Meta AI 基于 Llama 模型构建，集成了图像生成和编辑功能，可以了解用户的偏好、记住上下 文，能根据用户在应用程序上的活动提供个性化回复。到目前为止，这些个性化回复功能将在美国和 加拿大地区提供。用户还可以向 Meta 提供更多关于自己的信息，以便在未来与 AI ...

克雷西 明敏 发自 凹非寺 量子位 | 公众号 QbitAI 字节Seed首次开源代码模型！ Seed-Coder ，8B规模，超越Qwen3，拿下多个SOTA。 它证明 "只需极少人工参与，LLM就能自行管理代码训练数据" 。 通过 自身生成和筛选 高质量训练数据，可大幅提升模型代码生成能力。 这可以被视为对DeepSeek-R1模型自我生成和筛选训练数据策略的扩展。 一共包含三个版本： Base Instruct Reasoning 其中，Instruct在编程方面表现出色，拿下两个测试基准SOTA。 | Model | Size | | SWE-bench Verified | Multi-SWE-bench mini | | --- | --- | --- | --- | --- | | Agentless | | | OpenHands | Agentless | | ~8B Models | | | | | | Yi-Coder-9B-Chat | 9B | 0.0 | 1.6 | 0.0 | | Llama-3.1-8B-Instruct | 8B | 1.0 | 1.2 | 0.5 | | Q ...

Group 1: xAI Financing - xAI, an AI startup founded by Elon Musk, is negotiating a new round of financing with a potential valuation exceeding $120 billion (approximately 86.88 billion RMB) [1] - Investors are considering injecting $20 billion into xAI, although the specific amount may fluctuate as negotiations progress [1] - If successful, this financing would become the second-largest startup funding round in history, following OpenAI's $40 billion funding earlier this year, which valued OpenAI at $300 billion (approximately 217,000 million RMB) [1] Group 2: OpenAI Restructuring - OpenAI announced it will remain under the control of a non-profit organization, retracting a previous restructuring plan that aimed to shift control to a for-profit entity [2] - The for-profit LLC will transition to a Public Benefit Corporation (PBC), allowing it to pursue profit while also focusing on social missions [2] - The new structure will enable investors and employees to hold common stock without limits on appreciation, facilitating future fundraising efforts [2] Group 3: AI Programming Unicorn - Anysphere, the developer of the AI programming tool Cursor, completed a $900 million funding round, bringing its valuation to approximately $9 billion [5][6] - The funding round was led by Thrive Capital, with participation from notable investors such as a16z and Accel [5] - Cursor is recognized as one of the most popular AI tools in the programming sector, reflecting the growing interest in AI programming applications [6] Group 4: Google Market Value Drop - Google's parent company Alphabet experienced a market value loss of nearly $150 billion after Apple announced plans to introduce AI features in its Safari browser [4] - The stock price of Alphabet fell over 7% following the announcement, highlighting the competitive threat posed by AI technologies to traditional search engines [4] - The integration of AI into search functionalities is becoming a significant trend, with major players like Apple and OpenAI actively pursuing this direction [4] Group 5: Tencent's Video Generation Tool - Tencent's Hunyuan team released and open-sourced a new multimodal video generation tool called HunyuanCustom, which significantly improves performance over existing solutions [8] - The tool integrates various input modalities, including text, images, audio, and video, to generate videos [8] - This release is part of a broader trend of open-source video generation models competing with proprietary tools in the market [8] Group 6: Humanoid Robot Developments - Several humanoid robot manufacturers have updated their products, showcasing advancements in mobility and control [9] - The CL-3 humanoid robot by Zhijidongli features 31 degrees of freedom, enabling it to perform human-like movements [9] - The ongoing evolution of humanoid robots is highlighted by upcoming events such as the World Humanoid Robot Sports Competition [9]

Core Viewpoint - The automotive industry is experiencing a surge in power performance, but the ability of drivers to control this power is being overlooked. Lotus Cars aims to enhance driver safety through open access to its chassis database and the introduction of a free advanced safety driving training program [1][4]. Group 1: Industry Trends - The competition among automakers has intensified, with power outputs reaching unprecedented levels, such as 1,000 horsepower and 0-100 km/h in 3 seconds [2]. - Lotus Cars emphasizes that speed must be accompanied by safety, likening cars to tools rather than toys, and warns against the dangers of uncontrolled power [2][3]. Group 2: Technological Innovations - Lotus Cars has equipped its vehicles with 33 sensors, including 4 lidar units and multiple cameras, to ensure comprehensive environmental awareness and safety [2][3]. - The company has implemented a dual-redundancy control system, similar to those used in aviation, to minimize the risk of system failure [3]. Group 3: Safety Initiatives - Lotus Cars is opening its 77-year accumulated chassis database to the industry, aiming to improve safety standards across the automotive sector [4]. - The company is launching a free advanced safety driving training program in collaboration with the Shanghai Automobile and Motorcycle Sports Association, with a budget of 20 million yuan [5]. Group 4: Corporate Responsibility - Lotus Cars is committed to transforming its engineering expertise into accessible safety assets for the public, reflecting its dedication to corporate social responsibility [5]. - The company believes that understanding the physical limits of vehicles is crucial in the era of electrification and automation, prioritizing safety above speed [5].

Group 1: Founder Psychology and Roles - Eric Schmidt emphasizes the difference between founders and professional managers, stating that founders are visionaries while professional managers are "amplifiers" who help scale ideas [4][10] - Schmidt reflects on his experience at Google, noting that he was not a typical entrepreneur but rather a professional manager who contributed during the company's scaling phase [3][4] - He discusses the challenges of attracting talent, highlighting that many talented individuals often choose to start their own companies instead of joining established firms [3][5] Group 2: Market Dynamics and Startup Ecosystem - Schmidt points out that many startups are often acquired for their talent rather than their products, indicating a market structure that can be inefficient [6][7] - He notes that the majority of startups fail, with traditional venture capital experiences suggesting that 4 out of 10 will fail completely, and 5 will become "zombies" with no growth potential [7] - The conversation highlights the importance of competition for startups, suggesting that true leadership is demonstrated when facing challenges from larger companies [11][12] Group 3: AI and Future Trends - Schmidt believes that AI is currently underestimated rather than overhyped, citing the scaling laws that drive AI advancements [33][34] - He discusses the potential of AI to transform business processes and scientific breakthroughs, emphasizing the importance of understanding how humans will coexist with advanced AI systems [35][39] - The conversation touches on the competitive landscape between the U.S. and China in AI development, with China investing heavily in AI as a national strategy [41][42] Group 4: Talent Acquisition and Management - Schmidt stresses the importance of attracting top talent by creating an environment where individuals feel they are solving significant problems [18][20] - He differentiates between "rockstar" employees who drive change and "mediocre" employees who are self-serving, advocating for the retention of the former [21][22] - The discussion includes insights on how to identify and nurture high-potential talent within organizations [24][25] Group 5: Challenges in AI Development - Schmidt highlights the challenges of defining reward functions in reinforcement learning, which is crucial for AI's self-learning capabilities [51] - He warns about the potential pitfalls of over-investing in AI infrastructure without a clear path to profitability, suggesting that many companies may face economic traps [47][48] - The conversation concludes with a call for companies to focus on the most challenging problems in AI, as solving these will yield the most significant rewards [52][53]