中经记者 谭伦 北京报道 随着数据以指数级速度形成"数字洪流"，治理能力已成为当前政企数字化转型的核心命题。IDC数据显 示，2025年中国数据产量将达51.78ZB，但有效留存率仅5.1%，超九成数据因治理缺失沦为"沉睡资 产"。 其中，政企领域的矛盾更显突出。据信通院《数据治理成熟度报告》调研，2025年78%的国内企业已实 施数据治理，但仅有不足30%的企业实现数据资产化运营，技术标准混乱、安全风险凸显等问题成为普 遍痛点。 在此背景下，日前举行的北京第四届数据治理年会期间，百分点科技政企事业部总经理马伟凯在现场向 《中国经营报》记者表示，随着AI时代来临，数据治理的焦点必须从"如何管好数据"转向"如何用好数 据"，而利用具备深度行业认知的垂直模型，解决垂直场景中复杂的治理痛点，有望成为破局关键。 行业的隐痛：治理3.0时代的必然选择 "数据治理"并非新鲜词汇。从早期的数仓建设到后来的数据中台，企业为了理清数据资产，投入了巨大 的人力财力。但在马伟凯看来，这个行业正面临着成长的瓶颈。 "回顾过去十年，数据治理大致经历了三个阶段。"马伟凯分析道。在1.0时代，行业比拼的是功能，看 谁能更快汇聚数据；在2. ...

投资者关系活动记录表 证券代码：300925 证券简称：法本信息 深圳市法本信息技术股份有限公司 投资者关系活动记录表 编号：2025-002 | | □特定对象调研 □分析师会议 | | --- | --- | | 投资者关系活 | □媒体采访 □业绩说明会 | | | □新闻发布会 □路演活动 | | 动类别 | | | | □现场参观 | | | 其他 2025 年度深圳辖区上市公司投资者网上集体接待日活动 | | 参与单位名称 | 参与本次 2025 年度深圳辖区上市公司投资者网上集体接待日活动的广 | | 及人员姓名 | 大投资者 | | 时间 | 年 月 日 2025 11 20 14:30-17:00 | | 地点 | 全景网"投资者关系互动平台"https://ir.p5w.net | | | 董事长兼总经理 严华先生 | | 上市公司接待 | 董事、董事会秘书兼副总经理 吴超先生 | | 人员姓名 | 财务总监 刘芳女士 | | | 独立董事 胡振超先生 | | | 公司参加"传价值・促信任・共机遇，助力上市公司高质量发展— | | | 年度深圳辖区上市公司投资者网上集体接待日"活动，于 ...

Core Insights - Huatu Education held an AI strategy conference, revealing its strategic planning, product achievements, and industry forecasts, focusing on the vast potential of the non-degree vocational education market and the opportunities for industry transformation [1] - The company aims to explore business growth in lower-tier markets, leveraging vertical large models as a technological foundation to reconstruct the delivery model of educational services [1] Financial Performance - In the first three quarters of 2025, Huatu Shanding reported revenue of 2.464 billion yuan, a year-on-year increase of 15.65%, and a net profit of 249 million yuan, reflecting a significant year-on-year growth of 92.48% [3][4] Market Strategy - The lower-tier market is identified as a new growth engine for non-degree vocational education, with a focus on providing full-time, long-cycle preparatory services to users returning to their hometowns [2] - Huatu Education plans to deepen its market presence through three key initiatives: regional operational reform, optimizing product offerings, and enhancing service processes to improve user experience and operational efficiency [2] AI Product Development - Huatu Education has developed a comprehensive AI product matrix, including 20 AI products that cover all learning scenarios from training to assessment, with significant applications in AI interview feedback and essay grading [4][5] - The company has seen a rapid increase in user engagement with its AI products, with monthly usage doubling, indicating strong market demand and product effectiveness [4][5] Data Utilization and Organizational Efficiency - The company emphasizes the importance of high-quality data collection and organization, possessing over 200,000 grading samples and investing significantly in data governance to enhance AI capabilities [5] - AI strategies extend beyond student-facing products to organizational operations, with nearly 70% of employees using AI tools, resulting in a 35% increase in enrollment conversion rates and over 50% improvement in sales efficiency [5] Industry Outlook - The vocational education market in China is projected to exceed 900 billion yuan in 2024, with expectations to surpass 1.2 trillion yuan by 2030, driven by data-driven educational models [6] - Huatu Education anticipates an increase in market concentration, aiming to raise its market share from approximately 5% to 30% by leveraging high-quality curriculum and AI efficiency tools [6]

Core Insights - The article discusses Huatu Education's AI strategy, focusing on its planning, product implementation, and industry forecasts, emphasizing the potential of the non-degree vocational education market and the need for a shift in educational service delivery models [1][2] Group 1: Financial Performance - In the first three quarters of 2025, Huatu Shanding reported revenue of 2.464 billion yuan, a year-on-year increase of 15.65%, and a net profit of 249 million yuan, reflecting a significant growth of 92.48% [1][3] - The non-degree training business generated revenue of 2.443 billion yuan, indicating strong performance despite industry pressures [3] Group 2: Market Strategy - Huatu Education is focusing on the lower-tier markets, recognizing a demand for full-time, long-cycle preparatory services among users returning to their hometowns [1][2] - The company plans to deepen its market penetration through three key initiatives: regional operational reforms, optimized product offerings, and enhanced service processes [2] Group 3: AI Product Development - Huatu Education has developed a comprehensive AI product matrix, including 20 AI applications that cover all learning scenarios from training to assessment [3][4] - The AI interview evaluation and essay correction products have shown industry-leading user engagement, with monthly usage doubling [4][5] Group 4: Data and Technology - The company has invested significantly in data collection and organization, amassing over 200,000 correction samples and utilizing 3,000 teachers and 300,000 hours of data governance to create high-quality structured data [5][6] - Huatu's AI strategy extends beyond student-facing products to enhance organizational efficiency, with approximately 70% of its 7,000 employees using AI-driven tools to improve performance metrics [5][6] Group 5: Industry Outlook - The vocational education market in China is projected to exceed 900 billion yuan in 2024 and reach 1.2 trillion yuan by 2030, indicating substantial growth potential [6] - Huatu aims to increase its market share from approximately 5% to 30% by leveraging high-quality curriculum and AI efficiency tools, anticipating a rise in industry concentration [6]

Core Insights - The article discusses the current state of AI Agents, highlighting the hype surrounding them and questioning their actual competitiveness and effectiveness in the market [1][3][4] - It emphasizes the disparity between capital interest in AI Agents and user dissatisfaction, particularly focusing on the case of Manus and its product Wide Research [3][4][5] - The article explores the reasons behind the perceived bubble in the Agent market, including technological mismatches, capital-driven narratives, and misjudged application scenarios [1][2][4][8] Group 1: Market Dynamics - The rise of AI Agents has been driven by breakthroughs in tool-use capabilities, with a shift from merely providing answers to executing actions [2][4] - There is a growing concern about the high user drop-off rates after initial trials of Agent products, indicating a potential overextension of the "universal Agent" narrative [1][4][5] - The competition has shifted from model parameters to the combination of models and ecosystem tools, reflecting a change in market focus [2][4] Group 2: Product Competitiveness - Manus's Wide Research product has been criticized for its high resource consumption and lack of clear performance comparisons with existing solutions [4][5] - The product fails to address critical barriers such as specialized data, dedicated toolchains, and industry certifications, leading to a lack of competitive advantage [4][5] - The general sentiment is that while AI Agents promise efficiency, they often do not solve complex decision-making problems, resulting in low user retention [5][10] Group 3: Capital and Investment Trends - The article notes that the current investment climate is characterized by a speculative bubble, with many startups leveraging the term "Agent" to attract funding without delivering substantial value [8][9][10] - Investors are often driven by narratives of potential market disruption rather than actual product efficacy, leading to a disconnect between capital inflow and user experience [9][10] - The article highlights the risk of a rapid market correction as user experiences fail to meet inflated expectations set by marketing [9][10] Group 4: Technical Limitations - The article outlines several technical limitations faced by AI Agents, including issues with data quality, integration complexities, and the need for robust auditing capabilities [10][11][12] - It discusses the challenges of achieving reliable performance in real-world applications due to the inherent complexity of tasks and the limitations of current AI models [10][11][12] - The lack of a cohesive ecosystem and the reliance on outdated protocols hinder the effective deployment of AI Agents in various business contexts [15][26][27] Group 5: Future Outlook - The article suggests that the future of AI Agents lies in developing specialized, vertical solutions rather than attempting to create one-size-fits-all products [12][14][26] - It emphasizes the importance of integrating AI capabilities into existing ecosystems to enhance functionality and user experience [12][14][26] - The potential for a more mature Agent ecosystem is contingent upon overcoming current technological and market challenges, with a focus on delivering tangible value to users [12][14][26]

Core Insights - The global embodied robotics market is projected to grow from $8.5 billion in 2024 to $65 billion by 2030, with a compound annual growth rate (CAGR) of 40.2% [1] - Investment in the robotics sector has exceeded $12 billion this year, marking a 185% year-on-year increase, indicating strong growth momentum [1] - Humanoid robots are identified as the most promising market segment, with significant potential for future development [1] Industry Developments - The event "Robot Industry Academic-Research Connection Conference" gathered over 50 representatives from government, industry, academia, and investment institutions to align robotics technology with market needs [1] - The focus on user-centered and demand-driven solutions is emphasized as crucial for transitioning robots from production to practical applications [1][2] - The demand for four-legged robots, particularly in the pet market, is highlighted, especially in international markets, with an emphasis on emotional value in human-robot interaction [2] Investment Insights - Investment is considered a vital source of growth for the robotics industry, with predictions that the humanoid robot market will exceed $10 billion by 2030 [2] - Recommendations suggest that investment should target upstream components or downstream application scenarios as the robotics landscape stabilizes [2]

Group 1 - Musk announced that Grok 4 is now available for free to all users, with a daily query limit for free users [2] - NASA and Google are collaborating to develop an AI medical assistant called "Crew Medical Officer Digital Assistant" (CMO-DA), which aims to help astronauts diagnose and treat symptoms without a doctor [2] - The CMO-DA has shown accuracy rates of 74% for back pain, 80% for ear pain, and 88% for ankle injuries [2] Group 2 - A TBM big data mining community was established in Zhengzhou, attracting over 400 representatives from more than 160 domestic units [3] - The event also launched the first vertical large model in the tunnel and underground space field, named "Pioneer Tunnel Model" [3] - The model integrates AI and covers the entire lifecycle of tunnel construction, achieving safety, quality, efficiency, and sustainability in projects like the plateau railway tunnel and the Chongqing-Taiwan Yangtze River Tunnel [3]

Core Viewpoint - The introduction of China's first vertical large model in the tunnel and underground space sector, named "Pioneer·Tunnel Large Model v1.0," marks a significant advancement in the industry, leveraging extensive engineering data for enhanced decision-making in tunnel design, construction, and operation [1]. Group 1 - The model is based on data from 773 engineering lines and 120 billion construction data points, establishing a technical system for large model scheduling driven by smaller models [1]. - It aims to assist in multiple typical application scenarios, providing generative solutions for tunnel design, construction, equipment, and operation and maintenance [1]. - The model has already been validated in several major projects, including the plateau railway tunnel, Chongqing-Taiwan Yangtze River Tunnel, and Shenzhen-Jiangmen Pearl River Estuary Tunnel [1].

Core Insights - The "Pioneer Tunnel Model v1.0" is the first vertical large model in China's tunnel and underground space sector, showcasing advanced technology in this field [1] - The model is built on a substantial data foundation, utilizing 773 engineering lines and 120 billion construction data points, establishing a comprehensive technical system for vertical domain modeling [1] - The model aims to assist decision-making across various applications in tunnel design, construction, equipment, and operation, leading to generative solutions [1] - Successful validation of the model has been completed in significant projects, including the plateau railway tunnel and the Changjiang River tunnel [1]

Core Insights - OpenAI has introduced a learning mode in ChatGPT aimed at enhancing educational outcomes by guiding users through problem-solving rather than simply providing answers [1][2][4] - The learning mode is designed to help both students and teachers, potentially changing the way AI is utilized in educational settings and addressing concerns about its impact on traditional learning [2][4] - The introduction of this mode may pose a challenge to existing vertical AI education models, which currently excel at answering questions but lack the ability to provide comprehensive learning plans [3][4][5] Industry Trends - The rise of AI in education has led to a surge in AI applications and hardware, yet companies like Gaotu and TAL Education have not seen stock prices recover to pre-"double reduction" levels, indicating limited impact from the AI education concept [3] - Current vertical AI models are criticized for their strong problem-solving capabilities but weak teaching abilities, highlighting a gap in their effectiveness compared to the new ChatGPT learning mode [3][4] - The competitive landscape is shifting as OpenAI's advancements in general models, such as the learning mode, challenge the relevance of specialized vertical models, prompting concerns among AI entrepreneurs [5][6]