Siemiatkowski 表示，公司几年前确实曾计划停止招聘人工工作人员，并推出了 AI 代理，这不仅降低了 客户支持成本，还提升了每位员工的收入。他补充道，两年前公司员工人数为 5,500 人，而现在大约为 3,000 人；随着薪资成本的下降，Klarna 现在希望将大部分节省下来的资金重新投资于员工的现金奖励 和股权激励。 "我妻子教会了我一些东西，"Klarna CEO Sebastian Siemiatkowski 在伦敦 SXSW 的观众面前说道。他回 应了此前有报道指出该公司曾表示利用 人工智能完成相当于700名工人工作的消息，而如今又有消息称 公司将招聘人工工作人员。"两件事可以同时成立，"他说。 但他也意识到，AI 不仅仅与员工有关。他提到了诈骗案件的上升，以及这种情况对像他本土瑞典这样 高信任度社会带来的影响。最近，《金融时报》报道了金融科技诈骗案件的增加，并指出，例如，新加 坡居民因天生容易信任各类机构，因此更容易成为目标。 "而且 AI 显然在加速这一趋势，"Siemiatkowski 说道。 Siemiatkowski 还再次解释了为何公司停止使用 Salesforce和 Workd ...

Financial Data and Key Metrics Changes - The company has experienced a stable revenue environment, with most revenues being recurring and insulated from short-term volatility [12][13] - The guidance for the ratings business reflects expected ups and downs due to market volatility, with a flat year-over-year build in issuance anticipated [23][25] Business Line Data and Key Metrics Changes - The mobility division is being spun off, which had shown 8.4% growth last year with a 39% margin, indicating strong performance [15][17] - Private credit revenues have grown strongly, with a reported 21% growth in enterprise private markets revenues in the current year [28] Market Data and Key Metrics Changes - The global debt markets are experiencing volatility, but the company has a solid foundation for understanding investor behavior, which informs their guidance [22][23] - The company anticipates a flat M&A environment, with pent-up demand expected to manifest in future years [25] Company Strategy and Development Direction - The company is focusing on integrating data teams and applying generative AI to enhance capabilities across divisions, aiming for accelerated growth [6][10] - The strategic growth themes include private markets and generative AI, with more details expected at the upcoming Investor Day [11] Management's Comments on Operating Environment and Future Outlook - Management has noted reasonable stability in customer behavior and robust pipelines across divisions, indicating a positive outlook despite macro volatility [12][13] - The company is committed to a disciplined approach to capital allocation, maintaining an 85% guideline for capital return to shareholders [72][73] Other Important Information - The mobility business is viewed as better suited to operate as a standalone entity, with limited synergies expected post-spin [18][20] - The integration of generative AI is seen as a significant opportunity for operational efficiency and margin improvement across the organization [61][63] Q&A Session Summary Question: What is the vision for S&P Global over the next three to five years? - The company aims to leverage its comprehensive market coverage and capabilities in benchmarks, analytics, and data to deliver value and accelerate growth [9][10] Question: Are there any areas of the business experiencing revenue pressures? - Most revenues are recurring and stable, with no major changes in customer behavior observed so far [12][13] Question: What is the rationale behind the mobility spin-off? - The mobility division serves a distinct customer base and is expected to perform better as an independent entity, allowing for greater focus and growth opportunities [17][20] Question: How is the company addressing the impact of AI on its market intelligence platform? - The company is integrating generative AI across the organization, focusing on enhancing capabilities and operational efficiencies [46][55] Question: What are the expectations for margin improvement in the market intelligence business? - The company anticipates margin improvements driven by generative AI integration and disciplined execution [63][64]

当内容成为UGC平台的核心竞争力，当AI训练成为各互联网 大厂 数字军备竞赛的焦点，一张800万的罚单揭开了 大 数据战争的冰山一角，也 将 重塑内 容平台的竞争规则。 近期，百度百科官方公众号转发了《海淀法院审结全国首例涉百科词条数据竞争案》一文。文中指出，被告未经许可，大量抓取百度平台60余万条百科词 条数据，行为构成了不正当竞争，判决被告删除涉案词条，并赔偿原告经济损失500万元及合理开支300万元。 而据知情人士所称，该案件诉讼双方分别为百度百科和被字节收购的互动百科（现抖音快懂百科），原告为百度，被告为字节。 有 部分观点为小鸡词典 打抱 不平，认为这是"以小博大"的不公对决，也有 观点认为 这是财力、精力的全方面比拼 。 其实，无论是字节的败诉还是微博 的胜诉，都是相对公平的判决，而 倘若 抛开两起事件表面的喧嚣，深入审视 两种 结局之后，看到的 则是 数据时代 所有 UGC平台对优质内容的强烈需 求与深层焦虑。 流量竞赛下的"内容透支" 抖音、微博、小红书等 内容 社区平台的成功，向业界展示了UGC模式的 魔 力。在这种模式下，用户成为内容的主要生产者， 平台上 日均生产的内容量 高达2000万 ...

今天分享的是：沙利文：2024年AI生命组学市场研究报告 报告共计：18页 一、行业定义与核心特征 AI生命组学以多组学数据为基础，通过AI算法实现数据的高效管理与挖掘、多维度整合分析及疾病机理解析。其关键特征包 括： - 数据智能处理：针对临床队列数据的高维度、高噪声特性，AI技术可自动完成降维、去噪和特征选择，优化专病队列设计，加 速生物标志物发现。 - 多组学整合分析：整合DNA测序、RNA表达谱、代谢物谱等多源数据，通过算法融合不同层面信息，揭示基因调控网络与代 谢途径的动态关联。 - 疾病与药物研究赋能：在致病机理研究中，AI通过对比患者与健康个体数据识别分子标志物；在药物开发中，支持靶点发现、 抗体优化及虚拟筛选，显著缩短研发周期。 - 个性化医疗应用：基于患者组学数据与临床病史，AI提供定制化治疗方案，覆盖肿瘤免疫疗法、罕见病诊断等领域，提升治疗 精准度。 二、市场分类与产业链布局 AI生命组学市场分为五大核心领域： 1. AI队列数据中心解决方案：智能化管理临床数据与样本，优化患者招募和试验设计，提升研究效率。 《2024年AI生命组学市场研究报告》核心内容总结 AI生命组学作为生命科学与人工 ...

Core Insights - The report highlights the rapid growth of the AI genomics market, which is projected to expand from 16.4 billion yuan in 2020 to 70.3 billion yuan by 2028, with a compound annual growth rate (CAGR) of 24.79% from 2020 to 2023 and an expected CAGR of 17.12% from 2023 to 2028 [1][27][28]. Market Overview - AI genomics integrates artificial intelligence with life sciences, focusing on the analysis of multi-omics data such as genomics, transcriptomics, and proteomics to advance disease mechanism research, drug development, and personalized medicine [1][5]. - The market has evolved through several stages: the initial phase of genomics (2000-2010), the expansion of proteomics (2010-2020), the integration of multi-omics (2020-2023), and the current growth phase (2023-present) [17][18]. Key Applications - Core applications include AI cohort data centers, AI-BT software platforms, multi-omics data analysis, medical-engineering translation, and AI medical technology services [1][13][14]. - AI cohort data centers enhance clinical trial processes by optimizing patient recruitment and managing clinical data effectively [31]. - AI-BT software platforms streamline biobanking and laboratory information management, improving data handling and compliance [37][38]. Industry Drivers - The growth of the AI genomics market is driven by the demand for precision medicine, cost pressures in drug development, policy support (e.g., "Healthy China 2030"), and advancements in technology such as cloud computing and deep learning [2][27]. - The COVID-19 pandemic has accelerated the focus on life sciences technologies and clinical data collection, highlighting the importance of genomics in public health [27]. Challenges and Opportunities - The industry faces challenges such as data heterogeneity and insufficient cross-institutional collaboration [2]. - Future opportunities lie in vaccine development, veterinary and traditional Chinese medicine research, microbiome applications, and clinical diagnostics [2][23]. Data Integration and Analysis - AI genomics excels in integrating and analyzing diverse multi-omics data, addressing issues of data complexity and heterogeneity [6][42]. - The use of AI in disease mechanism research allows for the identification of key molecules and pathways associated with diseases, facilitating targeted therapies [7][23]. Drug Development - AI genomics provides revolutionary tools for drug discovery, optimization, and development, enhancing the efficiency of identifying drug targets and predicting drug interactions [8][51]. - The integration of AI in drug development processes helps reduce timelines and costs while improving the success rates of new therapies [51][52]. Personalized Medicine - AI genomics supports personalized medicine by analyzing patient-specific omics data to tailor treatment plans, improving therapeutic outcomes [9][57]. - The technology enables precise identification of disease subtypes, guiding treatment decisions and minimizing adverse effects [9][62]. Industry Ecosystem - The AI genomics ecosystem includes various stakeholders such as pharmaceutical companies, hospitals, academic institutions, data management providers, and AI technology firms, all contributing to the advancement of healthcare [58][60]. - Collaboration among these entities is crucial for leveraging AI capabilities to enhance drug development and clinical applications [58][62].

每经记者 林姿辰 许立波 每经编辑 杨夏 美国时间4月10日，美国食品药品监督管理局（FDA）官网更新了一则具有里程碑意义的政策调整：计 划逐步取消在单克隆抗体疗法和其他药物研发中对动物实验的强制性要求。 实际上，2022年FDA就通过了《FDA现代化法案2.0》，提倡寻找动物实验的替代方案，但最新政策更 加具体，指明了智能计算模型、类器官与器官芯片、跨物种数据整合的三大替代方向。上述计划发布后 立刻引发了医药行业的震动，由于对动物实验更为依赖，以昭衍新药（603127.SH，股价16.03元，市值 120亿元）、查尔斯河（CRL，股价99.75美元，市值49亿美元）为代表的传统CRO（合同研究组织）股 价承受了巨大冲击。美国时间4月10日，查尔斯河股价单日下跌28.13%；4月11日，昭衍新药股价下跌 9.98%。 不过，行业也对这项新政抱有更为理性的看法。4月12日，多位业内人士也在接受《每日经济新闻》记 者采访时指出，虽然新规指明了方向，但要想完全替代动物实验，还需更多临床数据来向监管部门证 明，类器官在毒性预测的有效性方面能够达到甚至超过动物实验。目前，监管对新技术的审慎态度不会 一夜转向，大规模替代 ...