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被踢出中国市场!垄断中国30年,却扬言绝不培养中国员工
Sou Hu Cai Jing· 2025-08-09 01:45
Core Perspective - Oracle's decline in the Chinese market illustrates the risks of complacency and neglecting local talent development after a long period of market dominance [2][28]. Group 1: Market Entry and Dominance - Oracle entered the Chinese market in the late 1980s, capitalizing on the demand for advanced information technology during China's reform and opening-up period [4]. - The company quickly established a monopoly in the database market, with enterprises paying substantial software and service fees, sometimes reaching hundreds of millions annually [5][8]. Group 2: Business Practices and Employee Treatment - Over time, Oracle's business practices became increasingly aggressive, with exorbitant pricing and mandatory maintenance fees, leading to strained relationships with clients like China Unicom [7][10]. - The company's founder publicly stated a refusal to train Chinese employees, creating a culture where local staff were relegated to menial tasks, while key technical roles remained occupied by foreign employees [12][14]. Group 3: Rise of Domestic Competitors - Discontent among Chinese enterprises led to a surge in domestic database development, with Alibaba, Huawei, and Tencent launching competitive products that outperformed Oracle's offerings at lower prices [16][19][21]. - The Chinese government's push for information technology localization and data security further accelerated the adoption of domestic databases, as critical sectors sought to reduce reliance on foreign technology [23][24]. Group 4: Oracle's Decline - Oracle failed to adapt to market changes, clinging to outdated technology while competitors embraced cloud computing and big data [26]. - A significant layoff in 2019, where over 900 employees were let go, signaled Oracle's retreat from the Chinese market, further confirmed by the removal of its database from key systems by major clients like Taobao [26][28].
从算力设施到软件工具 华为云全栈AI产品智能升级
Bei Ke Cai Jing· 2025-04-14 08:56
4月11日,在华为云生态大会2025上,华为云发布了全新升级的昇腾AI云服务、软件开发生产线 CodeArts、数字内容生产线MetaStudio、云原生安全体系、数据治理生产线DataArts、GaussDB数据库等 产品及解决方案。 "华为云将在打造对等多元算力基础设施、做好昇腾AI云服务支持百模千态、以核心软件与工具链重塑 应用、做好智能世界'黑土地'四大方面持续压强投入,解决AI落地时的算力、幻觉、数据、安全等复杂 挑战,加速千行万业智能升级。"华为公司副总裁、中国云业务部部长张修征表示。 当前,大模型正向多模态、长序列、MoE、深度思考演进,模型规模走向万亿甚至十万亿参数,算力、 网络和存力需求激增,AI基础设施体系面临重塑。 华为云EI服务产品部部长尤鹏指出,为了应对AI大模型时代"三高一低"挑战,华为云通过重构AI基础设 施,聚焦算力底座、资源调度、推理服务及集群可靠四个方面全面升级昇腾AI云服务。算力底座上, 华为云正式推出基于新型高速总线架构CloudMatrix 384超节点集群的新一代昇腾AI云服务,可提供大规 模、高性能、稳定可靠的AI算力集群。 大模型催生海量数据需求,但也让数据管理 ...
华为云创新系统架构 发布基于CloudMatrix 384超节点的全栈AI能力
Zhong Guo Jing Ji Wang· 2025-04-14 05:48
Core Insights - Huawei Cloud has launched a series of upgraded products and solutions including Ascend AI Cloud Services, CodeArts, MetaStudio, DataArts, and GaussDB to enhance its cloud offerings and address the challenges of AI implementation [1] Group 1: AI Infrastructure and Services - Huawei Cloud introduced the new generation of Ascend AI Cloud Services based on the CloudMatrix384 super-node cluster, providing large-scale, high-performance, and reliable AI computing power to meet complex model training and inference needs [2] - The GaussDB database has been upgraded to enhance its distributed performance by 50%, offering PB-level data management capabilities and a tenfold increase in online expansion speed [2] - Huawei Cloud has established a comprehensive cloud-native security system with multiple layers to ensure data security and compliance, launching four major security solutions including an end-to-end large model security solution [2] Group 2: Software Development and Support - The CodeArts toolchain has been enhanced to improve end-to-end development efficiency by 30%, covering various intelligent scenarios such as code generation, inspection, and testing [3] - Huawei Cloud supports Sina Weibo's product upgrades and innovations, providing robust elastic scaling capabilities during peak traffic periods and improving cluster utilization by 40% [3] - The Flexus enterprise large model application solution has been introduced to lower the barriers for small and medium-sized enterprises to utilize large models, significantly enhancing reliability and user experience [3]