Core Insights - The article emphasizes the successful path taken by JD Cloud in the context of domestic chip and software development, highlighting the shift from policy-driven to market-driven approaches by 2025 [1][14] - JD Cloud's methodology of "gradual true replacement" is gaining traction as a practical model for domestic innovation, focusing on real business scenarios to validate technology [4][6] Group 1: Domestic Chip and Software Development - By 2025, domestic chips are expected to account for 40% of the market, with a notable increase in the performance of domestic software stocks [1] - The AI industry in China is transitioning from "usable" to "usable and effective," with AI workloads projected to dominate cloud computing by 2029 [1][10] Group 2: Challenges in Domesticization - The main challenges in domesticization are categorized into three areas: usability, controllability, and trustworthiness [2][3] - Usability issues arise from the need for stability in high-demand environments, particularly with the coexistence of X86 and ARM architectures [2] - Controllability concerns involve hidden costs related to software restructuring and personnel training, which can delay domesticization efforts [3] - Trustworthiness is critical, as businesses cannot afford the risks associated with complete system overhauls [3] Group 3: JD Cloud's Approach - JD Cloud's strategy includes multi-chip management to ensure system availability despite individual chip failures [5] - The gradual replacement strategy allows for controlled risk and cost management by starting with small-scale pilot projects [5] - JD Cloud leverages real business scenarios to refine its hardware and software solutions, ensuring continuous upgrades [5][9] Group 4: Technological Advancements - JD Cloud's JoyScale AI computing platform integrates various domestic chips, providing efficient computing solutions validated through extensive real-world testing [12] - The JoyBuilder model development platform enhances training and inference speeds while significantly reducing application costs [13][14] - Data security is prioritized through the use of national encryption standards and secure sandbox technologies, ensuring compliance and safety [14] Group 5: Market Perception and Future Outlook - The market's perception of domesticization is shifting from mere availability to the effectiveness and value of solutions [14] - By 2025, the focus will transition from policy-driven initiatives to commercially viable solutions that withstand extreme testing scenarios [14] - Companies like JD Cloud, which continuously enhance their capabilities, are expected to thrive in the competitive landscape of digital sovereignty [14]

京东云这条路走对了。 2025年，国产化已从政策议题演变为资本市场的核心主线。TrendForce数据显示，2025年国产芯片预期占比将提升至40%，加之近来国产软件板块持续发力，相关概 念股逆势表现活跃。在"安全可控"成为数字基建底层逻辑的背景下，市场不再满足于概念炒作，而是聚焦于真正具备落地能力的技术路径与商业闭环。 当大模型迈入全栈国产化时代，中国AI产业正从"能用"向落地深度应用要"好用"跃迁。Gartner预测到2029年，AI工作负载将占据云计算的50%，这意味着未来几年 AI将成为云技术发展的核心驱动力。数智化水平的提升，不仅倒逼算力基础设施升级，更对系统的可用性、可控性和可信度提出前所未有的挑战。 在此过程中，京东云以"渐进式真替真用"为方法论，依托万亿级真实业务场景打磨技术底座，其路径选择正引发行业广泛关注。 国产化三大关卡：可用、可控、可信 多位信创领域专家指出，当前国产化推进的最大瓶颈并非技术空白，而是生态割裂与迁移成本过高。"很多企业愿意支持国产，但不敢'一步到位'。"一位行业人士 坦言，"一旦核心系统停摆几小时，损失可能远超替代收益。" 三是"可信"鸿沟。企业无法承受"推倒重来"的风 ...

JoyAI大模型深度应用京东11.11 全场景应用：JoyAI重塑超级供应链，释放增长新动能 在京东内部，AI的应用已从单点尝试走向全域覆盖，成为提升运营效率、降低综合成本的关键力量。 以商家运营为例，曾经最费工费时的商品素材生成，通过京点点平台，可以实现秒级的批量生成，在京 东11.11期间，京点点累计生产了2亿张商品图，已覆盖超4000万商品，帮助商家快速丰富营销素材库； 当商家纠结如何制定运营活动时，京麦商家AI助手可以结合店铺历史数据给出经营建议，每周它都会 辅助京东商家提供超3000万次经营决策。在物流运输上，物流超脑大模型搭配狼族机器人集群极大提升 了运输效率，与人仓对比存储坪效提升了200%，人效提升到300%。 在消费端，AI的价值不止于效率，有温度的交互为用户带来情绪价值，也让商家收入增长。京东11.11 期间，京东智能客服累计咨询服务量超42亿次，价保、活动、外卖售后等场景化Agent问题解决率达 85%；作为京东百万商家正在使用的智能客服和导购助手，京小智5.0在京东11.11期间累计服务1.6亿 次，为用户精准找到心仪商品。在一个深夜，飞鹤京东自营旗舰店的智能客服京小智，在面对一位焦虑 ...