专题：2025可持续全球领导者大会&首届绿色产业与可持续消费博览会 2025可持续全球领导者大会于10月16日-18日在上海市黄浦区世博园区召开。会议期间，愉悦资本创始 及执行合伙人刘二海与新浪财经等进行对话。 刘二海深入阐释了当前全球化的新特征与中国企业的关键角色。他指出，全球化已从过去由大型跨国公 司主导、强调股东利益优先的模式，转向以中国企业为主导的"生而全球化"新阶段。这一转变的核心动 力在于中国研发能力的显著提升、数字基础设施（如AI技术）的广泛应用，以及对本地利益相关者利 益的重视。 刘二海以实际案例说明"生而全球化"企业的运作模式：例如愉悦资本投资的智能短途出行品牌 LEMMO，其设计研发在中国完成，生产环节分布在匈牙利和波兰，销售市场则聚焦欧洲，展现了从创 立之初就进行全球资源整合的能力。此外，在拉美布局的智能物流仓储、美国的快递公司等投资，也体 现了中国企业在全球范围内优化供应链与市场策略的新思路。 他强调，中国持续的工程师红利是支撑全球化进阶的重要基础。"回顾一下中国的工程师，中国每年毕 业的学生，1000万到1500万大学毕业生。这个数可能大家不一定有印象，北欧四国加在一起也就3000万 ...

Core Viewpoint - The article discusses the decline of TensorFlow as an open-source framework, contrasting it with the rapid rise of PyTorch and other emerging projects in the AI open-source ecosystem [3][8][54]. Group 1: Decline of TensorFlow - TensorFlow's community activity peaked but has since declined to its lowest point, even lower than its inception [3][10]. - Ant Financial's open-source technology committee vice-chairman Wang Xu announced TensorFlow's removal from the latest open-source landscape map, indicating its diminishing relevance [6][8]. - The decline of TensorFlow reflects a broader trend in the AI open-source landscape, where project lifecycles are now measured in days rather than years [10][53]. Group 2: Open-Source Project Dynamics - The latest open-source landscape map (version 2.0) shows a significant turnover, with 39 new projects added and 60 existing projects removed, indicating a rapid evolution in the ecosystem [17][18]. - Projects that fail to maintain community engagement or lag in iteration speed are at risk of being excluded from the landscape [19][20][21]. - The competitive nature of the AI open-source ecosystem emphasizes the need for continuous innovation and effective community management to sustain project viability [24]. Group 3: New Paradigms in Open Source - The definition and operational model of open source are evolving, with some high-activity projects not adhering to traditional open-source licenses [26][30]. - The operational attributes of open source are becoming more pronounced, with platforms like GitHub serving as critical channels for product release and community engagement [31]. - New AI open-source projects are increasingly adopting customized licensing terms to balance community benefits with commercial interests, indicating a shift towards a more pragmatic approach to open source [32][33]. Group 4: Competitive Landscape - The focus of competition in the AI ecosystem has shifted from broad functionality to performance optimization, particularly in model serving and inference efficiency [35][44]. - The decline in activity for agent frameworks suggests a transition from exploratory phases to more practical, performance-driven applications [41][42]. - The emergence of high-performance inference engines highlights the importance of optimizing model serving to reduce operational costs and enhance application viability [43][44]. Group 5: Global Contribution Dynamics - The global AI open-source landscape is characterized by a "dual center" model, with the U.S. and China as the primary contributors, each excelling in different technological domains [46][49]. - U.S. developers lead in infrastructure contributions, while Chinese developers show strong growth in application innovation, driven by local market demands [51][52]. - The evolving contribution dynamics reflect a shift towards application-driven innovation, with real-world needs shaping the development of AI tools and solutions [50].