Core Insights - TensorFlow, once a dominant open-source framework, is now experiencing a significant decline in community activity, contrasting sharply with the rising popularity of PyTorch [3][8][11] - The analysis presented by Wang Xu at the recent Bund Conference highlights the rapid changes in the open-source landscape, where project viability is now measured in days rather than years [11][12] - The latest release of Ant Group's open-source ecosystem map has officially removed TensorFlow, indicating its diminished status in the AI open-source community [8][11] Group 1: Trends in Open Source Projects - The open-source ecosystem is witnessing a rapid turnover, with many projects being removed from the latest ecosystem map due to declining activity and relevance [11][12] - The OpenRank algorithm, which evaluates project influence based on collaboration networks, has been updated to reflect the current state of the ecosystem, resulting in a 35% replacement rate of projects in the new version [11][12] - Projects that fail to maintain community engagement or lag in iteration speed are particularly vulnerable to being excluded from the ecosystem map [12][14] Group 2: Evolution of Open Source Definition - The definition and operational model of open source are evolving, with many high-activity projects not adhering to traditional open-source licenses [17][20] - New licensing models are emerging that balance community engagement with commercial interests, indicating a shift towards a more pragmatic approach to open-source development [22][23] - The trend reflects a growing emphasis on community activity metrics over strict adherence to open-source principles, as projects seek to leverage community support for market success [21][22] Group 3: Shifts in Competitive Landscape - The focus of competition in the AI open-source space is shifting from broad functionality to performance optimization, particularly in model serving and inference efficiency [27][30] - High-performance inference engines are becoming critical as the industry transitions from exploration to practical implementation, with projects like vLLM and TensorRT-LLM leading the way [30][31] - The competitive landscape is increasingly defined by the ability to optimize model performance and reduce inference costs, marking a significant change in developer priorities [30][32] Group 4: Global Contribution Dynamics - The global AI open-source landscape is characterized by a "dual center" model, with the United States and China emerging as the primary contributors [33][35] - The U.S. leads in AI infrastructure contributions, while China shows strong growth in application innovation, reflecting a complementary dynamic between the two regions [35][36] - The active participation of Chinese developers in the AI agent domain is driven by the demand for AI solutions across various industries, highlighting a bottom-up innovation model [36]
昔日王者TensorFlow，已死
3 6 Ke·2025-09-15 01:29