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野田哲夫:AI大模型开闭源路线之争是伪命题,关键是……
Sou Hu Cai Jing·2025-10-10 02:08

Core Viewpoint - The competition between open-source and closed-source AI models is intensifying, with open-source models like DeepSeek and Qwen leading China's tech advancement globally. However, concerns about the profitability and economic impact of open-source models persist [1]. Group 1: Open-source vs Closed-source - Open-source software is characterized by community involvement beyond organizational boundaries, allowing for sustainable development and ecological growth [3]. - The distinction between open-source and closed-source languages is crucial, as open-source fosters broader participation and innovation [5]. - The coexistence of open-source and closed-source models is expected in the Web3.0 era, with both contributing to software development and user choice [6][9]. Group 2: Economic Impact of Open-source - Japan's experience with Ruby demonstrates that open-source languages can significantly enhance regional IT industries, allowing smaller contractors to engage in larger projects [10]. - The presence of Ruby in Shimane Prefecture has led to the establishment of a local ecosystem that attracts talent and fosters economic growth [10][11]. - The local government's support for Ruby-related projects has been successful, but there are concerns about potential complacency among companies due to guaranteed work [14]. Group 3: Lessons for China - China can learn from Japan's open-source initiatives to develop its own regional economic engines, particularly in the context of AI models like DeepSeek and Qwen [11]. - The importance of education in promoting open-source understanding and participation is emphasized, suggesting that fostering a culture of open-source can lead to better talent retention in local areas [13]. Group 4: Future of Programming Languages - The rise of AI may challenge traditional programming languages like Ruby, but the need for skilled programmers who understand both high-level and low-level languages remains critical [15]. - The potential for natural language programming through AI could lead to a divide between those who understand programming and those who rely solely on AI-generated solutions [17].