本文来自微信公众号： 新智元 ，作者：新智元，编辑：Aeneas、好困，原文标题：《美国码农，正 被AI"大屠杀"！Karpathy惊呼，26届毕业生崩溃》，题图来自：AI生成 美国码农这个物种，正在逐渐灭绝。 这不是什么危言耸听的预言，而是正在发生的事实。 由AI导致的全球大裁员，在2025年达到了117万，这是自2020年以来的最高纪录。 2026年的计算机专业毕业生们，一毕业就得面对水深火热的局面——根本找不到工作！ 而美国劳工统计局的数据显示，美国程序员的就业率，已经暴跌了27.5%。 也就是说，几乎被砍掉三分之一。 怎么办？"这场残暴的欢愉，终将以残暴终结。" 一、美国码农，已经快灭绝了？ 如今在美国，程序员的就业率已经暴跌。 劳工局的数据，是跌了27.5%。 而斯坦福大学的研究发现，自从2022年底AI工具的普及，22至25岁的程序员就业率下降了近20%。 （《Canaries in the Coal Mine？》论文地址： https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/） 根据一家美国咨询公司 ...

美国码农，正在经历一场「大屠杀」，就业率已经暴跌27.5%，将近1/3的工作岗位在消失。2026年的CS专业毕业生，已无路可走。一位多年 程序员说：这个职业要消失了，愿我们能荣耀离场、玩得痛快。 美国码农这个物种，正在逐渐灭绝。 这不是什么危言耸听的预言，而是正在发生的事实。 由AI导致的全球大裁员，在2025年达到了117万，这是自2020年以来的最高纪录。 2026年的计算机专业毕业生们，一毕业就得面对水深火热的局面——根本找不到工作！ 怎么办？「这场残暴的欢愉，终将以残暴终结。」 美国码农，已经快灭绝了？ 如今在美国，程序员的就业率已经暴跌。 劳工局的数据，是跌了27.5%。 而斯坦福大学的研究发现，自从2022年底AI工具的普及，22至25岁的程序员就业率下降了近20%。（《Canaries in the Coal Mine？》论文地址： https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/） 研究人员分析了美国最大薪酬公司ADP的工资记录，追踪了2021 年至2025年7月间数百万名在数万家公司工作的员 ...

机器之心报道 编辑：泽南、陈陈 「0 代码」计算机课在教啥东西？ 这就是现代的软件开发吗？ 在这门课上，主讲 Mihail Eric 告诉学生们，课程的主旨就是教你在不编写一行代码的情况下进行编程开发，「如果你能在整个课程中不写一行代码，那就太棒 了。」这不是开玩笑，听课的学生必须在提交 Git 的作业里附带和 AI 的对话记录。 在这里，老师教的不是 AI 的原理或是调优方法，而是教你如何 Vibe Coding，具体来说是使用 Cursor 和 Claude 等 AI 代码工具，并在开发的过程中应对 AI 的幻 觉。CS146S 在 9 月份第一次上线，直接被斯坦福的学生们挤爆，候补名单超过了 200 人， 看起来在快速发展的大语言模型（LLM）的冲击下，最令人焦虑的不再是 AI 写作业、写论文会不会认定为作弊，而是如何面对充满 AI 的世界了。 目前，CS146S 的 Slide 已经更新到了最后一周：Week 10: What's Next for AI Software Engineering，感兴趣的同学可以去观摩一下。 谁也想不到，斯坦福大学计算机系今年最热门的课程，居然明牌不鼓励你写代码。 近 ...

Core Insights - The automotive industry is facing unprecedented challenges in software engineering, with the proportion of software developers at Geely increasing from less than 10% to 40% in recent years, highlighting the exponential growth in complexity as the codebase for smart vehicles surpasses 100 million lines [3][5] - Geely is leveraging AI technology, specifically through collaboration with Alibaba Cloud's Tongyi Lingma, to enhance development efficiency, achieving a 20% increase in coding efficiency and over 30% of code generation being AI-driven [5][6] - The shift from hardware-dominated to software-centric automotive products necessitates a transformation in development models, moving towards agile and DevOps methodologies to support rapid iterations [8][19] Development Challenges - The automotive industry is transitioning from distributed ECU architectures to centralized computing and service-oriented architectures (SOA), which significantly increases system integration complexity [8] - Compliance with stringent international safety standards such as ISO 26262 and ASPICE poses additional challenges, creating tension between rapid agile development and necessary safety protocols [8] AI Integration - Geely's R&D system encompasses application software development, embedded development, and algorithm research, with AI tools like Tongyi Lingma being integrated across all areas [10][11] - AI is being utilized to automate repetitive tasks, allowing engineers to focus on system architecture and core business logic, leading to a 30% efficiency improvement in coding phases [16][18] Knowledge Management - AI's ability to quickly read and interpret legacy code helps mitigate the challenges of "technical debt," allowing new engineers to understand complex systems more rapidly [17][18] - The collaboration between Geely and Alibaba Cloud aims to create a proprietary knowledge base that enhances AI's contextual understanding of Geely's specific technical stack and business logic [14][15] Role Transformation - The role of engineers is evolving from executors to "AI commanders," where they define problems and oversee AI execution, shifting the focus from implementation to strategic oversight [20][21] - The ultimate goal is to achieve a highly automated R&D environment, where AI and human engineers collaborate throughout the entire development process [22][23] Industry Implications - The demand for cross-disciplinary talent that understands both mechanical hardware and software systems is increasing, highlighting a significant skills gap in the automotive industry [23] - The integration of AI in software development may lower technical barriers, enabling engineers with mechanical backgrounds to participate more actively in software engineering [23]

Group 1 - Nanjing University aims to provide the best undergraduate education in China, launching initiatives to cultivate top innovative talents [1] - The university has established a new Robotics and Automation College at its Suzhou campus, introducing a major in Automation (Robotics Direction) focused on smart manufacturing and robotics technology [1] - The "Zhicheng Class" is introduced to strengthen practical training and industry-education integration, featuring a talent cultivation system driven by industry needs and deep involvement from central enterprises [1] Group 2 - In 2025, Nanjing University will add "Intelligent Science" and "Electronic Science" directions to its Mathematics and Physics programs, respectively [2] - The Kuang Yaming College will continue to reform its outstanding talent cultivation model, allowing students to freely choose their academic paths and mentors, with over 80% of graduates pursuing further studies at prestigious institutions [2] - New dual-degree programs in Software Engineering combined with Business Management and Economics will be introduced to foster interdisciplinary talents [3] Group 3 - The university will continue to offer various dual-degree programs, including Computer Finance, German Law, Intelligent System Integration, and Big Data Communication, to provide students with ample choices and development opportunities [3]

Core Viewpoint - The article emphasizes that human programmers possess superior capabilities compared to large language models (LLMs), despite the usefulness of AI tools in assisting with programming tasks [3][10]. Group 1: Human vs. AI Capabilities - The article discusses a scenario where a complex bug in Redis was addressed, highlighting the limitations of LLMs in generating innovative solutions compared to human creativity [5][10]. - It is noted that while LLMs can assist in problem-solving, they often lack the ability to think outside conventional frameworks, which is a significant advantage of human programmers [10]. Group 2: Practical Applications of LLMs - The author shares experiences of using LLMs for code review and idea validation, indicating that these tools can enhance productivity but cannot fully replace the nuanced understanding required in software engineering [3][10]. - The article mentions that LLMs can serve as a sounding board for ideas, providing feedback that can help refine thought processes [13]. Group 3: Software Engineering Complexity - The article points out that software engineering encompasses much more than just coding, including understanding client needs and requirements, which LLMs are currently ill-equipped to handle [14]. - It emphasizes the social attributes of software engineering, where human interaction and comprehension of client demands play a crucial role [14].