当 AI 开始提笔写代码，程序员的饭碗真的要被抢了吗？ 关于这个话题，科技圈大佬们的看法堪称"神仙打架"。 Google 首席工程师 Jaana Dogan 曾分享过一个颇具冲击力的对比："谷歌团队耗时一整年攻坚的工作成果，Anthropic 的 Claude Code 仅用 1 小时，就拿出 了一份方向高度一致的方案。" 这并不是"AI 写几行脚本"的层面，而是直接对工程效率与组织协作方式的挑战。 而 Linux 之父 Linus Torvalds 则给出了截然不同的视角。他坦言："就我个人来说，因为我是维护者，代码审查对我非常重要，所以我对'用 AI 写代码'反 而没那么感兴趣。很多人一谈编程就想到用 AI 写代码，但在我看来，这远没有'用 AI 来帮助维护代码'有意思。" 话虽如此，这位嘴上说着 "不感兴趣" 的 大佬，不久前却悄悄体验了一把 Vibe coding，还顺势捣鼓出一个全新的开源项目AudioNoise，身体力行地打破了自己立下的 "flag"。 如果说 Jaana Dogan、 Linus 的态度仍然偏向克制与保留，那么 Redis 之父 Salvatore Sanfilippo（网名 ...

Core Insights - The article discusses the increasing productivity of coding due to AI tools like GitHub Copilot, but highlights the growing burden on code reviewers, particularly senior engineers, as code review becomes a new bottleneck [1][2][16] - AI can generate 70% of code quickly, but the remaining 30% involves complex issues that require human intervention, leading to a cycle of bugs and increased review time [8][9][16] Group 1: AI's Impact on Coding - AI tools are enhancing productivity, allowing junior developers to create functional code with minimal input, but this often results in technical debt and poorly structured code [4][5] - Senior engineers are facing increased pressure during code reviews as they must address the inadequacies of AI-generated code, which can lead to a significant increase in review workload [2][16] Group 2: Developer Trust and Skills - Developer trust in AI-generated code has declined, with only 60% expressing confidence compared to 70% two years ago, and 30% indicating a lack of trust [11] - There is a concern that over-reliance on AI may erode developers' ability to understand code and learn from mistakes, potentially impacting their coding skills [10] Group 3: Recommendations for Improvement - To mitigate the challenges posed by AI, teams are encouraged to implement "AI Free Sprint Days" to maintain problem-solving skills and create decision documentation to track key choices and pitfalls [12] - Emphasizing the importance of context in AI coding, developers should provide comprehensive information to improve code quality and ensure thorough testing of AI-generated outputs [13] Group 4: Real-World Productivity - Despite claims of AI boosting productivity by 5 to 10 times, evidence suggests that the actual efficiency gain is closer to 2 times, particularly when maintaining existing systems [14][16] - The increase in code review demands is primarily shouldered by senior engineers, whose limited availability exacerbates the bottleneck created by the influx of AI-generated code [16][17]

RT 𝘁𝗮𝗿𝗲𝘀𝗸𝘆 (@taresky)我自己能贡献一点的话，就是：1. 准备多个交易所日常有交易量、有提现记录的账号。最好升级高 VIP 联系客户经理再扩容。2. （国内/旅行用户）准备好固定的落地 IP，避免触发风控。3. 尽快开始用 AI 写代码，把小的需求自己快速实现。 ...

我自己能贡献一点的话，就是：1. 准备多个交易所日常有交易量、有提现记录的账号。最好升级高 VIP 联系客户经理再扩容。2. （国内/旅行用户）准备好固定的落地 IP，避免触发风控。3. 尽快开始用 AI 写代码，把小的需求自己快速实现。 ...