“CEO一登录，网站就崩了”，工程师紧急排查：AI写的Bug，差点甩锅给老板！

Core Viewpoint - The incident at Sketch.dev highlights the hidden risks associated with AI-generated code, where seemingly correct code can introduce significant bugs due to subtle changes during code refactoring [4][5][16]. Group 1: Incident Overview - Sketch.dev experienced a series of mini outages starting on July 15, attributed to CPU spikes and slow system responses [6][7]. - The initial investigation revealed that the outages coincided with the CEO logging into the system, leading to the temporary suspension of the CEO's account [3][9]. - Further analysis traced the root cause to a logic error introduced during a large-scale code refactoring, which involved AI-generated code [13][16]. Group 2: Technical Analysis - The problematic code was a result of moving code from one file to another, where a critical change from "break" to "continue" led to an infinite loop [15][16]. - The incident exposed the inadequacies of current tools in detecting minor code changes, particularly when large modifications obscure small but significant errors [16][18]. - AI-generated code is more prone to such errors due to its method of rewriting rather than directly copying and pasting, which increases the likelihood of transcription mistakes [18][22]. Group 3: Preventive Measures - To mitigate future risks, Sketch.dev has implemented a "clipboard" feature that allows the AI to copy and paste code, aiming to preserve the original logic [23]. - The team plans to integrate more advanced code formatting tools to ensure proper indentation and structure when pasting code [23][24]. - There is a call for Git to enhance its capabilities in detecting cross-file changes, which would significantly improve error detection in AI-generated code [24]. Group 4: Broader Implications - The incident at Sketch.dev is not isolated, as other developers have reported similar issues with AI tools leading to significant operational failures [25][28]. - A recent survey indicated that 66% of developers frequently encounter AI-generated code that is almost correct, leading to increased debugging time [35][36]. - Trust in AI tools remains low, with only 3% of developers expressing high confidence in their reliability, while 46% explicitly distrust them [37][39].