Core Viewpoint - The article presents a critical diagnosis of the current state of software quality, highlighting a systemic collapse exacerbated by increasing abstraction layers, AI automation, and energy consumption issues. It questions whether the current engineering quality can support the future digital world. Group 1: Software Quality Decline - Software quality is experiencing an exponential decline rather than a linear one, with many software incidents indicating that memory consumption metrics have lost their significance due to unaddressed memory leak bugs [7][8] - System-level crashes have become commonplace, with examples including Windows 11 updates causing failures and macOS Spotlight writing 26TB to SSDs in one night, exceeding normal levels by 52,000% [9][10] - A notable incident involved CrowdStrike, where a simple bug led to a global outage affecting 8.5 million Windows computers, resulting in at least $10 billion in economic losses [11][12] Group 2: AI's Role in Software Quality - The introduction of AI coding assistants has worsened the already precarious software quality situation, with AI-generated code exhibiting a 322% higher rate of security bugs compared to human-written code [21] - AI tools are amplifying the issues, as developers increasingly trust AI outputs over their own coding skills, leading to a potential crisis in the developer ecosystem [28][30] Group 3: Underlying Causes - The article identifies two main physical constraints affecting software quality: the "exponential tax" of abstraction layers, which can increase performance loss by 2 to 6 times, and the reality of energy consumption, with data centers consuming over 200 terawatt-hours annually [18][20] - Companies are spending 30% of their revenue on infrastructure to cope with these issues, a significant increase from the historical average of 12.5%, indicating a retreat rather than a proactive investment in quality [24] Group 4: Development Culture and Future Implications - The development culture has shifted to a mindset of "release first, fix later," leading to a lack of accountability and a growing gap in the developer ecosystem as junior developers are replaced by AI [11][28] - The article emphasizes the need for a return to fundamental engineering principles, such as proper memory management and algorithm complexity, to ensure sustainable software development practices [35][36]