Core Insights - The article discusses the phenomenon of "AI fatigue" among engineers, highlighting that increased efficiency in task completion does not equate to reduced workload, but rather leads to greater exhaustion due to expanded task volume and constant context switching [2][6][10]. - It emphasizes that the role of engineers has shifted from creators to evaluators of AI outputs, which can lead to decision fatigue and anxiety due to the unpredictability of AI-generated results [11][13][15]. - The article warns against the "FOMO treadmill," where engineers feel pressured to keep up with rapidly evolving tools and technologies, resulting in wasted time and knowledge decay [18][20][22]. Group 1 - AI can accelerate individual tasks, but this does not reduce the overall workload; instead, it leads to an increase in the number of tasks engineers undertake [10][11]. - The shift in work dynamics means engineers spend more time reviewing and evaluating AI outputs rather than creating, which is more mentally taxing [13][14]. - The unpredictability of AI outputs disrupts the foundational assumption of certainty that engineers rely on, leading to ongoing anxiety and stress [15][16]. Group 2 - The rapid pace of technological advancement creates a "FOMO treadmill," where engineers feel compelled to constantly adopt new tools, leading to inefficiencies and superficial knowledge [18][20]. - Engineers often find themselves in a cycle of switching between tools without achieving significant improvements, resulting in wasted effort and time [21][22]. - The article suggests that focusing on foundational infrastructure rather than chasing every new tool can lead to more sustainable practices [23]. Group 3 - The "prompt spiral" trap occurs when engineers become overly focused on refining AI prompts instead of addressing the core problem, leading to wasted time [25]. - Perfectionism in engineering can exacerbate frustration with AI outputs, which are often not perfect, causing engineers to spend excessive time making minor adjustments [26][27]. - The article highlights the importance of maintaining critical thinking skills, as reliance on AI can lead to a decline in independent problem-solving abilities [28][29]. Group 4 - The article advocates for setting boundaries around AI usage, such as time limits for tasks and accepting that AI outputs do not need to be perfect [34][37]. - It emphasizes the need for engineers to protect their cognitive resources and recognize that sustainable productivity is more valuable than merely increasing output [38][39]. - The conclusion stresses that the most successful engineers in the AI era will be those who know when to stop and prioritize their mental well-being over relentless productivity [40].