The article continues exploring Lisanne Bainbridge's insights from her 1983 paper on automation, focusing on how these ideas apply to today’s AI-driven work environments. Bainbridge emphasized the need for human operators to quickly recognize issues in automated systems, especially in high-stakes situations like industrial control. The author highlights that while white-collar work may seem less critical than industrial tasks, the pressure and speed at which employees must operate can create similar challenges. Companies often expect AI to boost productivity to superhuman levels, which can overwhelm human workers trying to keep pace. A significant point raised is the concept of "monitoring fatigue," where the sheer volume of information can lead to oversight. In AI systems, especially those driven by large language models (LLMs), the plans generated can be lengthy and filled with intricate details, sometimes obscuring critical errors. These plans can span over 100 lines, making it difficult for humans to spot mistakes, especially when those errors are buried within confident assertions. The article argues that this represents a poor user interface and experience, especially when human oversight is crucial to prevent unintended consequences. Bainbridge's recommendation is for systems to provide stronger artificial assistance in detecting low-probability events. This means designing alerts and controls that help operators quickly identify problems, rather than overwhelming them with excessive information. The challenge lies in balancing the efficiency of AI with the need for human oversight, ensuring that operators can make informed decisions without succumbing to stress or cognitive overload. The piece underscores the importance of adapting design and communication methods in AI to support human workers effectively.