What to do when AI is quietly making you worse (vtorosyan.gitHub.io)

A recent discussion on the Root Cause podcast highlights a critical concern within the AI/ML community: the potential erosion of deep system understanding among engineers as they increasingly rely on AI tools. While AI raises throughput efficiency in coding and problem-solving, it inadvertently diminishes crucial skills like first-principles reasoning and system intuition. The concern is particularly acute for junior engineers who, lacking the friction of tough debugging challenges, may not develop the mental frameworks essential for understanding complex systems, similar to how pilots who rely on autopilot can lose manual flying skills. To address this knowledge gap, the discussion emphasizes the importance of cultivating judgment and understanding through deliberate practice. Engineers are encouraged to actively engage with problems before consulting AI tools, manually debug production issues, and maintain a keen sense of mentorship by reading AI-generated code critically. Key practices include writing thoughts down before seeking AI solutions, fostering a deeper connection with customer needs, and creating an “experimental knowledge log” to capture insights without the burden of formal documentation. As AI continues to dominate execution tasks, the community must prioritize developing clarity and problem ownership to prevent the loss of vital engineering intuition.