Cal Newport warns that AI-driven speed in coding may erode the critical thinking skills that define good engineering. Instead of actively understanding code, developers risk passive recognition, becoming detached from the foundational understanding. While some advocate for a complete return to manual coding, the author seeks a balanced approach. This middle ground involves intentionally integrating friction, which is crucial for learning and skill development.Skill atrophy occurs not from using AI itself, but from delegating the internal modeling process. The author distinguishes between skill atrophy and AI's potential for generating subtly incorrect code. To maintain ownership and understanding, developers should re-derive complex code paths and ensure they can explain every change. Narrowing AI tasks to well-defined problems helps keep engineers involved and prevents overwhelming code reviews.Corey Schafer's methods, like detailed prompts and thorough code review, offer practical ways to preserve this friction. However, human attention is finite, necessitating automation of the remaining tasks. This is where "correctness by design" becomes vital, employing stricter system-level checks like strong typing and compile-time assertions.Rust exemplifies this by making invalid states unrepresentable, forcing AI compliance. In Python, type checkers and validation models serve a similar purpose. Ultimately, responsible AI use in development requires both retaining control over critical decisions to sharpen judgment and implementing strict rules to ensure AI's accountability. Software engineering remains a process of judgment and experience, with AI automating tedious aspects. The key takeaway is to identify and refuse to delegate the most important decisions to AI, ensuring true ownership and robust code.