[freshtake]

The article captures the two agendas at work. The reality is somewhat dependent on your situation.

If you understand _how_ you should be using AI in engineering, then AI can speed you up because you know what you're trying to build, you understand the fundamentals, and you are the pilot delegating granular tasks. When bugs pop up or requirements change, you'll have the knowledge required to frame and steer the AI to ensure goals are met, and met properly. When AI gets stuck, you'll be able to quickly jump in and work the problem. Your experience will continue to evolve and improve over time because you're plugged into the work and the code. You'll leverage your experience in unexpected ways in future projects, and perhaps your communication skills will improve as well.

Alternatively, if AI is used improperly, it may provide the illusion of velocity up until the point where you realize that your lack of knowledge or involvement actually prevents the AI from making progress. You want to move beyond a simple implementation, bugs pop up that can't be fixed, or new requirements can't be met. You aren't able to dive in yourself, either because you weren't paying attention to the work or were operating too far outside of your expertise. In either case, the progress you thought you were making might actually be a pile of wasted time and technical debt.