I think it all boils down to, which is higher risk, using AI too much, or using AI too little? Right now I see the former as being hugely risky. Hallucinated bugs, coaxed into dead-end architectures, security concerns, not being familiar with the code when a bug shows up in production, less sense of ownership, less hands-on learning, etc. This is true both at the personal level and at the business level. (And astounding that CEOs haven't made that connection yet). The latter, you may be less productive than optimal, but might the hands-on training and fundamental understanding of the codebase make up for it in the long run? Additionally, I personally find my best ideas often happen when knee deep in some codebase, hitting some weird edge case that doesn't fit, that would probably never come up if I was just reviewing an already-completed PR.
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