Excellent perspective! This raises a question that doesn't get nearly enough attention in discussions about AI-assisted development: Are we becoming better engineers, or simply becoming better at asking AI to do engineering for us?

Throughout my career as a software developer, I've learned that some of the most valuable lessons came from hours of debugging, incorrect assumptions, failed solutions, and those wonderful moments when a supposedly simple bug turned out to be an architectural problem.

Now an AI agent can investigate the issue, generate a fix, write regression tests, and confidently announce that everything is working.

Fantastic! We've eliminated the tedious work of thinking through the problem. What could possibly go wrong? :)

But I think there's an important distinction between delegating implementation and delegating understanding.

I regularly experiment with AI coding agents, and I've found them incredibly valuable for exploring unfamiliar codebases, generating repetitive code, investigating alternatives, and accelerating development.

However, I try to maintain one rule: If I cannot explain why the proposed solution works, what assumptions it makes, and under what conditions it might fail, I'm not ready to approve it.

One concern I would add is the effect on junior developers. Experienced engineers already have years of mistakes, debugging sessions, and architectural decisions to draw from. But what happens when new developers start their careers with AI solving nearly every difficult problem before they've developed the skills to recognize a bad solution?

We might end up producing developers who can orchestrate five AI agents simultaneously but struggle to debug a race condition without asking a sixth agent for help. :)

At the same time, I don't believe we should deliberately avoid AI just to preserve traditional skills. That would be like refusing to use a debugger because real programmers should understand everything from console logs.

The challenge is to use AI to expand our capabilities rather than replace our curiosity.

For me, the real measure of a senior engineer isn't how much code they personally write or how many tasks they delegate to AI. It's whether they can recognize incorrect assumptions, challenge architectural decisions, understand system behavior, and take responsibility when things fail.

And perhaps the greatest irony is that AI can make us so productive that we eventually need to schedule time to practice the skills that made us productive in the first place.

AI should make experienced engineers more capable, not merely more dependent.

Great topic, and an important reminder that engineering judgment is a skill we need to keep exercising, even when the machine offers to do all the heavy lifting.