There's definitely a trade-off, but it's a lopsided one that favors AI.
Before AI, you were often encumbered with the superficial aspects of a plan or implementation. So much that we often would start implementing first and then kinda feel it out as we go, saving advanced considerations and edge-cases for the future since we're not even sure what the impl will be.
That's useful for getting a visceral read on how a solution might feel in its fetal stage. But it takes a lot of time/energy/commitment to look into the future to think about edge cases, tests, potential requirement churn, alternative options, etc. and planning today around that.
With AI, agents are really good at running preformed ideas to their conclusion and then fortify it with edge-cases, tests, and trade-offs. Now your expertise is better spent deciding among trade-offs and deciding on what the surface area looks like.
Something that also just came to mind is that before AI, you would get married to a solution/abstraction because it would be too expensive to rewrite code/tests. But now, refactoring and updating tests is trivial. You aren't committed to a bad solution anymore. Or, your tests are kinda lame and brittle because they're vibe-coded (as opposed to not existing at all)? Ok, AI will change them for you.
I also think we accidentally put our foot on the scale in these comparisons. The pre-AI developer we'll imagine as a unicorn who always spends time getting into the weeds to suss out the ideal solution of every ticket with infinite time and energy and enthusiasm. The post-AI developer we'll imagine as someone who is incompetent. And we'll pit them against each other to say "See? There's a regression".