That has not been my experience at all. Whenever I tried asking the AI to do something, it took an inordinate amount of time and thought to go through its changes.

The mistakes were subtle but critical. Like copying a mutex by value. Now, if I would be writing the code, I would not make the mistake. But when reviewing, it almost slipped by.

And that's where the issue is: you have to review the code as if it's written by a clueless junior dev. So, building up the mental model when reviewing, going through all the cases and thinking of the mistakes that could possibly have happened... sorry, no help.

Maybe 10% of typing it out but when I think about it, it's taking more time because I have to create the mental model in my mind then create the mental model out of the thing that AI typed out and then have to compare the two. This latter is much more time consuming than creating the model in my mind and typing it out.

I think that programming languages (well, at least some of them, maybe not all) have succeeded in being good ways of expressing programmatic thought. If I know what I want in (say) C, it can be faster for me to write C code than to write English that describes C code.

I guess it depends on what you use it for. I found it quite relaxing to get AI to write a bunch of unit tests for existing code. Simple and easy to review, and not fun to write myself.