There are many cases in which I already understand the code before it is written. In these cases AI writing the code is pure gain. I do not need to spend 30 minutes learning how to hold the bazel rule. I do not need to spend 30 minutes to write client boilerplate. List goes on. All broad claims about AI's effects on productivity have counterexamples. It is situational. I think most competent engineers quietly using AI understand this.

As not all codebases are well-written, I have found useful once to get an LLM to produce code that does X, essentially distilling from a codebase that does XYZ. I found that reviewing the code the LLM producced, after feeding the original codebase in the context, was easier than going through the (not very well-written) codebase myself. Of course this was just the starting point, there was a ton of things the LLM "misunderstood", and then there was a ton of manual work, but it is an (admittedly rarer) example for me where "AI-generated" code is easier to read than code written by (those) humans, and it was actually useful having that at that point.

Very much disagree. When I type code I don't just press keys, I read, think, organize .. and the interplay between acting, seeing, watching, reevaluating was the fun part. There's a part of you that disappear if you only review the result of a generator. That's why it's less interesting imo

It makes a great code reading tool if you use it mindfully. For instance, you can check the integrity of your tests by having it fuzz the implementation and ensure the tests fail and then git checkout to get clean again.

AI makes people less productive because it’s speeding up the thing that was hard: training AI for better future AI.

The productivity gets siphoned to the AI companies owning the AI.