As they said, it depends on the task, so I wouldn't generalize, but based on the examples they gave, it tracks. Even when you already know what needs done, some undertakings involve a lot of yak shaving. I think transitioning to new tools that do the same as the old but with a different DSL (or newer versions of existing tools) qualifies.

Imagine that you've built an app with libraries A, B, and C and conceptually understand all that's involved. But now you're required to move everything to X, Y, and Z. There won't be anything fundamentally new or revolutionary to learn, but you'll have to sit and read those docs, potentially for hours (cost of task switching and all). Getting the AI to execute the changes gets you to skip much of the tedium. And even though you still don't really know much about the new libs, you'll get the gist of most of the produced code. You can piecemeal the docs to review the code at sensitive boundaries. And for the rest, you'll paint inside the frames as you normally would if you were joining a new project.

Even as a skeptic of the general AI productivity narrative, I can see how that could squeeze a week's worth of "ever postponed" tasks inside a day.

> but you'll have to sit and read those docs, potentially for hours (cost of task switching and all).

That is one of the assumptions that pro-AI people always bring. You don't read the new docs to learn the domain. As you've said, you've already learn it. You read it for the gotchas. Because most (good) libraries will provide examples that you can just copy-paste and be done with it. But we all know that things can vary between implementations.

> Even as a skeptic of the general AI productivity narrative, I can see how that could squeeze a week's worth of "ever postponed" tasks inside a day.

You could squeeze a week inside a day the normal way to. Just YOLO it, by copy pasting from GitHub, StackOverflow and the whole internet.