Here's the question. What is the difference in work between review and understanding the code versus writing the code in the first place?
I think the results vary a lot by scenario. In some cases, the business problem, the requirements, and the implementation are clearly separable, and exceptions can be clearly defined and partitioned a prior. These are amenable to automatic code generation and "higher level of abstraction".
In other cases, the business problem and requirements can only be untangled by asserting some sort processing logic, and then working from there. The more granular the logic that has to be specified, the closer you are to saving time by just writing the code yourself.
I suspect that the estimates of massive labor savings put almost all work into the first category. In reality, I believe quite a lot of work lands in the second category, and much of the work in the first has already been automated.
Case in point, TFA mentioned writing a calendar app for her family. There are a bajillion calendar apps for families, and nearly every piece of enterprise software now ships with calendaring and project management features of some sort.
So this was a hobby project, or her family has some extremely unique un-apped needs. Who knows, as an Amazon exec she may need to schedule their personal helicopter, private jet, and security detail and that probably is more complex than everyone else's soccer practices, doctor's appointments, and school forms. But I have a hard time seeing how there's a huge space of unmet demand for calendar apps. The biggest problem with calendar apps is all the stuff that is going on outside of them that people do not want to put in their calendar. AI does not change that.