A few weeks ago I watched a pull request go in that passed every test, read cleanly, and quietly got one edge case wrong. The model wrote it in under a minute. The person who approved it spent maybe ninety seconds on the review. The bug surfaced four days later in a place that cost ~$5000 (not that much, but not little either). Nobody had been careless in the way we usually mean. They had just put their attention where the work used to be, the producing, instead of where the work had moved to, the deciding and the checking.
That is the shift I want to name, because most career advice has not caught up to it. For a long time the slow, expensive part of knowledge work was making the thing and you would review and test as you went, because it took days/weeks/months to get something done. You'd find more edge cases as you worked. Writing the code, drafting the brief, building the model, updating the UI... So we organized everything around getting faster and cleaner at production, and we were right to, because that was the bottleneck. Remove a bottleneck and the value does not vanish. It moves. When generating ten plausible options takes seconds, the scarce thing is no longer the generating. It is knowing which of the ten is right, and catching the one that is plausible and wrong.
These are two specific skills, and they are not the same as being good at the task. The first is direction: stating clearly enough what you actually want that the output is worth having, and steering the machine when the first pass drifts, which it inevitably does. Willpower becomes more critical in the age of AI, to have a clear vision and a clear end goal, so you don't get derailed with AI suggestions. The second is verification: holding the result against reality hard enough to catch the failure that looks like success. That PR is the whole problem in miniature. The machine is very good at producing work that looks done, and the cost has moved to the person who can tell whether it is. In that story the one who catches the bug is worth more than the one who wrote the code.
The value of product vision and requirement definition has grown a lot. The value of strategic thinking and project planning (decomposing into achievable milestones, tasks, workstreams..) is also up. And the value of testing at all layers. All of these things are becoming more critical and important as part of the AI revolution, since these are the new bottlenecks, rather than writing code. And all of these are still best done by the engineers who used to write code.