I find the story of AI and radiology fascinating. Of course, Hinton's prediction was wrong* and tech advances don't automatically and straightforwardly cause job replacement — that's not the interesting part. Radiology has embraced AI enthusiastically, and the labor force is growing nevertheless. The augmentation-not-automation effect of AI is despite the fact that AFAICT there is no identified "task" at which human radiologists beat AI. So maybe the "jobs are bundles of tasks" model in labor economics is incomplete. Paraphrasing something @MelMitchell1 pointed out to me, if you define jobs in terms of tasks maybe you're actually defining away the most nuanced and hardest-to-automate aspects of jobs, which are at the boundaries between tasks. Can you break up your own job into a set of well-defined tasks such that if each of them is automated, your job as a whole can be automated? I suspect most people will say no. But when we think about *other people's jobs* that we don't understand as well as our own, the task model seems plausible because we don't appreciate all the nuances. If this is correct, it is irrelevant how good AI gets at task-based capability benchmarks. If you need to specify things precisely enough to be amenable to benchmarking, you will necessarily miss the fact that the lack of precise specification is often what makes jobs messy and complex in the first place. So benchmarks can tell us very little about automation vs augmentation. * Hinton insists that he was directionally correct but merely wrong in terms of timing. This is a classic motte-and-bailey retreat of forecasters who get it wrong. It has the benefit of being unfalsifiable! It's always possible to claim that we simply haven't waited long enough for the claimed prediction to come true.