If this is true, then companies should focus on hiring juniors out of college. The investment is less risky.
However, I don't personally believe this number and timeline is true, but if you do, the conclusion should be to wait and invest in humans.
At this point, the vast majority of the work required to make GenAI capable of producing that sufficiently reviewable/correctable content isn't improving model quality, but creating the harnesses, infrastructure, and workflows around the models. Companies aren't seeing returns yet because too many early adopting companies have conceived of AI as a drop in replacement for employees, or at least as a reason to cut staff immediately, without first building out the supporting systems needed to compensate for the inadequacies of the models.
I think any juniors who keep failing 10% of text based task will eventually get fired... So investing in those that don't fail seems only sensible move as usual.
Something I keep in mind is that the “goal” success/failure rate really needs to be appropriate in context. We sure can’t eliminate human error, but for my work a best case 80/20 would mean I’m losing customers and probably getting myself sued. I don’t have a problem “doing things by my own hand” in that case.