Are we overestimating how quickly AI capability turns into real productivity?

​ I’m not questioning whether AI models are powerful. They clearly are. But I’m starting to question whether people underestimate the distance between “capability” and “productivity.” A model can produce a good answer. But productivity in the real world often requires persistent context, judgment, tool access, process knowledge, responsibility, and integration into messy human systems. This seems especially important in the AGI discussion. Even if a model becomes extremely intelligent, does that automatically mean it can function as a productive worker inside a company, team, or market? Maybe the missing layer is not intelligence itself, but something like: \- workflow ownership \- reliability \- memory and context \- tool integration \- accountability \- ability to handle ambiguity \- economic alignment with business outcomes So I’m curious: are we overestimating how fast AI intelligence becomes real productivity? Where do you personally see the biggest gap?