That's the important parallel with using AI in production: on the surface it may seem like the solution to all productivity problems, but in reality it still takes a lot of human brainpower to review, assess, fix and maintain whatever the AI model spits out. Especially since generative AIs like to present their work with supreme confidence, and it's getting increasingly hard to separate the chaff from the wheat.
But it's like growing plants, in the sense that it only takes one crucial element to be missing to limit growth (or "output").
I've met a few ppl who were _reliable_ 10xers (could navigate a much wider range of techs / domains / situations than others) but I've seen even the best crash and burn at times when the circumstances aren't right.
These are the kind of people who cook up something like git when they take a one year sabbatical from their main project.
Or even hardware people like Jim Keller.
10x engineers are plentiful if you define the median engineer as 1x. There are a LOT of mediocre engineers out there to draw the median down.