The alternative is whatever one can learn without GPT-4 in whatever amount of time is reasonable/allocated for it at the moment.
Like, take Docker. Yes, you would not be wrong to say that "by getting help from GPT-4 to do stuff, you won't learn it as well as people who didn't get that help and slogged through it over the course of 2 years at one of their last job", but this ignores that I probably will, over the course of the next several months, learn a lot about Docker in a fraction of the time it took the hypothetical other person.
I don't doubt that leaning too much on LLMs as a crutch may have negative effects, but I find this to be a surprising line of argument on a tech forum; there is such an obvious difference in efficiency between learning using an LLM versus the slog of other materials--again, aside from books, though I think they offer, ideally, a different kind of learning.
> If you want to be an autodidact, seek out the information from primary sources, study it and organize it yourself. With GPT-4, you're essentially reading a sloppy cliffs notes of the topics. You'll be much better informed and be able to think more deeply about the subject matter if you adopt some ideas from Richard Feynman, wherein you try to explain what you think you know to a child, or at least someone with no knowledge of the subject. That will highlight the gaps in your understanding, and you can return to the source material to fill in the gaps.
The most immediate and fundamental problem with what you are suggesting is that it is infeasible. Nobody has time to learn everything they wish to at the level of depth they would ideally want. Nobody has time to read all the primary sources on which GPT-4 was trained. I find this so obvious I won't belabor the point.
I don't think any other tool can approximate the personalized "per-unit-of-time learning" afforded by a good LLM.