However, with recent grads flooding into IT for remote work and high pay, they could be hurting as AI reduces the need for entry level roles. Entry level was already saturated, and now it will be more saturated, with AI reducing the need for jobs.
But this is actually something I worry about. The best way to become an expert is to do things the hard way. I've talk a lot of people linux over the years and only 3 have really learned it. Every time I teach people I give them two options: the easy way, which is just how to use it and use the gui and a bit of terminal or the hard way, which hand them the arch wiki, tell them to come back after their third failed attempt to install. Those 3 people came back, but usually did more than 3 installs, but had all been successful at some point. All 3 mentioned they understood why and then we could really talk about how to use linux, make scripts (and that scripts aren't aliases...), and so on. All 3 still are terminally terminal, years later. The thing is that humans (and even machines) learn by struggling, getting things wrong, and learning from mistakes. The struggle is part of the learning process. I've found this both in myself and whenever I teach anything, that if I just feed someone the answer (or look it up and nothing more for myself) they (I) don't end up remembering, they don't end up playing, they don't end learning how to learn.
:-(
You don't go to your painter friend and ask them for coding help, much like you don't go to the general-purpose GPT; you'd go to the Copilot, who enjoys programming tasks or whatever.
Can GPT help? Sure. But the skeletons, rough jumping-off points, etc. all scream to me "I'm not going to do your homework for you," which I love.
In the end, both have been immensely helpful, but I use them for different things.
I don't see AI pushing out deep thinkers and the "annoying" nuance devs anytime soon. I'm that kinda person too and yeah, I'm not as fast as my colleagues. But another friend (who is similar) and I both are surprised how often other people in our lab and groups we work with (we're researchers) talk about how essential GPT and copilot are to their workflows. Because neither of us think this way. I use GPT(4) almost every day, but it's impossible for me to get it to write good quality code. It's great at giving me routines and skeletons, but the real engineering part takes far more time to talk the LLM into than it does to write it (including all the time to google or even collaborate with GPT[0]). LLMs can do tough things, but their abilities are clearly directly proportional to the frequency of the appearance of those tasks. So I think it is the coding bootcamp people that are in the most danger.
There are expert people that are also at risk though. These are the extremely narrow expertise people. Because you can target LLMs for specific tasks. But if your skills are the skills that define us as humans, I wouldn't lose too much sleep. I say this as a ML researcher myself. And I highly encourage everyone to get into the mindset of thinking with nuance. It has other benefits too. But I also think we need to think about how to transition into a post scarce world, because that is the goal and we don't need AGI for that.
[0] A common workflow for me is actually due to the shittiness of Google. Where it overfits certain words and ignores the advanced things like quotes or NOTs. Or similarly broaching into a new high level topic. I can't trust GPT's answer, but it will sure use keywords and vernacular I don't know that enable me to make a more powerful search. (But google employees should not take away that they should push LLMs into google search but rather that search is mostly good but that same nuance is important and being too forceful and repeating 5 pages of essentially the same garbage is not working. The SEO people attacked you and they won. It looks like you let them win too...)