Partly this is because the big companies are diverting vast amounts of money towards building data centers, instead of hiring new grads. I expect young people who learn AI skills will be fine, once the data center spending binge is over.
- directing the model and knowing when its going off the rails
- verification
- setting up the right loops, harness, graphs around the models
economics still apply to hiring people. if all your competitors are using ai and there's market share to fight over and people still are a productivity positive vs. only ai, hiring will increase as we've seen recently with swes
The more senior the candidate the less they took advantage of the tools. Most commonly they would manually copy/paste error or syntax errors and then run out of time. One candidate only copy pasted his questions into google and used the AI overview
Junior candidates tended to be overly ai eager, a lot of them oneshotted the problem but were unable to explain any of the details
That being said I think 80% of the skills should come easily to a capable dev that is willing to put in the effort to learn and get used to managing agents. Building agents that perform work themselves is a lot harder (and still pretty unsolved)
As for building agents that perform work themselves, in my opinion it boils down to understanding the problem space, isolating the key business logic, and determining what the pertinent requirements are. Kinda sounds like looping back around to software engineering skills IMO.
building agents is just a completely different ballgame, theres a lot of infra and harness engineering around handling the nondeterministic behaviors that are nonobvious unless you've shipped agents at scale
And I don't understand why you would wave away needing to know anything about the codebase's underlying tech because "The AI can take care of all that, we don't need to look at code anymore" while also not believing that any competent developer could prompt AI to get a good setup within a week or two max.
I noticed the newer batch of interns tends to have less mechanical coding skills but does well on understanding and planning on the design, DB design, etc.
The other 3 you literally just get for free as models improve. The "state of the art" of "prompting" changes literally every week. It was loops, then graphs, now its harnesses (and self automating harnesses)? Why are these not just obviated by better models? These are barely skills, and are imo, just the tech equivalent of tabloids advertising 5 minute exercises or pills to get rid of stubborn belly fat. No amount of prompt engineering or graphs or loops could get your previous version of GPT to perform like Fable, and yet somehow Fable can do all of that and more without any random built in "ai engineering skills."
The labor chart for SWEs will look like a slope up as productivity with AI increases, and then past a certain point where AI is like 99% good enough, employment will fall off a cliff. We are already seeing this with junior hiring, and who is to say that AI will magically only ever be as capable as a junior engineer?
Given that we can keep making infinite abstractions with software if we actually saturate software demand with AI then I don't see why every other industry is cooked (robotics is downstream of software).
If we apply the Gartner Hype Cycle to this disruptive technology, I do believe AI will hit a "slope of enlightenment" where an AI-assisted Analyst is properly understood in industries and can take productive roles. Conveniently, this will probably coincide with a lower interest rate where companies have funds for a talent acquisition spree/rapid growth.
With that being said, today's graduates are stuck in an awful market with a lot of charlatans selling crappy online courses and bad ideas.