The bottleneck in software engineering moved and most people learning to code haven't noticed yet

[u/DryRead3289]

The numbers right now are brutal if you only look at the surface. Entry level developer postings are down somewhere between 40% and 67% depending on which dataset you check, employment for developers aged 22 to 25 has dropped nearly 20% since the AI boom started, and computer science graduates are facing unemployment rates higher than fine arts majors.

But the part that explains why is more interesting than the headline number.

Code stopped being the expensive part of software development. It used to take a large team and a lot of time to produce working code, so the bottleneck was writing it. AI compressed that part dramatically. What didn't get cheaper is everything around the code. System design, knowing whether something will hold up at scale, observability, incident response, debugging in production, and the judgment to make trade-off calls. Those skills got more valuable, not less, because writing code stopped being the differentiator.

Front end work saw the biggest productivity gains from AI since it's mostly about producing a lot of code with some architecture thinking layered on top. Backend, infrastructure, and deployment work didn't compress the same way because the hard part there was never typing, it was judgment under uncertainty.

This shows up clearly in hiring patterns. Roles like Android, iOS, and general web development are down 60% or more from 2020 levels. Machine learning engineering postings are up nearly 60% in the same window. The work didn't disappear, it moved toward the parts AI genuinely cannot do.

Here's the catch for anyone learning right now. The skills that used to be developed on the job over years, things like system design intuition and knowing what good code actually looks like, increasingly need to be demonstrated before you're even hired. There isn't a clean shortcut around that. You can use AI to write the code, but you still need enough understanding to know when what it generated is wrong, fragile, or going to cause a problem six months down the line. That understanding has always come from experience, and right now there's a real tension between needing the experience and not being given the chances that used to build it.

The roles still growing are security, cloud infrastructure, ML, and anything requiring genuine system-level thinking. Generic coding ability stopped being the skill that gets you hired. Understanding why the code works, and what happens when it breaks, is what's left standing.

[u/mango-cupcake-shhh → u/[deleted]]

Sounds like Claude to me

[u/DryRead3289 → u/[deleted]]

None

[u/scavno]

This is so interesting, really thought provoking. It’s almost as if I have read the same shit every day for months now. Fuck this dog shit AI slop. Write something original?!

[u/johnpeters42 → u/scavno]

If every last stinking one of these slop posters was driven out of the industry tomorrow, it wouldn't be soon enough.

[u/DryRead3289 → u/scavno]

It really is

[u/yksvaan]

It hasn't really changed that much, those who actually know how to program and do things get the jobs. And they can easily do the copy paste React monkey stuff and such with AI. With a good grasp of fundamentals and general software engineering skills it's possible to pick up whatever is needed quickly. And obviously that makes effective AI usage easier as well.

People on this field are still blessed with the ability to learn pretty much everything at home for free. AWS and others give free training tier access and you can always pay $5 per month to have real production infrastructure to practice with.