Comment by jjmarr
15 hours ago
Juniors from non target schools are getting pushed out since the skill floor is too high.
I graduated 9 months ago. In that time I've merged more PRs than anyone else, reduced mean time to merge by 20% on a project with 300 developers with an automated code review tool, and in the past week vibe coded an entire Kubernetes cluster that can remotely execute our builds (working on making it more reliable before putting it into prod).
None of this matters.
The companies/teams like OpenAI or Google Deepmind that are allegedly hiring these super juniors at huge salaries only do so from target schools like Waterloo or MIT. If you don't work at a top company your compensation package is the same as ever. I am not getting promoted faster, my bonus went from 9% to 14% and I got a few thousand in spot bonuses.
From my perspective, this field is turning into finance or law, where the risk of a bad hire due to the heightened skill floor is so high that if you DIDN'T go to a target school you're not getting a top job no matter how good you are. Like how Yale goes to Big Law at $250k while non T14 gets $90k doing insurance defence and there's no movement between the categories. 20-30% of my classmates are still unemployed.
We cannot get around this by interviewing well because anyone can cheat on interviews with AI, so they don't even give interviews or coding assessments to my school. We cannot get around this with better projects because anyone can release a vibe coded library.
It appears the only thing that matters is pedigree of education because 4 years of in person exams from a top school aren't easy to fake.
Can I ask what you and others that posts things like this here -"What are you actually developing?"
People are posting about pull requests, use of AIs, yada yada. But they never tell us what they are trying to produce. Surely this should be the first thing in the post:
- I am developing an X
- I use an LLM to write some of the code for it ... etc.
- I have these ... testing problems
- I have these problems with the VCS/build system ...
Otherwise it is all generalised, well "stuff". And maybe, dare I say it, slop.
I'm hosting a Kubernetes cluster on Azure and trying to autoscale it to tens of thousands of vCPUs. The goal is to transparently replace dedicated developer workstations (edit: transparently replace compiling) because our codebase is really big and we've hired enough people this is viable.
edit: to clarify, I'm using recc which wraps the compiler commands like distcc or ccache. It doesn't require developers to give up their workspace.
Right now I'm using buildbarn. Originally, I used sccache but there's a hard cap on parallel jobs.
In terms of how LLMs help, they got me through all the gruntwork of writing jsonnet and dockerfiles. I have barely touched that syntax before so having AI churn it out was helpful to driving towards the proof of concept. Otherwise I'd be looking up "how do I copy a file into my Docker container".
AI also meant I didn't have to spend a lot of time evaluating competing solutions. I got sccache working in a day and when it didn't scale I threw away all that work and started over.
In terms of where the LLM fell short, it constantly lies to me. For example, it mounted the host filesystem into the docker image so it could get access to the toolchains instead of making the docker images self-contained like it said it would.
It also kept trying to not to the work, e.g. It randomly decides in the thinking tokens "let's fall back to a local caching solution since the distributed option didn't work" then spams me with checkmark emojis and claims in the chat message the distributed solution is complete.
A decent amount of it is slop, to be honest, but an 80% working solution means I am getting more money and resources to turn this into a real initiative. At which point I'll rewrite the code again but I'll pay closer attention now that I know docker better.
Isn't there a less convoluted way of making the best engineers leave? I am half serious here. If you want your software to run slow, IT could equally well install corporate security software on developer laptops. Oops, I did it again. Oh well, in all seriousness, I have never seen any performance problem being solved by running it on Azure's virtualization. I am afraid you are replacing the hardware layer by a software layer with ungodly complexity, which you are sure of will be functionally incomplete.
Are you sure they don't have to fix the build pipeline first? Tens of thousands of vCPUs for a single compilation run, or to accommodate 100 developers who try to compile their own changes?
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You seem exceptionally bright. Most people are not like this. This is why they are struggling.
It sounds like you have a job, right out of college, but you're griping about not getting promoted faster. People generally don't get promoted 9 months into a job.
I'm reading your post and I am genuinely impressed but what you claim to have done. At the same time I am confused about what you would like to achieve within the first year of your professional career. You seem to be doing quite well, even in this challenging environment.
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I hate the credentialism. What a bummer of a place to end up.
I mean you don’t need your first job go to top of the top companies. Your first job is to get you into the industry then you can flourish.
How many juniors OpenAI GDM are going to hire in a year, probably double digits at max, the chances are super slim and they are by nature are allowed to be as picky as they should be.
That being said, I do agree this industry is turning into finance/law, but that won’t last long either. I genuinely can’t foresee what if when AGI/ASI is really here, it should start giving human ideas to better itself, and there will be no incentive to hire any human for a large sum anymore, maybe a single digit individuals on earth perhaps
The problem is the lack of experience compounds.
Because AI accelerates the rate of knowledge gain, this gets even faster.