Labor market impacts of AI: A new measure and early evidence

> It feels very much like leading a team of junior engineers or even interns who are very fast but have no idea about why we're doing anything

Yes, yes!

And this is problem for me, because of the pace, my brain muscles are not developing enough compared to when I was doing those things myself.

before, I was changing my mind while implementing the code, because I see more things while typing, and digging deeper, but now, because juniors are doing things they don't offer me a refactoring or improvements while typing the code quickly, because they obey my command instead of having "aha" moment to suggest better ways

I can see someone who is very particular about their way being the right way having issues with it. I’m very much the kind of person who believes that if I can’t write a failing test, I don’t have a very serious case. A lot of devs aren’t like that.

I’m currently working across like 5 projects (was 4 last week but you know how it is). I now do more in days than others might in a week.

Yesterday a colleague didn’t quite manage to implement a loading container with a Vue directive instead of DOM hacks, it was easier for me to just throw AI at the problem and produced a working and tested solution and developer docs than to have a similarly long meeting and have them iterate for hours.

Then I got back to training a CNN to recognize crops from space (ploughing and mowing will need to be estimated alongside inference, since no markers in training data but can look at BSI changes for example), deployed a new version of an Ollama/OpenAI/Anthropic proxy that can work with AWS Bedrock and updated the docs site instructions, deployed a new app that will have a standup bot and on-demand AI code review (LiteLLM and Django) and am working on codegen to migrate some Oracle forms that have been stagnating otherwise.

It’s not funny how overworked I am and sure I still have to babysit parallel Claude Code sessions and sometimes test things manually and write out changes, but this is a completely different work compared to two or three years ago.

Maybe the problem spaces I’m dealing with are nothing novel, but I assume most devs are like that - and I’d be surprised at people’s productivity not increasing.

When people nag in meetings about needing to change something in a codebase, or not knowing how to implement something and its value add, I’ll often have something working shortly after the meeting is over (due to starting during it).

Instead of sending adding Vitest to the backlog graveyard, I had it integrated and running in one or two evenings with about 1200 tests (and fixed some bugs). Instead of talking about hypothetical Oxlint and Oxfmt performance improvements, I had both benchmarked against ESLint and Prettier within the hour.

Same for making server config changes with Ansible that I previously didn’t due to additional friction - it is mostly just gone (as long as I allow some free time planned in case things vet fucked up and I need to fix them).

Edit: oh and in my free time I built a Whisper + VLM + LLM pipeline based on OpenVINO so that I can feed it hours long stream VODs and get an EDL cut to desired length that I can then import in DaVinci Resolve and work on video editing after the first basic editing prepass is done (also PyScene detect and some audio alignment to prevent bad cuts). And then I integrated it with subscription Claude Code, not just LiteLLM and cloud providers with per-token costs for the actual cuts making part (scene description and audio transcriptions stay local since those don't need a complex LLM, but can use cloud for cuts).

Oh and I'm moving from my Contabo VPSes to running stuff inside of a Hetzner Server Auction server that now has Proxmox and VMs in that, except this time around I'm moving over to Ansible for managing it instead of manual scripts as well, and also I'm migrating over from Docker Swarm to regular Docker Compose + Tailscale networks (maybe Headscale later) and also using more upstream containers where needed instead of trying to build all of mine myself, since storage isn't a problem and consistency isn't that important. At the same time I also migrated from Drone CI to Woodpecker CI and from Nexus to Gitea Packages, since I'm already using Gitea and since Nexus is a maintenance burden.

If this becomes the new “normal” in regards to everyone’s productivity though, there will be an insane amount of burnout and devaluation of work.

The real question is, is it increasing their velocity?

They've already admitted they just 'throw the code away and start again'.

I think we've got another victim of perceived productivity gains vs actual productivity drop.

People sitting around watching Claude churn out poor code at a slower rate than if they just wrote it themselves.

Don't get me wrong, great for getting you started or writing a little prototype.

But the code is bad, riddled with subtle bugs and if you're not rewriting it and shoving large amounts of AI code into your codebase, good luck in 6-12 months time.

I don’t think the objections are not necessarily in terms of lack of productivity although my personal experience is not that of massive productivity increases. The fact that you are producing code much faster is likely just to push the bottleneck somewhere else. Software value cycles are long and complicated. What if you run into an issue in 5 years the LLM fails to diagnose or fix due to complex system interactions? How often would that happen? Would it be feasible to just generate the whole thing anew matching functionality precisely? Are you making the right architecture choices from the perspective of what the preferred modus operandi of an llm is in 5 years? We don’t know. The more experienced folks tend to be conservative as they have experienced how badly things can age. Maybe this time it’ll be different?