Posted by briankelly 4/3/2025
The main message I want to bring across is two-fold:
1. Senior developers are in a great position to make productive use of AI coding tools
2. I have (so far) identified three measures that make AI coding sessions much more successful
I hope the reworked version makes these more central and clear. I'm not a native English speaker, thus it's probably not possible for me to end up with an optimal version.
Still, I hope the new approach works a bit better for you — would love to receive another round of feedback.
I have a business which is turning in millions in ARR at the moment (made in the pandemic) it's a pest control business and we have got a small team with only 1 experienced senior engineer, we used to have 5 but with AI we reduced it to one which we are still paying well.
Even with maintenance, we plan ahead for this with an LLM and make changes accordingly.
I think we will see more organizations opting for smaller teams and reducing engineer count since now the code generated is to the point that it works, it speeds up development and that it is "good enough".
No, the article was just something about enjoying AI. This is hardly anything related to senior software developer skills.
There is no such thing as a measured approach. You can either use LLM agents to abdicate your intellectual honesty and produce slop, or you can refuse their use.
Also, Keyframing can be done in a more autonomous fashion. Sr Engineers can truly vibe code if they setup a proper framework for themselves. Key framing as described in the article is too manual.
Sorry, but as the wind blows, we only have indications of the opposite so far and it's wildly reported as a generating losing their ability for deep thought, relying too much on automated code generation. This is especially damaging for juniors, who never get the chance to learn the tricks of the trade in the first place, and they're the ones having a hard time explaining what the code the LLM generated even does.
Seniors will be few and far between, and we will charge premium for fixing up the crap LLM created, which will fit us great before retirement.
Good luck when we are retired.
Your comment addresses another issue, again in isolated temporal fashion. You and I do not understand assembly language. Previous generations of engineers did.
Having written z80 and x86 assembly since the 80s, being knee deep in several decompilation projects and a couple of emulators since then, I can safely tell you that I do know a handful assembly languages and at least 2 syntaxes.
I believe other seniors have similar skill sets, for now.
Other devs will say things like "AI is just a stupid glorified autocomplete, it will never be able to handle my Very Special Unique Codebase. I even spent 20 minutes one time trying out Cursor, and it just failed"
Nope, you're just not that good obviously. I am literally 10x more productive at this point. Sprint goals have become single afternoons. If you are not tuned in to what's going on here and embracing it, you are going to be completely obsolete in the next 6 months unless you are some extremely niche high level expert. It wont be a dramatic moment where anyone gets "fired for AI". Orgs will just simply not replace people through attrition when they see productivity staying the same (or even increasing) as headcount goes down.
I don't really need AI to write code for me, because that's the easy part. The aspect that it needs to be good at is helping me ship features that works. And to this date, there's never been a compelling showcase for that one.
My projects are much more complex than standard CRUD applications. If you're building simple back-office CRUD apps, you might see a 10x productivity improvement with AI, but that hasn't been my experience with more complex work.
It could be that other developers are not benefitting from AI as much as you because they don't understand it.
It could also be that you are benefitting more than them because you're less skilled than them, and AI can fill in your gaps but not theirs.
Grounding these conversations in an actual reality affords more context for people to evaluate your claims. Otherwise it’s just “trust me bro”.
And I say this as a Senior SWE who’s successfully worked with ChatGPT to code up some prototype stuff, but haven’t been able to dedicate 100+ hours to work through all the minutia of learning how to drive daily with it.
I'm not the OP, but on your other point about completing sprint goals fast - I'm building a video library app for myself, and wanted to add tagging of videos. I was out dropping the kids at classes and waiting for them. Had 20 minutes and said to Aider/Claude - "Give me an implementation for tagging videos." It came back with the changes it would make across multiple files: Creating a new model, a service, configuring the DI container, updating the DB context, updating the UI to add tags to videos and created a basic search form to click on tags and filter the videos. I hit build before the kids had finished and it all worked. Later, I found a small bug - but it saved me a fair bit of time. I've never been a fast coder - I stare at the screen and think way too much (function and variable names are my doom ... and the hardest problem in programming, and AI fixes this for me).
Some developers may be able to do all this in 20 minutes, but I know that I never could have. I've programmed for 25 years across many languages and frameworks, and know my limitations. A terrible memory is one of them. I would normally spend a good chunk of time on StackOverflow and the documentation sites for whatever frameworks/libraries I'm using. The AI has reduced that reliance and keeps me in the zone for longer.
But I have also spent hours asking Claude and ChatGPT with help trying to solve several annoying Django problems and I have reached the point multiple times where they circle back and give me answers that did not previously work in the same context window. Eventually when I figure out the issue, I have fun and ask it "well does it not work as expected because the existing code chained multiple filter calls in django?" and all of a sudden the AI knows what is wrong! To be fair, there was only one sentence in the django documentation that mentions not chaining filter calls on many to many relationships.