> In my experience, AI really lowered the bar for bad code in the name of delivering faster.
I would've believed that 6 months ago, but not now.
If you have a good codebase with proper rails, hygiene and architecture, AI will produce better code than most engineers out there.
People forget that 90% of the field has always been charlatans barely able to implement a fizz buzz or go much beyond trial and error googling.
I'll say even more. I'm in the 10%, and it's increasingly clear to me that AI writes in minutes code that's better than mine.
Even stellar and respected OSS engineers are nowadays leveraging AI and guiding it less and less everyday beyond giving indications of what kind of data structure they may want for a complex problem or the kind of architecture they are looking for.
In any case, I don't like this field anymore, I have no joy from it, way too much work, way too many changes a human can cope with both on product and technological level (not even counting AI and its tooling itself). The interesting parts of thinking an entire afternoon or week experimenting to get that design right disassembling the pros and cons are gone.
Even if you want to do that, it's just faster to launch 6/7 worktrees with the different ideas and judge the results. But you don't get as intimate with the problem and the amount of information is way more than you can process.
I'm hand rolling a project right now because even frontier models I use bloat things beyond comprehension. Because I'm intimately familiar with the domain, I know the shape of things, how the data should flow, and so on, and if l even if I spec it clearly AI will write 2x to 5x the amount of code necessary to make something work.
"beyond comprehension" is a good way of putting it. I've been genuinely baffled by some of these AI designs - why any intelligent thing would write >10 lines of bloat for what should be a one-liner.
> "beyond comprehension" is a good way of putting it. I've been genuinely baffled by some of these AI designs - why any intelligent thing would write >10 lines of bloat for what should be a one-liner.
As Anthropic's drones say: treat Claude as your genius coworker. Don't think yourself, don't judge, the machine must know better than you. It is the genius, after all, not you.
Forgive my ignorance, but if the corpus of coding data was always 90% bad, isn't that the same data being used for training LLMs? How are they magically any better than that average?
Proper rails, hygiene and architecture need to be actively maintained, they don’t just continue to exist in a developing codebase. Historically, a small proportion (the 10% as you say) had a disproportionate amount of influence on coding standards. When they can no longer keep up with that ongoing maintenance, which we’re seeing with the increased pressure to ship code, the hygiene will regress. We’re riding the tail of all the engineering practices we’ve developed as an industry.
This is what I’m seeing, anyways. Junior engineers are being rewarded for shipping so much code, it’s impossible to evaluate it all, and subtle changes in existing patterns are slipping through. Eventually all those subtle changes transform the rails.
I would've believed that 6 months ago, but not now.
If you have a good codebase with proper rails, hygiene and architecture, AI will produce better code than most engineers out there.
People forget that 90% of the field has always been charlatans barely able to implement a fizz buzz or go much beyond trial and error googling.
I'll say even more. I'm in the 10%, and it's increasingly clear to me that AI writes in minutes code that's better than mine.
Even stellar and respected OSS engineers are nowadays leveraging AI and guiding it less and less everyday beyond giving indications of what kind of data structure they may want for a complex problem or the kind of architecture they are looking for.
In any case, I don't like this field anymore, I have no joy from it, way too much work, way too many changes a human can cope with both on product and technological level (not even counting AI and its tooling itself). The interesting parts of thinking an entire afternoon or week experimenting to get that design right disassembling the pros and cons are gone.
Even if you want to do that, it's just faster to launch 6/7 worktrees with the different ideas and judge the results. But you don't get as intimate with the problem and the amount of information is way more than you can process.