Comment on I Went All-In on AI. The MIT Study Is Right.
Munkisquisher@lemmy.nz 2 weeks agoWhen the cost to generate new code has become so cheap,and the cost of devs maintaining code they didn’t write gets higher. There’s a huge shift happening to just throw out the code and regenerate it instead. Next year will be the find out phase, where the massive decline in code quality catches up with big projects.
MangoCats@feddit.it 2 weeks ago
That’s going to depend, as always, on how the projects are managed.
LLMs don’t “get it right” on the first pass, ever in my experience - at least for anything of non-trivial complexity. But, their power is that they’re right more than half of the time AND when they can be told they are wrong (whether by a compiler, or a syntax nanny tool, or a human tester) AND then they can try again, and again as long as necessary to get to a final state of “right,” as defined by their operators.
The trick, as always, is getting the managers to allow the developers to keep polishing the AI (or human developer’s) output until it’s actually good enough to ship.
The question is: which will take longer, which will require more developer “head count” during that time to get it right - or at least good enough for business?
I feel like the answers all depend on the particular scenarios - some places some applications current state of the art AI can deliver that “good enough” product that we have always had with lower developer head count and/or shorter delivery cycles. Other organizations with other product types, it will certainly take longer / more budget.
However, the needle is off 0, there are some places where it really does help, a lot. The other thing I have seen over the past 12 months: it’s improving rapidly.
Will that needle ever pass 90% of all software development benefitting from LLM agent application? I doubt it. In my outlook, I see that needle passing +50% in the near future - but not being there quite yet.