posted in Technology
80% of developers find AI coding more addictive than helpful
A new Coddy Developer Survey found that four in five developers, 80%, say their use of AI has felt more like a dependence than an advantage.
www.zdnet.com/article/80-of-developers-find-ai-coding-more-addictive-than-helpful/
Replying to @Nobody_Special@piefed.social
Maybe it’s just because Anthropic is so many more miles ahead than OpenAI, but I have not had the “it doesn’t work” experience that people are talking about here. I’ve set up agentic pipelines with code architecture guidelines, the orchestrator calls the implementer, tester, reviewer when needed. If the developed code isn’t up to standard or the work item isn’t fully captured, it’ll reject it and send it back to implementation. It invokes the architect if it runs into something unanticipated. The pipeline can run a feature with high accuracy and very little input.
I’ve even used it to run batteries of frozen experiments on self synthesized data using its own simulation harnesses to discover novel techniques that work in the real world. If it isn’t helpful, I feel like you are doing it wrong.
I use Claude all the time at work. It is good. But it makes massive mistakes, it misses tests, it confidently says something it screwed up will be fixed by something that certainly isn’t the right way to fix the problem.
I recently explained to a colleague: if you can use 1 AIU (arbitrary quantity of ai usage) and get 10% productivity bump, that doesn’t mean 5 AIU gets you 50% and 10 doubles your speed. The AI will do and say promising things, make you believe it’s on the verge of solving the problems, but it never quite arrives. There’s always one more problem and if you’re very lucky the AI will find it itself, but most likely it will be found when you pass it on to another person and it’s completely useless.
Let me put it this way: in addition to development, I use Claude to help with production support issues. It wrote some scripts I didn’t have time to and it pulls logs and data from multiple systems — honestly it works great and has saved me so much time. But I’m constantly in meetings and so I set Claude to investigate an incident so I can focus on my meeting and return when I have time, and it gets RCA wrong well over 50% of the time.
If it is so bad at RCA, how do you imagine it is fixing the bugs in the code it finds? Badly. It misunderstands the cause of problems, and so it fixes the wrong things until it has cobbled together the creakiest of code that passes the test. In fact I think AI is far worse at fixing code than it is at writing it in the first place.
I’m not anti AI. I’m trying to find ways to make it effective. And my teams are seeing 20-30% productivity gains - I think because they are skeptical about AI rather than trusting. But it has to be used appropriately, and everywhere I look, even within my own company, people are trying to do too much with it and creating huge problems I have to sort through.
This is a user issue. If it missed stuff, you didnt describe what you want clearly enough. This right here is exactly why im not worried about it taking over
I’m not worried about it taking over. Describe exactly what you want, clearly is exactly the job of a programmer and why we aren’t going to be replaced by middle management wielding an army of AI.
The difference is code largely can’t be ambiguous where human language is subject to interpretation. So the question is what it takes to describe what is required in a way that the AI cannot misinterpret ambiguity, and so that agentic AI doesn’t have ambiguity decided confidently wrong by the orchestration layer.
This is not strictly a user problem. AI will confidently make bad assumptions about ambiguity and propagate those to sub agents and you wind up with garbage. I’m good at this — would never claim to be the best and maybe you are better, but I’m good at it — and it still causes me grief. One criticism I have of the whole endeavor is that proficiency seems stubbornly difficult to transfer.