Comment by baumy

My experience is the opposite. I've yet to see a single example of AI working well for non trivial work that I consider relevant, based on 15+ years of experience in this field. It's good for brainstorming, writing tests, and greenfield work / prototyping. Add business context more complicated than can be explained in a short sentence, or any nuance or novelty, and it becomes garbage pretty much instantly.

Show me an AI agent adding a meaningful new feature or fixing a complicated bug in an existing codebase that serves the needs of a decent sized business. Or proposing and implementing a rearchitecture that simplifies such a codebase while maintaining existing behavior. Show me it doing a good job of that, without a prompt from an experienced engineer telling it how to write the code.

These types of tasks are what devs spend their days actually doing, as far as coding is concerned (never mind the non coding work, which is usually the harder part of the job). Current AI agents simply can't do these things in real world scenarios without very heavy hand holding from someone who thoroughly understands the work being done, and is basically using AI as an incredibly fast typing secretary + doc lookup tool.

With that level of hand holding, it does probably speed me up by anywhere from 10% to 50% depending on the task - although in hindsight it also slows me down sometimes. Net hours saved is anywhere from 0 to 10 per week depending on the week, erring more on the lower end of that distribution.