Some additional notes given the comments in the thread

* I wasn’t trying to be dismissive of the article or the study, just wanted to present a different context in which AI tools do help a lot

* It’s not just code. It also helps with a lot of tasks. For example, Claude Code figured out how to “manually” connect to the AWS cluster that hosted the source db, tested different commands via docker inside the project containers and overall helped immensely with discovery of the overall structure and infrastructure of the project

* My professional experience as a developer, has been that 80-90% of the time, results trump code quality. That’s just the projects and companies I’ve been personally involved with. Mostly saas products in which business goals are usually considered more important than the specifics of the tech stack used. This doesn’t mean that 80-90% of code is garbage, it just means that most of the time readability, maintainability and shipping are more important than DRY, clever solutions or optimizations

* I don’t know how helpful AI is or could be for things that require super clever algorithms or special data structures, or where code quality is incredibly important

* Having said that, the AI tools I’ve used can write pretty good quality code, as long as they are provided with good examples and references, and the developer is on top of properly managing the context

* Additionally, these tools are improving almost on a weekly or monthly basis. My experience with them has drastically changed even in the last 3 months

At the end of the day, AI is not magic, it’s a tool, and I as the developer, am still accountable for the code and results I’m expected to deliver