Here is the the methodology of the study: > To directly measure the real-world impact of AI tools on software development, we recruited 16 experienced developers from large open-source repositories (averaging 22k+ stars and 1M+ lines of code) that they’ve contributed to for multiple years. Developers provide lists of real issues (246 total) that would be valuable to the repository—bug fixes, features, and refactors t…
I think that's where you get 10-20x. When you're working on niche stuff it's either not gonna work or work poorly.
For example right now I need to figure out why an ffmpeg filter doesn't do X thing smoothly, even though the C code is tiny for the filter and it's self contained.. Gemini refuses to add comments to the code. It just apologizes for not being able to add comments to 150 lines of code lol.
However for building an ffmpeg pipeline in python I was dumbfounded how fast I was prototyping stuff and building fairly complex filter chains which if I had to do by hand just by reading the docs it would've taken me a whole lot more time, effort and frustration but was a joy to figure out with Gemini.
So going back to the study, IMO it's flawed because by definition working on new features for open source projects wouldn't be the bread and butter of LLMs however most people aren't working on stuff like this, they're rewriting the same code that 10000 other people have written but with their own tiny little twist or whatever.