I clearly find value in LLMs to some degree, but speeding up my coding is not yet one.. i'd love to, but i just don't understand. I can only imagine typing more in explanation for the LLM than it would take me to write it to begin with.
For instance I know I'm good at breaking up large tasks into smaller ones, but because planning and executive functioning is by far my worst energy consuming skill (adhd) I can save a lot of energy (but not time) by approaching it conversationally. I'd often use colleagues for the same thing, but then my productivity costs 2 salaries
Similarly, I have some trouble with memory and cognitive speed when I'm tired which is unfortunately often the case due to my health, I know well enough what I kinda "want" to do and I let the AI generate something that comes near what I need and I can work from there once I have the right starting point.
Just my personal experience I'd wanted to share.
I think the real problem with this is that people aren't differentiating these different types of work when they give these numbers.
1. After working for many years in Java I needed to build a service. I spent few days designing it and then a month on implementation. I used DBs and libraries I knew very well. I didn't need to access google/stackoverflow, I didn't need to look up names of (std)lib methods and their parameters and if something wasn't working it was fairly obvious what I needed to change.
2. Recently I wanted to create a simple page which fetched a bunch of stuff from some URLs and showed the results, simple stuff. But with React since that was what frontend team was using. I never used React and rarely touched web in the recent years. Most of my time was spent googling about React and how exactly CORS/SOP work in the browsers, and with polishing it took a couple of days.
I'm pretty sure that in case 1) AI wouldn't help me much. Maybe just as a more fancy code completion.
In case 2) AI would probably be a significant time save. I could just ask it to write some draft for me and then I could make few tweaks, without having to figure out React.
But somehow nobody quantifies their experience with the languages/tools when they are using AI - I'm sure there's a staggering difference between 1 month and 10 yoe.
Without getting into touchy questions of quality and talent and the whole 10x trope, there are a lot of working engineers out there that produce code a lot more slowly than others and that work on more common problems than others. My sense is copilot-like products provide the biggest boon to those people and are a lot harder for people who are more naturally prolific or who work on more esoteric things.
Would this have been difficult for me to just write? No. I wouldn't call it difficult. But it would have involved effort, which is a resource I'm happy to conserve. It's like the difference between a sandwich you make and a sandwich you ask someone to make.
Then I asked it to generate the integer ranges which correspond to each bucket. It screwed that one up, which I found out by copying the function to a scratch file and trying some representative values in the REPL. So I told it to iterate all the values between in and max and generate the ranges that way. That one worked. Net of less effort, plus I had a test for it which I could copy-paste from the REPL to the test suite.
Faster? Maybe, maybe not. But I'm rate-limited by gumption, not minutes. When it's easier to describe the function than write it, and it's simple enough that the robot won't screw the pooch, I hand it off to the LLM. It's a great addition to the toolkit.