- cross-posted to:
- pracownicze
- technology@lemmy.ml
- cross-posted to:
- pracownicze
- technology@lemmy.ml
The new global study, in partnership with The Upwork Research Institute, interviewed 2,500 global C-suite executives, full-time employees and freelancers. Results show that the optimistic expectations about AI’s impact are not aligning with the reality faced by many employees. The study identifies a disconnect between the high expectations of managers and the actual experiences of employees using AI.
Despite 96% of C-suite executives expecting AI to boost productivity, the study reveals that, 77% of employees using AI say it has added to their workload and created challenges in achieving the expected productivity gains. Not only is AI increasing the workloads of full-time employees, it’s hampering productivity and contributing to employee burnout.
The workload that’s starting now, is spotting bad code written by colleagues using AI, and persuading them to re-write it.
“But it works!”
‘It pulls in 15 libraries, 2 of which you need to manually install beforehand, to achieve something you can do in 5 lines using this default library’
TBH those same colleagues were probably just copy/pasting code from the first google result or stackoverflow answer, so arguably AI did make them more productive at what they do
yay!! do more stupid shit faster and with more baseless confidence!
2012 me feels personally called out by this. fuck 2012 me that lazy fucker. stackoverflow was my “get out of work early and hit the bar” card.
I asked it to spot a typo in my code, it worked but it rewrote my classes for each function that called them
I gave it a fair shake after my team members were raving about it saving time last year, I tried a SFTP function and some Terraform modules and man both of them just didn’t work. it did however do a really solid job of explaining some data operation functions I wrote, which I was really happy to see. I do try to add a detail block to my functions and be explicit with typing where appropriate so that probably helped some but yeah, was actually impressed by that. For generation though, maybe it’s better now, but I still prefer to pull up the documentation as I spent more time debugging the crap it gave me than piecing together myself.
I’d use a llm tool for interactive documentation and reverse engineering aids though, I personally think that’s where it shines, otherwise I’m not sold on the “gen ai will somehow fix all your problems” hype train.
I think the best current use case for AI when it comes to coding is autocomplete.
I hate coding without Github Copilot now. You’re still in full control of what you’re building, the AI just autocompletes the menial shit you’ve written thousands of times already.
When it comes to full applications/projects, AI still has some way to go.
I can get that for sure, I did see a client using it for debugging which seemed interesting as well, made an attempt to narrow down where the error occurred and what actually caused it.
I’ll do that too! In the actual code you can just write something like
// Q: Why isn't this working as expected? // A:and it’ll auto complete an answer based on the code. It’s not always 100% on point, but it usually leads you in the right direction.