I saw another article today saying how companies are laying off tech workers because AI can do the same job. But no concrete examples… again. I figure they are laying people off so they can pay to chase the AI dream. Just mortgaging tomorrow to pay for today’s stock price increase. Am I wrong?
Do the job? No. Noticeably increase productivity, and reduce time spent on menial tasks? Yes.
I suspect the layoffs are partly motivated by the expectation that remaining workers will be able to handle a larger workload with the help of AI.
US companies in particular are also heavily outsourcing jobs overseas, for cheaper. They just don’t like to be transparent about that aspect, so the AI excuse takes the focal point.
I agree completely.
We have an AI bot that scans the support tickets that come in for our business.
It has a pretty low success rate of maybe 10% or 20% accuracy in helping with the answer.
It puts its answer into the support ticket it does not reply to the customer directly. That would be a disaster.
But 10% or so of our workload has now been shouldered off to the AI, which means our existing team can be more efficient by approximately 10%.
It’s been relatively helpful in training new employees also. They can read what the AI suggests and see if it is correct or not. And in learning if it is correct or not, they are learning our systems.
What’s this process look like? Or are there any rails that prevent the new employee from blinding trusting what the AI is suggesting?
Well, as they are new and they are in training, the new employee has to show their response to their team members before they reply.
If they are going to reply incorrectly we stop them and show them what’s wrong with it.
We are quite small and it’s nice to just to help us with this process.
The bot is trained on our actual knowledge base data. Basic queries, it really does a great job, but when it’s something more system based or that is probably user error, then it can get a bit fuzzy.
That’s also true when processing bills. The AI can give you suggestions, which often require some tweaking. However, some times the proposed numbers are spot on, which is nice. If you measure the productivity of a particular step in a long process, I would estimate that AI can give it a pretty good boost. However, that’s just one step, so by the end of the week, the actual time savings are really marginal. Well, better than nothing, I guess.
Absolutely. It’s at the level where it can throw basic shit together without too much trouble, providing there is a competent human in the workflow to tune inputs and sanitise outputs.
I use it to write my PR descriptions, generate class and method docstrings, notate code I’m trying to grok or translate, etc and so forth. I don’t even use it to actually generate code, and it still saves me likely a couple hours a week.
I haven’t thought about using it to annotate my garbage rather than generating its own. Nice idea :)
I use it to (semi) automate bit repetitive tasks. Like adding a bulk set of getters, generating string maps to my types, adding handlers for each enum type, etc. Basic stuff, but nice to save keystrokes (it’s all auto complete).
Anything more complex though and I spend more time debugging than I saved. It’s hallucinated believable API calls way too often and wasted too much of my time.
Yeah I can see the API call shenanigans. I’m using super maven for code and it’s pretty good tbh, it gets me 30% of the way or something. But API calls is a no-go, it almost never gets it right because I’m pretty sure it’s very hard for AI to learn the differences in API endpoints.