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.
They can read what the AI suggests and see if it is correct or not.
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.
reduce time spent on menial tasks
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 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.
I haven’t thought about using it to annotate my garbage rather than generating its own. Nice idea :)
Nope. In fact, it’s actually generating more work for me, because managers are commiting their shitty generated code and then we have to debug and refactor it for productiuon. It would actually save time if they just made a ticket and let us write it traditionally.
But as long as they’re wasting their own time, I’m not complaining.
I’d seriously consider quitting my job if my managers sabotaged work like that
I actually quite enjoyed it. He called me on the weekend the other day because he couldn’t get his code to run (he tried for multiple hours). Took me about ten seconds to tell him he was missing two brackets, didn’t even need to share his screen, it was such an obvious amateur mistake.
Anyway, wrote down 15 minutes (smallest unit) of weekend overtime for a 1 minute call.
Well that’s kind of rewarding indeed :D
I’m seeing layoffs of US workers, who are then being replaced by Indian, South American and Ireland nationals… not AI. But they’re calling it AI.
I mean irelands not exactly cheap anymore.
I heard Ireland hiring is also for tax reasons. But I’m seeing them move to South Americans more and more. Uruguay especially. I know Big Blue hired thousands there after doing RTO in the US.
I think that is it is the eu sorta put a stop to them being a massive tax loophole to the area. Now the area is about as expensive as the rest but does not have the big tax undercut like it used to.
We need to figure out two words for AI that represent off shoring. I can’t think of any though.
AI = Actually Indians
I work for a web development agency. My coworkers create mobile apps, they start off with AI building the app skeleton, then they refine things manually.
I work with PHP and some JavaScript and AI supports me optimizing my code.
Right now AI is an automatization tool that helps developers save time for better code and it might reduce the size of development teams in the near future. But I don’t see it yet, and I certainly don’t see it replacing developers completely.
There are lots of types of work in the tech space. The layoffs I seen have impacted sales and marketing (probably happens elsewhere too) because AI makes the day to day work efficient enough they don’t need as many people.
At my multinational, we typically hire in the hundreds every month for customer service. It’s like a $15/hr job, very baseline entry level, no experience needed. Because of that, there’s a constant churn. Most folks go for a year and leave for other jobs, or get promoted.
Last year was the start of us rolling out AI tools. According to the year end report, our “customer score” skyrocketed, which tells the bosses that AI is great for customer service. Also a few months ago, I noticed we weren’t refilling Customer Service jobs as fast anymore.
So these are the people who are getting squeezed out.
Had a new hire try to do all his automation programming in python with an AI. It was horrifying.
Lists and lists and lists of if else statements they caught if a button errored but never caught if it did the right thing. 90% of their bug reports were directly due to their own code. Trivially provable.
Work keeps trying to tell us to use more AI but refuses to mention whether the training data is using company emails. If it is then a buttload of unlabeled non public data is getting fed into it. So only a matter of time until a “fun fact” from the AI turns into a nightmare.
Most of our stuff is in an obscure field with outdated code, so any coding assistance is not really that impressive.
Half of my job is now done with AI, mostly PowerShell scripting and creating PowerPoints / reports. I just play videogames or cook or clean for half of the workday now.
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No, it’s basically filling the role of an auto complete and search function for code based. We’ve had this for a while and it generally works better than a lot of stuff we’ve had in the past, but it’s certainly not replacing anyone any time soon.
Without saying too much, my company implemented innovative “AI” applications to reduce time wasted by certain workflows. I think I don’t have to worry about job security for the next decade…
Yeah kinda, my coworkers talk to ChatGPT like it actually knows stuff and use it to fix their broken terraform code.
It takes them a week or longer to get simple tickets done like this. One dude asked for my help last week, we actually LOOKED at the error codes and fixed his shit in about 15 minutes. Got his clusters up within an hour. Normally a week long ticket – crunched out in 60 minutes by hand.
It feels ridiculous because it’s primarily senior tech bro engineer types who fumble their work with this awful tool.
I have never seen a clearer divide and correlation between the value I observe being produced, and those that don’t understand the limitations and value of LLMs.
It’s exhausting, because, yes, LLMs are extremely valuables, but only as so far as to solve the problem of “possible suggestions”, and never as “answers and facts”. For some reason, and I suppose it’s the same as for why bullshit is a thing, people conflate the two. And, not just any “people” either, but IT developers and IT product managers, all the way up. The ones that have every reason to know better, are the ones that seem to be utterly clueless as to what problems it solves well, what is irresponsible for it do be used for, correctly evaluating ethics, privacy and security, etc. Sometimes I feel like I’m in a mad house or just haven’t found the same hallucinogenic that everyone else is on.
Yeah, I use it daily for coding. It’s a force multiplier. It basically makes me 2 - 3x more effective. My company laid off all our junior engineers and is not hiring juniors any longer.
I’d say more like 20% more productive for most developers. Maybe it suits your coding style better than most?
Most of the time spent developing software isn’t writing code, but understanding the problem you’re trying to solve and translating that into an algorithm. I see more utility in generating tests, since a lot of developers don’t have good testing skills.
That 20% is just way too optimistic for anything more serious so as it would normally prompt hiring of software engineers.
If the project currently requires human developers as paid employees, it will continue to require that. So in introducing today’s ai, you either pay for the employees and the language model expenses, or you pay reduced employee expenses and the language model expenses, and then figure out a way to fund a complete, unavoidable refactor/rewrite down the line and how to adapt the business model back to sustaining employing the original amount of engineers on top of that lump sum.
If the project never was going to employ anyone, then yeah, using a language model can be more productive. It’s never going to require the amount of stability and cohesiveness a serious application doing serious things would require.
Otherwise, it’s just going to add work and require effort in an amount of multiples that scales with the complexity and seriousness of the application.
And while it does this, it consumes ridiculous amounts of more energy and resources than a human person would. Especially those that are not sustainable, that humans do not generally require in such immense amounts.
It’s going to be a net negative for a good while. If we ever survive the burning of our resources with these current models, maybe we get to something actually serious and usable, but I doubt those two can ever work together.
I don’t know what tools you’re using, but that translating the problem into an algorithm is exactly what the AI is very good at.
I basically only architect stuff now, then fine tune the AI prompts and results.
That certainly won’t come back to haunt them in 10 years. /s
Very shortsighted, but that’s the market we live in. The people making those decisions know they’ll exit before this catches up with the company and leave someone else holding the bag.
In 10 years they’ll have laid off the senior developers and will be using actual AI to translate C-suite dorks’ grunts and marketing speak to code.
Unlikely.
I’m honestly not so sure anymore.
Funny you say this. I’m watching my local coding community say things like “We used to apply to 100+ jobs and get an interview. Now it’s like 300+ jobs.”
It’s a serious change
AI is just another reason for layoffs for companies that are underperforming. It’s more of a buzzword to sell the company to investors. I haven’t seen people actually use AI anywhere in my large ass corp yet.
I called Roku support for a TV that wasn’t working and 90% of it was a LLM.
All basic troubleshooting including factory resetting the device and such seemed like it was covered and then they would forward you onto the manufacturer if it wasn’t repaired because at that point they assume it is likely a hardware issue (backlight or LCD) and they want to get you to someone who can confirm and sell you a replacement I’m sure.
It has potential to increase quality but not take over the job. So coders already had various addons that can help complete a line and suggest variables and such. I found the auto commenting great. Not that it did a great job but its one of those things were without it im not doing enough commenting but when it auto comments Im inclined to correct it. I suppose at some point in the future the tech people could be writing better tasks and user stories and then commenting to have ai update the code output or just going in and correcting it. Maybe then comments would indicate ai code vs user intervened code or such. Utlimately though until it can plan the code its only going to be a useful tool and can’t really take over. Ill tell ya if ai could write code from an initiative the csuite wrote then we are at the singularity.
It also has potential to decrease the quality.
I think the main pivot point is whether it replaces human engineers or complements them.
I’ve seen people with no software engineering experience or education, or even no programming experience at all in any form, create working apps with AI.
I’ve also seen such code in multiple instances and have to wonder how any of it makes sense at all to anyone. There are no best practices seen, just a confusing set of barely working disconnected snippets of code that very rudimentarily work together to do what the creator wanted in a very approximate, inefficient and unpredictable way, while also lacking any benefits of such disconnect such as encapsulation or any real domain-separated design.
Extending and maintaining that app? Absolutely not possible without either a massive refactoring resembling a complete rewrite, or, you know, just a honest rewrite.
The problem is, someone who doesn’t know what they are doing, doesn’t know what to ask the language model to do. And the model is happy to just provide what is asked of it.
Even when provided proper, informed prompts, the disability to use the entire codebase as the context causes a lot of manual intervention and requires bespoke design in the code base to work with that.
It absolutely takes many more times more work to make it all work for ML in a proper, actually maintainable and workable way, and even then requires constant intervention, to the point that you end up doing the work you’d do manually, but in at least triple the amount of effort.
It can enhance some aspects, of which one worth a special mention is actually the commenting and automatic, basic documentation skeletons to work up from, but it certainly will not, for some while, replace anyone. Not unless the app really only has to work, maybe, sometimes, and stay as-is without any augmentations, be they maintenance or extending or whatever.
But yeah, it sort of makes sense. It’s a language model. Not a logical model or one that is capable of understanding given context, and being able to get even close to enough context, and maintain or even properly understand the architecture it works with.
It can mimic code, as it is a language model after all. It can get the syntax right, sure, and sometimes, in small applications, it works well enough. It can be useful to those who would not employ engineers in the first place, and it can be amazing for those cases, really, good for them! But anything that requires understanding of anything? Yeah, that’s going to do nothing other than confuse and trip everyone in the long run, requiring multiples of work to do in comparison to just doing it with actual people who can actually understand shit and retain tens of years worth of accumulated extremely complex and large context and experience applying it in practice.
But, again, for documentation, I think it is a perfect fit. It needs not any deeper context, and it can put into general language what it sees as code, and sometimes it even gets it right and requires minimal input from people.
So, it can increase quality in some sense, but we have to be really conscious of what that sense is, and how limited its usefulness ultimately is.
Maybe in due time, we’ll get there. But we are not even close to anything groundbreaking yet in this realm.
I don’t think we’ll ever get there, because we are very likely going to overextend our usage of natural resources and burn out the planet before we get there. Unless a miracle happens, such as stable fusion energy or something as yet inconceivable.
I find that is a big difference in the llm’s some you can challenge the answer and sorta get an update where they take into account what you said. closer to a conversation and thus collaboration. Others though seem to treat it like a new query and don’t take into whats been said and such. or just don’t do so well. My thought is it could be a replacement for paired programming but not many places were using that anyway.
What I’m reading out of this… there’s going to be a massive shortage of senior programmers in 20(?) years. If juniors aren’t being let go/not hired and AI is doing junior work…
AI will have to massively improve or else it’s going to be interesting when companies are trying to hold on to retirement age people and train up replacement seniors to verify the AI delivers proper code.
Some things like image recognition, text classification, are way way easier using pretrained transformers.
As for generating code, I already used to spent a lot of time chasing bugs juniors made but can’t figure out. The process of making such bugs has now been automated.
I took some obviously ai genetated code (it had comments so I know they didn’t write it) from an offshore senior engineer and asked chatgpt what was wrong with it, and sent the result back to the guy… cause it was right.
