This is where we laugh -- everyone who said "AI will allow us to eliminate all these jobs" is now discovering, no.... all it did was change the jobs. Now you have to hire the same skill levels to cross check the AI.
But now you have to pay even more money.
1. Because writing code is easy. Reading code is hard.
2. You now need to include devs "familiar with AI"
3. Not only is the dev writing new code, it's now considered refactoring.
Just wait, you haven't even seen the fun yet -- right now, AI companies are going "We're not responsible ... it's just software...."
We;'ll see how long that lasts -- when AI makes a fatal mistake somewhere, and it will, and no one thought to have people providing oversight to check it, well, who do the lawyers go after?
Look up Moffatt v Air Canada.
Tl;dr: Air Canada fired a Buchanan of support staff and replaced them with an AI chatbot on their website. Some guy asked the AI chatbot about bereavement fares, and the chatbot gave him wrong information about some options that were better than what Air Canada actually offered. He sued Air Canada and won, because the courts consider the AI chatbot to be a representative of the company, and everything that the chatbot says is just as binding for the company as any other offers they publish on their website.
Unlikely. Unless their lawyers were totally incompetent, the AI provider will have liability restrictions from "you can't sue us for more than what you paid us" to "you accept full responsibility for testing and any damages for issues not found".
I'm basing this on the contacts we use for normal consulting work.
I don’t get why people think this is some kind of mystery. Liability is always contractually established, the AI is a product, if the product works as advertised then the producers are not liable for misuse. If a doctor does not properly exercise their professional judgement when using AI tools, they are liable. If the tool can be shown to be fundamentally not fit-for-purpose then the AI vendor is liable.
> "We're not responsible ... it's just software...."
An example of how this is already happening:
I work for a company making EHR/EMR and a thousand other adjacent tools for doctors.
During a recent product showcase they announced an AI based tool that spits out recommended medication based on the live conversation (between the doctor and the patient) that's being recorded. Doctors can just glance at the recommendations and click "Prescribe" without having to spend more than a few seconds on it.
Someone asked what guardrails have been put in place. The response from the C-fuck-you-pleb-give-me-money-O was, and I quote: "BMW is not responsible for a driver who runs over a pedestrian at 150 miles an hour. Their job is to make a car that goes fast."
Yes, I should look for a new job, but I am jaded and have no faith left that any other company is going to be better either.
That person is an absolute psychopath. That's absolutely not the same, because there are other departments in BMW, very close ones, that ensure it respects regulations and also a lot of security standards and tests.
they won't sell it much then.
Reusing the BMW analogy. Cars need to make test to be as much pedestrian safe as they could (at least in Europe).
Imagine BMW selling a car saying "we make fast cars not safe ones". They would sell only a bunch.
Surely if it continues like that the company won't make good money.
If I was the doc using it, i would turn that off. Always wary of traps that can lead to getting sued, and there's a lot of distractions in clinical settings.
Prescribing is supposed to be an intentional act, even if "simple" decision in a given situation.
Well obviously Microsoft can’t be held responsible for their AI drivel powering an autonomous Boeing 787, which will crash into the sea in 5 years time.
See also: self driving cars.
Someone will be killed, and no one will be held responsible, because that will stop progress you stupid peon
What are you on about. Looks like you are insinuating flawed code puked up by an llm that leads to a fatality will somehow make ai companies pay a price. Already 90% of the code is broken and needs oversight, it makes no sense to talk about lawyers going after them - Though the other way for sure, you gonna try sue over flawed code you will get bulldozed by lawyers, thats a certainty.
> We;'ll see how long that lasts -- when AI makes a fatal mistake somewhere, and it will, and no one thought to have people providing oversight to check it, well, who do the lawyers go after?
https://www.reddit.com/r/Futurology/comments/1ls8mk1/rfk_jr_says_ai_will_approve_new_drugs_at_fda_very/
Sorry -- won't work. They'll say the software works fine, it's bad training data. That's like saying the Python people are guilty when the Google car hits a bus.
This is what I have been saying for fucking ages - reading code is not just hard, it is substantially harder, and the difficulty scales exponentially with codebase size.
"But now you have to pay even more money.
So if this is a win-win (companies get faster time to market, developers get paid more in the end), why is everyone complaining?
Yes, we're trading long-term maintainability for short term gains, but believe it or not, sometimes this is an actually desirable property. It's often better to be first today than best tomorrow.
There are some ways it saves money and some ways it costs money. You have to look at everything to determine if it's actually profitable. And generally, it is as long as you don't overestimate the ai.
As a journalist, it's the same thing. The actual part of writing is about as quick as whatever your typing speed is. The gathering and analyzing of credible information, and interviewing people, takes far longer.
It's a million times faster to just read the information from a credible source, getting it right the first time, than it is to check over, find and fix all the mistakes made by AI.
It's not refactoring. It's debugging, the practice which is usually at least twice as hard as programming.
With refactoring you do not change the programs behavior, just the structure or composition.
To debug you might need to refactor or even reengineer the code. But first you need to understand the code, what it does, what it should do, and why it should do that.
Yep. Debugging requires the person doing it to have at least some mental model of the system's design. Even the best engineers who are able to pick out the root cause quickly would need some time to understand the tangled mess they're working with.
That is for now.
Given the pace at which AI is improving, it's pretty obvious it's only a matter of time, and not a long time.
There are systems already that use a bunch of different agents to verify and validate the accuracy of the responses.
With better trained agents using the human pro as input, this will become trivial for AI.
And even if you were right, you need a way smaller workforce to cross check code, so it did eliminate a lot of jobs.
Is AI still improving? A lot of reporting is suggesting that we've hit a wall and new models are getting worse because they are fed too much AI generated garbage.
Yeah but there is a lot of hype and people are really committed to this financially so surely it must work.
It is amazing to me that after all this hype, ML is behaving roughly in the same way academics concluded it would 20 years ago. Almost as if computer scientists might understand computer science.
I don't think LLMs were predicted. When I was in college about 20 years ago, no one was saying, "Someday we'll have random text generators that somehow produce reasonably accurate summaries of articles and nearly working software code."
No evidence that AI is improving, especially not at an exponential rate like before. If anything, they're hitting a wall because LLMs can only go so far, even when you add all the agentic crap
the "if it keeps improving" part is a real question. There is are upper limits to these probability engines. I'm not entirely convinced their only real success has been to lie to get jobs it isn't fit to do.
I've been using AI to help me learn a few things (programming wise). I don't use it to build code. I use AI to help me figure out how to do some things.
I encountered a few situations yesterday where ChatGPT referenced library functions that didn't exist. I copied and pasted the offending lines into VS Code and searched through a vendor's documentation. Nope, the functions didn't exist.
You're doing it right -- AI is a talented assistant, a very capable librarian. It can find things and take a shot at explaining them, but you are still in charge.
I've been programming for a while, and I was a little hesitant on using AI, but it does help get my questions answered faster, sometimes much faster.
I am very aware that I can't trust it. Excluding my example above, I never copy and paste AI code.
One thing to note is that if you are good at defining the tasks in a clear and easy to understand fashion, an intern can get things done. More so if you can have a whole team of them working at the snap of your fingers.
BUT! The skillset of your "average developer" and whats required for herding interns has little overlap. It's a learning experience, and as an experienced dev it will be frustrating not being immediately good at it.
I use it mostly for looking up syntax, and sometimes I’ve been able to talk around a problem and have it suggest something useful. But I don’t use it to structure anything, and I’ll go to the documentation if there’s any question or contradiction.
It’s like asking a coworker who’s knowledgeable but also sometimes full of shit. It’s not like looking it up in a real reference or like the computer in Star Trek.
> Now you have to hire the same skill levels to cross check the AI.
Actually, hire better skills to fix more problems in the first place. The per hour rate is pretty good like Y2K!
Reading code is always harder than writing it, doubly so when you can't ask the author to explain. The minimum skill level you need to hire just increased.
And the comments aren't helpful cause they're in the style of example code
//wait for 5 minutes
await Task.Delay(TimeSpan.FromMinutes(5));
rather than
//We need to delay the task because creating a new tenants takes a while to propagate throughout all of Azure, so we'd get inconsistent API responses if we took the tenant in use right away.
message.Delay(TimeSpan.FromHours(24));
They didn’t even really train it to code, they just trained it to generate probable text. That’s like when a cab driver asks you where you want to go if you reply with “What do most people say? Wherever that is, take me there.”
You can’t solve complicated problems without knowing how anything works.
Enough for what? Have we really defined what we want it to do?
I’ve never been all that clear on what the real application for a machine that passes the Turing test was supposed to be. It was just a thought experiment, we weren’t supposed to build it.
Enough to mimic human thinking and decision making.
It doesn't have to be good, just good *enough.* Big companies are already jumping at AI chatbot for websites and in its current form it's annoying AF. But if it worked right, it would be a godsent.
But what you’re describing doesn’t sound better. Dashboards are good, I like having a dashboard. Having to describe what I want the machine to do in English would be time-consuming and imprecise.
And computers already don’t have manuals. This is exactly the problem with the way people talk about AI now, it’s all half-baked solutions to problems people don’t actually have.
Dashboards ARE good, they're better in fact. But they're inflexible. Someone has to design them, code them and maintain them. It doesn't make sense to build an interface for a once off task. Words are better for ad-hoc tasks.
I feel like you're being a little too literal about the manuals. Do you just want to argue? I think I'm gonna call it here. Good luck
It’s an encoding of humanity and human behavior. It’s for making inferences along that search space. Allowing machines to understand us, and our world, is what it’s for.
LLMs like ChatGPT are not a record of human behavior, they are models trained to recognize and reproduces complex patterns in text. Most of the text it was trained on was generated by humans, but that's not the same thing as teaching it human behavior.
It also doesn't understand anything. It can emulate us, to a degree and in terms of generating text, but it doesn't understand. That's actually really important to recognize. It doesn't have judgement. It can make connections between different pieces of information it was trained on, but it doesn't have a coherent mental model of the world or of people. If that's what its purpose is, then it fails.
You'd need a very different kind of AI to work as a control system in a bipedal robot. Honestly, I don't see a lot of practical utility in humanoid robots either.
I said ‘encoding’, not ‘recording’.
Also, it’s being done extensively:
https://www.sciencedirect.com/science/article/pii/S2667379723000451#:~:text=Abstract,potential%20avenues%20for%20further%20research.
https://www.youtube.com/watch?v=r0iVR1FEOxE&pp=0gcJCfwAo7VqN5tD&t=88
I'm reminded of a tweet I saw right after the SAG-AFTRA strikes concluded:
> It's amazing how quickly studios went from "Yeah we'll use AI, writers can go live under a bridge" to "Oh god we tried writing a sitcom with ChatGPT can we have the humans back now?"
It amazes me how so many businesses think the order to do things is (1) fire the staff, and only then (2) see if AI is fit to replace them. Not the other way around.
ChatGPT sounds like it knows what it’s talking about to anyone who only has a surface level understanding of whatever it is talking about. It’s kind of a perfect tool for convincing management that they don’t need their technical experts anymore.
High quality software development costs has no silver bullet. The quality barrier floor as dropped significantly lower thanks to AI though.
Before there was still a high minimum cost to deploy a low quality software product. AI has lowered that cost to near zero, so expect the number of low quality software products to drastically rise up.
Far from inevitable; model collapse looks more and more real. And all this generative model stuff is taking away from research that was being done on real AI.
Lawyers too. Turns out you can't cut out the lawyer, AI generate a contract, and slap it in front of someone to sign without taking a gigantic and embarrassing risk.
The lawyers can use AI for the bullshit work that they've been copy/pasting for decades, but they still have to review the thing.
Good to hear, but please make sure you're being paid GOOD good money.
Remember that these companies wanted to replace you and me with AI, now they're asking us to fix the shit AI built and make it work. For me, that's going to require a rather significant compensation package, because what happens when you decide you don't need me any more?
Protect yourself, get paid for the skills you bring and get paid well for it. I'm not against using AI, but it's a tool, not a replacement.
My firm is charging 370/hr for my time. And that's just for fixing normal bad code. Consulting companies are where the money is at now that the big names are doing mass layoffs.
Anthropic themselves are paying their engineers six figure salaries and constantly hiring: https://www.anthropic.com/jobs?team=4050633008
Even Claude Code has way over a thousand reported bugs: https://github.com/anthropics/claude-code/issues?q=is%3Aopen%20is%3Aissue%20label%3A"bug"
> way over a thousand reported bugs
FWIW this is just par for the course for production software used by a ton of people. Here's the Roslyn compiler and IDE with [nearly 2500 confirmed bugs](https://github.com/dotnet/roslyn/issues?q=sort%3Aupdated-desc%20is%3Aissue%20is%3Aopen%20label%3ABug).
If AI worked as advertised, then Claude Code could fix its own bugs and its count should be close to zero.
Roslyn is much older, much more complex, and far more users, and unlike CC, every change needs to reviewed for backwards compatibility and forward looking repercussions.
If CC can't even be used to fix itself, there's no chance of it being used to fix something as hard as Roslyn.
> If AI worked as advertised, then Claude Code could fix its own bugs and its count should be close to zero.
I don't see Anthropic advertising anything of the sort.
> Whether you need to fix bugs in a single function or rename variables across multiple files, you can instruct Claude with everyday language:
>
> “Refactor the logger to use the new API in logger.js”
> “Add input validation to signupForm.ts“
https://ai-claude.net/code/
Yes, you can ask it to fix bugs with natural language. That’s how these tools work. This says nothing about “can fix its own bugs” or your imagined advertisement wherein it can just fix issues on autopilot.
Show me where it says that it can fix bugs except it's own bugs.
Show me where it says it can rely on natural language unless that natural language is written into its own bug reports.
Do you always defend companies who put out misleading advertisements? Or is it just AI companies in particular that you'll bend over backwards to excuse their deception?
I’m not defending your head canon, no. Claude Code is a very sophisticated system used by a ton of people — often for things it wasn’t yet designed for — and so it has a lot of confirmed bugs. This is not a defense of anyone. What I’d recommend is actually using this tech so you can dispel both ends of the hype cycle from your brain and see it as a useful tool in the toolbelt that genuinely accelerates some work.
> Claude Code is a very sophisticated system used by a ton of people — often for things it wasn’t yet designed for
That's a great pitch for a venture capital fund.
But we're talking about what they claim it can do in their adverting material and comparing it to what's actually happening in their code base.
Correct, I am talking about what they claim. You are not. You are extrapolating one thing — it can fix bugs — to something they are not claiming — it can fix bugs sufficiently well that complex systems shouldn’t have a lot of bugs.
I understand where you’re coming from. You likely believe it’s just a 500 LoC agent like so many pet projects people have online. Or also perhaps that no “serious developers” (i.e., doing only things you personally grasp) work for Anthropic on Claude Code. These are commonly held opinions by people in this subreddit who don’t adopt technologies at earlier stages of their lifecycles. That is all fine and good, but it doesn’t take away from the fact the sophisticated software put to task for a heterogeneous set of use cases is not necessarily going to be excellent at those use cases all of the time.
That you’re confusing this with “so and so is advertising it as such and such” is a self-own.
Again, show me in the advertising where they want their customers believe that this doesn't work on complex projects.
This is what they call "plausible deniability". The intent is to make their customers think one thing, and then pretend like they said something else after they waste all their money.
Nobody is saying Claude Code doesn’t work on complex projects. At this point I think you’re just being difficult for the sake of it. For your sake, use these tools before you talk about them.
You just did. In your last reply, just an hour ago, you complained that I was unfairly accusing them of claiming that they could fix bugs in complex code.
> Watch as Claude Code tackles an unfamiliar Next.js project, builds new functionality, creates tests, and fixes what’s broken—all from the command line.
Source: https://www.anthropic.com/claude-code
Your point is? It's describing agentic coding. It is not describing itself to be a system that fixes all the bugs that may or may not be introduced, putting all software on autopilot.
/r/programming stop confusing your own head canon with reality challenge, I swear to god
> handle the entire workflow—reading issues, writing code, running tests, and submitting PR
This is what bug fixes are, they specifically advertise they can fix bug reports. You claimed they weren't.
I claimed nothing of the sort. Claude can indeed be given a bug report and attempt to fix it. It does sometimes, in my experience! And sometimes it does not.
But again, this is not at all what OP is claiming (in their head) is what is being advertised. I think you’re suffering the same problem.
> Claude can indeed be given a bug report and attempt to fix it. It does sometimes, in my experience! And sometimes it does not.
The same could be said for an RNG-fueled code remixer.
Yeah, them claiming Claude Code can do bug fixes definitely doesn't mean Claude Code can do bug fixes.
They specifically advertise it, but it can't work for their own tooling.
I also advertise to companies that I can fix bugs when I want them to hire me. However, that doesn't mean that it's either cost effective for me to fix \_all\_ bugs, nor that I can actually fix them all (I could imagine some bugs that I couldn't fix)
If they advertised "Claude code can fix \_all\_ bugs", then you might have a point.
They can write code, and they produce bugs. So do humans.
They can attempt to work on issue and submit a PR, it may be dumb and not working. So can human-submitted PRs.
Obviously in a lot of situations humans are better. But I still don't see Anthropic advertising that AI code will be bug free and completely replace humans.
Oh yeah I’m not saying it’s unusual.
My point was more that AI companies are paying professionals really good money to make their products usable. If you’re vibing anything with AI then you’re going to fall behind your competitors.
Too bad I'd rather make money building things, not fixing AI slop. Hopefully the industry snaps out of it and realizes AI hype juice is not worth the squeeze
Well hopefully there are enough people like me who do like fixing stuff to cover this mess so people like you still have a chance to work on greenfield applications.
it is just like the overseas programmer craze of the 80s and 90s. I made a lot of money from fixing crap from overseas bottom dollar software teams and I expect I also will with AI code.
While technically yeah they are making money fixing AI shit, it's not like it's extra money. In the story that web site copy used to be written by a human anyways, so it's not a net gain or anything for copywriters even in that instance.
The problem is poorly used AI. Like with programmers, skilled people can use AI to do more work than they used to be able to do, and unskilled people just create a mess wherever they go (whether it was their own code/copy before or AI generated now).
Overall it's still more work getting done by fewer people, but that's not necessarily a bad thing, the same with any other invention that increases productivity.
> While technically yeah they are making money fixing AI shit, it's not like it's extra money.
Sounds like your sales department needs to learn the phrase "emergency pricing".
I wish I’d heard that. I know it’s coming but so far the market is hard around me (and around me goes across several continents). The AI dreams are still funneling the only money out there
One of the positive effects of the US crashing the global economy is that it may kill funding for AI companies. When money becomes tight, people are going to demand results.
Unfortunately that's only positive in the long-term. While we're going through the process it is going to be very painful.
As a journalist, AI does not save time. It will always be faster to just get things right the first time. It takes a million times longer to fact check an LLM's work, find all of the errors, then go to the source and start from scratch, than it is to just read the information from the horse's mouth and fact check the source material.
Aside from not saving time, it also leads to less quality reporting because it's incapable of doing any of the things that make good reporting (which are also what take up the bulk of the time).
But I'm not a copywriter. So my work relies on factual reporting of meaningful, new information. I don't get to use — let alone rely on — adjectives like copywriters do.
It will vary by field, but here:
[**Generative AI isn't biting into wages, replacing workers, and isn't saving time, economists say**](https://www.techradar.com/pro/generative-ai-isnt-biting-into-wages-replacing-workers-or-saving-time-economists-say)
>However, the average time savings reported by users was only 2.8% – just over an hour on the basis that an employee is working a 40-hour week. Furthermore, only 8.4% of workers saw new jobs being created, such as teachers monitoring AI-assisted cheating, workers editing AI outputs and crafting better prompts.
Code isn't the tool in this context. The IDE is the tool, or the AI in this case, and it very much can create mistakes.
If a printer misfeeds and garbles a page of text, we don't blame the author of that text, nor do we blame the operator of the printer. We blame the tool, and then we fix or replace the tool.
There is absolutely nothing wrong with blaming your tools, if you then proceed to do something about those tools.
You've never had a printer?
If a printer misfeeds, no one replaces it. That doesn't make sense. All printers misfeed sometimes. It's just how printers are. Half the time it happens because of human error anyway.
Were the new printers truly of superior quality and completely jam-free, or could they still misfeed?
Say, if someone jammed them with a piece of paper inserted width-wise.
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Translators have been paid to fix issues created by AIs for nearly a decade. We've been calling it "post-editing" and commercial translation agencies have their translators correct machine translations. Machine translation with neural networks was introduced in 2014 already. If you want to see how an entire field has reacted to AI, look to translation.
In addition to what the other person said, the expectation on translators is now greater. They're expected to get through more per day and post editing pays less than translating from scratch. Some of the post editing work will just be hitting accept, some is correcting obviously wrong translations, and a bit is actually harder when the AI creates a wrong but plausible translation. You also need fewer translators to get through the same amount of work.
The younger translators are fine with it, especially because it's taught in schools now, but older translators much prefer working from scratch. In some domains that's more common.
I'm curious to see how it evolves in the next few years. I know organizers of machine translation conferences and some of them consider it to be a solved problem. But the top of the end models still aren't cheap enough to be deployable for every company.
most of the text is samey to begin with, and the fixes you make get committed back to the software. one of my translator friends was pretty glad she didn't need to do entire text by hand anymore.
I’m rewriting an app written in AI. I give it credit for doing the work needed for the company’s first client but it started to become a nightmare trying to make it configurable and scalable. The code is complete shit.
I have no worries about being replaced.
When ChatGPT came out and kept improving, I eventually got concerned that I could eventually lose my job to AI (also a dev). As I've become more aware of LLM's actual coding skills (or lack thereof), I stopped worrying.
I think it'll need a new leap / paradigm on AI before our job might be threatened. I don't think LLMs will ever be a threat to experienced devs.
I'm not saying AI won't become a threat. I'm saying I don't think LLMs specifically will, just like floppy disk never achieved the kind of memory that disks or flash achieved. My point is, I believe it will require a new leap or paradigm in AI before experienced devs are threatened. What do you think?
We’ve only just started to let coding agent LLMs run and test code. We haven’t even scratched the surface of what LLMs are capable of. Experienced devs will be highly threatened within the next 5 years, and that’s assuming they are already incorporating LLMs into their workflow.
AI is decent at basic task that have been extremely well documented in their training data. But this is stuff that in you are working in a decently powerful language or in a reasonable well equipped IDE, you could already automatic away. Generate a basic webpage with connection to standard tools like databases and payment processors? There are countless webpages documenting how to do this, and that's what the LLM is copying when it is asked to do it.
Anything where the problem is complex, long, novel, or not well documented, a LLM is going to fail because it won't have examples to copy in it's database or doesn't have the conceptual memory to keep a solution consistent. And it's not even smart enough to know that it is going to fail at the task. "Vibe Coders" are going to produce reams of broken and untested code, that pass the "vibe check" and the extremely limited set of "unit tests" that they ask the LLM to generate.
Not like this. I’ve worked for a few startups as a founding engineer. Yes, you sacrifice some quality to get to MVP faster, but as an engineer…as a human…you still try to maintain some modicum of readability and maintainability as you go along without spending too much time on it. AI simply doesn’t have the awareness to do this, nor will it ever.
As of now, I can tell you that it’s questionable if it was worth using AI to get to MVP rather than hiring an engineer in the first place. We’re having to refactor the codebase to adapt to newer clients while the competition is taking them away very rapidly.
> I can tell you that it’s questionable if it was worth using AI to get to MVP rather than hiring an engineer
I guess it depends on how fast AI could get the MVP (or let's call it what it is: POC) to market compared to an engineer. Maybe the company would have missed a window if it couldn't be made fast enough.
It’s the difference between garbage than can be cleaned up and garbage that simply needs to be thrown away. You’re not saving any time, resources, or money using AI to code.
And, if you’re a software engineer, you’re risking your skills becoming stale when you use AI.
I used to be on the fence on this matter, but I’ve seen a highly skilled principal engineer struggle solving junior level problems after months of using Copilot and Claude. It was scary seeing his transformation.
I guess I would say, redo the logic and rebuild bc debt is debt and likely full of vulnerabilities either way.
Have done enough web app pt in my time to see the debt.
In my experience, if you take the time to write the code properly in the first place then you get to MVP much faster.
But I'm normally comparing software engineering techniques to SOLID, Clean Code, and other fad-based methodologies. I haven't had to deal with AI-driven code yet because no one who says to me "We should use AI" has actually demonstrated that they can use AI.
AI coding that could do what it’s currently capable of doing (basically creating a fully functional app you can launch) didn’t exist a year ago, even though AI coding was a thing years ago.
I work very closely with a one of the top 20 technology universities in the US. 1) they’re forward thinking and have fully embraced AI in their curriculum. 2) they encourage its use 3) their entire curriculum/class structure is being rewritten and turned upside down to accommodate it. To the point where in a couple of yesrs they’re not planning on even having basic coding classes anymore, and their curriculum will be fully based on how to harness AI.
You might not (naively) be worried now, but in a few years what you’re doing now is going to be a few prompts away and be just as good.
lol what a complete lie. Did you have ChatGPT write that for you?
Nothing tells me more that I’m telling the truth than when people lie so blatantly like this. It’s desperation.
Ok bud! I actually work with two major universities very closely, endowed a scholarship at and one and was a member of their Board of Trustees about a decade ago. Now I mostly do fundraising for that one. Please tell me more though!
The problem is all these execs who think it is feasible to create an app by sewing the chunks of code generated by a chatbot.
It is the end of quality code, but apparently they don't care. Which in itself is telling of how fake this industry is.
*thinking* "Ah, at least that was two years ago when I, of course, had been ~much~ a bit less good of a programmer. Unlike the other location where git blame pointed to last month..."
All titles are made up. There's no standardization in the programming industry unless it's some guy who interned at Google and is trying to copy their hierarchy from memory. It varies wildly based on where you work.
AI is a wild horse. It's a powerful tool in the hands of an expert. Sadly, in the hands of inexperienced developers or worse yet "vibe coders" who have no idea how to read and analyze the code, it can be a disaster.
Right? This article sounds like anti ai propaganda. Ai writes bad code, but so do most people. People have been paid to fix someone else’s code since the beginning of programming.
I built my career coming in to reverse engineer and fix/support legacy PHP projects that were created by amateurs. I wonder if we are going to see a new sub-career spin off as software engineers specialized in fixing/supporting legacy systems built by AI.
I see two ways that won't happen.
1. LLMs are proven to be too unreliable/expensive and the whole sector dies.
2. LLMs are replaced by something that works far better.
Neither seems likely to me.
> Ms Warner says this has led to clients adding code to their website that has been suggested by ChatGPT. This, she says, has resulted in websites crashing and clients becoming vulnerable to hackers.
********
> "Human oversight is essential," he says.
>
> "We've seen companies generate low-quality website content or implement faulty code that breaks critical systems.
Maybe next time don't ask AI to summarize it for you and actually read the article.
> Maybe next time don't ask AI to summarize it for you and actually read the article.
Huh? I obviously read the article or I wouldn’t have known she was a copyrighter.
Your excerpts are about adding HTML to a company’s webpage and HTML is not a programming language.
From the sidebar:
“ Just because it has a computer in it doesn't make it programming. If there is no code in your link, it probably doesn't belong here.”
Nope, not gonna play that game. It explicitly says "code" and "critical systems". You're just denying it because.. well honestly I don't care why. People like you aren't worth my time.
I am being paid to analyse shit the annalists should have analysed. I am in a team with 1 dev, me, 3 analysts, 4 product managers, 1 other manager, (no clue what he does)
I used to be the only person in the team, and work went great. shit got done. now? Nothing, absolutely nothing. Fuck all these bloody meetings. AI isn't going to fix that crap.
I've been on projects work so many meetings that the managers never did any real work except in the middle of the night.
To this day i don't understand why none of them had the guts to say no. It wasn't like fewer meetings could make the pissed off client even mader.
What's even stranger is they dropped all the staff (not fired, just moved to other projects) but kept the managers.
There are no issues caused by AI, and there never will be. Also: we have always been at war with Eastasia.
From management's POV, AI's killer feature is that *it never says no*. Is this a feature or an anti-feature? You be the judge,
Yeah, but unless you’re being stupid about it, that was always the plan, right? The idea isn’t that AI can replace 100% of what a person does, but that the two working together can be more productive than an unassisted human.
Yeah, though, in hindsight, it's probably because I skipped the second step, which I thoughtlessly discarded as too obvious to mention.
Of course, programmers are still going to be replaced, it's just that the actual replacement is closer to 10 programmers with AI tools will replacing 15 programmers without AI tools.
The impact on total employment and wages for programmers could go either way, though, depending on elasticity of demand. I.e., it could be that, if development is cheaper, it will be profitable to do a lot more of it.
More like 10 programmers with AI tools plus 20 really expensive consultants will replacing 15 programmers without AI tools.
My company is going to make so much money.
AI tech is incredibly expensive. Last I checked, every AI company is burning money at an incredible rate.
The ONLY way this works for them is if they can convince their customers that AI will be cheaper than hiring professionals. They can't charge the rates they need to survive if companies like Disney can't cut their labor bill by firing most of their writers, artists, and programmers.
As an experienced programmer I notice that the AI often (> 75% of the time) suggests crazy solutions to problems. People who are more skilled at using it can reduce this a bit but still the AI is very often just going to lead you down an unproductive rabbit hole.
This leads to an interesting paradox where the AI is most helpful for people who are already very good programmers, who are also the most expensive. And of course, if we don't train new ones, they will eventually die out.
This is how automation affects works, generally. Manufacturing in the US didn't disappear work automation. But they are far fewer jobs. Those jobs, however, are higher skill and higher pay than assembly line work.
Yes this happened before, so we can learn from that. We have seen that this kind of labor that was considered "unskilled" and outsourced was actually not so easy. Currently many "developed" nations are finding it impossible to return this manufacturing capability. We can learn from that instead of blindly stumbling into the same hole again.
Manufacturing in the US is disappearing, just not in the way people expected.
We aren't replacing those high skilled machinists. So we they retire, we lose the ability to do their kind of work. There are a lot of products that we literally cannot make in the US because we no longer know how to.
I think the only reason this isn't going to happen with programming is that hobbyist programming provides an alternate training path. There are no hobbyist machinists with a 500 ton press in their garage.
I agree to an extent, but I think this is going to birth a new kind of programmer. People will become better at detecting AI mistakes and bullshit, at the same time as AI improves to make those mistakes less. Agents in the terminal debugging their own code and writing test cases and trying to use the stuff they create in browsers is already basically here (even if it doesn't work very good).
I have been programming most of my life and employed doing it for a long time now, rolling out proprietary software for companies.
I am NOT worried that Janet in HR is suddenly going to be the new programmer, thanks to AI, or that AI is suddenly going to be doing my job any time soon on its own. I am more concerned about "what does this look like in five or ten years when guys who ONLY had these tools growing up become efficient at using them in ways I didn't imagine"?.
We might also see companies arise where they employed just a couple of good programmers and then go sell programming "services" to companies that are 90% AI, trying to replace people like me. But stuff like that has always existed, with offshore, already.
Until AI can provision its own resources, I wouldn't be too worried. Most people who think they can program now that AI can "do it for them" don't know how to use a terminal or actually deploy code they create - it seems like such a small barrier but it has (thus far) been insurmountable for most people I encounter.
> I am more concerned about "what does this look like in five or ten years when guys who ONLY had these tools growing up become efficient at using them in ways I didn't imagine"?.
Early studies show that they are less capable of doing the work and anything more complex than what the AI can handle is beyond their ability.
It's the equivalent of giving first graders calculators instead of teaching them how to add and multiply. Yes, they can do the work quicker at a younger age. But most of them will never make it to algebra because they weren't forced to learn the basics.
> that was always the plan, right?
No? Who said it was the plan? The hype was all about replacing us for a while. Now things are starting to come back down to Earth, and people are realizing it won't replace us, but it can still be a great tool.
I dunno man, the way I’ve interpreted all this hype is that AI is coming for allll the jobs. White collar jobs first and once robotics catches up, blue collar jobs next. Every field is being sold this story that you can start cutting staffing costs thanks to AI. We’re obviously very focused on software but if you expand your search, the hype is everywhere.
No, that's just what we tell ourselves to make us feel better.
The idea is, and always had been, to replace expensive professionals with computers just like most farmers and factory workers were replaced by machines.
I think we can say with confidence that this story of moderation and careful proofreading is NOT the story being told to investors nor the type driving the “AI will replace us all” hype/panic.
So it’s important to highlight the practical limitations of AI as much as possible. The potential advantages are getting way too much airtime.
I work for /very large manufacturing corporation/ that's been pushing to find ways to integrate Ai. When it comes to programming what I've been telling management and upper mgmt is Ai is a great \_tool\_ to help a programmer write code a little faster, but the real person behind the keyboard has to have the skills to understand every line of code that the Ai is outputting because it will make mistakes that will cost us lots and lots of time and money to find later on. I simplify it like this - If there is a process that impacts real people somehow, and Ai is involved in that decision making process, the final decision \_must\_ be made by a real person. The Ai should be used to make 'suggestions' not 'decisions'.
What you've described management doing is the definition of a solution looking for a problem, and is sadly too common now.
You have some issue you want to fix? MAYBE LLM's are the way to do it, maybe not, but start with the _issue_, not some magic bullet.
There was a lot of money to be made in 2000 asking the question “could we apply the internet to this problem?” (E.g. Amazon)
And in 2010 about mobile (e.g. Uber).
Of course you need to apply the right tool to the right problem, but it is totally rational to brainstorm “given the existence of new tool X, how should our workflows adjust.” Nothing wrong with that kind of thinking at all.
Mandates to use it “or else” are pretty problematic but on the other hand, one is trying to overcome the inertia of “we have always done it this way” which is just as problematic.
If your company is high performing and healthy, then it will be the employees reporting how they did experiments with new tools (including and especially AI) and reporting back what did and didn’t work. And if management is also high performing and healthy then they will see that there is no need for mandates, because their employees are mature and professional enough to evaluate new tools without prodding.
Amazon's problem was "How do we make it easy to search for and buy books so people aren't limited to just what the bookstore happens to carry?".
Most of the companies that asked “could we apply the internet to this problem?” went bankrupt in the dot-com crash.
That’s some wild revisionist history you’ve got going there!
> Bezos first got the idea to start an Internet enterprise in 1994. While surfing the Internet in search of new ventures for D E Shaw to invest in, he came across the statistic that World Wide Web usage was growing by 2,300 percent a month. Bezos immediately recognized the expansive possibilities of selling online and began exploring the entrepreneurial possibilities of developing an Internet business.
> He drew up a list of 20 potential products he thought might sell well via the Internet, including software, CDs and books.
https://www.entrepreneur.com/growing-a-business/jeff-bezos-biography-how-he-started-amazon-and-more/197608#:~:text=After%20reviewing%20the%20list%2C%20books,could%20offer%20millions%20of%20titles.
> Although he thrived and was highly regarded at D. E. Shaw, his insatiable curiosity ensured he wouldn't stay long. Fascinated by the explosive 230,000% year-over-year growth of the internet in the early 90s, Bezos found this to be the catalyst for immersing himself in both entrepreneurship and the internet.
> He then began brainstorming business ideas with his Wall Street colleagues. A fun fact about this process is that Bezos categorized his colleagues by brilliance and filtered out ideas from those he wasn't particularly impressed with. Ultimately, his interest increasingly leaned towards e-commerce.
Bezos started by creating a list of 20 potential product categories for an online store, including items like software, office equipment, and music. He ultimately chose books, primarily because they represent a straightforward commodity. Consumers know exactly what they're getting: a book is identical regardless of where it's purchased.
https://quartr.com/insights/edge/jeff-bezos-building-an-empire-from-a-to-z
Jeff Bezos was following a tend li e exactly as Anthropic is today. But this time competitors are not dumb enough to let a single player dominate basically unopposed, so it’s a lot harder for e.f. Anthropic.
That's why I love the AI built into Visual Studio. Not Copilot, just the basic stuff that helps me type and refactor faster but doesn't get in the way.
That's the most dumbest thing ever. I press enter twice to clear a line and want to write some code but it presents some dumb ass statement I don't want there but it thinks it has to be there so I have to press esc to get rid of it. It's so obnoxious and annoying. Luckily they made an off-switch.
Granted, perhaps it's a bit smarter in C# than C++, what I write mostly
Step 3 in the AI journey.
Step 1: AI is useless for coding.
Step 2: okay maybe copying and pasting lines or functions from ChatGPT is occasionally useful.
Step 3: okay maybe line completions might be useful
Step 4: okay maybe full functions sometimes
Step 5: okay agents can sometimes solve problems faster than I can. As long as they are supervised
Step 6: okay agents can often solve problems faster than I can. As long as they’re supervised
Step. 7: ???
It does more than live completions. If it sees me make the same change in two or three places, then move to a similar line it offers to make the same change. Basically like a smart global find and replace.
It does this without having me to engage it in a conversation or fight to undo unwanted changes.
***
I liken it to the full self-driving car debate. The vast majority of self-driving car safety enhancements are obtainable with just things like automatic brake assist and adaptive cruise control. When you go beyond those features, then driver assist actually makes it more dangerous.
Or to use a cooking analogy, if a little bit of salt makes your soup taste better that doesn't mean you can dump in an entire box of salt.
These are fine analogies but there are tons of extremely experienced”cooks” who would say that your cooking tastes bland. I set the reminder because there is no use arguing about these things. Everyone moves along the path at their own pace depending on their problem domain, their working environment, their own personality, the tools they have tried etc.
It’s no different from any other developer tool transition from the past. You can argue with assembly programmers or you can just out-compete them. Only one of those is actually guaranteed to work in the long run.
You are saying nothing I haven't heard countless times before.
They made the same exaggerated claims about Computer Aided Software Engineering (CASE), OO databases, NoSQL databases, graph databases, blockchain, and many other fads.
Yes, there was a significant productivity jump from 2nd generation languages to 3rd generation languages. And another when we went to memory managed languages. But those advantages were obvious from the start. Very few people who tried them came back to say that they were a net negative. You didn't see FORTRAN programmers spending a lot of time hand-editing binaries to fix garbage emitted by the compiler.
In 20 years people will remember the influence of AI as “obvious from the start.”
The process has already begun. Two years ago everyone hated AI autocorrect and now they just take it for granted. As we all move up the spectrum, we will experience amnesia about our precious skepticism.
AI is also making its way into open source development like the [Linux Kernel](https://thenewstack.io/how-ai-helps-maintain-the-linux-kernel/) and [GPU kernels](https://huggingface.co/blog/codelion/openevolve-gpu-kernel-discovery)
CASE tools were never used this broadly or by this calibre of elite developer.
But I set the reminder because I know that words won’t convince you: you will need to see a coworker increase their productivity dramatically, as I have seen many times now.
> Two years ago everyone hated AI autocorrect and now most just take it for granted.
Autocorrect is more than 2 years old and I have it turned off because I still find it to be annoying.
EVERY coworker that I respect, whom I've subsequently asked, says that Copilot is a joke.
The ones I didn't redirect in the past are the same ones pushing me to use AI.
I doubt that this is a coincidence.
Step 7: the agents broke production and didn't tackle the issue in the user story they were handed. Instead, they spent $1500 in request thrashing chasing down the same bad architectural design that they were told to avoid explicitly.
Likewise - Cursor has worked great for me even though I almost never use the chat. After having rules files created (and updating them/changing dumb rules), it works even better.
["A computer can never be held accountable, therefore a computer must never make a management decision."](https://simonwillison.net/2025/Feb/3/a-computer-can-never-be-held-accountable/)
I've been using AI at work and also at home for a hobby project. At work, I work with it collaboratively where I give it targeted instructions like "look at this function, analyze the control flow and let's figure out how we will make this change..." Then we make a plan and I review every change. At home I've been testing out true vibe coding, where I just say what I want, let it go by itself, then if it doesn't work I'll just give it the error logs and let it figure it out. Obviously the former approach yields much better code than the latter.
If you don't give explicit instructions, LLMs will do some really wacky shit sometimes and generate messy unmaintainable code. But if you use it as a tool and make good suggestions like "extract this into its own method and make sure everything is testable" or "lets analyze all the options and search through these other repositories for examples", you can get much better results. Having a good initial instruction prompt set up is very useful as well, because it seems like the system prompt that Copilot or Claude has is geared more toward making it an independent developer and less as an assistant. They will regularly finish writing some code and then say "🎉 This feature is now ready for production!" before any tests have been run. If you put strict rules in the instruction prompt like "always run compilation, unit tests, linters etc before declaring a task is done" it reins in some of this overenthusiastic slop-code generation.
Thank you for your message — I take this seriously.
Can you clarify what part of the article or summary you found racist? If there's something in the content that reflects bias, stereotypes, or unfair treatment, I want to understand it fully so I can address it appropriately and make sure the response is respectful, accurate, and inclusive.
Your feedback matters.
>*For now, she's hearing of writers whose main role now is to fix copy churned-out by AI.*
>*"Someone connected with me and said that was 90% of their work right now. So, it's not only me making money off such missteps, there's other writers out there."*
That makes sense. It's the same with humans, who also make mistakes which other people (editorial staff) get paid to fix.
Ultimately, from the perspective of businesses, it doesn't matter if you have to pay people to fix the mistakes made by AI, if you are producing more correct text for a lower cost (and in less time) after you factor in the cost of paying people to fix errors.
It's really not. Businesses are using the technology because it is useful to them and saving them money. But you are welcome to keep your head in the sand and pretend like it's not happening if you want 🤷
I find that claim to be hilarious. It's the same thing that they said about countless fads that turned out to be huge money sinks. The bigger the corporation, the more they have a reputation for adopting counter-productive policies and technologies.
I strongly suspect that if they did come up with something that was actually saving the money, they wouldn't be talking about it. The directors who have actual innovative ideas keep them closely guarded secrets.
It's the directors who have no clue what they're doing who issue all the press releases and magazine interviews. They are the ones who are desperately trying to get people to believe that the stuff that they wasted so much company money on actually works.
If you think that the newfound ability for computers to both generate and interpret natural language text, process real-time speech, and convert natural language instructions into working computer code is "just a fad", I don't know how to help you. Best of luck to you.
> interpret natural language text
It doesn't. LLM style AI are literally just a weighted random text generator. The way they calculate the weights make it interesting, but it doesn't actually understand your code.
While there were natural language interpreters like Microsoft's long defunct Natural Language Query for SQL Server, they relied on a completely different technology.
> process real-time speech
That's a completely different technology. Speech to text has existed for decades and is still making incremental improvements, but the only people it is putting out of work are transcriptionists.
>
*That's a completely different technology. Speech to text has existed for decades*
No, modern speech to text / text to speech engines use exactly the same core technology: transformers. Maybe before you continue arguing about something you don't know about, you should take some time to read about how modern STT engines work. Transformer-based (sequence-to-sequence) speech to text models are a completely different technology from the STT models of the past, and have vastly higher transcription accuracy.
>*It doesn't \[interpret text\]. LLM style AI are literally just a weighted random text generator.*
Unless you have some wild new definition of the word "[interpret](https://www.merriam-webster.com/dictionary/interpret)", LLMs absolutely do interpret text. And they are not "random text generators". They are contextually aware text generators that are so good at not being "random" that they can successfully pass the Turing test, and are being used by hundreds of millions of people because of their utility for answering questions and solving real-world problems.
Again, you're welcome to live in denial about the utility of LLMs, and to believe that the hundreds of millions of people that are finding them useful are all just imagining things. You're welcome to make insanely false claims like saying that LLMs don't interpret text, or that LLMs/transformers haven't revolutionized voice-based computing. But the rest of the world is going to move on and keep using this technology, even if you keep trying to convince them that it can't possibly ACTUALLY be useful for anyone.
> hey are contextually aware text interpreters/generators that are so good at not being "random" that they can successfully pass the Turing test
Chat bots passed the Turing Test decades ago.
> And they are not "random text generators".
Explain "heat" in an LLM.
>Explain "temperature" in an LLM.
Yes, I think you clearly do need an explanation of the term if you think that the temperature parameter makes it a "random text generator".
Mislabeling LLM output as “random text” shows a basic misunderstanding of temperature and a conflation of **stochastic sampling** with **randomness**. The model deterministically assigns each next token a probability based on its learned patterns; only the *sampling step* injects variability, and the temperature knob merely sharpens (low *T*) or flattens (high *T*) that probability curve—never turning it into a uniform dice roll.
Even at a high temperature the sampler still obeys the model’s structured probability distribution, so the words you see reflect learned syntax and semantics, not chance. Conversely, setting *T = 0* (greedy decoding) produces the same output every time, proving the underlying process isn’t random at all; it’s controlled variation (temperature) layered atop deterministic prediction.
I find it hilarious how hard you had to try to avoid using the term random. But the word probability means the same thing in this context. It assigns different weights to each word and then it rolls and imaginary die to determine which one to give you.
At its core that's all it's doing. It looks the words emitted so far and uses the model to determine the probability of what the next word will be. Then it randomly chooses one instead of giving the one with the best chance because it makes the results look more interesting.
This is why it frequently makes mistakes like referencing libraries that don't exist. It has no idea if it's looking at code or a novel.
>I find it hilarious how hard you had to try to avoid using the term random
I didn't have to try hard at all. Because it's not a random text generator, for the reason I just explained. You clearly don't even understand what "randomness" means in this context, or what the temperature parameter that you cited even does.
I welcome you to go look up the actual meanings of the terms you're using, and come back when you can explain to me what I mean by the distinction between stochastic sampling from a learned distribution and "random text generation", or how LLMs are actually deterministic text generators that have variation ADDED during the sampling process precisely because they are NOT random, and will reproduce the exact same results for a given input, every time.
But then again, refusing to look up the meanings of basic English words like "interpret" so I won't hold my breath ...
Ok, let's look at the dictionary.
> stochastic. randomly determined; having a random probability distribution or pattern that may be analyzed statistically but may not be predicted precisely.
As a rule, don't ask people to look up the meaning of words until you've checked to see if they conform to your argument.
I know what stochastic means lol.
Again, LLMs are intently DETERMINISTIC, not random. They stochastically sample from a learned distribution ONLY when you (optionally) inject randomness into the system via increasing temperature. You have to do this if you want to increase variation in the sampling precisely because they are NOT random processes at all.
The randomness is not optional if you want it to have any semblance of working. When they tried building fully deterministic LLMs the results weren't good. So no major LLM system runs without a random element.
You want us to ignore that aspect because it's inconvenient for your sales pitch.
Here's a list of 100 words: tell me how many times you have to randomly sample from this to get a grammatically correct, complete sentence that answers the question *"What is the capital of France?" (*Please be honest I want you to come back after you've actually done the experiment)
\["The", "capital", "of", "France", "is", "Paris", "apple", "lantern", "ocean", "quantum", "zephyr", "marmot", "indigo", "glacier", "nexus", "harmonic", "pixel", "turbine", "ripple", "canary", "vortex", "eclipse", "nebula", "cactus", "prism", "summit", "fjord", "aurora", "ember", "timber", "cobalt", "basil", "orbit", "drift", "velvet", "meadow", "tundra", "dune", "mosaic", "comet", "geyser", "walnut", "lagoon", "drizzle", "mineral", "galaxy", "canyon", "horizon", "saffron", "thicket", "meander", "quartz", "amber", "silhouette", "cascade", "peridot", "pinnacle", "serene", "breeze", "crimson", "labyrinth", "auricle", "midnight", "juniper", "sequoia", "obsidian", "tapestry", "whistler", "sapphire", "lichen", "petrichor", "zeppelin", "wren", "glimmer", "opal", "basalt", "orchid", "phalanx", "meridian", "acorn", "stellar", "delta", "luminary", "sirocco", "citadel", "feather", "glyph", "helix", "incline", "jovial", "kindle", "lychee", "monsoon", "nocturne", "onyx", "prismarine", "quiver", "rhapsody", "solstice", "tranquil"\]
... of course in reality you'd be randomly selecting from every word in the English language (and millions of words from all of other language in the training set, including programming languages, etc). But I only want you to spend a few weeks randomly sampling words, so I am giving you an easy shortened list.
I'll be satisfied when you get back a couple weeks from now to hear your explanation of how a "random text generator" would manage to get so lucky that it "randomly" selects words in a way that are grammatically correct each time, and just so happen to usually be the answer to the question you asked?
What an utterly stupid question. Just because it's random doesn't mean it's an even distribution.
I'm bored with your pathetic attempts to gaslight me.
>I find that claim to be hilarious. It's the same thing that they said about countless fads that turned out to be huge money sinks. The bigger the corporation, the more they have a reputation for adopting counter-productive policies and technologies.
It's a technology that's been developing for decades - You've seen Deep Blue beat Kasparov in 1996 and the evolution of Stockfish, then AlphaGo defeating Lee Sedol in 2016. This is the same type of technology that underpins LLMs, and they're going to be increasingly integrated into your life. Writing it off as a fad seems possibly myopic?
What do you think coding AI will be capable of when it's achieved the same level of learning as AlphaGo? [Medical AI cases] (https://www.theguardian.com/technology/2025/jun/30/microsoft-ai-system-better-doctors-diagnosing-health-conditions-research) are clearly excelling in their potential, for example.
>Microsoft said that when paired with OpenAI’s advanced o3 AI model, its approach “solved” more than eight of 10 case studies specially chosen for the diagnostic challenge. When those case studies were tried on practising physicians – who had no access to colleagues, textbooks or chatbots – the accuracy rate was two out of 10.
There’s some kind of pro-AI propaganda campaign going on here in this discussion and it’s really obvious.
Yes, humans make mistakes that other people have to fix, but it’s not to the degree that we’ve seen coming from AI.
At least with a human written code you have some degree of cleanliness or structure especially from the more experienced devs.
With AI, the code is like it’s written from someone who just learned to code a month ago.
Don’t trust AI code. Dump the prompts.
My company wants to replace our entire UI with an AI prompt... we sell a complex b2b service that only experienced technicians use. How do the people get the kind of power to make a decision like this while being that stupid?
I'm being paid to fix issues caused by
[
Actual [
Interns |
International team that bid the lowest rate] |
Artificial Intelligence |
A.*holes In-charge
]
Same s.*t, different day.
Well, there is also this recent study put out by a research scientist at MIT where they did study on using ChatGPT AI for the task of writing SAT style essays - they studied the effect on the brain.
They had three groups - the ChatGPT AI group, a group permitted to use Google search engine, and a group that was only permitted to use their brains for said essay writing task.
The finds were that using ChatGPT AI for this task resulted on deleterious impact on the brain (basically brain atrophy impacts). And the deleterious effects persisted. When asked to write essays using no assist, the AI group did very poorly - their brains simply didn't function very well any more for this manner of intellectual exercise.
Now the group that used Google search fared nearly as well as the group that used only their brains, which indicates that the activity of finding, pulling in, and synthesizing of information found through a search engine still engages the brain in full on manner as the control group that had no artificial assist.
The findings found basically that prolonged use of AI to perform ones duties leads to a kind of brain damage, and it is persistent in nature (can it be recovered from?).
The lead scientist says that their next study emphasis will be specifically on the use of AI for software engineering. This scientist said that their findings are looking even more grim than the essay writing task.
The upshot here per this MIT study is that all the big corporations that are rushing to compel their staff to heavily use AI are basically going to produce a workforce of those that are significantly intellectually stunted.
> What is "vibe coding"?
It seems to be a style of AI-coding where you don't write or edit really any code yourself. Only what the AI generates.
Since it's somewhat common for AI to get stuck in a dead end, if you're having trouble directing the AI to do what you want, you just throw a bundle work out completely and start over.
It boils down to not really checking what the AI tool produces whenever you prompt it for something, just clicking "accept" and moving on to the next task.
Yes, in the sense that the sheer quantity of work may exceed our available time.
No, in the sense that most of the code won't be particularly "creative" and may be easier to fix than some of the things we deal with today.
All these anti-AI stories just scream "My horse can still outrun this new car thing" to me.
Enjoy making fun at the awkward period while AI's breasts grows in and it's discovering what it's periods are. In a couple of years it'll be a grown up woman.
My specialty is software remediation, in other words fixing the stuff incompetent code monkeys shit out.
I'm not ignoring it. I'm celebrating the job security I'm going to have.
This isn't a gotcha like most commenters are jumping to
This just means that companies are producing much more than average, and having enough "success" that they are now at the level where paying a consultant to better their copywrite makes sense
If AI is an MVP accelerator, it's not surprising that there would be an uptick of validated MVP's that now need to become actual products
It says nothing of the sort. Having to pay for emergency repairs after your website has been down for 3 days doesn't demonstrate success.
These are clients who previously contracted for services but thought, incorrectly, that AI would reduce their costs.
I have been saying this would be the end result for years now. There is so much tech debt being created it’s going to be a shit show - and job creator.
My decades of fixing bad code is going to really pay off in this next cycle. Maybe I should start a YouTube channel where I teach people how to repair the shit that AI produces.
I'm waiting for AT's "stray dog case".
You have a dog. You own it, you bought it from some puppy mill.
Most of the time, it's a great dog, but occasionally, it escapes the yard and goes wandering.
Occasionally, while it wanders, it bites people. Not often, but it does.
You try to say "Well, it's not my fault -- dogs are dogs and bite people right?"
That fails, and you try "Well, it's the fault of the puppy mill -- I just bought it"
The funniest thing is all these AI tools, the companies have smart lawyers and shoved the bag of liabilities to the users
You know those power bars where they have insurance of $____ for product damage? Why don't AI companies have that? Because they know they can't. They'll lose so much money even if they covered a tiny amount like $100, so they paid big bucks to good lawyers and made themselves completely not liable.
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