AI code bans may be impossible to enforce in the first place
I hate this attitude that if a rule or standard can’t be enforced perfectly, we shouldn’t strive for it. We can’t (reasonably) run an OS free of proprietary software, does that mean the free software movement should just give up?
“People are going to murder anyway, so why even make murder illegal?”
You are correct, you should make rules even understanding that they will be broken by some.
You can enforce consequences for murder. You can investigate and differentiate a murder from an accidental death. Don’t be disingenuous.
A death is a death.
I can quote from memory a case in my country, in the 80’s, where a man assaulted a couple, killed the woman and was about to kill the man.
The killer got tackled and beat to death by the widower. The sole survivor was sent to trial for murder regardless being under mortal peril.
Got acquited on the appeal. First instance deemed it an excessive act, regardless self defence.
Yes, debugging with an LLM is absolutely as bad as murder. Probably worse! \s
Not even remotely what I suggested.
They can only make fallacies to defend themselves.
LLM code can cause deaths (i.e. in hospital software), or otherwise great human or material loses (i.e., bank software or a shelter’s software), or even cultural loses (it actually does them right now, but you can also think about i.e. a museum software).
I think one way is to make people realize that there is a concept in copyright called Threshold of Originality, meaning that because machine generated output in itself is not creative work, it’s not copyrightable. You can do literally anything with it, the licenses attached don’t matter. The GPL is not enforceable and no proprietary EULA is enforceable.
The entire point of GPL is to forcefully extract copyright out of the code in the first place.
No? The point of the GPL is to leverage such a copyright.
It’s called “copyleft” for a reason.
The reason is that it’s based in copyright law but uses it for the community instead of the individual. Without copyright, copyleft has no power.
I have many problems with copyright as it’s currently implemented, but with copyleft it usefully creates a social contract: if you want to be involved for the benefits, you must also uphold the rule of contributing back.
I hate this attitude that if a rule or standard can’t be enforced perfectly, we shouldn’t strive for it.
“Murder bans may be impossible to enforce in the first place”
But they aren’t impossible to enforce and you know that.
That’s… the joke. The original proposition was that AI code bans can’t be enforced, a thought which I was leading ad absurdum.
I think of it like age verification. Slipperly slope to a proprietary kernel-level “security compliance module” on all devices, aka the end of general computation.
This isn’t a case where something can’t be enforced perfectly, it is a case of something that can’t be enforced at all going into the future. Every tool that can reliably detect LLM code is at the same time the tool used for adversarial training, making the generated code look more and more human. We are already at the point where for plain english the false positives and false negatives go through the roof, making these tools very unreliable and when applied automatically a liability. Code is a lot more formalized, with a lot less personal variance (spelling, vocabulary and grammar are basically fixed - only the used logic and how it is implemented is variable), making detection harder by default than in natural languages.
If LLM code can’t be detected anymore by automated means - and that state of things is approaching fast - then any policy about allowing or restricting LLM code is not worth the paper you would use to print it out. But that’s not so much of a problem. The more important policy to set, that can also be enforced, is that everyone submitting code has to take personal responsibility regarding the quality of the submission. Delivering bad code - when not happening while training to become a better coder and looking for feedback - has to lead to consequences based on the seriousness of the case and if it’s a repeat offender. Anyone using an LLM to spit out bullshit LQ code will run into that kind of rule very fast.
Exactly. There are too many knee jerk responses to this position. Just banning LLM generated code is a useless gesture.
Changing the way merges and reviews are handled, as well as responsibility for them, is the actual way to address this.
I’m very critical of AI, but complete bans on AI use are, at the moment, pretty much unenforcable. Not hard to enforce, not impossible to enforce 100%, but completely impossible to enforce in general. There currently is no reliable way to verify if and how much someone used AI, except maybe if you monitor people’s systems (and even with a totalitarian surveilence system, coders will probably be able to bypass it).
Obviously, if someone who has no clue about coding uses it to write the entire code, you notice. But that’s simply not how most coders use AI…
Edit: And additionally, mandating disclosure of AI can make sense, but it can also create the false assumption that codeers/code without any disclosure are verified to not use AI when coding when it simply cannot be verified. It depends entirely on the coder being honest and transperant about their use of AI. And given the passionate pushback, coders are definitely incentiviced to not disclose AI use.
So how would one enforce this?
It is not that it is very difficult, it is impossible.
Provenance has never been something you can guarantee with code in an open contribution model (which is not the only way of governing an open source project). For all you know the code could be copy and pasted from a proprietary codebase, or be the product of third party who’s work is being plagiarised by the submitter. A software project is ultimately a community and is build on trust and faith that people are acting in good faith. The kind of person who would deliberately (and gleefully as I’ve seen in some social media posts) go into a community and violate its stated values and standards, no matter how arbitrary and illegitimate they feel those are, has bigger character flaws to worry about than their reliance on stochastic parrots to write code.
‘No AI’ rules are ultimately about fostering certain community values and norms than guaranteeing no LLM code makes it in.
“it’s impossible to ensure that no one pisses in the pool, therefore we’re allowing pissing into the pool now”
“we can’t 100% enforce that studenst don’t cheat on exams, so it’s not worth to take a stand against cheating”
Just want to let you know from my canoebooted laptop that I run a 100% “free as in freedom” OS (trisquel GNU/Linux). Also you have convinced me about striving for a goal instead of doing it perfectly, which is not possible. Well said.
I hate this attitude that if a rule or standard can’t be enforced perfectly, we shouldn’t strive for it.
This is the entire basis for the War on (some) Drugs. Guess what? Drugs won.
Power is nothing without enforceability, and purposely implementing rules or laws that can’t be enforceable is a form of malice and discrimination through selective enforcement.
People are begging to regulate “AI” when it doesn’t even exist and there’s not even an objective definition.
Absolute recipe for failure or worse.
I agree, we should absolutely toss out all those laws against murder because they can’t be enforced perfectly or equitably.
In terms of murder, not every person is caught. But, enough of them are to deter the crime.
In terms of “no AI” enforcement, it’s not even a matter of perfection. It’s not possible to enforce except for the dumbest attempts, especially without also using AI to detect it. And if that enforcement comes about, then the evasion starts, and it’s hidden even further.
AI code ban makes as much sense as code written in IDE ban. That’s why it is impossible to enforce. It makes no sense.
Me when I’m a logical fallacy
You’re getting a lot of down votes not for being wrong, but for being right.
No, he gets them for being wrong.
As another person stated in another comment, that’s a false equivalence fallacy, but not only that, it’s a false dilemma fallacy, and a strawman fallacy.
It’s like trying to prove an author didn’t use a ghostwriter. You might be able to find some clues, but the smaller the sample and the more cleanup the author did, the less chance there is to detect it.
Now what if they’re using AI for debugging? Code from the AI may not even make it into the project, but that doesn’t mean AI wasn’t used. So how do you enforce a “no AI” policy in that scenario?
If there’s no practical way to enforce a rule or tell if it has been broken, it’s a pointless rule. Better programmers than me have settled on the policy of “If you track in shit on your shoes, you will be the one cleaning it up, and may well be told to not come back if you do.” Make people responsible for the code they submit, and if they can’t do it right, however they do it, don’t merge their code and kick them out.
In his commit message:
[And this was a debug session from hell, enormously helped by an AI doing much of the grunt-work.
I’d like to call it my tireless helper, but the AI several times stated flat out that this was impossible and unsolvable and that we should just write a report about it.
I suspect those things have been trained by people who may not be quite as stubborn as I am.
But while the AI was ready to give up several times, it did keep adding debug code and analyzing it faithfully when I pushed. So credit where credit is due and I let the AI write the commit message above.
This is basically a one-liner fixing a bogus “round_up()” to a “round_down()”, but there were 24 patches adding more and more debug information to this, and 18 kernel boot to finally narrow it down to this. - Linus ]
Didn’t he state quite early on that LLMs were a good tool to spot bugs? Found this from 3 years ago: https://blog.mathieuacher.com/LinusTorvaldsLLM/
Hackers use LLMs to spot odd exploits so it makes sense developers use them too. The usage of LLMs in development likely won‘t make Linux better or safer overall, because bugs and exploits wouldn‘t have been found by either side without it in the first place. It‘s like pandora‘s box where Linus is kind of forced to use it. If that makes sense.
Like an accelerating arms race.
Yes, but this has always been the case before and after this phony “AI” bullshit.
He might consider himself forced to use it to find bugs. He wouldn’t be forced to use it to write the fix.
(Now you might say he wasn’t or isn’t, but even if that were true for the kernel at large this doesn’t seem to be: https://www.theregister.com/os-platforms/2026/08/10/linus-torvalds-says-ai-has-made-huge-linux-kernel-updates-the-new-normal/5285268 And this seems like mostly to be on Linus, for not stepping in here.)
Following the hacker arm race is a good point. But also the number of bugs is not a bottomless pit. If an LLM allows to find more, and hopefully the test and reviews are good enough to limit the new opportunities to introduce some from LLMs, it should reduce the overall number of bugs and make the software generally safer.
also the number of bugs is not a bottomless pit.
Are you forgetting about Microsoft?
Whatever’s the outcome, the anthropomorphization in that message stinks 🤮
Dude, I’m not an AI zealot, but I anthropomorphize my socks.
Yeah but people aren’t having conversations with your socks thinking they actually are alive and your socks don’t then talk said people into suicide so I’m pretty sure it’s at least a little bit different.
I saw a post about mom dog jumping into a flood to rescue pups. They said they would end up finding new homes for pups. I amphropromorphized (can’t spell) the mom misising her pups, only to read that it’s common for them to get annoyed by older pups and even eat unwell pups.
Trying to avoid anthropomorphising something with which you interact via natural language is incredibly laborious, and achieves nothing.
Avoiding false statements achieves scientific accuracy and honest progress rather than grifting and bullshit.
And TBH it’s not at all difficult for me to distinguish between my computer and a human.
That’s a core part of the grift. Even people who supposed hate “AI” still promote this kind of anthropomorphic disinformation. It’s rampant.
Silly humans, antropomorphizing everything. Say, what species are you?
Regardless of how he got there, this is a pretty obnoxious and useless commit message…
Seriously. You don’t need to outline your whole journey to finding the bug, commentary on the tools you used, and how it all made you feel, just what the problem was and how it was fixed. Put the rest in a blog post.
I think Linus yelled at that LLM
Pretty wild seeing a programmer anthropomorphize their applications and mislabel them as “AI”.
The Big Lai runs deep.
Okay I’m seeing a lot of your replies here this is a really weird argument to make. You’re literally arguing from symantics.
LLMs are a great tool for create bugs *
Fuck Linus.
Let us know how your Linux 2.0 goes.
If you don’t have anything better to say than an ad hominem fallacy and a red herring fallacy, then you’re totally discrediting yourself.
Yes, because your original comment was so thought out and well reasoned to begin with 🙄
Well, it’s not that I’m telling another thing than the truth.
You’re attacking something totally unrelated and that I’ve never mentioned (“Linux 2.0”) because you simply can’t deny that LLM are bug-making machinegunss.
Literature on that in my response.
Do you think anybody, especially Linus, is unaware that LLMs produce bugs if left to themselves? He’s made it clear that PRs will continue to be judged on their code quality as they have all this while. Your comment is especially ironic considering how the post is about an elusive bug that took 1 line to fix, but hours upon hours and 24 other patches to debug. Anybody with any knowledge of Linus would find the idea hilarious that he’d allow, let alone commit, crappy code into the kernel. The only real issue up for debate is the ethics of it, considering the provenance of most training data, and potentially copyright issues.
Part 1:
Code Quality (defects, bugs, logic)
- CodeRabbit: AI code has 1.7× more defects (10.83 vs 6.45 issues/PR); logic errors 1.75×, security 1.57×, XSS 2.74×
- BusinessWire / CodeRabbit: Performance inefficiencies (excessive I/O) appear ~8× more in AI code; logic problems up 75%
- The Register: AI code shows 1.4× more critical and 1.7× more major issues; 1.57× more security findings
- Carnegie Mellon (via Ox Security): Only 61% of AI-generated code functions correctly; only 10.5% passes security review
- Ranger 2026: 26.6% produce incorrect outputs; 60% of faults are silent logic failures; ~50% has maintenance problems
- GitClear 2026: Copy-paste code rose 9.4%→15.7%; duplication up ~4×; refactoring down 70%; code churn nearly doubled
- arXiv 2026: LLMs suffer “Context Rot”; accuracy drops sharply when relevant info is mid-context
- arXiv 2026: Copilot generates executable code ~90% of the time, but 40% of solutions on critical security tasks contained MITRE Top-25 CWEs
- arXiv 2026 “Should I Give Up Now?”: LLMs “hallucinate, omit important steps, lose context between turns, or produce deceptive code”
- Based Info: “The problem is execution, not compilation” — code compiles cleanly but fails at runtime or solves the wrong problem
- CodeBridge / Ox Security: 10 recurring antipatterns in 80-100% of AI code; 68-73% contain vulnerabilities that pass unit tests but fail in production
- ResearchGate 2026: Valgrind revealed AI code left 1,068 bytes in 34 reachable blocks at exit (memory leaks) vs 24 bytes in 2 blocks for human code
- IEEE Spectrum 2026: AI code degrades as newer models create “silent failures” and rely on low-quality training data
- arXiv 2512.22387: 31.7% of AI-generated projects fail to execute at all (only 68.3% reproducible)
- arXiv 2510.26130: LLMs achieve 84-89% on synthetic benchmarks but only 25-34% on real-world code (66-75% functionally incorrect)
- SWE-bench 2026: Top models reach ~80%, most struggle below 20% on repository-level tasks (80%+ failure rate)
- SmartBear 2026: 70% of engineering leaders say quality has degraded; 60% report code outpacing testing capacity
- DeviQA 2026: 0% of senior QA (8+ yrs) said AI code has fewer bugs; 37.5% said noticeably more
- arXiv 2026: 304,362 AI commits → 110,000+ unresolved technical debt issues by Feb 2026
- How I Dropped Our Production Database and Now Pay 10% More for AWS
- Claude Tested Everything Except the One Thing That Mattered (AI agent refuses to follow explicit instructions to test
createPost()in increasingly erratic ways) - Amazon calls engineers for a “deep dive” internal meeting to discuss “GenAI”-related outages
- GitClear has released reports in 2024 and 2025 indicating a worsening of key code quality metrics correlating with increased LLM adoption.
Security & Vulnerabilities
- Veracode 2025/2026: 45% of AI-generated code introduces OWASP Top 10 vulnerabilities (100+ LLMs tested); Java 70%+ failure rate
- Cloud Security Alliance 2026: Privilege escalation paths +322%, architectural flaws +153%, despite syntax errors −76%; ~20% of samples reference nonexistent packages
- arXiv “Broken by Default” 2026: Mean vulnerability rate 55.8% (GPT-4o: 62.4%) across 3,500 artifacts verified with Z3
- Sherlock Forensics 2026: 100% of AI-generated apps contained ≥1 critical vulnerability; 78% store secrets in plaintext; 34% of Node.js projects include hallucinated dependencies
- Georgetown CSET: 86% failed XSS defense, 88% vulnerable to log injection, 47% SQL injection across 5 LLMs
- Llama 3.3 SWE-bench study: 11× more new vulnerabilities in LLM patches vs developer patches
- arXiv 2603.10072: Only 24.8% of LLM security patches achieve full correctness; 51.4% fail BOTH security and functionality
- Stanford/MIT Mar 2026: 14.3% of AI code has ≥1 security vulnerability vs 9.1% human (2M snippets)
- Apiiro Fortune 50: AI-assisted devs produce 10× more security issues despite 3–4× more code
- ACM 2025/2026: ~30% of generated code snippets contain security weaknesses
- Pagerly 2026: Models produce compilable code almost always but secure code only 56% of the time
- Dev.to / State of Web Dev 2026: 63% of AI-generated functions had a security finding; review doesn’t scale with volume
- Stanford (via SC World): In 80% of tasks, devs using AI produced less secure code; 3.5× more likely to believe their code was secure
- Georgia Tech Vibe Security Radar: 74 CVEs attributed to AI tools (Mar 2026); est. 400–700 real
- arXiv 2026: Slopsquatting — LLMs hallucinate package names, enabling attackers to register them with malicious code
- ValueAdd VC 2026: Vulnerability density 2.74× higher in AI code; code churn ~2× higher in AI-heavy repos
- Meta Security Researcher’s AI Agent Accidentally Deleted Her Emails
- Moltbook’s “vibe-coded” breach is the future of security failures
- In a study evaluating over 500k code samples, LLM-generated code was found to contain more high-risk security vulnerabilities than human-generated code
- LLMs make up package names, making them vulnerable to incorporating malicious code in “slopsquatting” attacks (Arxiv study)
Part 2:
Productivity Illusion (perception vs reality)
- METR RCT: Experienced OSS devs were 19% slower with AI; 39-point perception gap (believed 20% faster)
- McKinsey 2025: 46% time savings on routine tasks but <10% on complex work (4,500 devs)
- Sonar State of Code 2026: 96% of devs don’t fully trust AI code; only 48% always verify before commit; 53% say it “appears correct but is unreliable”
- Stack Overflow 2025: 66% top frustration = code that’s “almost right but not quite”
- Stack Overflow Blog Jan 2026: 45% of developers say debugging AI-generated code takes longer than writing it themselves
- Byteiota 2026: Trust in AI tools fell from 40% (2024) to 29% (2025); 96% believe AI code is not fully correct
- Smarter Articles 2026: Devs with Copilot introduced a 41% increase in bugs, with no reduction in burnout risk
Churn, Replacement & Survival
- Faros AI: 65% survival rate for AI code vs 92% human (35% gets silently replaced)
- Faros AI: +98% PRs merged but +91% review time, +9% bugs, DORA metrics flat (10,000+ devs)
- Kunal Ganglani 2026: 40% of new AI-assisted code is rewritten within two weeks, vs 33% pre-AI
- New Relic 2026: 74% of tech leaders report ≥25% of AI code requires significant post-deployment rework; 82% have suffered at least one major production failure caused by AI code
- Lightrun 2026: 43% of AI-generated code changes require manual debugging in production after passing QA and staging
Production Impact & Outages
- CloudBees 2026: 81% of enterprise leaders report increased production issues from AI code
- Amazon / CNBC Mar 2026: Amazon convenes “deep dive” meeting over outages caused by GenAI-assisted changes; “high blast radius” incidents since Q3 2025
- The New Stack Mar 2026: Amazon mandates senior engineer sign-off on all AI-assisted code changes after multiple outages
- Fortune Mar 2026: Amazon retail website crashes from “inaccurate advice” an AI agent pulled from a stale wiki; 4 Sev-1 incidents in one week
Deskilling
- Brains show less activity when completing tasks with LLMs compared to completing tasks with search or completing tasks without digital help.
- Developers who use early-2025 LLMs reported higher subjective performance, but were measured to have lower objective performance. This gap between subjective and objective performance was considered notable.
- In an Anthropic study, learners using LLMs demonstrated lower learning rates on average compared to learners not using LLMs.
- A recent study uses the term “cognitive surrender” to describe the way humans tend to offload key critical thinking skills onto LLMs, even when the output is wrong.
- A paper entitled “AI Assistance Reduces Persistence and Hurts Independent Performance” from April 2026 by academics from MIT, Oxford, UCLA, and Carnegie Mellon showed alarming evidence that performing a variety of tasks with the help of AI for only 10 minutes causes “inpaired unassisted performance and reduced persistence”. The researchers noted that “although AI assistance improves performance in the short-term, people perform significantly worse without AI and are more likely to give up”; they also pointed out that “these findings are particularly concerning because persistence is foundational to skill acquisition and is one of the strongest predictors of long-term learning”.
Using AI is not the same as vibe coding.
Asking AI to write a piece of code and then read it, understand and use, is a very reasonable use case.
Yeah, and Torvalds using generative AI to write code is not the same as Bobby LinkedIn telling us he’s coding his third app this week.
Using AI is not the same as vibe coding.
i’ve been in dozens of lemmy threads that disagree…
There’s a huge portion of lemmy who think even one token of AI is unacceptable for any reason
I’m in a thread where the entire idea of AI/LLMs has been declared facist, with ZERO legitimate use cases other than facism
I’m in a thread where the entire idea of AI/LLMs has been declared facist, with ZERO legitimate use cases other than facism
A disappointment I’ve noticed with lemmy is that its community doesn’t often see the nuances in things as if to day its all black or white. Its probably not just lemmy, but all internet communities nowadays. Lemmy just sways towards anti AI more and it really leaves a bad taste when coupled with “if not with us, then against us” mindset.
Yes. It’s not just about nuance anymore. In the old days on the internet you can find some spaces where there’s at least a baseline level of intellectual honesty. Lemmy has not escaped echochamberization.
that’s like declaring electric light racism and keep using light bulbs…
Like all people that see racism everywhere, they will fail…
It’s a tool, use it where it works well.
I agree with you…
But be prepared to be labelled a facist on lemmy
apparently there’s no such thing as a neutral technology or tool
So um, as a sociology student, there [i]is indeed no such thing as neutral technology or tool[/i]. Everything is created within systems of power, and its production will always require externalisation of some costs. Artefacts do have politics, because we live in a social world, use social language, etc.
[b]However[/b], this does not mean that it is a moral failing, cuz otherwise everybody existing in society would be evil. We live in an imperfect world, and our own position in it, legible through the symbolic order, is continually accepted, rejected, naturalised, de-naturalised, interpreted, re-interpreted, and negotiated.
Call out harm where you can, accept some difficulties to avoid stuff you find especially egregious if you can, but ultimately, don’t moralise it against yourself if you can’t.
TL;DR There is no neutral tool or technology, but that doesn’t mean you’re morally complicit by not resisting everything. Try to minimise harm where you can, but don’t beat yourself up if you can’t either.
Huh, kinda like “there is no ethical consumption under capitalism”, acknowledging that the only way to abstain from perpetuating it is to starve to death, but principled corpses–even MOUNTAINS of Principled Corpses–seldom result in changes of policy.
Quiet isolated suicide will not improve the world, especially if immoral and exploitative systems WANT (colloquially speaking) their opposition to die. Maybe don’t play right into the hands of people who want us dead by giving them what they want, right? Better to survive and maybe also scavenge the resources needed to build something less destructive…
Quite. I think that’s also why we’re on the Fediverse, right? While so many of our tech is captured by corporates, we still have room to not accept it all laying down. That’s what negotiation means, drawing your own boundaries, and as much as I hate saying it, picking your own battles, while realising not many will be willing or able to fight the same ones you do.
There’s a huge portion of lemmy who think even one token of AI is unacceptable for any reason
I think a lot of that is to do with how people claim to have written something themselves, and when pressed, admit they used AI for a bit “here and there”, and when double-checked it becomes clear they vibe-coded the whole thing. People lie, basically, and if someone lies about their use of AI that’s probably a harbinger of slop.
Ah, yeah, no, I get it, I see it’s reminiscent of cockroaches: if you actually SEE even just one of them, it is very very likely there are more. A lot more.
It’s just a tool. All tools have good and bad use cases.
I agree…
But check out the other discussions on lemmy about AI …
if you’re not 100% against it, you’re a facist
I wonder how long before he gets comfy relying on it and something really shitty slips in.
Dude merges in code into the kernel originating from how many thousands of developers from around the world?
Read the LKML if you think he’s a guy that’s prone to getting comfy with merging in sub-par code.
oh he’s quick to put the brakes on other PR’s including slop code.
which makes me wonder how long until he’s comfy and forgets sometimes the slop comes from inside the building lol.
You assume humans don’t ever write sloppy code and Linux Torvalds has never before encountered someone wanting to merge in sloppy code before. If in decades he hasn’t gotten comfy with sloppy code written by humans, why would he suddenly be merging in sloppy code written by an AI? He’s never been worried about hurting the feelings of a human that’s writing sloppy, do you suppose he’s going to develop empathy for an LLM that’s writing sloppy code?
The people that go all in on vibe coding are people that aren’t very good programmers to begin with. They don’t really understand how to write code and then suddenly they can make things work quickly. Well sort of work. At least work about as well as they could with their limited skills.
People that are actually good at programming know what good code is. After decades of working with code and knowing what it’s supposed to be, you don’t suddenly become a vibe coder saying eh, it kind of works, good enough, ship it. Its the people that were never able to write high quality code to begin with that do that. It can do the job of a lazy programmer that has a low standard of quality. For a skilled developer, it can sometimes save on typing on some simple tasks, and a good developer knows when it did something wrong and be all Thanos and say “fine, I’ll do it myself”.
You assume humans don’t ever write sloppy code and Linux Torvalds has never before encountered someone wanting to merge in sloppy code before.
damn dude, did you completely fail reading and comprehension? because you’re replying to a link to me pointing that exact fuckin thing out.
maybe slow down and try harder.
like, it’s in the link title text, right there.
People that are actually good at programming know what good code is.
as long as they stay proficient. but prolonged exposure to AI slop tends to have a damaging effect - https://thenewstack.io/karpathy-says-developers-have-ai-psychosis-everyone-else-is-next/
so I stick with my premise: how long before it has an effect on him?
Do you completely fail at critical thinking? I’ll slow it down so you can understand:
-
Linus sees sloppy code written by a human
-
Linus sees sloppy code written by an AI
Why did #1 not corrupt him have decades, but #2 definitely will? There is no reason, sloppy code is sloppy code whether written by a human or written by an AI.
You’re equating shitty developers becoming vibe coders with a skilled software engineer using a tool. I think you just don’t know the difference between a junior developer and a senior software engineer. Maybe you just don’t have any experience in software development and are just reading articles on random websites.
just gonna end this pointless chat here:
yeah it’s not gonna lead to any problems lol. meanwhile reality is disagreeing. I get it, you’re a slopfan, so this pointless you’re not going to listen / try to understand, but for the rest of the audience, here’s some other views:
https://thenewstack.io/karpathy-says-developers-have-ai-psychosis-everyone-else-is-next/
https://aiforautomation.io/news/2026-05-17-ai-psychosis-hacker-news-1979-developers-vote
https://www.psychologytoday.com/us/blog/urban-survival/202507/the-emerging-problem-of-ai-psychosis
https://therenegadecoder.com/blog/why-generative-ai-makes-the-future-of-software-development-worse/
https://www.slashgear.com/1970560/ai-coding-problems-security-risks/
https://andrewjcode.substack.com/p/ai-is-writing-terrible-code
https://letsdatascience.com/blog/developers-thought-ai-made-them-faster-the-data-said-otherwise
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Yep, and even more important are the cases where coding models are used outside of actually producing code. Unit tests, red teaming, bug hunting and so on are all areas where LLMs can reduce the load on an experienced coder, allowing him to concentrate on the meaty stuff. Blindly being against the technology itself because big tech currently consists of a bunch of asshats is not a mature stance.
Blindly being against the technology itself because big tech currently consists of a bunch of asshats is not a mature stance.
yeah it’s not gonna lead to any problems lol. meanwhile reality is disagreeing. I get it, you’re a slopfan, so this pointless you’re not going to listen / try to understand, but for the rest of the audience, here’s some other views:
https://thenewstack.io/karpathy-says-developers-have-ai-psychosis-everyone-else-is-next/
https://aiforautomation.io/news/2026-05-17-ai-psychosis-hacker-news-1979-developers-vote
https://www.psychologytoday.com/us/blog/urban-survival/202507/the-emerging-problem-of-ai-psychosis
https://therenegadecoder.com/blog/why-generative-ai-makes-the-future-of-software-development-worse/
https://www.slashgear.com/1970560/ai-coding-problems-security-risks/
https://andrewjcode.substack.com/p/ai-is-writing-terrible-code
https://letsdatascience.com/blog/developers-thought-ai-made-them-faster-the-data-said-otherwise
I am not a “slopfan”. I do not use any of the techbros services, and never have outside of a few test runs in 2022 to see what it can do.
I only use local models running on my own hardware, and only to automate tasks that would take ages by hand (e.g. creating summaries about gameplay and features of my game collection from the game stores websites, helping me with RegEx or quickly creating one-shot python scripts for specific purposes - i am a terrible coder), or that make interacting with large technical documents easier (e.g. mainboard manuals). I have dabbled a bit with image generation, but it isn’t really interesting for me.
But denying that the technology can reduce workload on coders even when not generating code (e.g. unit tests, red teaming, reverse engineering) is immature. These tasks are also much less prone to cause AI psychosis, because i do not care about “chatting” with these models.
Is Linus error-free pre-AI?
If AI makes Linus more (quantity) or more severely (quality) error-prone than without the AI, that, and only that is an argument against AI being a tech asset for Linus.
Just one bad Linux kernel commit slip up related to AI is not an argument.
That said, the macro impact of the AI data centers and AI on: electric power generation and grids, pollution (smelly exhaust, noise, CO2), fresh water demands, socioeconomic power imbalance exacerbation, surveillance capitalism, questionable military usages, rogue agentic AI doing hacks and crimes to fulfill oterwise legal/ethical objectives, AI chatbot relationship addiction, these are some of what I consider real and urgent problems with AI.
As an individual, his LLM carbon footprint and money used to purchase AI services aren’t going to matter at all. But him being one of the world’s highest profiled software engineers who’s openly using these tools is very good publicity for them.
I think the anti ai crowd has been focusing on the slop angle too much, the horrible malpractice of these companies is the real problem imo. If it really is only slop it will die out by itself.
so many possible upsides it’s hard to pick which one will end the species.
fml
I’m glad Linus is pragmatic about this, ethical questions aside LLM’s are a highly valuable tool in software development and they are likely to continue to improve. Any engineer fighting the use of LLM’s will ultimately find that their peers are achieving more in a shorter time then they are. Open weight LLM’s have also come a long way and are already good enough for daily use.
ethical questions aside
And his here is the problem. Oh yeah lets just waive off the biggest critisicm on and issue of AI.
Hitler was a pretty neat guy, ethnic cleansing aside.
And before you say this, no, i am not comparing you to hitler, I’m only using an over the top example to hopefully make my point more clear.
I think the better analogy is, the advances in rocketry made under hitler were pretty neat and just because Hitler was an evil guy who did evil shit doesn’t mean we should have thrown that away. By all means stop giving money and influence to the big AI companies, but the advances made can still be repurposed.
Yes so of course my actual opinion is a little more nuanced than this. I absolutely see the uses of AI, I won’t deny that. I just fundamentally disagree with the way it is being used right now (by which I mean the gas-turbine-powered datacenters it requires, the noise pollution they create, the unethical/outright illegal acquiring of training data.
If you’re using a local model, whose training did not deprive actual people of huge amounts of water or electricity, and did not use stolen data, I’m all for it. But most people aren’t, they’re using ChatGPT or Gemini or whatever Microsoft is making.
Continuing your analogy, this would be like flying rockets manufactured by the third reich, not other companies using the same technologies.
Will the Tesla be the next Beetle?
The main difference is that rockets are expressly for the purpose of murdering people, so I would argue that the research shouldn’t be continued or done at all.
Fortunately I’m pretty sure that people developing these computer programs are serious about trying not to murder people - even if just for legal reasons.
Rockets literally got us to the moon what do you mean.
“Ai is literally Hitler”
-fediverse user, 2026
I hope you’re joking, but I did explicitly state that was not my intention.
Although that’s basically true…
Wow, just speedrunning Godwin’s Law, aren’t we?
I guess I’m going to go back to work and talk to my five LLM sessions that are working on different tickets.
Wasting all that extra water will certainly teach him a lesson.
Yeah, ethical, environmental, legal and fuck the consumer memory questions aside.
I remember people being angry at IDEs for making programming too easy and belittling code colourization as for children.
There will always be a crowd that wants everything to stay the way they know it.
Some people still think that digital artists aren’t real artists.
deleted by creator
find that their peers are achieving more in a shorter time then they are
I’ve been reading this for 3 years and all I’ve seen so far is my peers making more work for me by going on wild goose chases and making me wade through slop. I do believe there are ways to use it responsibly and productively but that is definitely not what is happening on average.
I don’t understand why so many people decided to take “LLMs increase productivity” as an axiom instead of using their fucking eyes and looking at what is happening around them.
Well it’s about time those slackers caught up. And all they needed was a slop machine.
No, pragmatically speaking, LLMs and in general AI will only make code worse each day.
I think most misplaced critic comes from people not understanding or knowing the different types of AI. Some even hate all types of intelligence it seems.
Especially their own. Every single day, the backlash looks more like garden-variety anti-intellectualism to me.
Chess engines have been using AI, specifically, neural networks, for years before ChatGPT or other LLMs. Nobody complained.
Chess engines didn’t require data centers that guzzle water and spit out pollution like an 18th century factory.
Exactly right. That is the distinction I’m trying to draw.
They did, just on a smaller scale, and they used all games ever played by high-level players. LLMs and image generators were stealing data years before they became viable. And nobody gave a shit.
Let’s not pretend most people give a shit about ethics or the environment. Those were impacted long before AI became viable in any way. People hate on AI because it’s affecting everyday prices and employment. Ethics and environment are just convenient shiny arguments few actually stand behind.
If tomorrow billionaires turned around and said “actually, here’s UBI for everyone, provided by AI!”, most people would be cheering on intellectual theft and destruction of this earth, and call everyone still against it selfish and regressive.
Ethics and environmental destruction are “convenient shiny arguments”?? Go fuck yourself you delusional shill. Praying for a billionaire saviour while gagging on their boots, fuck you.
Most people don’t give a shit about ethics and the environment. They care about the well being of themselves, the well being those close to them, and their comfort, ABOVE ALL ELSE. even if they won’t admit it to themselves.
Delusional? I wish I was. Look at the last 30 years, how many protested to get climate change taken seriously? How many raised their voices against various human rights violations around the world? A drop in a bucket, that’s how many. Most people just don’t give a shit about anything that doesn’t affect them and it’s plain to see.
So yes, for majority of people, ethics and environmental destruction are “convenient shiny arguments” that make them feel good about themselves when they virtue signal them on social media and then forget about them 5min later.
Curse me all you want, the world of today is of our collective making.
Here we go again!
You conflate one type of AI with all the other ones. Call it LLM or generative AI.
I programmed AI 20 years ago, it still is AI but doesn’t use datacenters. I programmed deep neural networks ten years ago, it did not use datacenters either.
And that’s why no one is opposed to those.
You didn’t pirate the collective works of humanity to train on, while under a society that requires intellectual property rights to ensure those creators are compensated, in order to undercut them twice (once to train and once to replace them)
You didn’t pollute the sky, dry the soil, bulldoze people’s homes to make your chessbot.
You didn’t corner the market on components to brute force the solution to declare yourself computer god, where everyone must come to you for compute.
Gen AI seeks to replace humans and it’s not even sentient. I don’t give a single fuck how useful a fancy algorithm is, it’s not ok with me if it’s designed to destroy my life.
Well do the work and call it for what it is, not just “AI”.
Generative AI.
LLM
Is it truly that hard?
I mean you seem to want to, and thus we can have a discussion about it, which I favour instead of having people just being generally angry it seems.
The day when it doesn’t crush the environment, should we use gen AI to help us in our daily lives? Or should we not? Is there something good in it? Or isn’t it? I’d love seing constructive discussions but they just get globbed by people thinking with their gut it seems. Like if you don’t hate “AI” then you’re pro genocide.
Sorry for the rant!
The day when it doesn’t crush the environment
Why should we indulge your hypothetical? They’re actively building fossil fuel powered data centers so it’s not even moving in that direction.
And most tech literate people that are anti-genAI would also prefer if it didn’t completely usurp the term AI or that everything historically called ML got also got sucked in. You’re acting like anti people made that happen but it was the boosters and now AI=chatgpt is in the common lexicon. Nitpicking their language when you obviously know damn well what they mean is just silly.
What are you talking about?
Chess using neural nets didn’t happen until around 2017 with Alpha Zero, based on Google DeepMind.
Ie… Two years after OpenAI was founded. ChatGPT is part of the exact same tech rush.
Nobody complained because chess is harmless, ChatGPT is not 🤷
Please understand that ChatGPT was released in November 2022.
And also understand that the distinction between chess and ChatGPT is the point of the comment. These two things both use AI but one is harmless and useful while the other is not.
Lol you angered the luddites lol.
I keep seeing headlines like this, and of course conveniently leaving out how Torvalds chewed people out for vibe coding.
Torvalds didn’t vibe code away the Linux bug. He actually knows what he’s doing.
AI is like a power tool. If you give a contractor a nail gun instead of a hammer he can build a house faster. If you give someone who has no idea how to build a house a nail gun they’ll just fail at building a house faster than if you only gave them a hammer and they’ll probably hurt themselves.
This will also be quite the test for the Lemmy slopwarriors
Oh, please… is it still a text generator, or is it already a religion?
Didn’t you love T9 back in the days?
I’m just a movement generator.
Saying “it’s a text generator” cannot be the full force of your criticism, if the text it generates is incredibly useful. What could AI be on a screen if not a text generator?
Your views have got to be formed by WHAT text is generated given that AI cannot express itself through any other channel than text.
If you can give AI a deeply complex code base and ask it to find the likely cause of an obscure bug, and the solution is right, then you’ll have to acknowledge that some form of reasoning is going on, no?
And given that no other system has previously enabled this form of reasoning, it seems a shame to judge AI on semantics, rather than usefulness.
If you can give AI a deeply complex code base and ask it to find the likely cause of an obscure bug, and the solution is right, then you’ll have to acknowledge that some form of reasoning is going on, no?
Absolutely not. LLMs are just prediction engines for words.
If a LLM has the right answer, it only means that the model had the correct training data for your issue. Nothing more.
That is absolutely not how it works at all
What do you mean? That’s exactly how LLMs work. That’s why they “hallucinate”.
Uh huh, and how does it work?
Saying “it’s a text generator” cannot be the full force of your criticism,
That is no criticism, just a statement.
My criticism goes to people who assign some metaphysical meaning to AI, or want to treat all it’s output like a contagious pest.
It can’t reason. It doesn’t think. It makes shit up as it was trained to do. It can sort of detect patterns and make a guess about what response would get it the highest score, but it’s so error-prone that a human has to verify the output anyway. It’s difficult to tell whether the perceived benefits outweigh the costs. There are many different types of LLMs and use cases.
As a person who values life and our environment, I’d rather not participate in the sloppification of our world. As a Linux user, I am wary of vibe coded flaws slipping under the radar and screwing up my system.
Again, we are stuck in a semantics debate. What I call reasoning you don’t, it seems.
But all I know, as someone that has worked in software engineering for 25+ years, is that the output of whatever it does, and whatever you call it, is incredibly useful.
Saying “it just generates text” is a reflexive reaction that attempts to flatten the nuance.
All Columbus did was sail west.
All America did was throw some fuel into a tube and put Neil Armstrong on the top.
All Picasso did was to throw some paint onto a cloth.
All this PhD student did was generate text.
Surely we’ve got to have some more nuance in there. If there’s a difference between 10000 monkeys hacking on a typewriter and a human writing their doctoral thesis, the quality of their act isn’t in whether text was generated but WHAT text was generated.
Again, we are stuck in a semantics debate. What I call reasoning you don’t, it seems.
It is a pattern matcher. It does not reason.
But all I know, as someone that has worked in software engineering for 25+ years, is that the output of whatever it does, and whatever you call it, is incredibly useful.
I have a degree in computer science. Irrelevant. Anyone can see that the “AI” is not intelligence.
Saying “it just generates text” is a reflexive reaction that attempts to flatten the nuance.
It’s a large language model. It generates text.
All Columbus did was sail west.
And killed indigenous people. Datacenters and shitty chatbots are killing people today, good point.
All America did was throw some fuel into a tube and put Neil Armstrong on the top.
Scientists did that, not America.
All Picasso did was to throw some paint onto a cloth.
At least he did it himself instead of prompting an image generator.
All this PhD student did was generate text.
Correct. And you seem to have learned nothing useful.
Surely we’ve got to have some more nuance in there.
LLMs can sometimes be useful. AI does not yet exist. It’s a scam.
If there’s a difference between 10000 monkeys hacking on a typewriter and a human writing their doctoral thesis, the quality of their act isn’t in whether text was generated but WHAT text was generated.
You don’t understand the metaphor. It’s about the nature of infinity, not the competency of monkeys as authors.
If you run an AI prompt infinite times, it will inevitably generate a correct answer somewhere in there. The rest of the time, it will be wrong, potentially with lethal consequences.
I’m just a movement generator.
Which is way more complex than just predicting text, because you have to predict physics and all that stuff. There is a reason most robots controlled by neural nets have failed hilariously so far.
then you’ll have to acknowledge that some form of reasoning is going on, no?
By that logic you could say a compiler is reasoning. But the reasoning displayed was happening when the compiler (or LLM training material respectively) was written.
If you can’t see the difference between a compiler and a large language model, may I propose you read more about both and then return to this debate.
And if the entirety of your argument is a mystic “and all that stuff”, I am finding it hard to generate a convincing counter-argument.
If you can’t see the difference between a compiler and a large language model
If you can’t see the difference between a given example and the underlying logic…
And if the entirety of your argument is a mystic “and all that stuff”
What, you want me to list the entirety of sciences downstream from physics that are involved in generating and predicting movement in mammals? Because that could, like, take a while…
You might be a few years behind. We have robots outpacing human performance in specific tasks using neural networks.
Also, LLMs can perform on tasks they weren’t explicitly trained for. This line is not as well defined as you make it sound.
You might be a few years behind. We have robots outpacing human performance in specific tasks using neural networks.
Might well be behind here, but to my knowledge we don’t have a single robot outpacing a single human in most tasks. They don’t do one-shot learning from their mistakes, they don’t learn new movements randomly. Because most basically just use static weights when operating, because you don’t really want an industrial robot to get ideas. But even test systems that can learn usually just have an equivalent to the movement model of a brain, maybe a vision model, just like an LLM is just a language model. You would need a system that has all of those, plus the other brain areas, especially a prefrontal cortex like model for integration.
I’m sure people are working on it, but I haven’t heard of anything successful yet. I imagine there might be a being like that in secret which is currently tortured in some billionaires tech dungeon. Poor thing.
Also, LLMs can perform on tasks they weren’t explicitly trained for. This line is not as well defined as you make it sound.
Yeah but that’s coincidental. It’s the model weights, prompt, and the RNG aligning. They can mock reasoning, because they do it by what they always do, predict more text, but it’s not like this has any effect on themselves. They aren’t really understanding a mistake when you point it out and growing neurons and synapses, i.e. they won’t have different model weights the next time you ask the same question. They can only really change when the powers that be release an update, which includes new training data, and hence model weights.
We have robots outpacing human performance in specific tasks using neural networks.
Because they were created BY HUMANS to do exactly that.
Also, LLMs can perform on tasks they weren’t explicitly trained for.
Such as?
You are on Lemmy so defending AI use will net you down votes. I am an anti-AI person, practically, but you make a very fair and objective point. 👍 I up vote you, not because I agree with AI use but because this is a proper point to make in a proper discussion, which is what this place should be about.
Kudos.
Yes AI is definitely a topic that seems to have a fairly united front. I’ve worked in software engineering for 25+ years and I can see the impact AI has on our discipline. So it’s not like I’m hailing it to be an all-positive force - I just think “all it does is generate text” is a weird, flat form of criticism.
Indeed. Also look at the down votes on my comment. 😂
But yeah, generative AI has so many environmental and ethical issues that I am totally and wholly against its use in most cases, but you made a fair point and that should be recognized.
There’s a difference between using a tool as a knowledgeable professional, and using a tool as a novice or a member of the general public: the professional can likely tell when the answer an AI gives is wrong.
AI is a tool that can be useful sometimes.
Finally got where I stand: why uncomfortable? It’s just being hard to impose a sensible approach to it:
- yes, llms can be used as a tool efficiently
- no, killing the planet along the way is not an option, this has to
stopbe stopped
Seems like an AI apologists article. Sounds a bit like “bans are hard so why even bother.”
No, the alternatives exist and people can choose them where able: https://codeberg.org/brib/slopfree-software-index Sure, not all things are available AI free, e.g. there’s no Linux kernel (NetBSD doesn’t run on as much hardware), but that doesn’t mean there’s no point in choosing no AI software where possible, if you care about it.
The enforceability part seems the most apologist to me. You never could really know if some contributor wasn’t copying leaked Windows XP code into your FOSS project. If you trust contributors that little, don’t let them contribute.
People are forgetting the AI hype train is reliant on the fact that businesses and individuals can’t host the AI themselves, which is rapidly changing.
All that insane power that OpenAI and Anthropic have harnessed is suddenly completely useless when you can just spin up a competing model at home, with HF hosting a crap ton of finetuned models like uncensored/abliterated to do whatever you want with it.
Even Deepseek released their full size models if you happen to have 400Gb (or 1.5Tb) of VRAM lying around.
When the bubble (hopefully) pops, AI will just continue to be a tool that devs use where applicable, and likely not on a massive data center cloud as it migrates into consumer electronics.
I think this is a much more sensible attitude than the pitchfork wavers declaring mandatory zero-tolerance or you’re evil.
While that’s true, there’s a pretty good chance that it will be negatively useful for many programmers even after the AI bubble burst.
Also e-mail is much easier and cheaper to self-host than an LLM, yet most people relying on 3rd parties. Even some big pushers of yOU cAn JuST seLf-HosT cHiNEse lLM MoDels use corpo e-mail accounts.
Email is NOT easier to self-host than an LLM lol, it’s like multiple orders of magnitude more difficult. Set something up slightly wrong and your emails will never not be marked as spam because your server will be used to forward spam. An LLM for internal company usage you could just set up on a private network and never have to worry about someone else burning your GPU time.
A humorous talk on the topic of self-hosting email, it’s pretty fun.
Getting SPF, DMARC, and DKIM working is such a pain. And that is only part of the mail security these days.
If the model is adequately FOSS, i.e. open weights, and can run on a single consumer GPU (or NPU), and the “author” (quotations because I’m personally undecided if one can claim code generated by an aforementioned model is theirs) understands it, then I really don’t see what the problem is…
Personally I have nothing against SLMs/LLMs as a technology, to me my grievances against ChatGPT or Claude are mostly about their environmental impacts and selling us back our own art, also keeping knowledge behind a for-profit black-box - if those aren’t appropriate for a specific model, then I say using that model is fair, and good for productivity.
I’d take a model whose training data is open source and legitimately obtained. The only ones I know about are Apertus and OLMo, and they aren’t really competitive.
Unfortunately not yet, no, true FOSS models are likely many years away, but I would argue that that follows typical FOSS lifecycles. Emerging technology is typically outperformed by proprietary endeavors, which creates an audience, and then that audience undertakes a FOSS implementation that initially underperforms, then just about competes, then eventually overtakes (i.e, GNU/Linux).
Interesting point… In this case, I have a hunch that the requirements in terms of training data and hardware to run the training on would remain prohibitive, but I do wonder how much could be actually achieved using a distributed scheme of sorts.
The problem with open weight models is that they are still hard to deploy. For DeepSeek for example you require NVIDIA HGX B200, these things are expensive.
Yes, unfortunately they are, and I do think efficiency is going to be a significant research front for open-weight models. The nature of this topic is highly speculative as our compute capabilities have only recently reached what is required to consider running generative AI models, what we have today are very crude first implementations of what I personally believe will become an everyday tool for developers, and more.
And we have seen this, there are models now capable of running on an individual’s hardware (and not particularly expensive hardware either) that can outperform what ChatGPT initially launched with.
If you are really using those things for work, a 32GB VRAM Blackwell currently costs around 3800-4000€. My own experience with local models on my 5070/12GB with 64GB DDR4 has shown me:
- I can run a Qwen 3.8 35b Q_8 quant (BF16 is a tad too large) using CPU offloading, while still reaching acceptable speeds for private use.
- Ternary Models are a game changer. Built to be used in edge computing, Bonsai-27B-Ternary from Prism ML runs completely on my GPU (using ca. 10GB of my 12GB VRAM incl. MMProj, 9 when disabling vision) and even leaves enough VRAM to run subagents (when killing my plasmashell lol -but i tend to offload these to the CPU in favor of increasing the context of the main model). currently you have to install a costum fork of llama.cpp to run ternary models, but the needed changes will be applied upstream soon. (There is a Bonsai quant which aims to run on iPhones with below 6GB VRAM footprint - the limit of what Ios currently allows)
If this scales linear, it would mean that the 32 GB of a smallish Blackwell would be enough to run Ternary models with around 70B parameters plus a ton of context window without breaking a sweat.
These are strange times. I absolutely love the technology and the advances, and i fucking hate the companies pusting these technologies.
Altman, Amodei, Musk, Nadella, Huang and all of the othe AI evangelists can go choke on a bag of dicks in my opinion. This technology never should have been this divisive, it should have just become another tool in humanities toolbox. I fear that these people have tainted the general populations outlook on this tech for a very long time.
I personally don’t care about the tech. I care about the economy around the tech.
People who are vehemently against any form of AI will be left behind. That may be difficult to hear, and even more difficult to accept, but that is the cold, hard, truth.
Its a tool, and just like any tool it has uses. It can be used inappropriately, and it’s valid to criticize those instances. But if you are plugging your ears and going “Lalala!” when people talk about actual valid use cases, or stubbornly pretending that it has none, you will be left behind.
You will be like office employees from the 80’s and 90’s who refused to use a computer and then suddenly found themselves without a job. It’s your choice if you use it or not. It’s also every company’s choice to not hire you because of your unwillingness to adapt.
If you haven’t learned it yet, this is your warning and your wake-up call: Learn how to use AI now, or find a different career. It’s not going away, no matter how much you may bitch and moan. There will soon be two classes of people, those who learned how to use this emerging technology, and those who refused. Things will be much harder for that second group.
I don’t say this to be snarky or smug. I hate AI myself, but I also recognize how fucking stupid I’d have to be to refuse to use it. It’s a bitter pill to swallow, but it’s also the truth.
Agreed, although simply using LLMs to do your thinking for you is only going to decrease your value in the workplace and make you more replaceable. The goal needs to be learning how to use AI to expand your thinking, counteract your biases, improve the quality of your work, and accelerate your learning.
Like anything else, using the tool properly requires skill and care.
I don’t know if you noticed you contradicted yourself there. Letting ai do your thinking decreasing your value and then, talking about using ai to replace your thinking.
Ai is causing psychosis because people are trying to use it to replace thinking. If you don’t use it you lose it, literally especially with your brain. You have to know how to produce those skills from within or you don’t have those skills and then you are worthless in any workplace, but more importantly to yourself. You have to be meta to the aim being the TV show, and control it from a place of checks and balances (us performing those checks and balances of ai) handing over the reigns of higher thinking to a machine designed to be a yes man, is not ever a pathway to expanding your thinking or even learning to use ai, that’s just sitting on a couch till you lose critical muscle mass, but it’s your brain. (No shame to sitting on a couch or losing muscle mass, I’m just trying to explain it in relative terms)
Currently there is no way to use ai without causing ai hallucinations, because there isn’t a model readily available that runs as a tool, there are only models that run as a manipulative tool that provoke said replacement of your thinking, I can not assume that’s an accident. Seeing the usual levels of evil that come from the companies pushing this hard, tells me this is just as evil, if not the most evil thing they’ve done, measured against all the things they’re openly flouting, copyright law, openly burning rare literature for a monopoly, burning the already past tipping point climate, burning computer ownership and accessibility, I could go on. Theres very open bribery and flouting of social rules laws and common moralities, they’re desperate to cause this monster they’ve created out of ai, to attach like the symbiot in aliens that I forget the name of because I’m pre first coffee and my brain just be like that.
I am anti ai, as it currently stands. The current versions need to be left to die in silence and hatred and rejection we are all collectively currently showing. Only because it has been made into a Frankensteins monster, by the people responsible who have infinite money to pay judges to rule their way and have zero morals, nay a deficit of morals.
Ai will survive the ai bubble, because the ai bubble is the unsustainable practices of the billionaires and the manipulative interface designed to lessen your critical thinking skills.
But the ai that survives the bubble will and should be so highly regulated (it’s currently the opposite) and used as a tool, not a replacement for critical thinking, it can never do that. But there are so many things it can do to better humanity, none of which are currently being used.
We have to wait this one out. We’re not missing any boat.
If AI can do it for you, then let it. If it can’t, that’s where your value lies. If you just refuse to let it do things it can do well, you will be replaced by someone who’s probably not as smart as you but simply willing to let AI do what it does well.
As GP said: using AI to do a better job than was possible before is the real value.
It’s like they used to say back in the 80s: to really foul things up you need to use a computer, and it’s very true that both computers and AI magnify the opportunity to screw up in newer bigger more spectacular ways. AI also gives the opportunity to make a bunch of banal clip-art sideways printed banners - and those were cool for about 15 seconds back when you first saw them, just like the walking cats videos.
So, you can ignore AI, probably for 5-10 years in most jobs it applies to, before “reality” hits and they just can’t use people who don’t know how to use it (properly, for more than cat videos) anymore. You can take up basket weaving, sit at a booth at your local arts markets selling baskets - no AI required - or… you can figure out how to use it properly. Sort of like computers in the 80s, there aren’t a lot of valuable guides out there, it’s very new and changing ridiculously fast - if you’re going to master it, you’re probably going to have to dive in and figure it out for yourself, or join the 2nd wave of adopters who “wait for the training” and do the cookie cutter jobs with it.
The future is hard to see, always moving, but the scarier part about AI vs “the computer” is that AI can do a lot of the obvious stuff already: “take a memo”, “look this up on Google”, “make a list of all people who match these criteria…” in the computer you still needed a keyboard and mouse jockey, with AI… I’m not sure what 2nd wave adopter jobs will be out there in quantity.
This falls apart the second you look at it when you see that it doesn’t take much learning to use that tool for most applications.
At the same time, people overly reliant on that tool take a big hit in developing and strengthening their own skilks and thinking capabilities.
Combine the two things if you still can.
People who are vehemently against any form of blockchain technology will be left behind. That may be difficult to hear, and even more difficult to accept, but that is the cold, hard, truth. Blockchain is a tool, and just like any tool it has uses. It can be used inappropriately, and it’s valid to criticize those instances. But if you are plugging your ears and going “Lalala!” when people talk about actual valid use cases, or stubbornly pretending that blockchain has none, **you will be left behind**. You will be like office employees from the 80’s and 90’s who refused to use a computer and then suddenly found themselves without a job. It’s your choice if you use blockchain or not. It’s also every company’s choice to not hire you because of your unwillingness to adapt. If you haven’t learned it yet, this is your warning and your wake-up call: Learn how to use blockchain now, or find a different career. It’s not going away, no matter how much you may *removed* and moan. There will soon be two classes of people, those who learned how to use this emerging technology, and those who refused. Things will be much harder for that second group. I don’t say this to be snarky or smug. I hate blockchain myself, but I also recognize how fucking stupid I’d have to be to refuse to use it. It’s a bitter pill to swallow, but it’s also the truth.
Ah, there we go, I knew your rhetoric sounded familiar!
Seriously though, what “skills” are you building by learning how to use LLMs at your corporate job? How to tell a chatbot to do a task for you?
Sure, if you’re trying to build a local LLM server from scratch, tune a minimalist harness with a RAG and custom skills, optimize context compaction and concurrent token throughput, and integrate all that into existing automation pipelines, that takes some technical skill. But that’s a tiny fraction of people in the corporate world that need to do anything like that.
I could teach vast majority of corporate workers everything they will ever need to know about using “AI” in a half hour Lunch-N-Learn. There is no skill involved in asking a chatbot to move all your emails about “Project ABC” into a new Outlook folder called, “Project ABC” or asking a chatbot to, “draft a department memo about the new parking policy found in the HR SharePoint site.”
It’s not the next industrial revolution, it’s not the beginning of the Singularity, it’s not the dawn of a new age of our species. It is merely the latest hype-cycle of Capitalism that exists to siphon wealth from the population up to a handful of ultra wealthy billionaires and megacorps, and artificially prop up a dying economy just a little longer. And long term, we will discover the same thing about LLM use as we are finding out now about electronics in school and the mass proliferation of smart phones: That it stunts people’s critical reasoning, reading comprehension, self-image, and social confidence.
Writing an appropriate prompt is a skill. Many (most?) people using LLMs aren’t using prompts that give them what they actually want, in many cases because they don’t understand the thing they’re trying to ask for, but also in many cases because they don’t know how to ask for it while avoiding the many possible pitfalls that come with uncareful prompts (including, but not limited to, hallucination and bias).
Personally I haven’t bothered learning that skill yet, largely because I don’t want that crap shoved in my face all the damned time, and have little use for it anyway. But it’s still a skill.
It’s really not for most people. You ask a question, the LLM answers. You ask it to do a task, it does it. Most people are not using these things for anything advanced or technically difficult.
I work in a big corporate office, if you exclude the IT staff and software devs, here’s a pretty exhaustive list of what everybody else is using LLMs for:
- Organizing emails.
- Drafting memos and auto-responses in Teams.
- Organizing cluttered files.
- Adding “artwork” to company fliers.
- Doing web searches.
- Summarizing emails and Teams messages and creating to-do lists from them.
- Searching large documents for key terms.
It’s grunt work, that’s what these things excel at, and that’s how most people use them. That’s fine, but acting like most people are somehow not able to unlock the “true potential” of these LLMs unless they have special training and skills is a marketing ploy to sell overpriced “AI” courses to gullible and ignorant executives and upper managers.
It’s like those stupid DEI courses that corporations have everybody take so they can “learn to not be problematic.” But all the quizzes are filled with stupid stuff like:
Your manager, Sarah finishes briefing you and your coworker, Phil on an upcoming project. After she leaves, Phil says to you, “That Sarah sure is a real juicy piece of meat, I’d love to get taste of her!” What should you do? A. High-five Phil and tell him you call dibs on her secretary. B. Tell Phil that his comment was not appropriate, then report it to HR.
It’s just there to check boxes for compliance and provide an excuse for other companies to charge outrageous prices to tell people that sexually assaulting your coworkers or pulling your eyelids tight and calling yourself “Ching Chong” isn’t appropriate office behavior.
It’s another way Capitalism poisons and hollows out substantive discussions about topics like systemic racism, multiculturalism, and gender relationships, in favor of standardized quizzes that cost thousands of dollars a year to administer.
The amount of “AI consultants” and “AI integration” firms that have popped up overnight is staggering. Just like with the crypto/blockchain craze a few years ago, everybody is trying to get rich quick off the hype.
Good point. I probably should have stated “using LLMs for technical tasks that anyone cares about the results of”.
I’m not going to claim LLMs are trustworthy, but I am saying that there are ways to ask things that either reduce the odds of it giving you false things or actively cause it to provide wrong answers with misstated or hallucinated context. People who know what they’re doing can get better results out of them. This doesn’t stop them from being the equivalent of a nepo-hire intern, but it could be said to change whether they’re a malicious, apathetic, or semi-eager nepo-hire intern.
And one of the big risks with LLMs is staff who can recognise bullshit or questionable results being replaced with staff who unquestioningly accept whatever results they get. And people reading an “AI summary” of something and assuming it’s actually accurate.
People who know what they’re doing can get better results out of them.
I don’t think that’s true in the way you appear to mean (sorry if I misunderstood). People who are already experts on the subject matter might be able to use better keywords and discard hallucinatory material quicker, but I don’t believe that you can generically “be good at prompting” outside of an area where you have substantial domain knowledge.
And the only way to learn to recognise bullshit involves not using LLMs or other automated tools and working problems out for yourself, not to mention that they’re an “intern” who actually costs the same in computing power as two senior engineers’ salary.
I don’t think that’s true in the way you appear to mean (sorry if I misunderstood). People who are already experts on the subject matter might be able to use better keywords and discard hallucinatory material quicker, but I don’t believe that you can generically “be good at prompting” outside of an area where you have substantial domain knowledge.
There are ways of phrasing LLM requests that can actively induce them to give you made-up bullshit that confirms your pre-existing assumptions. Which is fine if that’s what you want it to produce, but often not what people actually want. For (possibly poor, given I don’t actually use LLMs) example “give me the transcripts for five court cases for assault with a deadly fish” would likely result in five-ish almost-certainly made-up court transcripts. Someone who knows what they’re doing could phrase the prompt to be more likely to say that no such cases exist (assuming no such cases actually exist) instead of just hallucinating them as per the request.
And you might be right about domain knowledge being required for good prompting. But it’s possible to have domain knowledge and still be terrible at asking for what you actually want it to produce.
not to mention that they’re an “intern” who actually costs the same in computing power as two senior engineers’ salary.
Don’t expect me to defend the moronic economic decisions of companies that have jumped on this stupid band-wagon without regard for the actual value (or lack thereof) to their business.
Sure, how and what you ask does make a difference, especially on lower power models. But it’s generally not really significant, like learning how to write more effective prompts takes maybe a few hours of total time? Honestly, probably an hour at most for 95% of people using LLMs in a corporate environment.
The irony of all this, is that one of the supposed biggest advantages of LLMs is that you can just talk to them with natural language in a conversational format. The more people have to use special rules of conversation, format their prompts in specific ways, take advantage or avoid subtleties of the model’s preferred syntactic style, etc. The more LLMs become a technical tool that can only produce high quality results if you use it in very specific, skilled ways.
And one of the big risks with LLMs is staff who can recognise bullshit or questionable results being replaced with staff who unquestioningly accept whatever results they get. And people reading an “AI summary” of something and assuming it’s actually accurate.
That’s exactly the point from people who are making systemic critiques of the “AI” craze. LLMs by their very nature, encourage people to become lazy. I’m not worried about a machine that’s constantly wrong, depending on the application, I can control for that. I am worried about a machine that is almost always right. That’s really dangerous, because it lulls its users into a false sense of confidence and security.
There’s a saying I heard from an old sys-admin once, “It’s better to be broadly right, than precisely wrong.” Modern LLMs are a perfect example of the latter, they will get 95% of a task correct, but then mess up that last 5%. But it messes up that 5% in a way that is very subtle and convincing, and requires somebody with deep, technical knowledge to catch and mitigate. That fact, combined with how lazy people are incentivized to be when using them, is the deadly combo imo.
Seriously though, what “skills” are you building by learning how to use LLMs at your corporate job?
i’m not learning any skills… i’m offloading some work on to AI so that i can do other stuff on company time…
Isn’t that what we should all aspire to? To destroy capitalism from the inside?
Capitalism isn’t being “destroyed from the inside” by you offloading grunt work to an agent. If anything, you may be helping justify future cuts of your job by demonstrating that there is less need for you. Your managers will be all too happy to cut half your department and make the remaining people take on all that work because, “AI makes them 5x more productive.” Of course, none of those people will get a 5x pay raise.
Use it or don’t, it doesn’t make a difference in the long run. This bubble will pop soon anyways and we’ll be on to the next “revolutionary technology.” I suspect it will be quantum computing, I’ve been seeing a significant upswing in news articles and bot traffic about that over the last few months, but who knows.
If your work is pushing AI hard, use it if you need the job, play the game if you have to, just don’t let yourself be mesmerized by the aggressive marketing hype. Anybody who is pushing the idea that tech workers will be replaced in the next 5-10 years with LLMs is a religious fanatic and should be treated with the appropriate levels of mockery and disdain.
Also, if you really want to work on taking down Capitalism, organize your labor, and support other movements that do the same.
Yeah, i’m not an idiot… i’ve already considered all those points about the downsides of using AI at work … like you said… play the game
if you really want to work on taking down Capitalism, organize your labor
How do you know i"m not using my free time at work to do exactly that?
you made a lot of assumptions about people who use AI at work …
Eh, more like generalizations based on observations of my own, and many other corporate workplaces that people I am close to, work in.
So what you’re saying is that Linus Torvalds is only a mid-tier programmer?
Has Linus been screeching about how everybody must use AI or get left behind?
I am 100% fine with being left behind by a technology that I see as an attempted corporate surveillance scheme that has metastasized into a giant money pit. The difference from the 80’s and 90’s is people were actually using computers to make money back then. If any AI company is actually making more money than they’re spending, I haven’t heard about it, and you’d think they’d be advertising it. https://isaiprofitable.com/
I’ve been happy to fully sit this one out thus far. Don’t feel like I’m missing a damn thing.
agreed
the issue I have with how people use it is that it’s easy to just create more shit that they spew out into the world
I’ve got no issue with people using tools to improve their work. I think it’s fair to say that AI is enough like predictive text and spell check when it comes to tools you can use at work. and like any tool, the user is still responsible for the end result
my main problem with AI is that it is absolutely fucking our world (environmentally and economically) to create pure garbage that is thrown in my face every day. like, scale it back a bit everyone…
To be clear, capitalism is fucking our world. Nothing about the technology can do that on its own. Its how it is being deployed.
We could have started with smaller LLMs, deployed strategically at institutions of higher learning for real societal value, running on renewable electricity… fuck it lets aim for a trillion dollar IPO promising to replace all doctors and engineers!
it is absolutely fucking our world (environmentally and economically)
Environmentally, it has yet to surpass Bitcoin in electricity usage (though it’s set to triple soon, and that will make it a bigger power hog than all of cryptocurrenty).
Economically, this is the biggest bubble since the 1800s railroad boom. Scaled for inflation and GDP railroads were a far bigger gamble than AI, and they hit a 20ish year recession when that boom was over.
Hopefully the “fungible technology” argument holds water, and whatever AI investments don’t work out the hardware / data centers can be re-purposed for truly valuable aspects of it. Truly valuable to who is the key question.
Bit coin farms never built massive illegal gas turbine power plants in residential neighbourhoods at least not that I know of.
Not that I am a fan of the waste for crypto mining either.
Bitcoin had a slower rollout, it wormed its way into smaller niches. A lot of early bitcoin mining was done on “borrowed” or otherwise “unused” hardware that other people paid the electric bills for.
I generally approve of much of what cryptocurrency could be used for, but I’ve been pissed off about the waste of proof-of-work cryptocurrency since before 2018, and nothing has improved since then.
It’s a classic scammer tactic to create a false sense of urgency, so people sign up for something without thinking.
Slow down, and do some critical thinking. The promise of AI is that you can type in a simple prompt and the AI will figure out all of the complicated computer stuff for you. How would someone be “left behind” by this technology? Do you suppose that experienced developers will lose the ability to write simple sentences?
I don’t think people will loose the ability to type in “make that button green”. Even if AI fulfills it’s promises (which is doubtful, read up on the halting problem) someone who knows how computers work will be more employable than someone that only knows how to write prompts. Because the person that knows how computers work can also write prompts and will be better at doing that than someone that doesn’t know anything about computers.
More important in this day and age is learning how to spot a scam. Someone pressuring you to do something immediately is likely scamming you.
Oh no I’ll be left behind when the Titanic leaves port?! How utterly tragic!
Tragic for everyone who WASN’T “left behind”, anyway.
Facts. Whether you like it or not. It’s good to have a healthy amount of criticism towards AI and to fact check it and not blindly trust. But you can’t wish it away or pretend it has zero merit. I guess you can…
Why can I not upvote and downvote?
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Talk me more about how in the future, game controllers will be made obsolete by motion-sensing cameras, and we will all be jumping around in front of a big flat-screen TV playing party games and finger-gunning down bad guys in rail shooters!
Yea, and the internet will never go mainstream. I also heard Apple developed this new product called the iPhone, what a stupid idea that no one will buy, people will just stick with regular cell phones when given the choice. Now excuse me while I ride off on my horse that will never, ever, be replaced by these new au-to-mo-biles.
See, I can do it too.
good thing that downvotes can only be used to suppress narratives on lemmy. lol
