I've come down pretty hard on friends who, when I ask for advice about something, come back with a ChatGPT snippet (mostly D&D-related, not work-related).
I know ChatGPT exists. I could have fucking copied-and-pasted my question myself. I'm not asking you to be the interface between me and it. I'm asking you, what you think, what your thoughts and opinions are.
I’ve noticed this trend in comments across the internet. Someone will ask or say something, the someone else will reply with “I asked ChatGPT and it says…” or “According to AI…”
ChatGPT is free and available to everyone, and so are a dozen other LLMs. If the person making the comment wanted to know what ChatGPT had to say, they could just ask it themselves. I guess people feel like they’re being helpful, but I just don’t get it.
Though with that said, I’m happy when they at least say it’s from an LLM. At least then I know I can ignore
It. Worse is replying as if it’s their own answer, but really it’s just copy pasted from an LLM. Those are more insidious.
I have coworkers who, on a zoom meeting, will respond to questions and discussions by spamming the zoom chat with paste-dumps of ChatGPT, etc. So frustrating and tiresome.
Exactly what I came here to say. It's not wrong to ask ChatGPT for advice; it can occasionally be very useful. But if I want chatGPT's opinion, I can ask for it myself. It's pointless to ask ChatGPT to review someone else's code. Use it to review your own before you submit it for review by your coworkers.
This is not a problem I've run into fortunately, but I'm appalled that this exists.
The scenario the author describes is bound to happen more and more frequently, and IMO the way to address it is by evolving the culture and best practices for code reviews.
A simple solution would be to mandate that while posting coversations with AI in PR comments is fine, all actions and suggested changes should be human generated.
They human generated actions can’t be a lazy: “Please look at AI suggestion and incorporate as appropriate. ”, or “what do you think about this AI suggestion”.
Acceptable comments could be:
- I agree with the AI for xyz reasons, please fix.
- I thought about AIs suggestions, and here’s the pros and cons. Based on that I feel we should make xyz changes for abc reasons.
If these best practices are documented, and the reviewer does not follow them, the PR author can simply link to the best practices and kindly ask the reviewer to re-review.
It's kinda hilarious to watch people make themselves redundant. Like you're essentially saying "you don't need me, you could have just asked ChatGPT for a review".
I wrote before about just sending me the prompt[0], but if your prompt is literally my code then I don't need you at all.
I do not use AI for engineering work and never will, because doing the work of thinking for myself is how I maintain the neural capacity for forming my own original thoughts and ideas no AI has seen before.
If anyone gives me an opinion from an AI, they disrespect me and themselves to a point they are dead to me in an engineering capacity. Once someone outsources their brain they are unlikely to keep learning or evolving from that point, and are unlikely to have a future in this industry as they are so easily replaceable.
Soc. At the Egyptian city of Naucratis, there was a famous old god, whose name was Theuth; the bird which is called the Ibis is sacred to him, and he was the inventor of many arts, such as arithmetic and calculation and geometry and astronomy and draughts and dice, but his great discovery was the use of letters. Now in those days the god Thamus was the king of the whole country of Egypt; and he dwelt in that great city of Upper Egypt which the Hellenes call Egyptian Thebes, and the god himself is called by them Ammon. To him came Theuth and showed his inventions, desiring that the other Egyptians might be allowed to have the benefit of them; he enumerated them, and Thamus enquired about their several uses, and praised some of them and censured others, as he approved or disapproved of them. It would take a long time to repeat all that Thamus said to Theuth in praise or blame of the various arts. But when they came to letters, This, said Theuth, will make the Egyptians wiser and give them better memories; it is a specific both for the memory and for the wit. Thamus replied: O most ingenious Theuth, the parent or inventor of an art is not always the best judge of the utility or inutility of his own inventions to the users of them. And in this instance, you who are the father of letters, from a paternal love of your own children have been led to attribute to them a quality which they cannot have; for this discovery of yours will create forgetfulness in the learners' souls, because they will not use their memories; they will trust to the external written characters and not remember of themselves. The specific which you have discovered is an aid not to memory, but to reminiscence, and you give your disciples not truth, but only the semblance of truth; they will be hearers of many things and will have learned nothing; they will appear to be omniscient and will generally know nothing; they will be tiresome company, having the show of wisdom without the reality.
Phaedr. Yes, Socrates, you can easily invent tales of Egypt, or of any other country.
It is increasingly incredibly important here to make a distinction between using an LLM with and without search. Without search, I agree with you.
But e.g. ChatGPT with search enabled is often an invaluable research tool, and dramatically speeds up finding relevant sources. It basically automates the spidering of references and links, and also handles the basic checks for semantic relevance quite well, and this task requires little real intelligence or thought. Only once you hit a highly specific and niche technical domain will it start to fail you here (since it will match on common-language semantics that do not often align with technical usage).
For a lot of topics, I now have the reverse feeling that a person who has NOT used an LLM to facilitate search—on basic or even intermediate questions—is increasingly more of a concern.
> I do not use AI for engineering work and never will
> Once someone outsources their brain they are unlikely to keep learning or evolving from that point
It doesn't piss me off, it makes me feel sorry for you. Sorry that you're incapable of imagining those with curiosity who use AI to do exactly what you're claiming they don't - learning. The uncurious were uncurious before AI and remain so. They never asked why, they still don't. Similarly, the curious still ask why as much as ever. Rather than Google though, they now have a more reliable source to ask things to, that explains things much better and more quickly.
I hear you grunt! Reliable? Hah! It's a stochastic parrot hallucinating half the time!
Note that I said more reliable than Google, which was the status quo. Google is unreliable. Yes, even when consulting three separate sources. Not that one has the time for that, not in reality.
You've got it the wrong way around. LLMs do the exact opposite - they increase the gap between the curious and the nots. It accelerates the learning rate gap between them. The nots.. they're in for a tough time. LLMs are so convenient, they'll cruise through life copypasting their answers, until they are asked to demonstrate competence in a setting where none are available and everything falls apart.
If you still find this hard to imagine, here's how it goes. In your mind LLM usage by definition goes like this - and for the uncurious, this is indeed how it would go.
User: Question. LLM: Answer. End of conversation.
By the curious, it's used like this.
User: Question. LLM: Answer. User: Why A? Why B? LLM: Answer. User: But C. What's the tradeoff? LLM: Answer. User: Couldn't also X? LLM: Answer. User: I'm not familiar with Y, explain.
>I do not use AI for engineering work and never will, because doing the work of thinking for myself is how I [...]
This is a framing issue that's keeping you from doing clearer thinking, faster. LLMs, especially in agentic configurations, are a cognitive tool. Just because many people use a tool unskillfully doesn't mean that tool is useless.
>If anyone gives me an opinion from an AI, they disrespect me and themselves to a point they are dead to me in an engineering capacity.
Hmmm...
> Once someone outsources their brain they are unlikely to keep learning or evolving
Agreed. But there are many ways in which people can "outsource their brains". LLMs are one way. Dogmas are another.
>If this pisses you off, ask yourself why.
It would definitely piss me off to see an engineering leader fail to grasp the obvious due to some poorly reasoned beliefs.
I'm starting to run into the other end of this as a reviewer, and I hate it.
Stories full of nonsensical, clearly LLM-generated acceptance requirements containing implementation details which are completely unrelated to how the feature actually needs to work in our product. Fine, I didn't need them anyway.
PRs with those useless, uniformly-formatted LLM-generated descriptions which don't do what a PR description should do, with a half-arsed LLM attempt at summary of the code changes and links to the files in the PR description. It would have been nice if you had told me what your PR is for and what your intent as the author is, and maybe to call out things which were relevant to the implementation I might have "why?" questions about. But fine, I guess, being able to read, understand and evaluate the code is part of my job as a reviewer.
---- < the line
PRs littered with obvious LLM comments you didn't care enough to take out, where something minor and harmless, but _completely pointless_ has been added (as in if you'd read and understood what this code does, you'd have removed it), with an LLM comment left in above it AND at the end of the line, where it feels like I'm the first person to have tried to read and understand the code, and I feel like asking open-ended questions like "Why was this line added?" to get you to actually read and think about what's supposed to be your code, rather than a review comment explaining why it's not needed acting as a direct conduit from me to your LLM's "You're absolutely right!" response.
This absolutely has been my more recent frustration as well, specifically this:
> uniformly-formatted LLM-generated descriptions which don't do what a PR description should do, with a half-arsed LLM attempt at summary of the code changes and links to the files in the PR description. It would have been nice if you had told me what your PR is for and what your intent as the author is, and maybe to call out things which were relevant to the implementation I might have "why?" questions about.
If I want to see what the code changes do, I will read the code. I want your PR description to tell me things like:
- What the tradeoffs, if any, to this implementation are
- If there were potential other approaches you decided not to follow for XYZ reason so that I don't make a comment asking about it
- If there is more work to be done, and if so what it is
- Any impacts this change might have on other systems
- etc.
Sure, if you want to add a handful of sentences summarizing the change at a high level just to get me in context, that's fine, but again if I want to see what changed, I will go look at what changed.
Counterpoint: "Chatgpt said this" is an entirely legitimate approach in many contexts and this attitude is toxic.
One example: Code reviews are inherently asymmetrical. You may have spent days building up context, experimenting, and refactoring to make a PR. Then the reviewer is expected to have meaningful insight in (generously) an hour? AI code reviews help bring balance; it may notice stuff a human wouldn't, and it's ok for the human reviewer to say "hey, chatgpt says this is an issue but I'm not sure - what do you think?"
We run all our PRs through automated (claude) reviews automatically, and it helps a LOT.
Another example: Lots of times we have several people debugging an issue and nobody has full context. Folks are looking at code, folks are running LLM prompts, folks are searching slack, etc. Sometimes the LLMs come up with good ideas but nobody is sure, because none of us have all the context we need. "Chatgpt says..." is a way of bringing it to everyone's attention.
I think this can be generalized to forum posts. "Chatgpt says" is similar to "Wikipedia says". It's not the end of the conversation, but it helps get everyone on the same page, especially when nobody is an expert.
I'd agree. Certainly mentioning that information came from an LLM is important so people know to discount or manage it. It's possibly incorrect but still useful as an averaged answer of some parts of the internet.
Certainly citing GPT is better than just assuming it's right and not citing it along with an assertion.
Relying heavily on information supplied by LLMs is a problem, but so is this toxic negativity towards technology. It's a tool, sometimes useful, and other times crap. Critical thinking and literacy is the key skill that helps you tell the difference, and a blanket rejection (just like absolute reliance) is the opposite of critical thinking.
I just throw everyone who tells me “chatgpt said” or “ask chatgpt” into the idiot pile in my brain. It’s not nice but usually these are people who tell me incorrect things in the first place. Or turn in half finished unoptimized work. Maybe Llms are just a way to identify the mentally lazy?
No one I know who says this kind of thing would read this article. People love being lazy.
I'm surprised nobody else has gone meta yet, so I suppose I must. Anyway, "ChatGPT said this" ... about this thread.
----
In many of the Hacker News comments, a core complaint was not just that AI is sometimes used lazily, but that LLM outputs are fundamentally unreliable—that they generate confidently stated nonsense (hallucinations, bullshit in the Frankfurtian philosophical sense: speech unconcerned with truth).
Here’s a more explicitly framed summary of that sentiment:
⸻
Central Critique: AI as a Bullshit Generator
Many commenters argue that:
• LLMs don’t “know” things—they generate plausible language based on patterns, not truth.
• Therefore, any use of them without rigorous verification is inherently flawed.
• Even when they produce correct answers, users can’t trust them without external confirmation, which defeats many of the supposed productivity gains.
• Some assert that AI output should be treated not as knowledge but as an unreliable guess-machine.
Examples of the underlying sentiment:
• “LLMs produce bullshit that looks authoritative, and people post it without doing the work to separate truth from hallucination.”
• “It costs almost nothing to generate plausible nonsense now, and that cheapness is actively polluting technical discourse.”
• “‘I asked ChatGPT’ is not a disclaimer; it’s an admission that you didn’t verify anything.”
A few participants referenced Harry Frankfurt’s definition of bullshit:
• The bullshitter’s goal isn’t to lie (which requires knowledge of the truth), but simply to produce something that sounds right.
• Many commenters argue LLMs embody this: they’re indifferent to truth, tailored to maximize coherence, authority, and user satisfaction.
This wasn’t a side issue—it was a core rejection of uncritical AI use.
⸻
So to clarify: the strong anti-AI sentiment isn’t just about laziness.
It’s about:
• Epistemic corruption: degrading the reliability of discourse.
• False confidence: turning uncertainty into authoritative prose.
• Pollution of knowledge spaces: burying truth under fluent fabrication.
I personally think this could pop up as policy at work. I'd personally push for that. "If you're pasting AI responses without filtering through the lens of your own thoughts and experience..."
Like, it's fine for you to use AI, just like one would use Google. But you wouldn't paste "here are 10 results I got from Google". So don't paste whatever AI said without doing the work, yourself, of reviewing and making sense of it. Don't push that work onto others.
Actually I think its called Fraud: If you payed someone else to do your job, and didnt even confirm correctness, then you should be fired.
Wait no, if your boss is making you use this tech, then its his fault.
Wait no, if the companies are selling this as the holy grail of firing everyone to save money, its the fault of whomever is buying this trash without testing it
Wait no, if this is trained on the entirety of human information, then we are all wrong for not deleting all of our bad answers written on the internet
34 comments
[ 3.1 ms ] story [ 90.2 ms ] threadI know ChatGPT exists. I could have fucking copied-and-pasted my question myself. I'm not asking you to be the interface between me and it. I'm asking you, what you think, what your thoughts and opinions are.
ChatGPT is free and available to everyone, and so are a dozen other LLMs. If the person making the comment wanted to know what ChatGPT had to say, they could just ask it themselves. I guess people feel like they’re being helpful, but I just don’t get it.
Though with that said, I’m happy when they at least say it’s from an LLM. At least then I know I can ignore It. Worse is replying as if it’s their own answer, but really it’s just copy pasted from an LLM. Those are more insidious.
If a stupid LLM doesn't understand what something might be, chances are your user won't either.
This is not a problem I've run into fortunately, but I'm appalled that this exists.
A simple solution would be to mandate that while posting coversations with AI in PR comments is fine, all actions and suggested changes should be human generated.
They human generated actions can’t be a lazy: “Please look at AI suggestion and incorporate as appropriate. ”, or “what do you think about this AI suggestion”.
Acceptable comments could be: - I agree with the AI for xyz reasons, please fix. - I thought about AIs suggestions, and here’s the pros and cons. Based on that I feel we should make xyz changes for abc reasons.
If these best practices are documented, and the reviewer does not follow them, the PR author can simply link to the best practices and kindly ask the reviewer to re-review.
I wrote before about just sending me the prompt[0], but if your prompt is literally my code then I don't need you at all.
[0] https://blog.gpkb.org/posts/just-send-me-the-prompt/
If anyone gives me an opinion from an AI, they disrespect me and themselves to a point they are dead to me in an engineering capacity. Once someone outsources their brain they are unlikely to keep learning or evolving from that point, and are unlikely to have a future in this industry as they are so easily replaceable.
If this pisses you off, ask yourself why.
But e.g. ChatGPT with search enabled is often an invaluable research tool, and dramatically speeds up finding relevant sources. It basically automates the spidering of references and links, and also handles the basic checks for semantic relevance quite well, and this task requires little real intelligence or thought. Only once you hit a highly specific and niche technical domain will it start to fail you here (since it will match on common-language semantics that do not often align with technical usage).
For a lot of topics, I now have the reverse feeling that a person who has NOT used an LLM to facilitate search—on basic or even intermediate questions—is increasingly more of a concern.
> Once someone outsources their brain they are unlikely to keep learning or evolving from that point
It doesn't piss me off, it makes me feel sorry for you. Sorry that you're incapable of imagining those with curiosity who use AI to do exactly what you're claiming they don't - learning. The uncurious were uncurious before AI and remain so. They never asked why, they still don't. Similarly, the curious still ask why as much as ever. Rather than Google though, they now have a more reliable source to ask things to, that explains things much better and more quickly.
I hear you grunt! Reliable? Hah! It's a stochastic parrot hallucinating half the time!
Note that I said more reliable than Google, which was the status quo. Google is unreliable. Yes, even when consulting three separate sources. Not that one has the time for that, not in reality.
You've got it the wrong way around. LLMs do the exact opposite - they increase the gap between the curious and the nots. It accelerates the learning rate gap between them. The nots.. they're in for a tough time. LLMs are so convenient, they'll cruise through life copypasting their answers, until they are asked to demonstrate competence in a setting where none are available and everything falls apart.
If you still find this hard to imagine, here's how it goes. In your mind LLM usage by definition goes like this - and for the uncurious, this is indeed how it would go.
User: Question. LLM: Answer. End of conversation.
By the curious, it's used like this.
User: Question. LLM: Answer. User: Why A? Why B? LLM: Answer. User: But C. What's the tradeoff? LLM: Answer. User: Couldn't also X? LLM: Answer. User: I'm not familiar with Y, explain.
This is a framing issue that's keeping you from doing clearer thinking, faster. LLMs, especially in agentic configurations, are a cognitive tool. Just because many people use a tool unskillfully doesn't mean that tool is useless.
>If anyone gives me an opinion from an AI, they disrespect me and themselves to a point they are dead to me in an engineering capacity.
Hmmm...
> Once someone outsources their brain they are unlikely to keep learning or evolving
Agreed. But there are many ways in which people can "outsource their brains". LLMs are one way. Dogmas are another.
>If this pisses you off, ask yourself why.
It would definitely piss me off to see an engineering leader fail to grasp the obvious due to some poorly reasoned beliefs.
Stories full of nonsensical, clearly LLM-generated acceptance requirements containing implementation details which are completely unrelated to how the feature actually needs to work in our product. Fine, I didn't need them anyway.
PRs with those useless, uniformly-formatted LLM-generated descriptions which don't do what a PR description should do, with a half-arsed LLM attempt at summary of the code changes and links to the files in the PR description. It would have been nice if you had told me what your PR is for and what your intent as the author is, and maybe to call out things which were relevant to the implementation I might have "why?" questions about. But fine, I guess, being able to read, understand and evaluate the code is part of my job as a reviewer.
---- < the line
PRs littered with obvious LLM comments you didn't care enough to take out, where something minor and harmless, but _completely pointless_ has been added (as in if you'd read and understood what this code does, you'd have removed it), with an LLM comment left in above it AND at the end of the line, where it feels like I'm the first person to have tried to read and understand the code, and I feel like asking open-ended questions like "Why was this line added?" to get you to actually read and think about what's supposed to be your code, rather than a review comment explaining why it's not needed acting as a direct conduit from me to your LLM's "You're absolutely right!" response.
> uniformly-formatted LLM-generated descriptions which don't do what a PR description should do, with a half-arsed LLM attempt at summary of the code changes and links to the files in the PR description. It would have been nice if you had told me what your PR is for and what your intent as the author is, and maybe to call out things which were relevant to the implementation I might have "why?" questions about.
If I want to see what the code changes do, I will read the code. I want your PR description to tell me things like:
- What the tradeoffs, if any, to this implementation are
- If there were potential other approaches you decided not to follow for XYZ reason so that I don't make a comment asking about it
- If there is more work to be done, and if so what it is
- Any impacts this change might have on other systems
- etc.
Sure, if you want to add a handful of sentences summarizing the change at a high level just to get me in context, that's fine, but again if I want to see what changed, I will go look at what changed.
One example: Code reviews are inherently asymmetrical. You may have spent days building up context, experimenting, and refactoring to make a PR. Then the reviewer is expected to have meaningful insight in (generously) an hour? AI code reviews help bring balance; it may notice stuff a human wouldn't, and it's ok for the human reviewer to say "hey, chatgpt says this is an issue but I'm not sure - what do you think?"
We run all our PRs through automated (claude) reviews automatically, and it helps a LOT.
Another example: Lots of times we have several people debugging an issue and nobody has full context. Folks are looking at code, folks are running LLM prompts, folks are searching slack, etc. Sometimes the LLMs come up with good ideas but nobody is sure, because none of us have all the context we need. "Chatgpt says..." is a way of bringing it to everyone's attention.
I think this can be generalized to forum posts. "Chatgpt says" is similar to "Wikipedia says". It's not the end of the conversation, but it helps get everyone on the same page, especially when nobody is an expert.
Certainly citing GPT is better than just assuming it's right and not citing it along with an assertion.
Think of it as a dynamic opinion poll -- the probabilistic take on this thing is such and such.
As a bonus you can prime the respondent's persona.
// After posting, I see another comment at bottom opening with "Counterpoint:"... Different point though.
No one I know who says this kind of thing would read this article. People love being lazy.
----
In many of the Hacker News comments, a core complaint was not just that AI is sometimes used lazily, but that LLM outputs are fundamentally unreliable—that they generate confidently stated nonsense (hallucinations, bullshit in the Frankfurtian philosophical sense: speech unconcerned with truth).
Here’s a more explicitly framed summary of that sentiment:
⸻
Central Critique: AI as a Bullshit Generator
Many commenters argue that: • LLMs don’t “know” things—they generate plausible language based on patterns, not truth. • Therefore, any use of them without rigorous verification is inherently flawed. • Even when they produce correct answers, users can’t trust them without external confirmation, which defeats many of the supposed productivity gains. • Some assert that AI output should be treated not as knowledge but as an unreliable guess-machine.
Examples of the underlying sentiment: • “LLMs produce bullshit that looks authoritative, and people post it without doing the work to separate truth from hallucination.” • “It costs almost nothing to generate plausible nonsense now, and that cheapness is actively polluting technical discourse.” • “‘I asked ChatGPT’ is not a disclaimer; it’s an admission that you didn’t verify anything.”
⸻
Philosophical framing (which commenters alluded to)
A few participants referenced Harry Frankfurt’s definition of bullshit: • The bullshitter’s goal isn’t to lie (which requires knowledge of the truth), but simply to produce something that sounds right. • Many commenters argue LLMs embody this: they’re indifferent to truth, tailored to maximize coherence, authority, and user satisfaction.
This wasn’t a side issue—it was a core rejection of uncritical AI use.
⸻
So to clarify: the strong anti-AI sentiment isn’t just about laziness.
It’s about: • Epistemic corruption: degrading the reliability of discourse. • False confidence: turning uncertainty into authoritative prose. • Pollution of knowledge spaces: burying truth under fluent fabrication.
Like, it's fine for you to use AI, just like one would use Google. But you wouldn't paste "here are 10 results I got from Google". So don't paste whatever AI said without doing the work, yourself, of reviewing and making sense of it. Don't push that work onto others.
Wait no, if your boss is making you use this tech, then its his fault.
Wait no, if the companies are selling this as the holy grail of firing everyone to save money, its the fault of whomever is buying this trash without testing it
Wait no, if this is trained on the entirety of human information, then we are all wrong for not deleting all of our bad answers written on the internet