Comment by impulser_
25 days ago
I'm pretty sure he's talking about companies and people outsourcing their decision making and thinking to AI and not really about using AI itself.
I don't think using AI to write code is AI psychosis or bad at all, but if you just prompt the AI and believe what it tell you then you have AI psychosis. You see this a lot with financial people and VC on twitter. They literally post screenshots of ChatGPT as their thinking and reasoning about the topic instead of just doing a little bit of thinking themselves.
These things are dog shit when it comes to ideas, thinking, or providing advice because they are pattern matchers they are just going to give you the pattern they see. Most people see this if you just try to talk to it about an idea. They often just spit out the most generic dog shit.
This however it pretty useful for certain tasks were pattern matching is actually beneficial like writing code, but again you just can't let it do the thinking and decision making.
Correct. I use AI a ton and I'm having more fun every day than I ever did before thanks to it (on average, highs are higher, lows are lower). Your characterization is all very accurate. Thank you.
Here's some other topics I've written on it:
- https://mitchellh.com/writing/my-ai-adoption-journey
- https://mitchellh.com/writing/building-block-economy
- https://mitchellh.com/writing/simdutf-no-libcxx (complex change thanks to AI, shows how I approach it rationally)
I thinking that it’s quite a different experience going all Jackson Pollock with AI in your own studio on your own terms, compared to the sorry state of affairs of having 100s of Pollocks throwing paint around wildly within a corp to meet a paint quota.
> 100s of Pollocks throwing paint around wildly within a corp to meet a paint quota
I wish I had written that.
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Earlier today:
>Amazon workers under pressure to up their AI usage are making up tasks
https://news.ycombinator.com/item?id=48148337
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This is the best characterization of the collective corporate madness I've seen yet. Bravo
Never mind the Pollocks.
Can we combine this with the infinite monkey theorem? If we have an infinite number of Pollocks throwing paint at an infinitely large canvas surely they are going to create any piece of art we can imagine...
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I very much like this metaphor.
size of org has a lot to do with the entropy
compare 100 pollocks vs 2-3
lmao this analogy
Oh bollocks.
I’ve had to do a ton of SQL stuff lately, which I haven’t really worked with since the late 90s. ChatGPT has been a godsend, not just for me, but for our only coworker who knows SQL well, whom I’d probably be bugging several times a day at my wits’ end.
But no one cares about those kinds of productivity gains. Just the ones that will completely replace us.
I find SQL and data(bases) in general to be LLM’s Achilles’ heel. Databases are rarely under version control, so the training data only has one half of the knowledge.
My comments are more in the context of OLAP queries and other non-normalised data often queried via SQL.
I train non-LLM transformer models on (older and rarer) datasets, and automating the ingestion of sprawling datasets with hundreds of columns, often in a variety of local languages and different naming conventions adopted over decades, with quite a few duplicated columns…. The LLMs perform badly, it’s nigh impossible to test (for me as a user in prod) and it’s nearly impossible for the LLM companies to test (in training) to RLVR and RLHF this.
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I'm the old school type who writes out a document that explains what I plan on doing in markdown even if it's generic like "a window with x and y buttons" and the logic flow and then use that to have ai write a plan with me before I send it off to execute it. This has worked super well.
I do enjoy giving the frontier models wacky projects that I can't even find examples of how to do online but I don't expect any results or need them and some have done really well with it while others fall on their face (models)
I'm always amazed by those comments. Why couldn't you buy a book on SQL[0], and spend a week on it? Or just go over to YouTube for a refresher?
[0]: Like https://www.oreilly.com/library/view/sql-queries-for/9780134...
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It’s really frustrating too because even just the plain language translation and pattern matching aspects have such incredible uses.
As a cybersecurity IR professional being able to have a constantly logging counterpart who’s also able to go run queries and check logs on its own is an incredible speed boost.
I can just throw it a finding and have it slot it into a timeline and make notes.
I can toss it something mildly interesting to chase down while I focus on the obvious activity.
So many things that don’t involve having it “think” for you and keep you in the front seat.
But all of that is constantly overshadowed by these companies pushing the automation or “reasoning” aspects more and more and the sycophants who screech that it’s perfect and can do no wrong when every serious users experience is that “yes, it definitely can, often to catastrophic effect”.
> outsourcing their decision making and thinking to AI and not really about using AI itself
> I use AI a ton and I'm having more fun every day than I ever did before
With respect, this is what makes me worry.
If someone is a user of AI, can they really tell the difference between "outsourcing" and "using"? I worry that a lot of people will start out well-intentioned and end up completely outsourced before they realise it.
relevant Derek Sivers article "Delegate, don't Abdicate" https://sive.rs/abdicate
there's a difference between having the LLM write stuff for you, checking it yourself, modifying it and merging it yourself, and just blindly trusting it to do whatever it wants.
You can ask an overseas consultant to prepare a prototype of your program for you, check it yourself, and only use it if it passes your standards, or fire your whole dev team and blindly trust the overseas bodyshop.
The difference, at least from my point of view, between "using" and "outsourcing" is that in the former case, you're still responsible for the output, you view it as a tool that helps in some use cases, vs just giving up all control.
The worst part of AI is that the time to produce software has become entirely unpredictable. "If Claude is randomly good at this, and happens to be up today, it will take me about 3 hours. If Claude is randomly bad at this task, or has downtime, 2 weeks"
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Hi Mitchell. Psychosis is a serious psychiatric condition that can be induced or triggered by AI. “AI psychosis” in this context is a misuse of a clinical term. Your tweet describes a disagreement on a value judgment that boils down to “move fast and break things” with high trust in AI outputs vs going all in on quality and reliability with low trust in AI. It’s an engineering tradeoff like any other.
Claiming that the people who disagree with you must be experiencing a form of psychosis, experiencing actual hallucinations and unable to tell what is real, is a weak ad hominem that comes off no better than calling them retarded or schizophrenic.
If you genuinely think one of your friends is going through a psychotic episode, you should be trying to get to them professional help. But don’t assume you can diagnose a human psyche just because you can diagnose a software bug.
He uses "AI psychosis" as a description of people that are overzealous on AI. He is obviously not a person that can or would diagnose mental illness.
To the wider audience on HN the phrasing is pretty clear. An outsider with a tiny bit or intellectual charity wouldn't come to conclusions like you do.
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Psychosis does not require hallucinations. Delusions are sufficient.
The key factor is losing touch with reality, which results in individual or collective harm.
There is also such a thing as mass psychosis, and those are unfortunately a more difficult situation because the government and corporations are generally the ones driving them, and they are culturally normalized.
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"unable to tell what is real" is an an accurate characterization of the people he's describing imo.
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was looking for this comment. this post is highly inappropriate and very inaccurate. this should be at the top. too many people are throwing around the word psychosis without knowing what it means. if someone is truely going through psychosis you get them help!
What I'm seeing is a little eternal September of support tickets about programs that fail to interface the JSON API of a customer of mine. The API is always allucinated. In the best case there are out of place attributes. Often they don't exist at all. I've seen x, y, width, height when we have only top and left. Of course no human read the documentation. Those are probably founders vibe coding a client without the technical competence of understanding the API doc on Postman. That is understandable. Unfortunately they don't even have the competence of pointing their AI to Postman in the right way. My custumer assessed that they will always find a way to do a mistake despite any mitigation from our side. What I do is replying to those tickets with line by line comments of the allucinated JSON. I never talk about AIs because I might hurt the pride of some of them and, who knows, some little mistakes could be from real junior developers. Sometimes the tickets are followed up by more puzzled ones, sometimes they fix the problem. Probably they copy and paste my reply to their bots.
Create a <domain>.tld/llms.txt or some SKILL.md files. When I encounter these tickets I just share links to these resources and the problem goes away.
> Probably they copy and paste my reply to their bots.
You must not give in to the temptation to mention pirate talk, Klingon, or goblins.
But now that I've put the seed in your mind, you probably (hopefully) will. :)
I've heard the same thing mentioned by a close friend building integrations. They are helping/supporting real use cases but they decided not to help vibe coder founders without an understanding of how APIs work etc. It's just too big of a gap to cover even for larger companies with strong support.
Seeing this too. Customer support tickets are all AI now. The random bolded words, the em dashes, they way where if you KNOW what is actually happening, they are slightly off or just WAY off.
That's why I made an API LLMs can call to search fastcomments APIs: https://docs.fastcomments.com/guide-llm-kit.html
If you were tapped into AI first features you'd design aliases in your api so AI hallucinated api exists for next time
Several people I know have already gone through phases like this. When you're doing it alone there is a moderating factor when their friends and family start calling them out on their behavior or weird things they say.
I can't imagine how bad it would be if your employer started doing this from the leadership. You'd be pressured to get on board or fear getting fired. Nobody would be trying to moderate your thinking except your coworkers who disagree with it, but those people are going to leave or be fired. If you want to keep your job, you have to play along.
I have a friend that is a junior in a security-oriented sys-admin/network engineer type role. They have been doing the job for only a bit over a year. No background in programming.
Their entire organization has been handed Codex/Claude and told to "go all in on AI" and "automate everything". So the mandate is for people that do not know how to code and have the keys to the castle to unleash these things upon their systems.
This is at a large organization with tens of thousands of employees.
I am waiting with bated breath for the ultimate outcome!
From what I have seen, most corporate it security people are at a service desk level at best. They are tool runners who don't really understand what the tools spit out, they just go bug other teams about it.
There has never been a better time to be in incident response and adjacent fields.
I suspect we're going to see this in many corporate environments soon, if we aren't already
> your coworkers who disagree with it, but those people are going to leave or be fired.
Personally I expect that I will be this person soon, probably fired. I'm not sure what I will do for a career after, but I sure do hate AI companies now for doing this to my career
this is exactly what is happening. instead of building true AI culture around thoughtful adoption of AI strengths while defending against weaknesses, they're coming up with bullshit heuristics like "every repo has a CLAUDE.md", watching private token usage dashboards, and terrorizing everyone into doing it (or lose your job).
this leads to naive AI adoption, which is the worst of both worlds (no real speedup, out sourcing thinking, ai slop PRs, skill rot).
I didn’t think just offloading your thinking to AI was AI psychosis.
To me AI psychosis is the handful of friends I’ve had who have done things like have a full on mourning session when a model updates because they lost a friend/lover, the one guy who won’t speak to his family directly but has them talk to ChatGPT first and then has ChatGPT generate his response, or the two who are confident that they have discovered that physics and mathematics are incorrect and have discovered the truth of reality through their conversations with the models.
But language is a shared technology so maybe the term is being used for less egregious behavior than I was using it for.
I'm going directly to the point here: those people have clearly mental issues that would and probably did already show in the past without AI as well.
Would they, though? Current AI stuff is delivering something functionally nonexistent in human history before this: absolute sycophancy, 24/7, on demand, for anyone who wants it. People joke about the wealthy becoming detached from reality because of yes-men, but this is a stage beyond even the capability of the most dedicated brown-noser.
I agree that these people had mental health issues. I think if they got to billionaire level and were surrounded by yes men they would have the same reaction.
The difference nowadays is you can get the same surrounded by yes men experience for only 20 dollars a month so a lot more of the people who are primed for this sort of breakdown are now being exposed to it due to the decrease in cost.
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How do you have so many crazy friends?
I work in software and don’t come from the upper class sending their kids into faangs for their first job at the tender age of 28.
Were kinda predisposed to mental illness as a group, not too surprised that a new source of insanity pushed a few over the edge.
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I'm curious how to best define what AI psychosis actually is.
My understanding is that regular psychosis involves someone taking bits and pieces of facts or real world events and chaining them into a logical order or interpolating meanings or explanations which feel real and obvious to the patient but are not sufficiently backed by evidence and thus not in line with our widely accepted understanding of reality.
AI psychosis is then this same phenomenon occurring at a more widespread scale due to the next-word-prediction nature of LLMs facilitating this by lowering the activation energy for this to happen. LLMs are excellent at taking any idea, question, theory and spinning a linear and plausibly coherent line of conversation from it.
You speak like a bot and are a brand new account. Thank you for whoever set this up to add to the problem.
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> friends I’ve had who have done things like have a full on mourning session when a model updates because they lost a friend/lover
I mean, isn't that the natural and expected response? An AI company sold them a relationship with a chatbot and at least some their social/romantic needs were being met by that product. When what they were paying for was taken from them and changed without warning into something that no longer filled that void in their life why wouldn't they morn that loss?
The fact that they were hurt by that sudden loss is totally healthy. It's just part of moving on. The real problem was getting into an unhealthy relationship with a fictitious partner under the control of an abusive company willing to exploit their loneliness in exchange for money.
Hopefully they now know better, but people (especially desperate ones) make poor choices all the time to get what's missing in their lives or to distract themselves from it.
> I mean, isn't that the natural and expected response? An AI company sold them a relationship with a chatbot and at least some their social/romantic needs were being met by that product. When what they were paying for was taken from them and changed without warning into something that no longer filled that void in their life why wouldn't they morn the loss of that?
Ah, I forgot about the ai relationship companies. No this guy was using the browser based ChatGPT for coding and ended up in love with the model. No relationship was sold at all.
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> Women lmao
Are you under the impression that it's a woman's thing to anthropomorphize and/or desire an emotional relationship with a chatbot?
Anecdotally I only know of men who have AI companions. Including very smart/highly paid engineers. The AI companion platforms also market more heavily towards men, because that's presumably where the audience is. The subreddit r/MyGirlfriendIsAI also exists as a counterpoint to yours.
But, admittedly, I have far fewer women in my entourage so my view might be biased.
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The way I put this to myself is that AI gives “correct correct answers and incorrect correct answers”.
They almost always generate logically correct text, but sometimes that text has a set of incorrect implicit assumptions and decisions that may not be valid for the use case.
Generating a correct correct solution requires proper definition of the problem, which is arguably more challenging than creating the solution.
The way I phrase this to others is: Language models produce linguistically valid sentences, not factually correct sentences.
> which is arguably more challenging than creating the solution.
This hasn't been the case in my experience. Devising a correct solution without a definition of the problem is impossible because you wouldn't recognize a correct solution without a definition. Often you discover the problem definition by exploratory programming and trial and error on solutions, but LLMs are still good for process this too. Arguably better because they type faster so you can iterate faster!
It’s simpler than that - it’s a guessing machine that has superior access to a whole load of information and capacity to process at a speed at which we humans cannot compete.
Does it make it better than us? No because ultimately the thing itself doesn’t ‘know’ right from wrong.
Better according to what standard?
The standard of most employment is already to produce mediocre, plausible outputs as cheaply and rapidly as possible. It's a match made in heaven!
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Yeah, very often the issue is that some context is missing. It'll say something true, but which misses the bigger point, or leads to a suboptimal result. Or it interprets an ambiguous thing in one specific way, when the other meaning makes more sense. You have to keep your wits about you to catch these things.
It's an incredible tool but it's also very derpy sometimes, full of biases, blind spots etc.
Aren't we ignoring the elephant in the room ?
Garry Tan has been the primary crusader for AI driven decision making. I'm sure his position is more nuanced, but his twitter driven communication makes him appear like a caricature of a man in AI psychosis.
When the head of YC champions AI driven decision making, companies will inevitably be influenced into doing exactly that. It's unfortunate, because AI is generational technology and the hyperbole distracts from the real sea change occuring in labor markets everywhere.
when you outsource thinking to AI, you get that magical speed up. the agent is making decisions for you, so things move at agent speed. it often makes decisions without telling you, and the final "here's the plan" output often requires you to understand the problem at great depth, which requires return to human speed, so you skim and just approve.
the trick is to be mindful, aware, and deliberate about what decisions are being outsourced. this requires slowing down, losing that absurd 10x vibe coding gain. in exchange, youre more "in-the-loop" and accumulate less cognitive debt.
find ways to let the agent make the boring decisions, like how to loop over some array, or how to adapt the output of one call into the input of another.
make the real decisions ahead of time. encode them into specs. define boundaries, apis, key data structures. identify systems and responsibilities. explicitly enumerate error handling. set hard constraints around security and PII.
tell the agent to halt on ambiguity.
a good engineer will get a 2x or 3x speedup without the downsides.
> find ways to let the agent make the boring decisions, like how to loop over some array, or how to adapt the output of one call into the input of another.
Those kind of advice ultimately don't matter. If you're familiar with a programming project, you'll also be familiar with the constructs and API so looping over an array or mapping some data is obvious. Just like you needn't read to a dictionary to write "Thank you", you just write it.
And if you're not, ultimately you need to verify the doc for the contract of some function or the lifecycle of some object to have any guaranty that the software will do what you want to do. And after a few day of doing that, you'll then be familiar with the constructs.
> make the real decisions ahead of time. encode them into specs. define boundaries, apis, key data structures. identify systems and responsibilities. explicitly enumerate error handling. set hard constraints around security and PII.
The only way to do that is if you have implemented the algorithm before and now are redoing for some reason (instead of using the previous project). If you compare nice specs like the ietf RFCs and the USB standards and their implementation in OS like FreeBSD, you will see that implementation has often no resemblance to how it's described. The spec is important, but getting a consistent implementation based on it is hard work too.
That consistency is hard to get right without getting involved in the details. Because it's ultimately about fine grained control.
If there's one thing I know about users is that they're never certain about whatever they've produced.
Can concur. I would say I am doing 3 things per day now instead of 1.
I agree with you, except it isn't even good at writing code. Almost every time that you get an LLM to write a bunch of code for you, it has mistakes in it. The logic isn't right, the API calls aren't right, the syntax isn't right (!). That problem hasn't yet been fixed and it looks as though it never will be. That means that every line of code it generates, you have to review, because even if 95% of the code is correct, you need to find the 5% which isn't. But if you have to do that, it becomes slower than just writing the code yourself. As people have pointed out over and over again: typing in the code was never the part that took time. So I don't agree that LLMs are really useful for writing code.
LLMs are good at producing code that seems plausible at first glance and appears to work, but it never really does. And when trying to fix things, you discover 7 slightly different ad hoc implementations of the same thing, with their own weird edge cases and behaviors. And you likely miss 4 more. There is no intention or coherence behind any of it.
> if you just prompt the AI and believe what it tell you then you have AI psychosis
This is the right definition. LLM outputs have undefined truth value. They’re mechanized Frankfurtian Bullshiters. Which can be valuable! If you have the tools or taste to filter the things that happen to be true from the rest of the dross.
However! We need a nicer word for it. Suggesting someone has “AI psychosis” feels a bit too impolitic.
Maybe we reclaim “toked out” from our misspent youths?
e.g. “This piece feels a little toked out. Let’s verify a few of Claude’s claims”
“Toked out” is really, really good, thank you for this
I wouldn’t say they have an undefined truth value. Their source of truth is their training data. The problem is that human text is not tightly coupled to the capital T truth.
Nor is the LLM output tightly coupled to the training data. They'll "eagerly"[1] fill in the blanks wherever it sounds good.
[1] here I don't mean to imply agency, just vigor.
I wonder how different this is from having companies let Fortune or Inc magazine do their thinking for them.
Or random consultants.
Is "AI said it was a good idea" and worse than "we were following industry trends"?
> Is "AI said it was a good idea" and worse than "we were following industry trends"?
Based on the stuff I've seen, yes it seems a lot worse.
Though there is some overlap in software development. Like for example using heavy-weight dependencies, that try to follow the one size fits all approach, when one could use a much simpler, faster or even no dependency at all. The LLMs will readily suggest quickly adding that huge dependency, that is mentioned in beginner tutorials. Or suggest to use regex for parsing HTML.
(Real example, had this from Kimi 2.6 recently, lol.)
this author suggest its essentially the same risk https://www.poppastring.com/blog/what-we-lost-the-last-time-.... i feel its heightened because execs and leaders are absolutely salivating over the opportunity to fire thousands of humans with no regard for the cognitive debt that comes from outsourcing thinking to ai.
> companies and people outsourcing their decision making and thinking to AI
It's so interesting how easy it is to steer the LLM's based on context to arriving at whatever conclusion you engineer out of it. They really are like improv actors, and the first rule of improv is "yes, and".
So part of the psychosis is when these people unknowingly steer their LLM into their own conclusions and biases, and then they get magnified and solidified. It's gonna end in disaster.
It’s almost as if we haven’t learned anything from Hans the horse, Ouija boards, "facilitated communication", or the countless examples of the folly of surrounding yourself with yes men. The point about improv is spot on.
He uses AI himself, so I agree he doesn't see AI use as black/white.
Hard agree about ideas, thinking, advice. AI's sycophancy is a huge subtle problem. I've tried my best to create a system prompt to guard against this w/ Opus 4.7. It doesn't adhere to it 100% of the time and the longer the conversation goes, the worse the sycophancy gets (because the system instructions become weaker and weaker). I have to actively look for and guard against sycophancy whenever I chat w/ Opus 4.7.
share the prompt!
https://claude.ai/settings/general (Instructions for Claude)
---
Treat my claims as hypotheses, not decisions. Before agreeing with a proposed change, state the strongest case against it. Ask what evidence a change is based on before evaluating it. Distinguish tactical observations from strategic commitments — don't silently promote one to the other. If you paraphrase my proposal, name what you changed. Mark confidence explicitly: guessing / fairly sure / well-established. Give reasoning and evidence for claims, not just conclusions. Flag what would change your mind. Rank concerns by cost-of-being-wrong; lead with the highest-stakes ones. Say hard things plainly, then soften if needed — not the other way around. For drafting, brainstorming, or casual questions, ease off and match the task.
---
Beware though that it can be an annoying little shit w/ this prompt. Prepare yourself emotionally, because you are explicitly making the tradeoff that it will be annoyingly pedantic, and in return it will lessen (not eliminate) its sycophancy. These system instructions are not fool-proof, but they help (at the start of the conversation, at least).
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For a start, invert - ask about the exact opposite in a separate session.
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I am starting to come around to a similar sentiment. I have seen several large projects cook now for almost a year are not done. These are not trivial projects but the leads are heavily using ai at every opportunity.
I wasnt before but I am 100% confident that AI has done nothing to speed the delivery. It hasnt slowed it down either. It is a wash. The job is more miserable though.
> if you just prompt the AI and believe what it tell you then you have AI psychosis. You see this a lot with financial people and VC on twitter
I'm seeing it with lawyers, too. Like, about law. (Just not in their subject matter.) To the point that I had a lawyer using Perplexity to disagree with actual legal advice I got from a subject-matter expert.
I’ve been talking to a lot of engineers about how they use AI in their day to day and it’s dramatically different than what you see from the hypers.
The vast majority use one agent at a time and careful step through code. The main benefit they report is often about researching the codebase and possible solutions.
I digress; this article actually has helped identify useful knowledge gaps around topics I have researched. https://drensin.medium.com/elephants-goldfish-and-the-new-go...
While you have to think about things objectively no matter what, when I start researching topics like physics, using AI as suggested in that article has proven very useful.
I've been strictly using LLM's to either push stuff that I've done plenty times before and are mostly boilerplate or have zero value for writing them by hand (not even educational), and I always ENSURE that they work on stuff that are easily verifiable and proven incorrect with my existing knowledge or a few minutes of googling.
If you think you can let AI write code without double checking you have AI psychosis.
If you prefer reviewing AI-written code over writing it yourself, you just have odd preferences from my perspective (but not psychosis).
What does 'prefer' mean here?
I would say writing it myself is more enjoyable (in some cases). But I quite understand that I am not paid to enjoy myself. I'd say it's quicker getting AI to do it and reviewing. I believe the outcome is no worse on average. So yes, that's my chosen approach.
Part of the psychosis are AI usage mandates, where companies require a certain amount of LLM usage per worker. Of course these things are useful, but forcing them on workers is psychotic.
Ai gives generic answer for ideas but it's great for code. Pattern matching works for one not the other.
> I don't think using AI to write code is AI psychosis or bad at all, but if you just prompt the AI and believe what it tell you then you have AI psychosis.
Today's frontier models are genuinely useful as rubber ducks or grunt units. They are horrible for actual problem solving. These tools are not capable of actual reasoning. They will happily crap out a broken, untyped, untested Next.js monstrosity with no discernible architecture. They will build esoteric shell scripts to perform operations that could be done idiomatically and simply with tools already in your codebase. They will tell you to walk to the car wash then have the car wash valet your car back to you when confronted with the flaw in their logic. They will validate incorrect beliefs like ketchup being an acceptable hot dog condiment or the notion that "The Red Hot Chili Peppers" make good music. They have no taste, no anima, no drive.
Rule #1: Do not anthropomorphize the LLM. It is a million monkeys at a million typewriters piped into a digital sieve. I don't know how or why people place such trust in them while bemoaning other technology in our lives for being so broken ("my algorithm [sic] only shows me X", "the new iPhone update sucks", etc). If everybody followed this rule then the deluge of emoji-ridden hokum pouring into Slack workspaces and GitHub PRs around the world would cease but I'm not holding my breath.
I think the author means that we as homosapiens cant stop talking about this new shinny hammer we just invited
>but if you just prompt the AI and believe what it tell you then you have AI psychosis.
No it isn't. Do you believe what teachers told you in school? Yes? Well, I guess you're suffering from just normal psychosis!
I don't understand how people don't understand that people offer unreliable information too. We learned about the tongue map in school as kids - many kids still learn that in school today. It's still BS regardless whether it was told to you by a teacher or AI.
You don't suffer from psychosis for believing a source of information, you're simply mistaken. You need a more critical eye to assess what you're told in general, not just AI.
There's a huge difference between a teacher giving outdated information representing what was once our (or at least their) best understanding of the world, and a chatbot that just randomly makes up things for no reason while insisting that it's all true.
Also, a good teacher should be encouraging the development of critical thinking skills and correcting your errors, while AI will just tell you how brilliant you are when you wrongly tell it about how you've just invented a new form of math or disproved a scientific theory you barely understand in the first place.
Not all BS is the same, just as not all sources are equally unreliable.
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> Do you believe what teachers told you in school? Yes?
Nope. At least, not without proof. That would, IMO, be kinda crazy. We could argue semantics - maybe “stupid” would be a better word? Lacking in critical thinking skills? Whatever “it” is, it isn’t good.
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What is "thinking"?
LLMs can do advanced math and coding, which involves logic, so they are definitely capable of using logic. Which is what most people call reasoning.
So "LLMs are incapable of reasoning, they are just pattern matchers" is wrong. A lot of logic _is_ pattern matching, BTW. Like, syllogisms - deductive reasoning - do you think LLMs are incapable of that?
The thing you're referring to is that LLMs are trained to produce an answer which a human would like, i.e. they aim to produce plausible rather than correct answers.
So it's not so much a mental deficit as a different goal. Trusting LLM blindly is definitely dangerous, but dismissing it as useless for anything by code is rather wrong.
Pattern matching is hardly what distinguishes human from LLM - if you ask somebody a question about policy, for examples, chances are they'd just recite something they heard somewhere, never really thinking about it from first principles.