It is weirdly religious in a way, because if you were to present contrary evidence (e.g. experts in a field weighing in about how plausible sounding responses are bunk), you would only be told you don’t believe enough in the long term potential and capabilities.
Don’t get me wrong, I think we all agree capabilities will eventually improve (and farther-future capabilities could reasonably surpass experts), but really is unclear if the current transformer architectures with their probabilistic/hallucinatory outputs will plateau before they surpass current experts abilities in all promised fields.
And it's so much like listening to someone in a church congregation sharing their experiences with god. Clear and obvious gaps are hand-waved away exactly how you're describing.
The problem is that AI psychosis is fundamentally the belief that an LLM is "thinking" at all. Outputs are just believable word vomit which resembles factual information.
The problem is real but I don't think positing a philosophical root is helpful
If "agency" is making decisions and performing corresponding actions in the real world, then LLMs most definitely LOOK LIKE they're making decisions (what's the next token? which tool to use? what's to say, in general? what idea to convey?) and performing actions (tool use). Can we tell whether they are ACTUALLY making decisions? Well, are the people around me "actually" making decisions? Or are they simply pushed around by circumstances and external forces?
Am I actually making decisions? Did I like DECIDE to write this comment? Maybe? I have no clue...
While I can understand being skeptical of non-experts' claims that such answers are enough, I don't understand why you call it "psychosis" and not simply naivety or lack of expertise.
At the same time, the new so-called "models" haven't been pure transformer-based LLMs, but entire systems with tools (with access to the Internet), data storage, and the options to trigger additional instances for different tasks.
A lot of the models up to this point have been benefitted - like Google did - from essentially ‘pre SEO’ internet.
Now the same tools are being used to generate nigh infinite good sounding bullshit, which poisons the dataset in all sorts of hard to detect ways.
To add insult to injury, the human experts are also not as. Naive, and have many incentives to poison their own input in subtle ways too.
For one, if your website/book is poisoned, who is going to trust it for anything at all, much less for training models?
For two, all the major AI labs hire or contract for subject matter experts to create curated data sets, evaluate model performance, etc.
Unless they hire malicious experts, this will provide a growing, high quality data set that should drown out any poisoned pretraining data.
If it's easy enough that some randos can do it for fun, what do you think happens when there's commercial interest behind it?
Obviously companies are going try nudging AI towards recommending whatever they're selling. It's a logical extension of SEO - and that's a 100 billion USD industry.
Additionally, if I believed myself to be in some sort of spending - err - AI race, I'd try to poison the data sets of my competitors by putting crap out there for others to ingest.
Yes AI scrapers can easily spoof user-agent, but they fall out of date as the browser updates.
Bit harder to catch them in tarpits and then serve nonsense to whoever ever triggered the tarpit.
It’s a hell of a lot easier for a company to ensure that its scrapers all report the latest user agent string than it is to get everyone and their mother to update their browsers in a timely fashion.
OpenEvidence claims
"More than 40% of U.S. physicians use it daily, and it handled around 20 million clinical consultations per month. Over 100 million Americans were treated by a doctor using it in 2025."
Here is an example. My provider sent me this note. I'm quoting verbatim here from my MyChart record:
"Your liver enzymes are high, I would like to order acetaminophen containing medication like Tylenol, I would like to order liver ultrasound I placed ultrasound order in the system, make an appointment for radiology, I would like you to get hepatitis panel lab work done, obtain blood work order, please schedule a well visit to get it done"
When I queried it, this is what I got back. It was a dictation error. You could almost hear the panic in the message:
"Sorry for wrong message earlier, I was dictated message- so could not realize that it was written to take Tylenol type of medicines- I DO NOT RECOMMEND ACETAMINOPHEN CONTAINING MEDICINE - LIKE TYLENOL AND ALCOHOL DUE TO ELEVATED LIVER ENZYMES."
Again the problem is not dictation, or LLMs. The problem is humans ignoring their responsibility to check the output of a machine.
If a physician uses Google to search for a dosage chart for some drug they rarely prescribe, you wouldn’t say they are using Google to diagnose the patient. You wouldn’t say that either if they used Google to search for the most recent studies on a topic.
The fact that they use it doesn't make what the result is any worse or less trustworthy - arguably it makes it better.
It only becomes a problem if they offload all of the thinking to AI.
An expert already knows they don't know everything. That was never the point. Critical thinking cannot be delegated to AI any more than it can be delegated to a book. There is nothing new going on here.
We've known since the beginning that AIs confidently say incorrect things. But now that they can speak confidently about very complex topics, and mostly say correct things, we are letting our guard down and lots of subtle falsehoods are slipping through.
*In one case, I was able to put things back on track because the AI suggested my colleague talk to me; somehow it figured out we were co-workers.
Absolutely agree. Have seen this first hand
Yes, this is exactly so. AI is able to confidently sound plausible enough to convince laypersons or anyone who isn't very familiar with the subject matter, which is a big part of the mass-appeal "magic" of ChatGPT and other similar tools. It's like having a know-it-all friend (who also makes shit up to bridge their own knowledge gaps).
In many non-advanced non-specialized situations, AI is right enough to be at best useful or at worst not harmful (usually landing in the middle somewhere).
But speaking for myself, in areas where I consider myself quite proficient, I can very easily spot the subtle inconsistencies and naive conclusions that AI responses provide, and I have to guide/steer/correct it a lot to get good results when the subject matter is complex enough.
Similarly with LLMs, you can't just write them off entirely because they sometimes provide misleading or incorrect advice. The positive utility maximizing view is to learn when you need to call in an expert. I recently moved in to a new house and have used Claude extensively to figure out basic things (e.g., adjusting the garage door height, how to mount a TV). However, when the HVAC suddenly stopped working, I gave Claude a shot for an hour and tried some non-destructive fixes, but then realized I had to call in an HVAC expert.
I find Claude is surprisingly similar to a confident but incorrect coworker, with the benefit that Claude will reevaluate when I correct it.
I guess to me it has to be comparable to be an alternative.
Like, I don’t consider doomscrolling x an alternative to reading Wikipedia but I might consider it an alternative to CNN, even though they’re all technically and very broadly activities that I could use to inform myself.
In that same way I don’t consider the multitude of ways I could use my free will necessarily alternatives to each other even though they technically are. It kinda sucks but going that broad feels to me like it breaks the concept of alternative and makes it kind of meaningless.
The LLM may have, from its "perspective", implicitly thought the OP was telling it that he had strong reason to believe there was no calcification and was not considering the bigger picture of possibly receiving an incomplete/poor assessment from the medical staff. In fact, the issue here may be the LLM overly trusting doctors vs. trusting its own expertise.
In fields where I'm an expert... it makes a lot of silly mistakes that are annoying and I feel like they would just cascade if I didn't correct them early. (I still think it's a net win, but... I watch it and it watches me, and we both do better work. I'd even apply the "magical" adjective when it does stuff I hate but know how to do, like edit Helm charts. What would normally be 20 minutes of me griping about YAML indentation is just a correct diff in seconds. I'll take it!)
So with that in mind, I tend to distrust output that I can't verify. If a doctor was recommending surgery and I thought the plan was too aggressive, I'd get a second opinion. I don't expect Claude Code to have much medical diagnostic ability, as that is really not what the model is trained for, and I know how it performs on work that it's trained and fine-tuned for. That is not to say the output is wrong and that it can't have diagnostic value, just that I personally wouldn't feel safe trusting it. Wrap up the same model with fine-tuning in the domain and a harness that reminds Claude to do a lot of sanity checks, perhaps with a human in the loop to guide it back onto the rails when it gets hyperfixated on something that doesn't matter? That could very much be a useful AI product.
I always recommend people try asking LLMs a lot of questions on something they know first. Programmers should start by asking LLMs to work on a codebase they’re familiar with first.
You’re overstating the problem, though. Even for an expert the LLM will get a lot of things right and can be helpful under a watchful eye.
The real problem is knowing how to identify when it’s on the right track and when you need to correct it, because both cases are presented with the same tone and confidence.
An expert can better identify when the LLM output doesn’t sound plausible. Someone unfamiliar with the topic will think everything it says looks correct.
"Be wowed by the convenience and speed", or merely "take advantage of the mere availability"? What most people find to be damning about expert advice is that they simply can't get it anywhere, at any cost that they can afford.
Properly emotionally processing this fact and your complete inability to do anything about it is called an "existential crisis" and if you haven't had one or several yet, you will.
Putting that aside, your philosophy sounds shallow. Death is certain, but how long you have to live and the quality of that life are not predefined. An incompetent passenger-pilot trying to save you from a crash will at worst make no difference. But an incompetent doctor can teach to you that death isn’t necessarily the worst outcome.
Who do you choose to be coached by an expert on the ground?
The first: Has no clue about anything and therefore no useful knowledge and cannot challenge me
The second one: Is proven to willfully give wrong information and will make me do mistakes for sure.
The LLMs will do their best, even if imperfect, since they summarizes what appeared in books.
I prefer to be grounded on what Airbus / Boeing manuals, or on what pilots training book said, than two far more unreliable sources.
Ok for pain in your shoulder it might not, but how about a woman with a lump in her breast waiting for the mammogram interpretation? How about someone trying to understand disturbing lab results? People are also often pushed these days to move through visits with doctors at a breakneck speed, but the AI will "hear you out" all day.
Part of this is a problem with the AI, part of it a problem with our healthcare systems, and part of it is simply human nature. If you think that OpenAI, Anthropic, Google and the rest weren't aware of this going in you must have very little faith in the intelligence of their members. It's not hard to imagine the future of LLM's should involve a hell of a lot of liability on the companies running it, but for now it's the Wild West.
Whatever scenario you come up with my answer is the same.
As an adult I’d like to be able to choose what tools I use to learn about my condition regardless of how well it works or even if it’s likely to mislead me.
There’s risk in every aspect of life and we can’t baby proof everything.
Even if it "works" so poorly that you're not actually learning about your condition?
So if you MUST have answers that are at most random guesses, I'd suggest saving a few bucks and asking a coin before flipping it.
More on topic: if the article's author arrived at a definitively negative result would this have shown up on HN?
For example, we had to advocate for certain practices during the birth of our first child that became routine during our second several years later.
So, neither side is guaranteed correct, doctor or citizen researcher (which did not include LLMs in my case, for the record). The truest answer is also the most useless one, applicable to all fields: it depends.
The real question is: if you embrace being a layman, whom do you trust more: LLMs/the internet or experts, like doctors? I think the answer is pretty clearly experts.
It has been like this since the rise of "AI". The only people enthusiastic about it are usually the ones hoping to make a profit in one way or another.
The term for when the press "gets it wrong" is Gell-Mann Amnesia (https://en.wiktionary.org/wiki/Gell-Mann_Amnesia_effect).
In that case, when you have personal knowledge of the facts, or know the specific domain area, you can see where the reporter mixed things up.
AI is no different, it's just a bunch of matrix math substituting for "the reporter" regurgitating what it was previously told. So the Gell-Mann Amnesia effect would apply just the same. If you have domain knowledge, you immediately see where the AI got it wrong. When you do not have domain knowledge, you have less chance of seeing where the AI was wrong.
Then to say "Aha, but all of that is AI psychosis" makes obviously no sense: Why would we trust experts when they offer critique but not when they say "this is helpful"?
Overall: People are not insane. AI makes mistakes and, often, fails completely. AI also helps them do things better, quicker, increasingly so. The jaggedness of AI is confusing and real.
There is a huge difference between having a chance of a good result, which can be useful for experts able to filter out the bullshit, and consistent success. I would generate code as a helper, I would never allow a guy from marketing to merge unreviewed AI code.
As an industry we've been promising people for decades that if they put all their data into our special softwares they can get all sorts of information back out that will make life easier for them, reveal new insights and otherwise improve their understanding. But the unspoken caveat has always been that you have to put the right data into the right places, in the right format, in the right way and then you have to ask the right questions, in the right syntax, with the right tools. And if you get any one of those parts wrong, you're not going to get the right answers (or possibly even any answer at all). How many people have had their excel worksheet that they (or someone else they asked/employed) built for some task that has been working fine for the last year suddenly stop working or start throwing out nonsense numbers because some input changed? Or how many people have experienced their system seemingly throw out meaningless garbage because daylight savings changed right at the moment the report was being run? Or spent months operating on wrong data because the person who wrote the query misplaced a parenthesis and the query was searching for "(foo AND bar) OR baz" and not "foo AND (bar OR baz)". For most people, the computer and the programs they use to do their jobs are magical black boxes that most of the time produce mostly the right answers and sometimes get things very very wrong with no indication of what has changed. Which is effectively the same experience they will have with an AI, but now instead of needing to figure out some arcane excel pivot table and VBA script, they can just dump some raw data and a "natural language" question into the AI.
And that's not counting the fact that their experience with looking information up online is about the same as well. How many absolutely confident wrong takes have you encountered online for things you're an expert in? How many of those wrong takes have come straight from supposedly trustworthy sources like news companies or even other people in the field?
For most people, using a computer has always come with the asterisk that you should always be aware that the source you're reading could be very wrong, that the output is only correct assuming all the inputs and all the parts processing that input are also correct and that everything you do should be accompanied by vetting by experts, whether those experts were software developers or domain experts. For most people the only thing that's changed with AI is that it's a one stop shop for their "probably directionally right, almost certainly wrong in the details" access to the digital oracles.
I.e. nothing this radiologist said was related to the LLM’s advice.
AI isn't even the first instance of this phenomenon, news articles are like this as well.
Apply that to the Internet at large, and realize where LLMs got their training. They're basically ConfidentlyIncorrect personified.
Welcome to the club? This new awareness you've found over the true quality of LLM based GenAI output has been what "all the haters" have been mad about for-ever. That the output of LLMs are clearly defective, and merely have found a cute trick towards making humans think they're less defective than they are actually measured to be.
And the corresponding anger and frustration to push the risks of genai output out onto others, while also aggressively pushing it as a feature you should be using already. You're behind don't you know, and whatever other lie I have to tell to trick you into enough FOMO to pay me 200USD/mo so I can sell FOSS back to you.
An LLM can only output the mean next likely token, and then add a bunch of extra noise on top of that so it feels interesting and not repetitive. None of this is new, the problem is, 50% of humans are below the mean, but have no idea. So when an LLM tells them some lie: well, it sounds so helpful! It's impossible for someone who sounds this helpful to lie to me, liars never sound confident! It must be PERFECT! I'm gonna tell everyone how perfect it is. so the bottom 0-33% think LLMs are fantastic tools that make nearly 0 mistakes in comparison to the bottom 33%. 33-66%-ish aren't sure, some times it's great, but it will make that random mistake sometimes, but I can catch most (or all of them depending on ego). and the 66%+ are angry about how many people are getting tricked by something so obviously low quality, or are lucky enough to not have to care.
So when an LLM was asked to analyze the unit distance conjecture, it just spat out a bunch of average-or-random tokens that coincidentally happened to correspond to a valid proof that had eluded humans for decades?