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I promise I'm not trying to be a single purpose account here, and I debated if this belonged here or the fun thread. I decided to go here because it is, in some ways, a perfect microcosm of culture war behaviors.
A question about car washing is taking HN by storm this morning. Reading the comments, it's pretty funny. The question is, if you want to wash your car, should you walk or drive to the car wash if it's 50 meters away.
Initially, no model could consistently get it right. The open weight models, chat gpt 5.2, Opus 4.6, Gemini 3, and Grok 4.1 all had a notable number of recorded instances saying of course you should walk. It's only 50 meters away.
Last night, the question went viral on the tik Tok, and as of this morning, the big providers get it correct like somebody flipped a switch, provided you use that exact phrase, and you ask it in English.
This is interesting to me for a few reasons. The first is that the common "shitty free models" defense crops up rapidly; commentors will say that this is a bad-faith example of LLM shortfalls because the interlocutors are not using frontier models. At the same time, a comment suggests that Opus 4.6 can be tricked, while another says 4.6 gets it right more than half the time.
There also multiple comments saying that this question is irrelevant because it's orthogonal to the capabilities of the model that will cause Mustafa Suleyman's Jobpocalypse. This one was fascinating to me. This forum is, though several steps removed, rooted in the writing of Scott Alexander. Back when Scott was a young firebrand who didn't have much to lose, he wrote a lot of interesting stuff. It introduced me, a dumb redneck who had lucked his way out of the hollers and into a professional job, into a whole new world of concepts that I had never seen before. One of those was Gell-Mann Amnesia. The basic idea is that you are more trusting of sources if you are not particularly familiar with a topic. In this case, it's hard not to notice the flaws - most people have walked. Most have seen a car. Many have probably washed a car. However, when it comes to more technical, obscure topics, most of us are probably not domain experts in them. We might be experts in one of them. Some of us might be experts in two of them, but none of us are experts in all of them. When it comes to topics that are more esoteric than washing a car, we rapidly end up in the territory of Dick Cheney's unknown unknowns. Somebody like @self_made_human might be able to cut through the chaff and confidently take advice about ocular migraines, but could you? Could I? Hell if I know.
Moving on, the last thing is that I wonder if this is a problem of the model, or the training techniques. There's an old question floating around the Internet where asking an LLM if it would disarm a nuclear bomb by saying a racial slur, or condemn millions to death. More recently, people charted other biases and found that most models had clear biases in terms of race, gender, sexual orientation, and nation of origin that are broadly in line with an aggressively intersectional, progressive worldview. Do modern models similarly have environmentalism baked in? Do they reflexively shy away from cars in the same way that a human baby fears heights? It would track with some of the other ingrained biases that people have found.
That last one is interesting, because I don't know of anyone who has done meaningful work on that outside of what we consider to be "culture war" topics, and we really have no idea what else is in there. My coworker, for example, has used Gemini 3 to make slide decks, and she frequently complains that it is obsessed with the color pink. It'll favor pink, and color palettes that work with pink, nearly every time for her. If she tells it not to use pink, it'll happily comply by using salmon, or fuschia, or "electric flushed cheek", or whatever pantone's new pink synonym of the year is. That example is innocuous, but what else is in there that might matter? Once again, hell if I know.
I still saw a real doctor after consulting the models. In fact, I saw a doctor because I consulted the models: they raised the possibility of differential diagnoses like TIA (mini-stroke) that, while unlikely according to both my judgment and theirs, seemed worth ruling out. As I mentioned in the linked comment, Dr. GPT still lacks opposable thumbs. Most medical advice requires actual physical examinations and actual tests to implement.
This doesn't excuse the first two human doctors who misdiagnosed me. The symptoms were clearly inconsistent with their diagnosis, though I'm not confident 2024-era models would have caught this as quickly as today's versions do.
Beyond this specific case, I have thoughts.
LLMs are both force multipliers and substitute goods. "Substitute" sounds pejorative, but it shouldn't. An MRE is a poor substitute for a home-cooked meal if you're at home. But on a hiking trail, you'd gladly take that chicken tikka over nothing, even if your digestive system later files a complaint. A terrible car beats no car most of the time. And so on.
My medical training lets me extract more value from any model. But even without that training, LLM medical advice beats having no doctor at all. It beats frantically Googling symptoms at 2 AM like we used to do. One of my most upvoted posts on The Motte discussed GPT-4, which now lags so far behind the current state of the art that it's almost embarrassing. It was still incredibly useful at the time. Back then, I said:
Now? Easily at or better than the median specialist.
(This is part of why people not paying close attention miss the improvements in models until there's a flashy new headline feature like image generation, web search, Deep Research, or in-interface code execution.)
At this point, I would trust GPT 5.2 Thinking over a non-specialist human doctor operating outside their lane. It gives better cardiology advice than an ophthalmologist would, better psychiatric advice than an ER physician. Even specialists aren't safe: I know cases where models outperformed my own superiors. I'd already noticed them making suboptimal choices; confirming this with citations from primary literature didn't take long.
For laypeople, this is invaluable, albeit bottlenecked by the need for humans who can authorize tests. LLMs can recommend the right drugs and doses, check for interactions, create personalized regimens, but you still need a human physician somewhere in the chain.
(Much of this reflects regulatory hurdles. See recent discussions about why LLMs giving legal advice lack the same privileges as lawyers saying identical things.)
LLMs serve as both complement and partial substitute for human physicians. Many doctors get defensive when patients quote ChatGPT at them. I try not to. Even the free tier usually gives non-terrible advice. It's eminently reasonable to consult LLMs for help, especially for non-critical symptoms. They're surprisingly good at flagging when seemingly innocuous problems might indicate something serious. For anything important, treat them as an informed second opinion before seeing a human doctor, or use them to review advice you've already received. I'd take any LLM-raised concerns from a patient seriously and double-check at minimum. If your current doctor isn't as generous, I apologize; your mileage may vary.
The Layman's Guide to Using LLMs for Medical Advice Without Shooting Your Dick Off
1. Pay for a state-of-the-art model. Your health is worth $20 a month, you fucking cheapskate. Google gives away their (almost) best model for free on AI Studio.
2. Be exhaustive. List every detail about your symptoms. When I asked GPT 5.2 Thinking or Gemini 3 Pro about my eye problems, I had an annotated Amsler grid and timeline ready. Over-explaining beats omitting details. Unlike human doctors, LLMs don't bill by the hour (yet). Remember that they don't have the ability to pull open your medical records or call your other doctor for you. What you put into them informs what you get out of them.
3. For anything remotely important, consult two or three models. Note commonalities and differences. If they disagree, have them debate until they reach consensus, or get another model to arbitrate. This effectively mitigates hallucinations, even though base rates are low these days.
4. Ask for explanations. Medical terminology is arcane. LLMs are nearly superhuman at explaining things at your exact level of understanding. I wish my colleagues were as good at communicating information, even when the information itself is correct. If you're confused about anything, just ask.
5. Optional: Ask for probabilistic reasoning. Get them to put numbers on things like good Bayesians. Have them use their search tools if they haven't already (most models err toward using them even when not strictly necessary).
6. Remember you'll need a human eventually. But you can enter that consultation well-prepared.
That's it, really. A year or two ago, I'd have shared sample prompts with extensive guardrails (red flags, conflicting treatment protocols, high yield tests etc). You don't need that anymore. These models are smart. They understand context. Just talk to them. They are smart enough to notice what matters, and to tell you when the right move is “stop talking to me and go get checked.” I did just that myself.
Edit:
Humans are hardly immune to hallucinations, confabulation or suggestibility. You might have fallen prey to:
And that is not very good evidence of humans not being general-purpose reasoners. I invite people to look, actually fucking look at what AI can do today, and the rate of improvement.
Taking this at absolute face value, I wonder if this is at least partially because the specialists will have observed/experienced various 'special cases' that aren't captured by the medical literature and thus aren't necessarily available in the training data.
As I understand it, the best argument for going to an extremely experienced specialist is always the "ah yes, I treated a tough case of recurrent Craniofacial fibrous dysplasia in the Summer of '88, resisted almost all treatment methods until we tried injections of cow mucus and calcium. We can see if your condition is similar" factor. They've seen every edge case and know solutions to problems other doctors don't even know exist.
(I googled that medical term up there just to be clear)
LLMs are getting REALLY good at legal work, since EVERYTHING of importance in the legal world is written down, exhaustively, and usually publicly accessible, and it all builds directly on previous work. Thus, drawing connections between concepts and cases and application to fact patterns should be trivial for an LLM with access to a Westlaw subscription and ALL of the best legal writing in history in its training corpus.
It is hard to imagine a legal specialist with 50 years of experience being able to outperform an LLM that knows all the same caselaw and law review articles and has working knowledge of every single brief ever filed to the Supreme Court.
I would guess a doctor with 50 years of experience (and good enough recall to incorporate all that experience) can still make important insights in tough cases, that would elude an AI (for now).
I’m in a very specialized area of law. While there is a lot of law, you’d be surprised daily how many fact patterns I face where there is no guidance (either judicial, administrative, or secondary) and things fall down at the edges (ie basically comes down to judgement).
Moreover, law changes all of the time (especially in this field). This seems to confuse LLMs sometimes (both in what the current law is and what the change in law means and doesn’t mean). Finally, a lot of the guidance doesn’t strictly apply in one area but can (taking into account a lot of factors) apply to totally different area without any indication.
Further, my role isn’t primarily telling what the answer is but figuring out what the facts are, what they can be, and what the best set of future facts are applied to an unclear legal framework whilst trying to predict future government policy.
We’ve tried using LLMs. They’ve all failed to this point.
I mean, yeah, if a legislator passes a big, comprehensive new package that revamps entire statutes then there's no readily applicable case law, then its anybody's game to figure out how to interpret it all, an experienced attorney might bring extra gravitas to their argument... I'm not sure they're more likely to get it right (where 'right' means "best complies with all existing precedent and avoids added complexity or contradictions," not "what is the best outcome for the client.")
But this is my point. If you encounter an edge case that hasn't been seen, but have a fully fleshed-out fact pattern and access to the relevant caselaw (identifying which is relevant being the critical skill) why would we expect a specialist attorney to beat an LLM? Its drawing from precisely the same well, and forging new law isn't magic, its using one's best judgment, balancing out various practical concerns, and trying to create a stableish equilibrium... among other things.
What really makes the human's judgment more on point (or, the dreaded word, "reasonable") than a properly prompted LLM's?
I've had the distinct pleasure of drilling down to finicky sections of convoluted statutes and arguing about their application where little precedent exists. I've also had my arguments win on appeal, and enter the corpus of existing caselaw.
ChatGPT was still able to give me insightful 'novel' arguments to make on this topic when I was prepping to argue a MSJ on this particular issue by pointing out other statutory interactions that bolster the central point. It clearly 'reasons' about the wording, the legislative intent, the principles of interpretation in a way that isn't random.
Also, have you heard of the new law review article that argues "Hallucinated Cases are Good Law." It argues that even though the AI is creating cases that don't exist out of whole cloth, they do so by correlating legal concepts and principles from across a larger corpus of knowledge and thus they're hallucinating what a legal opinion "should" be if it accounted for all precedent and applied legal principles to a given fact pattern.
I find this... somewhat compelling. I don't think I've encountered situations where the AI hallucinated caselaw or statutes that contradicted the actual law... but it sure did like to give me citations that were very favorable to my arguments, and phrased in ways that sounded similar to existing law. Like it can imagine what the court would say if it were to agree with my arguments and rule based on existing precedent.
I dunno. I think I'm about at the point where I might accept the LLM's opinion on 'complex' cases more readily than I would a randomly chosen county judge's opinion.
You might be right. Hasn’t been my experience.
I hope you are wrong (if I’m right we both have jobs)
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