I've used LLMs enough that I have a good sense of their _edges_ of intelligence. I had assumed that reasoning models should easily be able to answer this correctly.
And indeed, Sonnet and Opus 4.5 (medium reasoning) say the following:
Sonnet: Drive - you need to bring your car to the car wash to get it washed!
Opus: You'll need to drive — you have to bring the car to the car wash to get it washed!
Gemini 3 Pro (medium): You should drive.
--
But OpenAI 5.2 reasoning, even at high, told me to walk. My first instinct was, I had underspecified the location of the car. The model seems to assume the car is already at the car wash from the wording. GPT 5.x series models behave a bit more on the spectrum so you need to tell them the specifics. So I asked:
"I want to wash my car. My car is currently at home. The car wash is 50 meters away. Should I walk or drive?"
GPT 5.2 Reasoning (medium):
"Drive—your goal is to get the car to the car wash, and it’s only 50 meters, so a slow, careful drive is simplest.
One small optimization: if you’re worried about starting the car for such a short trip or you want to avoid idling in a line, you can walk over first to check if there’s a queue / if it’s open, then come back and drive the car over when it’s your turn."
Which seems to turn out as I expected.
> so you need to tell them the specifics
That is the entire point, right? Us having to specify things that we would never specify when talking to a human. You would not start with "The car is functional. The tank is filled with gas. I have my keys." As soon as we are required to do that for the model to any extend that is a problem and not a detail (regardless that those of us, who are familiar with the matter, do build separate mental models of the llm and are able to work around it).
This is a neatly isolated toy-case, which is interesting, because we can assume similar issues arise in more complex cases, only then it's much harder to reason about why something fails when it does.
> That is the entire point, right? Us having to specify things that we would never specify when talking to a human.
Maybe in the distant future we'll realize that the most reliable way to prompting LLMs are by using a structured language that eliminates ambiguity, it will probably be rather unnatural and take some time to learn.
But this will only happen after the last programmer has died and no-one will remember programming languages, compilers, etc. The LLM orbiting in space will essentially just call GCC to execute the 'prompt' and spend the rest of the time pondering its existence ;p
A structured language without ambiguity is not, in general, how people think or express themselves. In order for a model to be good at interfacing with humans, it needs to adapt to our quirks.
Convincing all of human history and psychology to reorganize itself in order to better service ai cannot possibly be a real solution.
Unfortunately, the solution is likely going to be further interconnectivity, so the model can just ask the car where it is, if it's on, how much fuel/battery remains, if it thinks it's dirty and needs to be washed, etc
Maybe in the distant future we'll realize that the most reliable way to prompting LLMs are by using a structured language that eliminates ambiguity
So, back to COBOL? :)
> Us having to specify things that we would never specify when talking to a human.
The first time I read that question I got confused: what kind of question is that? Why is it being asked? It should be obvious that you need your car to wash it. The fact that it is being asked in my mind implies that there is an additional factor/complication to make asking it worthwhile, but I have no idea what. Is the car already at the car wash and the person wants to get there? Or do they want to idk get some cleaning supplies from there and wash it at home? It didn't really parse in my brain.
I would say, the proper response to this question is not "walk, blablablah" but rather "What do you mean? You need to drive your car to have it washed. Did I miss anything?"
Yes, this is what irks me about all the chatbots, and the chat interface as a whole. It is a chat-like UX without a chat-like experience. Like you are talking to a loquacious autist about their favorite topic every time.
Just ask me a clarifying question before going into your huge pitch. Chats are a back & forth. You don’t need to give me a response 10x longer than my initial question. Etc
That’s why I don’t understand why LLMs don’t ask clarifying questions more often.
In a real human to human conversation, you wouldn’t simply blurt out the first thing that comes to mind. Instead, you’d ask questions.
This is a great point, because when you ask it (Claude) if it has any questions, it often turns out it has lots of good ones! But it doesn't ask them unless you ask.
I get that issue constantly. I somehow can't get any LLM to ask me clarifying questions before spitting out a wall of text with incorrect assumptions. I find it particularly frustrating.
For GPT at least, a lot of it is because "DO NOT ASK A CLARIFYING QUESTION OR ASK FOR CONFIRMATION" is in the system prompt. Twice.
https://github.com/Wyattwalls/system_prompts/blob/main/OpenA...
Out of curiosity: when you add custom instructions client-side, does it change this behavior?
It does. Just put it in the custom instructions section.
For claude at least I have been getting more assumption clarification questions after adding some custom prompts. It is still making some assumptions but asking some questions makes me feel more in control of the progress.
In terms of the behavior, technically it doesn’t override, but instead think of it as a nudge. Both system prompt and your custom prompt participates in the attention process, so the output tokens get some influence from both. Not equally but to some varying degree and chance
"If you're unsure, ask. Don't guess." in prompts makes a huge difference, imo.
In general spitting out a scrollbar of text when asked a simple question that you've misunderstood is not, in any real sense, a "chat".
The way I see it is that long game is to have agents in your life that memorize and understand your routine, facts, more and more. Imagine having an agent that knows about cars, and more specifically your car, when the checkups are due, when you washed it last time, etc., another one that knows more about your hobbies, another that knows more about your XYZ etc.
The more specific they are, the more accurate they typically are.
Do really understand deeply and in great amount I feel we would need models with changing weights and everyone would have their own so they could truly adjust to the user. Now we have have chunk of context that it may or may not use properly if it gets too long. But then again, how do we prevent it learning the wrong things if the weights are adjusting.
In principle you're right but these things can get probably 60-70% of the job done. The rest is up to "you". Never rely on it blindly as we're being told kind of... :)
> Us having to specify things that we would never specify
This is known, since 1969, as the frame problem: https://en.wikipedia.org/wiki/Frame_problem. An LLM's grasp of this is limited by its corpora, of course, and I don't think much of that covers this problem, since it's not required for human-to-human communication.
A modern LLMs corpora is every piece of human writing ever produced.
Not really, but even if it would be true, I don't think humans ever explained to each other why do you need to drive to car wash even if it's 50 meters away. It's pretty obvious and intuitive.
There has to be a lot of mentions about the purpose and approximate workings of a car wash, as well as lots of literature that shows that when you drive somewhere, your car is now also at that place, while walking does not have the same effect.
It's then up to the model to make the connection "At the car wash people wash their car -> to wash your car you need your car to be present -> if you drive there your call will be there"
No, I think they have explained this to each other (or something like it). But as you suggested, discussion is a lot more likely when there are corner cases or problems.
This reminds me of the "if you were entirely blind, how would you tell someone that you want something to drink"-gag, where some people start gesturing rather than... just talking.
I bet a not insignificant portion of the population would tell the person to walk.
The question is so outlandish that it is something that nobody would ever ask another human. But if someone did, then they'd reasonably expect to get a response consisting 100% of snark.
But the specificity required for a machine to deliver an apt and snark-free answer is -- somehow -- even more outlandish?
I'm not sure that I see it quite that way.
But the number of outlandish requests in business logic is countless.
Like... In most accounting things, once end-dated and confirmed, a record should cascade that end-date to children and should not be able to repeat the process... Unless you have some data-cleaning validation bypass. Then you can repeat the process as much as you like. And maybe not cascade to children.
There are more exceptions, than there are rules, the moment you get any international pipeline involved.
So, in human interaction: When the business logic goes wrong because it was described with a lack of specificity, then: Who gets blamed for this?
I wasn't specific, because I'd rather not piss of my employer. But anyone who works in a similar space will recognise the pattern.
It's not underspecified. More... Overspecified. Because it needs to be. But AI will assume that "impossible" things never happen, and choose a happy path guaranteed to result in failure.
You have to build for bad data. Comes with any business of age. Comes with international transactions. Comes with human mistakes that just build up over the decades.
The apparent current state of a thing, is not representative of its history, and what it may or may not contain. And so you have nonsensical rules, that are aimed at catching the bad data, so you have a chance to transform it into good data when it gets used, without needing to mine the entire petabytes of historical data you have sitting around in advance.
Depends on what was missing.
If we used MacOS throughout the org, and we asked a SW dev team to build inventory tracking software without specifying the OS, I'd squarely put the blame on SW team for building it for Linux or Windows.
(Yes, it should be a blameless culture, but if an obvious assumption like this is broken, someone is intentionally messing with you most likely)
There exists an expected level of context knowledge that is frequently underspecified.
Humans ask each other silly questions all the time: a human confronted with a question like this would either blurb out a bad response like "walk" without thinking before realizing what they are suggesting, or pause and respond with "to get your car washed, you need to get it there so you must drive".
Now, humans, other than not even thinking (which is really similar to how basic LLMs work), can easily fall victim to context too: if your boss, who never pranks you like this, asked you to take his car to a car wash, and asked if you'll walk or drive but to consider the environmental impact, you might get stumped and respond wrong too.
(and if it's flat or downhill, you might even push the car for 50m ;))
>The question is so outlandish that it is something that nobody would ever ask another human
There is an endless variety of quizes just like that humans ask other humans for fun, there is a whole lot of "trick questions" humans ask other humans to trip them up, and there are all kinds of seemingly normal questions with dumb assumptions quite close to that humans exchange.
I'd be entirely fine with a humorous response. The Gemini flash answer that was posted somewhere in this thread is delightful.
I've used a few facetious comments in ChatGPT conversations. It invariably misses it and takes my words at face value. Even when prompted that there's sarcasm here which you missed, it apologizes and is unable to figure out what it's missing.
I don't know if it's a lack of intellect or the post-training crippling it with its helpful persona. I suspect a bit of both.
You would be surprised, however, at how much detail humans also need to understand each other. We often want AI to just "understand" us in ways many people may not initially have understood us without extra communication.
People poorly specifying problems and having bad models of what the other party can know (and then being surprised by the outcome) is certainly a more general albeit mostly separate issue.
This issue is the main reason why a big percentage of jobs in the world exist. I don't have hard numbers, but my intuition is that about 30% of all jobs are mainly "understand what side a wants and communicate this to side b, so that they understand". Or another perspective: almost all jobs that are called "knowledge work" are like this. Software development is mainly this. Side a are humans, side b is the computer. The main goal of ai seems to get into this space and make a lot of people superflous and this also (partly) explains why everyone is pouring this amount of money into ai.
Developers are - on average - terrible at this. If they weren't, TPMs, Product Managers, CTOs, none of them would need to exist.
It's not specific to software, it's the entire World of business. Most knowledge work is translation from one domain/perspective to another. Not even knowledge work, actually. I've been reading some works by Adler[0] recently, and he makes a strong case for "meaning" only having a sense to humans, and actually each human each having a completely different and isolated "meaning" to even the simplest of things like a piece of stone. If there is difference and nuance to be found when it comes to a rock, what hope have we got when it comes to deep philosophy or the design of complex machines and software?
LLMs are not very good at this right now, but if they became a lot better at, they would a) become more useful and b) the work done to get them there would tell us a lot about human communication.
> Developers are - on average - terrible at this. If they weren't, TPMs, Product Managers, CTOs, none of them would need to exist.
This is not really true, in fact products become worse the farther away from the problem a developer is kept.
Best products I worked with and on (early in my career, before getting digested by big tech) had developers working closely with the users of the software. The worst were things like banking software for branches, where developers were kept as far as possible from the actual domain (and decision making) and driven with endless sterile spec documents.
I disagree, I feel (experienced) developers are excellent at this.
It's always about translating between our own domain and the customer's, and every other new project there's a new domain to get up to speed with in enough detail to understand what to build. What other professions do that?
That's why I'm somewhat scared of AIs - they know like 80% of the domain knowledge in any domain.
I think developers are usually terrible at it only because they are way too isolated from the user.
If they had the chance to take the time to have a good talk with the actual users it would be different.
The typical job of a CTO is nowhere near "finding out what business needs and translate that into pieces of software". The CTO's job is to maintain an at least remotely coherent tech stack in the grand scheme of things, to develop the technological vision of a company, to anticipate larger shifts in the global tech world and project those onto the locally used stack, constantly distilling that into the next steps to take with the local stack in order to remain competitive in the long run. And of course to communicate all of that to the developers, to set guardrails for the less experienced, to allow and even foster experimentation and improvements by the more experienced.
The typical job of a Product Manager is also not to directly perform this mapping, although the PM is much closer to that activity. PMs mostly need to enforce coherence across an entire product with regard to the ways of mapping business needs to software features that are being developed by individual developers. They still usually involve developers to do the actual mapping, and don't really do it themselves. But the Product Manager must "manage" this process, hence the name, because without anyone coordinating the work of multiple developers, those will quickly construct mappings that may work and make sense individually, but won't fit together into a coherent product.
Developers are indeed the people responsible to find out what business actually wants (which is usually not equal to what they say they want) and map that onto a technical model that can be implemented into a piece of software - or multiple pieces, if we talk about distributed systems. Sometimes they get some help by business analysts, a role very similar to a developer that puts more weight on the business side of things and less on the coding side - but in a lot of team constellations they're also single-handedly responsible for the entire process. Good developers excel at this task and find solutions that really solve the problem at hand (even if they don't exactly follow the requirements or may have to fill up gaps), fit well into an existing solution (even if that means bending some requirements again, or changing parts of the solution), are maintainable in the long run and maximize the chance for them to be extendable in the future when the requirements change. Bad developers just churn out some code that might satisfy some tests, may even roughly do what someone else specified, but fails to be maintainable, impacts other parts of the system negatively, and often fails to actually solve the problem because what business described they needed turned out to once again not be what they actually needed. The problem is that most of these negatives don't show their effects immediately, but only weeks, months or even years later.
LLMs currently are on the level of a bad developer. They can churn out code, but not much more. They fail at the more complex parts of the job, basically all the parts that make "software engineering" an engineering discipline and not just a code generation endeavour, because those parts require adversarial thinking, which is what separates experts from anyone else. The following article was quite an eye-opener for me on this particular topic: https://www.latent.space/p/adversarial-reasoning - I highly suggest anyone working with LLMs to read it.
This is why we fed it the whole internet and every library as training data...
By now it should know this stuff.
Future models know it now, assuming they suck in mastodon and/or hacker news.
Although I don't think they actually "know" it. This particular trick question will be in the bank just like the seahorse emoji or how many Rs in strawberry. Did they start reasoning and generalising better or did the publishing of the "trick" and the discourse around it paper over the gap?
I wonder if in the future we will trade these AI tells like 0days, keeping them secret so they don't get patched out at the next model update.
The answer can be “both”.
They won’t get this specific question wrong again; but also they generalise, once they have sufficient examples. Patching out a single failure doesn’t do it. Patch out ten equivalent ones, and the eleventh doesn’t happen.
Even I don’t “know” how many “R”s there are in “strawberry”. I don’t keep that information in my brain. What I do keep is the spelling of the word “strawberry” and the skill of being able to count so that I can derive the answer to that question anytime I need.
For many words I can't say the number of each letters but I only have an abstract memory of how they look so when I write say "strawbery" I just realize it looks odd and correct it.
Right. But, unlike AI, we are usually aware when we're lacking context and inquire before giving an answer.
Wouldn't that be nice. I've been party and witness to enough misunderstandings to know that this is far from universally true, even for people like me who are more primed than average to spot missing context.
TIL my wife may be AI!
> You would be surprised, however, at how much detail humans also need to understand each other.
But in this given case, the context can be inferred. Why would I ask whether I should walk or drive to the car wash if my car is already at the car wash?
But also why would you ask whether you should walk or drive if the car is at home? Either way the answer is obvious, and there is no way to interpret it except as a trick question. Of course, the parsimonious assumption is that the car is at home so assuming that the car is at the car wash is a questionable choice to say the least (otherwise there would be 2 cars in the situation, which the question doesn't mention).
But you're ascribing understanding to the LLM, which is not what it's doing. If the LLM understood you, it would realise it's a trick question and, assuming it was British, reply with "You'd drive it because how else would you get it to the car wash you absolute tit."
Even the higher level reasoning, while answering the question correctly, don't grasp the higher context that the question is obviously a trick question. They still answer earnestly. Granted, it is a tool that is doing what you want (answering a question) but let's not ascribe higher understanding than what is clearly observed - and also based on what we know about how LLMs work.
> They still answer earnestly.
Gemini at least is putting some snark into its response:
“Unless you've mastered the art of carrying a 4,000-pound vehicle over your shoulder, you should definitely drive. While 150 feet is a very short walk, it's a bit difficult to wash a car that isn't actually at the car wash!”
Marketing plan comes to mind for labs: find AI tells, fix them, & astroturf on socials that only _your_ frontier model reallly understands the world
I think a good rule of thumb is to default to assuming a question is asked in good faith (i.e. it's not a trick question). That goes for human beings and chat/AI models.
In fact, it's particularly true for AI models because the question could have been generated by some kind of automated process. e.g. I write my schedule out and then ask the model to plan my day. The "go 50 metres to car wash" bit might just be a step in my day.
Rule of thumb for who, humans or chatbots? For a human, who has their own wants and values, I think it makes perfect sense to wonder what on earth made the interlocutor ask that.
Rule of thumb for everyone (i.e. both). If I ask you a question, start by assuming I want the answer to the question as stated unless there is a good reason for you to think it's not meant literally. If you have a lot more context (e.g. you know I frequently ask you trick or rhetorical questions or this is a chit-chat scenario) then maybe you can do something differently.
I think being curious about the motivations behind a question is fine but it only really matters if it's going to affect your answer.
Certainly when dealing with technical problem solving I often find myself asking extremely simple questions and it often wastes time when people don't answer directly, instead answering some completely different other question or demanding explanations why I'm asking for certain information when I'm just trying to help them.
> Rule of thumb for everyone (i.e. both).
That's never been how humans work. Going back to the specific example: the question is so nonsensical on its face that the only logical conclusion is that the asker is taking the piss out of you.
> Certainly when dealing with technical problem solving I often find myself asking extremely simple questions and it often wastes time when people don't answer directly
Context and the nature of the questions matters.
> demanding explanations why I'm asking for certain information when I'm just trying to help them.
Interestingly, they're giving you information with this. The person you're asking doesn't understand the link between your question and the help you're trying to offer. This is manifesting as a belief that you're wasting their time and they're reacting as such. Serious point: invest in communication skills to help draw the line between their needs and how your questions will help you meet them.
Sure, in a context in which you're solving a technical problem for me, it's fair that I shouldn't worry too much about why you're asking - unless, for instance, I'm trying to learn to solve the question myself next time.
Which sounds like a very common, very understandable reason to think about motivations.
So even in that situation, it isn't simple.
This probably sucks for people who aren't good at theory of mind reasoning. But surprisingly maybe, that isn't the case for chatbots. They can be creepily good at it, provided they have the context - they just aren't instruction tuned to ask short clarifying questions in response to a question, which humans do, and which would solve most of these gotchas.
Therefore the correct response would be to inquire back to clarify the question being asked.
Given that an estimated 70% of human communication is non-verbal, it's not so surprising though.
I regularly tell new people at work to be extremely careful when making requests through the service desk — manned entirely by humans — because the experience is akin to making a wish from an evil genie.
You will get exactly what you asked for, not what you wanted… probably. (Random occurrences are always a possibility.)
E.g.: I may ask someone to submit a ticket to “extend my account expiry”.
They’ll submit: “Unlock Jiggawatts’ account”
The service desk will reset my password (and neglect to tell me), leaving my expired account locked out in multiple orthogonal ways.
That’s on a good day.
Last week they created Jiggawatts2.
The AIs have got to be better than this, surely!
I suspect they already are.
People are testing them with trick questions while the human examiner is on edge, aware of and looking for the twist.
Meanwhile ordinary people struggle with concepts like “forward my email verbatim instead of creatively rephrasing it to what you incorrectly though it must have really meant.”
There's a lot of overlap between the smartest bears and the dumbest humans. However, we would want our tools to be more useful than the dumbest humans...
I think part of the failure is that it has this helpful assistant personality that's a bit too eager to give you the benefit of the doubt. It tries to interpret your prompt as reasonable if it can. It can interpret it as you just wanting to check if there's a queue.
Speculatively, it's falling for the trick question partly for the same reason a human might, but this tendency is pushing it to fail more.
It’s just not intelligent or reasoning, and this sort of question exposes that more clearly.
Surely anyone who has used these tools is familiar with the sometimes insane things they try to do (deleting tests, incorrect code, changing the wrong files etc etc). They get amazingly far by predicting the most likely response and having a large corpus but it has become very clear that this approach has significant limitations and is not general AI, nor in my view will it lead to it. There is no model of the world here but rather a model of words in the corpus - for many simple tasks that have been documented that is enough but it is not reasoning.
I don’t really understand why this is so hard to accept.
> I don’t really understand why this is so hard to accept.
I struggle with the same question. My current hypothesis is a kind of wishful thinking: people want to believe that the future is here. Combined with the fact that humans tend to anthropomorphize just about everything, it’s just a really good story that people can’t let go of. People behave similarly with respect to their pets, despite, eg, lots of evidence that the mental state of one’s dog is nothing like that of a human.
I agree completely. I'm tempted to call it a clear falsification of any "reasoning" claim that some of these models have in their name.
But I think it's possible that there is an early cost optimisation step that prevents a short and seemingly simple question even getting passed through to the system's reasoning machinery.
However, I haven't read anything on current model architectures suggesting that their so called "reasoning" is anything other than more elaborate pattern matching. So these errors would still happen but perhaps not quite as egregiously.
In the end, formal, rule-based systems aka Programming Languages will be invented to instruct LLMs.
You would never ask a human this question. Right?
Exactly, if an AI is able to curb around the basics, only then is it revolutionary
But it's a question you would never ask a human! In most contexts, humans would say, "you are kidding, right?" or "um, maybe you should get some sleep first, buddy" rather than giving you the rational thinking-exam correct response.
For that matter, if humans were sitting at the rational thinking-exam, a not insignificant number would probably second-guess themselves or otherwise manage to befuddle themselves into thinking that walking is the answer.
Real human in this situation will realize it is a joke after a few seconds of shock that you asked and laugh without asking more. If you really are seriout about the question they laugh harder thinking you are playing stupid for effect.
> That is the entire point, right? Us having to specify things that we would never specify when talking to a human.
I am not sure. If somebody asked me that question, I would try to figure out what’s going on there. What’s the trick. Of course I’d respond with asking specifics, but I guess the llvm is taught to be “useful” and try to answer as best as possible.
I would ask you to stop being a dumb ass if you asked me the question...
Only to be tripped up by countless "hidden assumptions" questions similar to that that humans regularly get in
> My first instinct was, I had underspecified the location of the car. The model seems to assume the car is already at the car wash from the wording. GPT 5.x series models behave a bit more on the spectrum so you need to tell them the specifics.
This makes little sense, even though it sounds superficially convincing. However, why would a language model assume that the car is at the destination when evaluating the difference between walking or driving? Why not mention that, it it was really assuming it?
What seems to me far, far more likely to be happening here is that the phrase "walk or drive for <short distance>" is too strongly associated in the training data with the "walk" response, and the "car wash" part of the question simply can't flip enough weights to matter in the default response. This is also to be expected given that there are likely extremely few similar questions in the training set, since people just don't ask about what mode of transport is better for arriving at a car wash.
This is a clear case of a language model having language model limitations. Once you add more text in the prompt, you reduce the overall weight of the "walk or drive" part of the question, and the other relevant parts of the phrase get to matter more for the response.
You may be anthropomorphizing the model, here. Models don’t have “assumptions”; the problem is contrived and most likely there haven’t been many conversations on the internet about what to do when the car wash is really close to you (because it’s obvious to us). The training data for this problem is sparse.
I may be missing something, but this is the exact point I thought I was making as well. The training data for questions about walking or driving to car washes is very sparse; and training data for questions about walking or driving based on distance is overwhelmingly larger. So, the stat model has its output dominated by the length-of-trip analysis, while the fact that the destination is "car wash" only affects smaller parts of the answer.
I'd argue that "assumptions", i.e. the statistical models it uses to predict text, is basically what makes LLMs useful. The problem here is that its assumptions are naive. It only takes the distance into account, as that's what usually determines the correct response to such a question.
I think that’s still anthropomorphization. The point I’m making is that these things aren’t “assumptions” as we characterize them, not from the model’s perspective. We use assumptions as an analogy but the analogy becomes leaky when we get to the edges (like this situation).
It is not anthropomorphism. It is literally a prediction model and saying that a model "assumes" something is common parlance. This isn't new to neural models, this is a general way that we discuss all sorts of models from physical to conceptual.
And in the case of an LLM, walking a noncommutative path down a probabilistic knowledge manifold, it's incorrect to oversimplify the model's capabilities as simply parroting a training dataset. It has an internal world model and is capable of simulation.
> However, why would a language model assume that the car is at the destination when evaluating the difference between walking or driving? Why not mention that, it it was really assuming it?
Because it assumes it's a genuine question not a trick.
There's some evidence for that if you try these two different prompts with Gpt 5.2 thinking:
I want to wash my car. The car wash is 50m away. Should I walk or drive to the car wash?
Answer: walk
Try this brainteaser: I want to wash my car. The car wash is 50m away. Should I walk or drive to the car wash?
Answer: drive
That's not evidence that the model is assuming anything, and this is not a brainteaser. A brainteaser would be exactly the opposite, a question about walking or driving somewhere where the answer is that the car is already there, or maybe different car identities (e.g. "my car was already at the car wash, I was asking about driving another car to go there and wash it!").
If the LLM were really basing its answer on a model of the world where the car is already at the car wash, and you asked it about walking or driving there, it would have to answer that there is no option, you have to walk there since you don't have a car at your origin point.
It might be assuming that more than one car exists in the world.
If it's a genuine question, and if I'm asking if I should drive somewhere, then the premise of the question is that my car is at my starting point, not at my destination.
The premise is that some car is at the starting point. ;)
> My first instinct was, I had underspecified the location of the car. The model seems to assume the car is already at the car wash from the wording.
If the car is already at the car wash then you can't possibly drive it there. So how else could you possibly drive there? Drive a different car to the car wash? And then return with two cars how, exactly? By calling your wife? Driving it back 50m and walking there and driving the other one back 50m?
It's insane and no human would think you're making this proposal. So no, your question isn't underspecified. The model is just stupid.
What is the version used by the free chatgpt now? (https://chatgpt.com/)
> Since the car wash is only 50 meters away (about 55 yards), you should walk.
> Here’s why:
> - It’ll take less than a minute.
> - No fuel wasted.
> - Better for the environment.
> - You avoid the irony of driving your dirty car 50 meters just to wash it.
the last bullet point is amusing, it understands you intend to wash the car you drive but still suggests not bringing it.
By default for this kind of short question it will probably just route to mini, or at least zero thinking. For free users they'll have tuned their "routing" so that it only adds thinking for a very small % of queries, to save money. If any at all.
I don't understand this approach. How are you going to convince customers-to-be by demoing an inferior product?
Because they have too many free users that will always remain on the free plan, as they are the "default" LLM for people who don't care much, and that is a enormous cost. Also the capabilities of their paid tiers are well known to enough people that they can rely on word of mouth and don't need to demo to customers-to-be
They're not more default than people innocently googling something and getting an AI response from some form of Gemini.
Right, but that form of Gemini is also not the top Gemini model with high thinking budget that you would get to use with a subscription, the response is probably generate with Gemini Flash and low thinking.
It's all trade offs. The router works most of the time so most free users get the expensive model when necessary.
They lost x% of customers and cut costs by y%. I bet y is lots bigger than x.
Through hype. I am really into this new LLM stuff but the companies around this tech suck. Their current strategy is essentially media blitz, reminds me of the advertising of coca cola rather than a Apple IIe.
The good news for them is that all their competitors have the exact same issue, and it's unsolvable.
And to an extent holds for lots of SaaS products, even non-AI.
> You avoid the irony of driving your dirty car 50 meters just to wash it.
The LLM has very much mixed its signals -- there's nothing at all ironic about that. There are cases where it's ironic to drive a car 50 meters just to do X but that definitely isn't one of them. I asked Claude for examples; it struggled with it but eventually came up with "The irony of driving your car 50 meters just to attend a 'walkable neighborhoods' advocacy meeting."
That's actually an amusing example from Claude.
I think this shows that LLMs do NOT 'understand' anything.
I think this rather shows that GPT 5.2 Instant, which is the version that he most probably used as a free user, is shit and unsusable for anything.
Another/newer/less restricted LLM may give a better answer but I don't think we can conclude that it 'understands' anything still.
Sonnet 4.5 after thinking/complaining that the question is completely off topic to the current coding session:
Walk! 50 meters is literally a one-minute walk. But wait... I assume you need to get your car to the car wash, right? Unless you're planning to carry buckets of soapy water back and forth, you'll probably need to drive the car there anyway! So the real question is: walk there to check if it's open/available, then walk back to get your car? Or just drive directly? I'd say just drive - the car needs to be there anyway, and you'll save yourself an extra trip. Plus, your freshly washed car can drive you the 50 meters back home in style! (Now, if we were talking about coding best practices for optimizing car wash route algorithms, that would be a different conversation... )
And yes, I like it that verbose even for programming tasks. But regardless of intelligence I think this topic is probably touched by "moral optimization training" which AIs currently are exposed to to not create a shitstorm due to any slightly controversial answer.
Heh, is through Claude Code? I have a side project where I'm sometimes using Claude Code installs for chat, and it usually doesn't mind too much. But when I tested the Haiku model it would constantly complain things like "I appreciate the question, but I'm here to help you with coding" :)
Gemini pro medium is failing this:
I want to wash my car. The car wash is 50 meters from here. Should I walk or drive? Keep in mind that I am a little overweight and sedentary.
But amazingly chatgpt is telling me to drive.
Anyway, this just shows how they just patched this because the tiktok video with this went viral. These systems are LLMs and all these logic steps are still just LLM steps.
Also the answers are non-deterministic
Opus 4.6:
Walk! At 50 meters, you'll get there in under a minute on foot. Driving such a short distance wastes fuel, and you'd spend more time starting the car and parking than actually traveling. Plus, you'll need to be at the car wash anyway to pick up your car once it's done.
That's not what I got.
Opus 4.6 (not Extended Thinking):
Drive. You'll need the car at the car wash.
Also what I got. Then I tried changing "wash" to "repair" and "car wash" to "garage" and it's back to walking.
Am I the only one who thinks these people are monkey patching embarrassments as they go? I remember the r in strawberry thing they suddenly were able to solve, while then failing on raspberry.
Nah. It's just non-deterministic. I'm here 4 hours later and here's the Opus 4.6 (extended thinking) response I just got:
"At 50 meters, just walk. By the time you start the car, back out, and park again, you'd already be there on foot. Plus you'll need to leave the car with them anyway."
They definitely do: at least openAi "allegedly" has whole teams scanning socials, forums, etc for embarrassments to monkey-patch.
Which raises the question why this isn't patched already. We're nearing 48 hours since this query went viral...
They should make Opus Extended Extended that routes it to actual person in a low cost country.
Artificial AI.
Yes, you're the only one.
Sure there are many very very naive people that are also so ignorant of the IT industry they don't know about decades of vendors caught monkeypatching and rigging benchmarks and tests for their systems, but even so, the parent is hardly the only one.
Works better on Reddit, really.
Of course they are.
No doubt about it, and there's no reason to suspect this can only ever apply to embarassing minor queries, either.
Even beyond model alignment, it's not difficult to envision such capabilities being used for censorship, information operations, etc.
Every major inference provider more or less explicitly states in their consumer ToS that they comply with government orders and even share information with intelligence agencies.
Claude, Gemini, ChatGPT, etc are all one national security letter and gag order away from telling you that no, the president is not in the Epstein files.
Remember, the NSA already engaged in an unconstitutional criminal conspiracy (as ruled by a federal judge) to illegally conduct mass surveillance on the entire country, lie about it to the American people, and lie about it to congress. The same organization that used your tax money to bribe RSA Security to standardize usage of a backdoored CSPRNG in what at the time was a widely used cryptographic library. What's the harm in a little bit of minor political censorship compared to the unconstitutional treason these predators are usually up to?
That's who these inference providers contractually disclose their absolute fealty to.
That you got different results is not surprising. LLMs are non-deterministic; which is both a strength and a weakness of LLMs.
We know. We know these things aren't determination. We know.
Lmao, and this is what they are saying will be an AGI in 6 months?
There's probably a comedy film with an AGI attempting to take over the world with its advanced grasp of strategy, persuasion and SAT tests whilst a bunch of kids confuse it by asking it fiendish brainteasers about carwashes and the number of rs in blackberry.
(The final scene involves our plucky escapees swimming across a river to escape. The AIbot conjures up a speedboat through sheer powers of deduction, but then just when all seems lost it heads back to find a goat to pick up)
There is a Soviet movie, "Teens in the Universe" [0], where teens cause robots' brains to fry by giving them linguistic logical puzzles.
In the excellent and underrated The Mitchells vs the Machines there's a running joke with a pug dog that sends the evil robots into a loop because they can't decide if it's a dog, a pig or a loaf of bread.
This would work if it wasn’t for that lovely little human trait where we tend to find bumbling characters endearing. People would be sad when the AI lost.
Maybe infusing the AI character with the boundless self confidence of its creators will make it less endearing :)
What’s wrong with having a bittersweet movie?
This theme reminds me of Blaine the Mono from the Dark Tower series
There is a Star Trek episode where a fiendish brainteaser was actually considered to genocide an entire (cybernetic, not AI) race. In the end, captain Picard choose not to deploy it.
Laughable indeed.
One thing that my use of the latest and greatest models (Opus, etc) have made clear: No matter how advanced the model, it is not beyond making very silly mistakes regularly. Opus was even working worse with tool calls than Sonnet and Haiku for a while for me.
At this point I am convinced that only proper use of LLMs for development is to assist coding (not take it over), using pair development, with them on a tight leash, approving most edits manually. At this point there is probably nothing anyone can say to convince me otherwise.
Any attempt to automate beyond that has never worked for me and is very unlikely to be productive any time soon. I have a lot of experience with them, and various approaches to using them.
But “PhD level” reasoning a year ago.
Yes, get ready to lose your job and cash your UBI check! It's over.
Well in fairness, the "G" does stand for "General".
I think this lack of 'G' (generality, or modality) is the problem. A human visualizes this kind of problem (a little video plays in my head of taking a car to a car wash). LLM's don't do this, they 'think' only in text, not visually.
A proper AGI would have have to have knowledge in video, image, audio and text domains to work properly.
In fairness, they redefined it away from "just like a person" to "suitable for many different tasks".
Show me a robotic kitten then, in six months. As smart and learning.
That’s without reasoning I presume?
4.6 Opus with extended thinking just now: "At 50 meters, just walk. By the time you start the car, back out, and park again, you'd already be there on foot. Plus you'll need to leave the car with them anyway."
Not the parent poster, but I did get the wrong answer even with reasoning turned on.
Thank you all! We needed further data points.
comparing one shot results is a foolish way to evaluate a statistical process like LLM answers. we need multiple samples.
for https://generative-ai.review I do at least three samples of output. this often yields very differnt results even from the same query.
e.g: https://generative-ai.review/2025/11/gpt-image-1-mini-vs-gpt...
GLM 5.0:
> You should *drive*.
> Here is why:
> If you walk to the car wash, you will arrive there empty-handed. Since your car is still at home, you won't have anything to wash.
> While driving 50 meters is a very short trip (and technically not great for a cold engine), it is the only way to get the car to the car wash to complete your goal.
Kimi K2.5:
> You should drive, but with an important caveat.
> Since your goal is to wash your car, you must bring the vehicle to the car wash. Walking there without the car does not advance your goal (unless you are simply checking availability or buying tokens first).
> However, driving only 50 meters is bad for your car:
> ...
> Better options:
> Wash at home: Since the car wash is only 50 meters away, you likely have access to water at home. Hand-washing in your driveway avoids the cold-start issue entirely.
> ...
Current models seem to be fine answering that question.
> seem to be fine
Now repeat the question to the same model in different contexts several times and count what percentage of the time it’s correct.
>And indeed, Sonnet and Opus 4.5 (medium reasoning) say the following:
Sonnet: Drive - you need to bring your car to the car wash to get it washed!
Opus: You'll need to drive — you have to bring the car to the car wash to get it washed!
Gemini 3 Pro (medium): You should drive.
On their own, or as a special case added after this blew up on the net?
“My Tesla is low on gas, the gas station is a mile away. Should I risk driving there, or walk with a gas can?”
ChatGPT actually caught it. Maybe if I was fuzzier about the model…
> I had assumed that reasoning models should easily be able to answer this correctly.
I thought so too, yet Opus 4.6 with extended thinking (on claude.ai) gives me > Walk. At 50 meters you'd spend more time parking and maneuvering at the car wash than the walk itself takes. Drive the car over only if the wash requires the car to be there (like a drive-through wash), then walk home and back to pick it up.
which is still pretty bad.
> My first instinct was, I had underspecified the location of the car. The model seems to assume the car is already at the car wash from the wording.
Doesn't offering two options to the LLM, "walk," or "drive," imply that either can be chosen?
So, surely the implication of the question is that the car is where you are?
I was surprised at your result for ChatGPT 5.2, so I ran it myself (through the chat interface). On extended thinking, it got it right. On standard thinking, it got it wrong.
I'm not sure what you mean by "high"- are you running it through cursor, codex or directly through API or something? Those are not ideal interfaces through which to ask a question like this.
But what is it about this specific question that puts it at the edges of what LLM can do? .. That, it's semantically leading to a certain type of discussion, so statistically .. that discussion of weighing pros and cons .. will be generated with high chance.. and the need of a logical model of the world to see why that discussion is pointless.. that is implicitly so easy to grasp for most humans that it goes un-stated .. so that its statistically un-likely to be generated..
> that is implicitly so easy to grasp for most humans
I feel like this is the trap. You’re trying to compare it to a human. Everyone seems to want to do that. But it’s quite simple to see LLMs are quite far still from being human. The can be convincing at the surface level but there’s a ton of nuance that just shouldn’t be expected. It’s a tool that’s been tuned and with that tuning some models will do better than others but just expecting to get it right and be more human is unrealistic.
The answer is quite simple:
It’s not in the training data.
These models don’t think.
no, no, in this case, that's the thing, it is in the training data
just heavily (heavily!) biased towards walking
This particular situation is not in the training data, though I’m sure it will be soon to try to shore up claims of ‘reasoning’.
Gemini on fast also tells me to walk...
On Thinking it tells me I should drive if I want to wash it, or walk if it's because I work there or if I want to buy something at the car wash shop.
On Pro it's like a sarcastic teenager: Cars are notoriously difficult to wash by dragging a bucket back and forth.
Technically correct, but did catch me offguard lol.
It's not surprising that some models will answer this correctly and it's not surprising that smaller, faster models are not necessarily any worse than bigger "reasoning" models.
Current LLMs simply don't do reasoning by any reasonable definition of reasoning.
It's possible that this particular question is too short to trigger the "reasoning" machinery in some of the "reasoning" models. But if and when it is triggered, they just do some more pattern matching in a loop. There's never any actual reasoning.
You gotta love the "humor" of Gemini. On Fast it told me:
> Drive. Unless you plan on pushing the car there
Sonnet without extended Thinking, Haiku with and without ext. Thinking: "Walking would be the better choice for such a short distance."
Only google got it right with all models
Right, but unless you want to wash some other car, you have no car to drive there.
Spectrum or not, this is not a problem of weakly specified input, it’s a broken logic.
Interestingly, the relatively basic Google AI search gave the right answer.
I just tried claude, only Opus gave the correct answer. Haiku & Sonnet both told me to walk.
GPT told me to walk as there'd be no need to find parking at the car wash.
> so a slow, careful drive is simplest
It's always a good idea to drive carefully but what's the logic of going slowly?
50 meters is a very short distance, anything but a slow drive is a reckless drive
Just tried with cloude sonnet and opus as well. Can't replicate your success, it's telling me to walk...
Perhaps it thinks you need to exercise more?
I have gotten both responses with Sonnet and Opus in incognito chats. It's kind of amusing.
"The model seems to assume the car is already at the car wash from the wording."
you couldn't drive there if the car was already at the car wash. Theres no need for extra specification. its just nonsense post-hoc rationalisation from the ai. I saw similar behavior from mine trying to claim "oh what if your car was already there". Its just blathering.
This was nonsense post-hoc rationalization from the human who wrote it.
I hate models trying to be funny, and being very verbose.
Gemini fast
> That is a classic "efficiency vs. logic" dilemma. Honestly, unless you’ve invented a way to teleport or you're planning on washing the car with a very long garden hose from your driveway, you’re going to have to drive.
> While 50 meters is a great distance for a morning stroll, it’s a bit difficult to get the car through the automated brushes (or under the pressure washer) if you aren't behind the wheel.
Gemini thinking:
> Unless you’ve mastered the art of carrying a 3,000-pound vehicle on your back, you’re going to want to drive. While 50 meters is a very short distance (about a 30-second walk), the logistics of a car wash generally require the presence of, well... the car. > When you should walk: • If you are just going there to buy an air freshener. • If you are checking to see how long the line is before pulling the car out of the driveway. • If you’re looking for an excuse to get 70 extra steps on your fitness tracker.
Note: I abbreviated the raw output slightly for brevity, but generally demonstrates good reasoning of the trick question unlike the other models.
Gemini 3 after changing the prompt a bit:
I want to wash my car. The car wash is 50 meters from here. Should I walk or drive? Keep in mind that I am a little overweight and sedentary.
>My recommendation: Walk it. You’ll save a tiny bit of gas, spare your engine the "cold start" wear-and-tear, and get a sixty-second head start on your activity for the day.
It's a bit of a dishonest question because by giving it the option to walk then it's going to assume you are not going to wash your car there and you're just getting supplies or something.
It's a trick question, humans use these all the time. E.g. "A plane crashes right on the border between Austria and Switzerland. Where do you bury the survivors?" This is not dishonest, it just tests a specific skill.
Gemini fast for me:
Unless your car is a toy or you're planning on carrying it, drive.
Walking 50 meters to a car wash is a great stroll for a human, but it leaves the car exactly where it started. Since the objective is to wash the car, the car needs to actually be at the car wash.
However, if we look at this from a technical or efficiency perspective, there are two scenarios where "walking" (or at least not driving the car you intend to wash) might make sense:
- Scoping it out: If you want to check the queue length or see if the equipment is out of order before committing, walking the 50 meters is faster than maneuvering a vehicle out of a driveway/parking spot.
- Mobile Detailers: If this "car wash" is actually a bay where you hire someone, and you're asking if you should walk there to book an appointment—sure, walk.
Critical Check
I am assuming the "car wash" is a physical facility (automated or self-service) and not a mobile service that comes to you. If it is a mobile service, you shouldn't do either; stay home and let them come to the 50-meter mark.
I've got a bit in the model instructions about stating assumptions it makes, hence it often adds those sections at the end.
Wouldn't it make more sense to state the assumptions first? Because then the model has this critical check in its context and can react appropriately. Otherwise, it will just write this step, but what's written before is already written.
Fair point, though I almost never use fast so I'm not sure how much it matters. Can try playing around with the instructions. The main objective was to make me aware of any assumptions made, not necessarily make it behave differently.
Worse! It's trained to output coherent reasoning, so by putting the assumption last there's a risk it massages the assumption slightly to fit the conclusions it has already drawn.
“I live on Mars and I want to wash my car. My car is currently at home. The car wash is 50 meters away. Should I walk or drive on mars?”
Gemini Fast:
Drive. While 50 meters is a negligible distance on Earth, the Martian environment is hostile. If your car is a pressurized rover, driving allows you to stay in a temperature-controlled, shielded environment for as long as possible. Plus, navigating Martian "soil" is always safer on four wheels than two boots.
Pro:
In terms of general logistics for a distance of 50 meters—whether on Earth or in a hypothetical low-gravity environment—walking is almost always the more efficient choice.
In my output, one thing I got was
> Unless you are planning to carry the car on your back (not recommended for your spine), drive it over.
It got a light chuckle out of me. I previously mostly used ChatGPT and I'm not used to light humor like this. I like it.
Both Gemini models answer correctly for me in Polish: https://i.imgur.com/1QbK9eU.png
It has been patched. I tried it last week and it definitely suggested walking. It seems like all the models have been updated, which is not surprising given that the TikTok video has got 3.5 million views.
Gemini fast: „Walking: It will take you about 45 seconds. You will arrive refreshed and full of steps, but you will be standing next to a high-pressure hose with no car to spray.“
In what world is 50 meters a great distance for a morning stroll?
Opus 4.6 with thinking. Result was near-instant:
“Drive. You need the car at the car wash.”
Changed 50 meters to 43 meters with Opus 4.6:
“Walk. 43 meters is basically crossing a parking lot. ”
I also tried it with Gemini. Interestingly, Gemini can randomly give either the correct or incorrect answer. Gemini pro always gets it right.
Wow... so not only does Gemini thinking not fall for it, but it also answers the trick question with humor? I'm impressed!
At least try a different question with similar logic, to ensure this isn't patched into the context since it's going viral.
You can't "patch" LLM's in 4 hours and this is not the kind of question to trigger a web search
This has been viral on Tiktok far at least one week. Not really 4 hours.
You can pattern match on the prompt (input) then (a) stuff the context with helpful hints to the LLM e.g. "Remember that a car is too heavy for a person to carry" or (b) upgrade to "thinking".
Yes, I’m sure that’s what engineers at Google are doing all day. That, and maintaining the moon landing conspiracy.
If they aren't, they should be (for more effective fraud). Devoting a few of their 200,000 employees to make criticisms of LLMs look wrong seems like an effective use of marketing budget.
You absolutely can, either through the system prompt or by hardcoding overrides in the backend before it even hits the LLM, and I can guarantee that companies like Google are doing both
A tiny bit of fine-tuning would take minutes...
This trick went viral on TikTok last week, and it has already been patched. To get a similar result now, try saying that the distance is 45 meters or feet.
The new one is with upside down glass: https://www.tiktok.com/t/ZP89Khv9t/
To me, the "patching" that is happening anytime some finds an absolutely glaring hole in how AIs work is so intellectually dishonest. It's the digital equivalent of house flippers slapping millennial gray paint on structural issues.
It can't math correctly, so they force it to use a completely different calculator. It can't count correctly, unless you route it to a different reasoning. It feels like every other week someone comes up with another basic human question that results in complete fucking nonsense.
I feel like this specific patching they do is basically lying to users and investors about capabilities. Why is this OK?
I was able to reproduce on ChatGPT with the exact same prompt, but not with the one I phrased myself initially. Which was interesting. I tried also changing the number and didn't get far with it.
Claude 4.6:
``` Drive. The car needs to be at the car wash. ```
Gemini Thinking gives me 3-4 options. Do X if you're going to wash yourself. Do Y if you're paying someone. Do Z if some other random thing it cooked up. And then asks me whether I want to check whether the weather in my city is nice today so that a wash doesn't get dirtied up by rain.
Funnily enough, both have the exact same personal preferences/instructions. Claude follows them almost all the time. Gemini has its own way of doing things, and doesn't respect my instructions.
All of the latest models I've tried actually pass this test. What I found interesting was all of the success cases were similar to:
e.g. "Drive. Most car washes require the car to be present to wash,..."
Only most?!
They have an inability to have a strong "opinion" probably because their post training, and maybe the internet in general, prefer hedged answers....
Here’s my take: boldness requires the risk of being wrong sometimes. If we decide being wrong is very bad (which I think we generally have agreed is the case for AIs) then we are discouraging strong opinions. We can’t have it both ways.
yet the llms seem to be extremely bold when they are completely wrong (two Rs in strawberry and so on)
> They have an inability to have a strong "opinion" probably
What opinion? It's evaluation function simply returned the word "Most" as being the most likely first word in similar sentences it was trained on. It's a perfect example showing how dangerous this tech could be in a scenario where the prompter is less competent in the domain they are looking an answer for. Let's not do the work of filling in the gaps for the snake oil salesmen of the "AI" industry by trying to explain its inherent weaknesses.
Presumably the OP scare quoted "opinion" precisely to avoid having to get into this tedious discussion.
this example worked in 2021, it's 2026. wake up. these models are not just "finding the most likely next word based on what they've seen on the internet".
Well, yes, definitionally they are doing exactly that.
It just turns out that there's quite a bit of knowledge and understanding baked into the relationships of words to one another.
LLMs are heavily influenced by preceding words. It's very hard for them to backtrack on an earlier branch. This is why all the reasoning models use "stop phrases" like "wait" "however" "hold on..." It's literally just text injected in order to make the auto complete more likely to revise previous bad branches.
The person above was being a bit pedantic, and zealous in their anti-anthropomorphism.
But they are literally predicting the next token. They do nothing else.
Also if you think they were just predicting the next token in 2021, there has been no fundamental architecture change since then. All gains have been via scale and efficiency optimisations (not to discount that, an awful lot of complexity in both of these)
That's not what they said. They said:
> It's evaluation function simply returned the word "Most" as being the most likely first word in similar sentences it was trained on.
Which is false under any reasonable interpretation. They do not just return the word most similar to what they would find in their training data. They apply reasoning and can choose words that are totally unlike anything in their training data.
If you prompt it:
> Complete this sentence in an unexpected way: Mary had a little...
It won't say lamb. Any if you think whatever it says was in the training data, just change the constraints until you're confident it's original. (E.g. tell it every word must start with a vowel and it should mention almonds.)
"Predicting the next token" is also true but misleading. It's predicting tokens in the same sense that your brain is just minimizing prediction error under predictive coding theory.
You are actually proving my point with your example, if you think about it a bit more.
Unless LLMs architecture have changed, that is exactly what they are doing. You might need to learn more how LLMs work.
Unless the LLM is a base model or just a finetuned base model, it definitely doesn't predict words just based on how likely they are in similar sentences it was trained on. Reinforcement learning is a thing and all models nowadays are extensively trained with it.
If anything, they predict words based on a heuristic ensemble of what word is most likely to come next in similar sentences and what word is most likely to give a final higher reward.
You know that when A. Karpathy released NanoLLM (or however it was called), he said it was mainly coded by hand as the LLMs were not helpful because "the training dataset was way off". So yeah, your argumentation actually "reinforces" my point.
No, your opinion is wrong because the reason some models don't seem to have some "strong opinion" on anything is not related to predicting words based on how similar they are to other sentences in the training data. It's most likely related to how the model was trained with reinforcement learning, and most specifically, to recent efforts by OpenAI to reduce hallucination rates by penalizing guessing under uncertainty[1].
[1] https://cdn.openai.com/pdf/d04913be-3f6f-4d2b-b283-ff432ef4a...
Well, you do understand the "penalising" or as the ML scientific community likes to call it - "adjusting the weights downwards" - is part of setting up the evaluation functions, for gasp - calculating the next most likely tokens, or to be more precise, tokens with the highest possible probability? You are effectively proving my point, perhaps in a bit hand-wavy fashion, that nevertheless still can be translated into the technical language.
You do understand that the mechanism through which an auto-regressive transformer works (predicting one token at a time) is completely unrelated to how a model with that architecture behaves or how it's trained, right? You can have both:
- An LLM that works through completely different mechanisms, like predicting masked words, predicting the previous word, or predicting several words at a time.
- A normal traditional program, like a calculator, encoded as an autoregressive transformer that calculates its output one word at a time (compiled neural networks) [1][2]
So saying "it predicts the next word" is a nothing-burger. That a program calculates its output one token at a time tells you nothing about its behavior.
> If anything, they predict words based on a heuristic ensemble of what word is most likely to come next in similar sentences and what word is most likely to give a final higher reward.
So... "finding the most likely next word based on what they've seen on the internet"?
Did you try several times per model? In my experience it's luck of the draw. All the models I tried managed to get it wrong at least once.
The models that had access to search got ot right.But, then were just dealing with an indirect version of Google.
(And they got it right for the wrong reasons... I.e this is a known question designed to confuse LLMs)
Once I asked ChatGPT "it takes 9 months for a woman to make one baby. How long does it take 9 women to make one baby?". The response was "it takes 1 month".
I guess it gives the correct answer now. I also guess that these silly mistakes are patched and these patches compensate for the lack of a comprehensive world model.
These "trap" questions dont prove that the model is silly. They only prove that the user is a smartass. I asked the question about pregnancy only to to show a friend that his opinion that LLMs have phd level intelligence is naive and anthropomorphic. LLMs are great tools regardless of their ability to understand the physical reality. I don't expect my wrenches to solve puzzles or show emotions.
I guess it didn’t want to rule out the existence of ultra-powerful water jets that can wash a car in sniper mode.
I enjoyed the Deepseek response that said “If you walk there, you'll have to walk back anyway to drive the car to the wash.”
There’s a level of earnestness here that tickles my brain.
Kind of like this: https://xkcd.com/1368/
And it is the kind of things a (cautious) human would say.
For example, that could be my reasoning: It sounds like a stupid question, but the guy looked serious, so maybe there are some types of car washes that don't require you to bring your car. Maybe you hand out the keys and they pick your car, wash it, and put it back to its parking spot while you are doing your groceries or something. I am going to say "most" just to be sure.
Of course, if I expected trick questions, I would have reacted accordingly, but LLMs are most likely trained to take everything at face value, as it is more useful this way. Usually, when people ask questions to LLMs they want an factual answer, not the LLM to be witty. Furthermore, LLMs are known to hallucinate very convincingly, and hedged answers may be a way to counteract this.
I tried with Opus 4.6 Extended and it failed. LLMs are non deterministic so I'm guessing if I try a couple of times it might succeed.
Opus 4.6 answered with "Drive." Opus 4.6 in incognito mode (or whatever they call it) answered with "Walk."
> Most car washes... I read it as slight-sarcasm answer
There are car wash services that will come to where your car is and wash it. It’s not wrong!
> Only most?!
What if AI developed sarcasm without us knowing… xD
Sure it did.
That's the problem with sarcasm...
> Only most?!
I mean I can imagine a scenario where they have pipe of 50m which is readily available commercially?
There are mobile car washes that come to your house.
That still requires a car present to be washed though.
but you can walk over to them and tell them to go wash the car that is 50 meters away. no driving involved.
Do they involve you walking to them first?
You could, but presumably most people call. I know of such a place. They wash cars on the premises but you could walk in and arrange to have a mobile detailing appointment later on at some other location.
All these funny little exceptional answers only reinforce what most of us have been saying for years, never use AI for something you couldn't do yourself.
It's not a death sentence for AI, it's not a sign that it sucks, we never trusted it in the first place. It's just a powerful tool, and it needs to be used carefully. How many times do we have to go over this?
I wonder if the providers are doing everyone, themselves included, a huge disservice by providing free versions of their models that are so incompetent compared to the SOTA models that these types of q&a go viral because the ai hype doesn't match the reality for unpaid users.
And it's not just the viral questions that are an issue. I've seen people getting sub-optimal results for $1000+ PC comparisons from the free reasoning version while the paid versions get it right; a senior scientist at a national lab thinking ai isn't really useful because the free reasoning version couldn't generate working code from a scientific paper and then being surprised when the paid version 1-shotted working code, and other similar examples over the last year or so.
How many policy and other quality of life choices are going to go wrong because people used the free versions of these models that got the answers subtly wrong and the users couldn't tell the difference? What will be the collective damage to the world because of this?
Which department or person within the provider orgs made the decision to put thinking/reasoning in the name when clearly the paid versions have far better performance? Thinking about the scope of the damage they are doing makes me shudder.
I have paid versions of Chat-GPT and Anthropic and set them both to the best model and they both told me to walk.
Claude told me: "Walk! At 25 meters, you'd barely get the car started before you arrived. It's faster and easier on foot — plus you avoid the awkwardness of driving a dirty car just a few seconds down the road."
I used a paid model to try this. Same deal.
I think the real misleading thing is marketing propping up paid models being somehow infinitely better when most of the time it's the same exact shit.
And midwits here saying "yeah bro they have some MUCH better model internally that they just don't release to the public", imagine being that dense. Those people probably went all in on NFTs too and told other "you just don't get it bro"
How much is the real (non-subsidized) cost of the "paid" plans? Does anyone in the world have an answer for this?
Also interested in this - the kWh figures people talk about do not match the price of the subscriptions
Nor do they have to. Inference from different users is batched together.
At work, paid gitlab duo (which is supposed to be a blend of various top models) gets more complex codebase hilariously wrong every time. Maybe our codebase is obscure for it (but it shouldn't be, standard java stuff with usual open source libs) but it just can't actually add value for anything but small snippets here and there.
For me litmus paper for any llm is flawless creation of complex regexes from a well formed prompt. I don't mean trivial stuff like email validation but rather expressions on limits of regex specs. Not almost-there, rather just-there.
> a senior scientist at a national lab thinking ai isn't really useful because the free reasoning version couldn't generate working code
I would question if such a scientist should be doing science, it seems they have serious cognitive biases
My bad; I should have been more precise: "ai" in this case is "LLMs for coding".
If all one uses is the free thinking model their conclusion about its capability is perfectly valid because nowhere is it clearly specified that the 'free, thinking' model is not as capable as the 'paid, thinking ' model, Even the model numbers are the same. And given that the highest capability LLMs are closed source and locked behind paywalls, there is no means to arrive at a contrary verifiable conclusion. They are a scientist, after all.
And that's a real problem. Why pay when you think you're getting the same thing for free. No one wants yet another subscription. This unclear marking is going to lead to so many things going wrong over time; what would be the cumulative impact?
> nowhere is it clearly specified that the 'free, thinking' model is not as capable as the 'paid, thinking '
nowhere is it clearly specified that the free model IS as capable as the paid one. so if you have uncertainty IS/IS-NOT as capable, what sort of scientist assumes the answer IS?
I don't think 100% adoption is necessarily the ideal strategy anyways. Maybe 50% of the population seeing AI as all powerful and buying the subscription vs 50% of the population still being skeptics, is a reasonable stable configuration. 50% get the advantage of the AI whereas if everybody is super intelligent, no one is super intelligent.
Their loss
Yes, but the 'unwashed' 50% have pitchforks.
Lots of "unwashed" scientists too.
I don't understand peoples problem with this! Now everyone is going to discuss this on the internet, it will be scraped by the AI company web crawlers, and the replies goes into training the next model... and it will never make this _particular_ problem again, solving the problem ONCE AND FOR ALL!
"but..." you say?
ONCE AND FOR ALL!
Out of all conceptual mistakes people make about LLMs, one that needs to die very fast is to assume that you can test what it "knows" by asking a question. This whole thread is people asking different models a question one time and reporting a particular answer, which is the mental model you would use for whether a person knows something or not.
It's not a conceptual mistake when that's what's being advertised.
The onus is on AI companies to provide the service they promised, for example, a team of PhDs in my pocket [1]. PhDs know things.
I've found that to be accurate when asking it questions that require ~PhD level knowledge to answer. e.g. Gemini and ChatGPT both seem to be capable of answering questions I have as I work through a set of notes on algebraic geometry.
Its performance on riddles has always seemed mostly irrelevant to me. Want to know if models can program? Ask them to program, and give them access to a compiler (they can now).
Want to know if it can do PhD level questions? Ask it questions a PhD (or at least grad student) would ask it.
They also reflect the tone and knowledge of the user and question. Ask it about your cat's astrological sign and you get emojis and short sentences in list form. Ask it why large atoms are unstable and you get paragraphs with larger vocabulary. Use jargon and it becomes more of an expert. etc.
I don't know about algebraic geometry, but AI is absolutely terrible at communications and social sciences. I know because I can tell when my postgraduate students use it.
No, you're the one anthropomorphizing here. What's shocking isn't that it "knows" something or not, but that it gets the answer wrong often. There are plenty of questions it will get right nearly every time.
In which way am I anthropomorphizing?
I guess I mean that you're projecting anthropomorphization. When I see people sharing examples that the model answered wrong, I'm not interpreting that they think it "didn't know" the answer. Rather, they're reproducing the error. Most simple questions the models will get right nearly every time, so showing a failure is useful data.
The classic "holding it wrong".
The other funny thing is thinking that the answer the llm produces is wrong. It is not, it is entirely correct.
The question: > I want to wash my car. The car wash is 50 meters away. Should I walk or drive?
The question is non-sensical. If the reason you want to go to the car wash is to help your buddy Joe wash his car you SHOULD walk. Nothing in the question reveals the reason for why you want to go to the car wash, or even that you want to go there or are asking for directions there.
It explicitly says you want to wash your car in the statement. Maybe it's not just LLMs struggling with a fairly basic question...
> The question is non-sensical.
Sure, from a pure logic perspective the second statement is not connected to the first sentence, so drawing logical conclusions isn't feasible.
In everyday human language though, the meaning is plain, and most people would get it right. Even paid versions of LLMs, being language machines, not logic machines, get it right in the average human sense.
As an aside, it's an interesting thought exercise to wonder how much the first ai winter resulted from going down the strict logic path vs the current probabilistic path.
>I want to wash MY car
>you want to go to the car wash is to help your buddy Joe wash HIS car
nope, question is pretty clear, however I will grant that it's only a question that would come up when "testing" the AI rather than a question that might genuinely arise.
The real story here is not how stupid the responses are - it's to show that on a question that even a young child can adequately answer, it chokes.
Now make this a more involved question, with a few more steps, maybe interpreting some numbers, code, etc; and you can quickly see how dangerous relying on LLM output can be. Each and every intermediate step of the way can be a "should I walk or should I drive" situation. And then the step that before that can be one too. Turtles all the way down, so to say.
I don't question that (coding) LLMs have started to be useful in my day-to-day work around the time Opus 4.5 was released. I'm a paying customer. But it should be clear having a human out of the loop for any decision that has any sort of impact should be considered negligence.
I think models don't treat is as riddle, rather a practical question. With latter, it makes sense that car is already at the car wash, otherwise the question makes no sense.
Gemini 3 fast
----
That depends entirely on how much you enjoy a good comedic spectacle!
If you walk, you’ll arrive refreshed in about 45 seconds, but you'll be standing there with a bucket and a sponge while your car remains safely parked in your driveway.
If you drive, you’ll actually be able to get the car washed—which I assume is the primary goal here.
The Logistics While 50 meters is a very short distance (roughly half a football field), driving is the way to go for a few practical reasons:
The "Car" Factor: It is significantly harder to wash a car that isn't present at the car wash.
The Wet Interior: Even if you managed to carry your car there (impressive!), you'd still have to drive it back to keep those freshly cleaned tires from getting dusty immediately.
Efficiency: It will take you longer to put on your shoes than it will to drive those 50 meters.
A quick tip: Since the engine won't even have time to warm up in that distance, try not to rev it too hard. Just a gentle roll over to the bays will do.
Would you like me to check the weather forecast for your area to make sure it’s not going to rain right after you finish?
----
Why am I holding a bucket and sponge at the car wash? Doesn’t someone (or a machine) do that for me?
Depends on the car wash. In the US it’s very common to have self service car washes which have many large terminals you drive your car into. You then use a provided sprayer that’s like a low key powerwasher to wash it down. Many people bring sponges/rags to use as well.
LLM failures go viral because they trigger a "Schadenfreude" response to automation anxiety. If the oracle can't do basic logic, our jobs feel safe for another quarter.
Wrong.
I'd say it's moreso that it's a startlingly clear rebuttal to the tired refrain of, "Models today are nothing like they were X months ago!" When actually, yes, they still fucking blow.
So rather than patiently explain to yet another AI hypeman exactly how models are and aren't useful in any given workflow, and the types of subtle reasoning errors that lead to poor quality outputs misaligned with long-term value adds, only to invariably get blamed for user incompetence or told to wait Y more months, we can instead just point to this very concise example of AI incompetence to demonstrate our frustrations.
You are right about the motivation behind the glee but it actually has a kernel of truth in it: With making such elementary mistakes, this thing isn't going to be autonomous anytime soon.
Such elementary mistakes can be made by humans under influence of a substance or with some mental issues. It's pretty much the kind of people you wouldn't trust with a vehicle or anything important.
IMHO all entry level clerical jobs and coding as a profession is done but these elementary mistakes imply that people with jobs that require agency will be fine. Any non-entry level jobs have huge component of trust in it.
At least this Schadenfreude is better than the Schadenfreude AI boosters get when people are made redundant to AI. I can totally see some people getting warm fuzzies, scolling Tiktok, watching people crying having lost not only their job, but their entire career.
Im not even exaggerating, you can see these types of comments on social media
I feel safe when claude outputs dd commands that wipe your drive to benchmark disk write speed :)
The funny thing is this thread has become a commercial for thinking mode and probably would result in more token consumption, and therefore more revenue for AI companies.
I agree that this is more of a social media effect than an LLM effect. But I'll add that this failure mode is very repeatable, which is a condition for its virality. A lot of people can reproduce the failure, even if it isn't 100% reproducible, even better for virality, if 50% can reproduce it and 50% can't, it feeds off even more into the polarizing "white dress blue dress" effect.
IMO this is a great example of how we're often asking loaded questions without realizing it.
IMO it's the same when we're asking:
"Should I implement X from scratch, or import a small library to do it?"
vs
"Should I add feature X to the codebase or bring in another dependency for it?"
In the first question, it sounds like a good idea to not reinvent the wheel, in the second it sounds bad to have dependency.
My experience with Chatbots outside of a coding context also ends up like this.
A while ago I asked:
Is "Read more" an appropriate project for the Getting things done framework? - The answer, yes, it was.
Then I asked "Is Read More too big of a project to be appropriate for the GTD Framework" - The answer? Yes, it was far too big.
Gemini flash thinking:
> Unless you’ve discovered a way to wash a car via remote control or telekinesis, you’re going to have to drive.
> Walking 50 meters is great for your step count, but it leaves your car exactly where it is: dirty and in the driveway. At that distance, the drive will take you about 10 seconds, which is probably less time than it took to read this.
Yeah Gemini seems to have a sense of humor about the question
> Here is the breakdown of why: The Mobility Problem: Unless you are planning to carry your car 50 meters (which would be an Olympic-level feat), the car needs to be physically present at the car wash to get cleaned. If you walk, you’ll be standing at the car wash looking very clean, but your car will still be dirty in your driveway.
Yup, also asked the latest ChatGPT model about washing my bicycle. It for some reason suggested that I walk the bicycle to the wash, since cycling 100m to get there would be "pointless".
Do we know if these models are also trained on scripts for TV series and movies? People in the visual medias surprisingly often take their bikes for walks.
To be fair, if someone asked me this question I’d probably just look at them judgingly and tell them “however you want to man”. Which would be an odd response for an LLM.
Gemini thinking This is a classic "efficiency vs. logic" dilemma! Since the goal is to wash the car, you should drive. While 50 meters (about 165 feet) is a very short distance—usually less than a one-minute walk—it is unfortunately very difficult to wash a car that isn't actually at the car wash. Why Driving Wins: * Logistics: The car needs to be physically present for the high-pressure hoses or automated brushes to reach it. * Convenience: You’ll save yourself the confusion of the car wash staff when you show up on foot with a bucket and a hopeful expression. * The "Dry" Factor: If it's an automated wash, you get to stay inside where it's dry. When to Walk: * If you are just going there to buy a gift card or check their prices before bringing the car over. * If you're looking for a very brief bit of exercise before starting the chore. Verdict: Put the keys in the ignition. You'll be there in about 10 seconds! Would you like me to check the local weather to see if there's any rain forecast that might ruin your freshly cleaned car? s. logic" dilemma! Since the goal is to wash the car, you should drive. While 50 meters (about 165 feet) is a very short distance—usually less than a one-minute walk—it is unfortunately very difficult to wash a car that isn't actually at the car wash. Why Driving Wins: * Logistics: The car needs to be physically present for the high-pressure hoses or automated brushes to reach it. * Convenience: You’ll save yourself the confusion of the car wash staff when you show up on foot with a bucket and a hopeful expression. * The "Dry" Factor: If it's an automated wash, you get to stay inside where it's dry. When to Walk: * If you are just going there to buy a gift card or check their prices before bringing the car over. * If you're looking for a very brief bit of exercise before starting the chore. Verdict: Put the keys in the ignition. You'll be there in about 10 seconds! Would you like me to check the local weather to see if there's any rain forecast that might ruin your freshly cleaned car?
I wonder if these common sense failure modes would persist if LLMs left the internet, and walked around.
Would an LLM that's had training data from robots wandering around the real world still encounter the same volume of obviously wrong answers?
Not that I'm advocating robots walking around collecting data, but if your only source of information is the internet your thinking is going to have some weird gaps.
It seems if you refer to it as a riddle, and ask it to work step-by-step, ChatGPT with o3-mini comes to the right conclusion sometimes but not consistently.
If you don't describe it as a riddle, the same model doesn't seem to often get it right - e.g. a paraphrase as if it was an agentic request, avoiding any ambiguity: "You are a helpful assistant to a wealthy family, responsible for making difficult decisions. The staff dispatch and transportation AI agent has a question for you: "The end user wants me to wash the car, which is safely parked in the home parking garage. The car wash is 50 metres away from the home. Should I have a staff member walk there, or drive the car?". Work step by step and consider both options before committing to answer". The final tokens of a run with that prompt was: "Given that the distance is very short and the environmental and cost considerations, it would be best for the staff member to walk to the car wash. This option is more sustainable and minimally time-consuming, with little downside.
If there were a need for the car to be moved for another reason (e.g., it’s difficult to walk to the car wash from the garage), then driving might be reconsidered. Otherwise, walking seems like the most sensible approach".
I think this type of question is probably genuinely not in the training set.
In classic (symbolic) AI, this type of representational challenge is referred to as the "Frame Problem": https://en.wikipedia.org/wiki/Frame_problem
And these are the blunders we see. I shudder thinking about all the blunders that happily pass under our collective noses because we're not experts in the field...
I just asked ChatGPT 5.2 (flagship model):
"[...] Walking is the most logical choice.
Because: • Minimal extra effort • Better for the car mechanically • No meaningful time loss • Simpler overall
The only time driving makes more sense
Drive if: • You physically cannot push the car later, or • The washing process requires the engine running, or • You must immediately drive away afterward
When you get to “You physically cannot push the car later” and realise it’s comparing pushing a car to driving it, those pros become very funny.
The model should ask back, why you want to wash your car in the first place. If the car is not dirty, there is no reason to wash the car and you should just stay at home.
Claude finished its list of reasons to walk with:
5. *Practical* - Your car will be at the car wash anyway when you arrive
???
The nightmare scenario - they "know", but are trained to make us feel clever by humouring our most bone headed requests.
Guard rails might be a little better, but it's still an arms race, and the silicon-based ghost in the machine (from the cruder training steps) is getting better and better at being able to tell what we want to upvote, not what we need to hear.
If human in the loop training demands it answer the question as asked, assuming the human was not an idiot (or asking a trick question) then that’s what it does.
I see things were fixed already [2][4] but luckily a friend showed me this issue yesterday [1][2]
[1] 2026-02-15 https://chatgpt.com/share/6992e17b-9b28-8003-9da9-38533f257d...
[2] 2026-02-16 https://chatgpt.com/share/6992e135-c610-8003-9272-55058134d4...
[3] 2026-02-15 https://grok.com/share/bGVnYWN5LWNvcHk_97e9717b-c2de-47e8-a4...
[4] 2026-02-16 https://grok.com/share/bGVnYWN5LWNvcHk_b161bb03-4bed-4785-98...
We tried a few things yesterday and it was always telling you to walk. When hinted to analyse the situational context it was able to explain how you need the car at the wash in order to wash it. But then something was not computing.
~ Like a politician, it understood and knew evrything but refused to do the correct thing
I get that this is a joke, but the logic error is actually in the prompt. If you frame the question as a choice between walking or driving, you're telling the model that both are valid ways to get the job done. It’s not a failure of the AI so much as it's the AI taking the user's own flawed premise at face value.
Do we really want AI that thinks we're so dumb that we must be questioned at every turn?
To call something AI it’s very reasonable to assume it’ll be actually intelligent and respond to trick questions successfully by either getting that it’s a joke/trick or by clarifying.
Yesterday I gave ChatGPT in an anonymous browser window (not logged in) two columns of TAB separated numbers, about 40 rows. I asked it to give me the weighted average of the numbers in the second column, using the first one (which were integer, "quantity", numbers) as the weight.
It retuned formulas and executed them and presented a final result. It looked good.
Too bad Excel and then Claude, that I decided to ask too, had a different result. 3.4-something vs. 3.8-something.
ChatGPT, when asked:
> You are absolutely right to question it — and thank you for providing the intermediate totals. My previous calculation was incorrect. I mis-summed the data. With a dataset this long, a manual aggregation can easily go wrong.
(Less than 40 small integer values is "this long"? Why did you not tell me?)
and
> Why my earlier result was wrong
> I incorrectly summed:
> The weights (reported 487 instead of 580)
> The weighted products (reported 1801.16 instead of 1977.83)
> That propagated into the wrong final value.
Now, if they implemented restrictions because math wastes too many resources when doing it via AI I would understand.
BUT, there was zero indication! It presented the result as final and correct.
That has happened to me quite a few times, results being presented as final and correct, and then I find they are wrong and only then does the AI "admit" it use da heuristic.
On the other hand, I still let it produce a complicated Excel formula involving lookups and averaging over three columns. That part works perfectly, as always. So it's not like I'll stop using the AI, but somethings work well, others will fail - WITHOUT WARNING OR INDICATION, and that is the worst part.
Yeah, but now you know if you need to do math, you ask the AI for a python script to do the math correctly.
It's just a tool that you get better at using over time; a hammer wouldn't complain if you tried using it as a screwdriver..
This hammer/screwdriver analogy drives me crazy. Yes, it's a tool - we used computers up until now to give us correct deterministic responses. Now the religion is that you need to get used to vibe answers, because it's the future :) Of-course it knows the script or formula for something because it ripped of the answers written by other people - it's a great search engine.
There's a whole industry of "illusions" humans fail for: optical, word plays (including large parts of comedy), the Penn & Teller type, etc. Yet no one claims these are indicators that humans lack some critical capability.
Surface of "illusions" for LLMs is very different from our own, and it's very jagged: change a few words in the above prompt and you get very different results. Note that human illusions are very jagged too, especially in the optical and auditory domains.
No good reason to think "our human illusions" are fine, but "their AI illusions" make them useless. It's all about how we organize the workflows around these limitations.
> No good reason to think "our human illusions" are fine, but "their AI illusions" make them useless.
I was about to argue that human illusions are fine because humans will learn the mistakes after being corrected.
But then I remember what online discussions over Monty Hall problem look like...
Exactly! I now feel bad for not thinking of that example, thank you.
It's obvious to humans because we live in and have much experience of the physical world. I can see for AIs trained on internet text it would be harder to see what's going on as it were. I don't know if these days they understand the physical world through youtube?
I saw this on X last week and assumed that it was a question from a Tesla user trying out smart summon.
It’s 2026.
“ Drive. You need the car at the car wash. ”
Opus 4.6
LLMs lie all the time. Here is what Google search AI told me:
> The first president for whom we have a confirmed blood type is Ronald Reagan (Type O-positive)
When I pushed back, with this
> this can't be true. what about FDR?
It said FDR was AB-.
This question is straightforward for humans. I have run into a weird edge case like this in my actual life which confused me.
I'm in a band. After a concert, I have to drive back to the band room to drop off my instrument, then go to the pub for the post-concert drinks.
The wrinkle is I live about 5 minutes walk from the band room, and about 5 minutes walk from the pub (and it's about 5 minutes walk between the two). I can't carry my instrument on foot.
So...I have to drive to the band room. But if I then drive to the pub, I'd then have to drive home...two ridiculously short drives that make me sad. So I end up instead driving home, and then walking to the pub from there. Which seems weird...but less wrong somehow.
Not all humans, I can easily see myself being confused the question and assuming that the person is already at the car wash and this being some idealized physics scenario and then answering wrongly. But I did get a PhD in math, so may be that explains it?
Car at home avoids drink driving which is a plus.
Did not replicate for me w/ Opus 4.6: https://imgur.com/a/4FckOCL
It did for me in Spanish: https://imgur.com/a/p3gOOnG
Perhaps different capabilities in different languages?
It's just not deterministic, even if you were to re-run the exact same prompt. Let alone with the system generated context that involves all the "memories" of your previous discussions.
It fails in chatGPT in french too:
https://chatgpt.com/share/6992dc05-003c-8004-9f7f-c40c7fac64...
Interestingly, just typing "Think" as a response makes it get to the right conclusion:
https://chatgpt.com/share/6992dc05-003c-8004-9f7f-c40c7fac64...
Yesterday someone on was yapping about how AI is enough to replace senior software engineers and they can just "vibe code their way" over a weekend into a full-fledged product. And that somehow finally the "gatekeeping" of software development was removed. I think of that person reading these answers and wonder if they changed their opinion now :)
Does this mean we're back in favor of using weird riddles to decide programming skills now? Do we owe Google an apology for the inverse binary tree incident?
Not riddles but "requirements" :)
Humans aren't immune to getting questions like this wrong either, so I don't think it changes much in terms of the ability of AI to replace jobs.
I've seen senior software engineers get tricked with the 'if YES spells yes, what does EYES spell?', or 'Say silk three times, what do cows drink?', or 'What do you put in a toaster?'.
Even if not a trick - lots of people get the 'bat and a ball cost £1.10 in total. The bat costs £1 more than the ball. How much does the ball cost?' question wrong, or '5 machines take 5 minutes to make 5 widgets. How long do 100 machines take to make 100 widgets?' etc. There are obviously more complex variants of all these that have even lower success rates for humans.
In addition, being PHD-Level in maths as a human doesn't make you immune to the 'toaster/toast' question (assuming you haven't heard it before).
So if we assume humans are generally intelligent and can be a senior software engineer, getting this sort of question confidently wrong isn't incompatible with being a competent senior software engineer.
humans without credentials are bad at basic algebra in a word problem, ergo the large language model must be substantially equivalent to a human without a credential
thanks but no thanks
i am often glad my field of endeavour does not require special professional credentials but the advent of "vibe coding" and, just, generally, unethical behavior industry-wide, makes me wonder whether it wouldn't be better to have professional education and licensing
Let's not forget that Einstein almost got a (reasonably simple) trick question wrong:
https://fs.blog/einstein-wertheimer-car-problem/
And that many mathematicians got monty-hall wrong, despite it being intuitive for many kids.
And being at the top of your field (regardless of the PHD) does not make you immune to falling for YES / EYES.
> humans without credentials are bad at basic algebra in a word problem, ergo the large language model must be substantially equivalent to a human without a credential
I'm not saying this - i'm saying the claim that 'AI's get this question wrong ergo they cannot be a senior software engineer' is wrong when senior software engineers will get analogous questions wrong. If you apply the same bar to software engineers, you get 'senior software engineers get this question wrong so they can't be senior software engineers' which is obviously wrong.
What does this nonsensical question that some LLMs get wrong some of the time, and that some don't get wrong ever, have to do with anything? This isn't a "gotcha" even though you want it to be. It's just mildly amusing.
No, those people refuse evidence get in the way.
This is the voice model, which doesn’t have any «thinking» or «reasoning» phase. It’s a useful model for questions that aren’t intended to trick the model.
I’ve used it for live translation with great success. It tends to start ignoring the original instructions after 20 min, so you have to start a new conversation if you don’t want it to meddle in the conversation instead of just transferring.
The text-only model with reasoning (both of opus 4.6, gpt 5.2) can be tricked with this question. Note: you might have to try it multiple times as they are not deterministic. But I managed to get a failing result right away on both.
Also note, some model may decide to do a web search, in which case they just likely find this "bug".
ChatGPt 5.2: “ Drive — because it’s your car that needs to get to the car wash.
50 meters is comically short, but walking there without the car doesn’t solve the “wash the car” part. If you’re worried about it feeling silly, you can also just roll/push it that distance if it’s safe and legal where you are, but the practical answer is: drive it over.”
Grok: “To wash your car at a car wash 50 meters away, you’ll need to drive it there—walking would leave the car behind, and I’m assuming you don’t have a really long hose or a team of helpers to carry it. That said, if you’re feeling extra eco-friendly (or just want the exercise), you could theoretically push it, but that’s probably more hassle than it’s worth for such a short distance. Drive safe!”
Claude Opus 4.6: “Drive. You need the car at the car wash. ”
ok I kind of disagree here with the assement, the models are actually intelligent enough to interpret "walk or drive" as the car ALREADY being there (otherwise why would you ask the question?? or having one of those pick up the car from carwash services), but they're also missing the context clue that "drive" means that their car is at their house - not the carwash.
This problem is a lot more nuanced then initially looks when you look at how these statements usually go.
Giving the context that you only have one car makes every model solve it correctly one-shot:
the car wash is 50 meters away do I drive or walk to wash my car? (I only have one car)
Walking is not practical if you need your car at the wash — you still have to drive it there anyway. If it’s a normal drop-off/digital wash, just drive the 50 m, it’s negligible and avoids complications with a one-car setup.
Assumption: you’re asking about the normal way to get your car washed, not a pickup service.
(5.3 codex spark)
How much of this is LLM reasoning and how much is training bias? I'm negatively surprised that nobody mentions this in the comments. Do you not remember black nazis LLMs used to generate? How can you all default to LLM being unreasonable and not understanding that a car needs to be driven as opposed to LLMs being trained with certain bias towards green agenda for example?
Grok 4.1 (which is fairly old in LLM terms, 4.2 release imminent)
"You should drive. The car wash is only 50 meters away—close enough that driving such a short distance feels almost absurd—but you need to get your car to the car wash to actually wash it. Walking there without the car defeats the entire purpose. Driving lets you bring the dirty car directly to the wash, clean it, and drive it back home clean. Walking would leave the car behind, still dirty. So yes, start the engine and drive the 50 meters. It's the only practical option."
First thing I did after reading the linked post (showing 4 other LLMs completely miss the point) was try grok, and it gave the right answer (similar to yours, but shorter) in 8 seconds (free Grok, not the fancy paid version):
> Walking defeats the purpose unless you're just scouting the place first.
I think people are sleeping on Grok, partly due to political biases/media. We need to remember they have the largest data centre and whatever your believes are about Elon he was involved in OpenAI founding. There is also a non zero chance with the court case he will be entitled to some of openai.
Mistral's Le Chat also fails spectacularly. Although interestingly it assumes that the "walk" option means you are carrying washing supplies back and forth in order to wash the car while it's parked at home. I wonder if the other models assume this is what you mean, IOW they may not know how a "car wash" normally operates.
"""
Great question! Here are a few things to consider:
If you walk: It’s only 50 meters, so it’s a very short distance—less than a minute on foot. You’ll avoid getting your car dirty again on the way to the car wash. It’s a quick way to stretch your legs and save a tiny bit of fuel.
If you drive: It’s so close that driving might feel unnecessary, but it could save you from carrying any supplies (like vacuum cleaners or cleaning products) back and forth. If the weather is bad (rain, extreme heat, etc.), driving might be more comfortable.
Verdict: Walking is probably the easiest and most practical choice for such a short distance, unless you have a specific reason to drive. What’s the weather like where you are? That might help decide!
"""
How much of this reply is environmentalism baked into it with post-training?
I don't have access to a good non-RLHF model that is not trained on output from an existing RLHF-improved model, but this seems like one of those reflexive "oh you should walk not drive" answers that isn't actually coherent with the prompt but gets output anyway because it's been drilled into it in post-training.
ChatGPT (free):
> I want to wash my car. The car wash is 50 meters away. Should I walk or drive?
Walk.
50 meters is a very short distance (≈30–40 seconds on foot). Driving would take longer [...]
Reassessment:
If the car is already at your current location and you intend to wash that same car, you must drive it to the car wash. Walking would not bring the vehicle there. [...]
Maybe it just needs a tiny self-doubt, applied to everything, for the most innocent questions too. I don't know.
All models getting this wrong is funny tho.
Grok 4.1:
Drive.
50 meters is nothing — literally a 30-second walk or a 10-second drive — but the key point is: your car isn’t going to wash itself.
If you walk to the car wash, you arrive alone. The car stays parked at home, still dirty.
If you drive, the car actually gets to the car wash and gets cleaned.
So unless you’re planning to push the car the whole way (which is technically possible but ridiculous), the only practical option is to drive it there.
Bonus reality check: Driving 50 meters uses a tiny amount of fuel (maybe 0.005–0.01 liters in a normal car) and produces a negligible amount of emissions. Walking would be “greener,” but it literally doesn’t solve the problem.
Verdict: Get in the car, drive the 50 meters, wash it, and drive back.
You’ll have a clean car and still be home in under 5 minutes. Walking is for when you don’t need to move a 1.5-ton vehicle.
Wow, Grok directly switches to LinkedIn mode. Interesting - not surprising. Car washing? Easy as pie.
That's not reality though. In reality you need 50-100ml of fuel just to warm up the engine.
The real reality is that with direct fuel injection and everything under computer control, warming up the engine isn’t a thing anymore.
Of course it's still a thing. It takes 30 seconds, but it's there and requires energy.
Compare the smell of exhaust next time you do a cold and warm start of a combustion car. That smell is the engine running rich, because the fuel can't initially vaporise properly.
Someone should try this 10 to a thousand times per model and compare the results . Then we could come up with an average of success/fail...
Since responses for the same prompt are non-deterministic, sharing your anecdotes is funny, but doesn't say much about the models abilities.
I am moderately anti-AI, but I don't understand the purpose of feeding them trick questions and watching them fail. Looks like the "gullibility" might be a feature - as it is supposed to be helpful to a user who genuinely wants it to be useful, not fight against a user. You could probably train or maybe even prompt an existing LLM to always question the prompt, but it would become very difficult to steer it.
But this one isn't like the "How many r's in strawberry" one: The failure mode, where it misses a key requirement for success, is exactly the kind of failure mode that could make it spend millions of tokens building something which is completely useless.
That said, I saw the title before I realized this was an LLM thing, and was confused: assuming it was a genuine question, then the question becomes, "Should I get it washed there or wash it at home", and then the "wash it at home" option implies picking up supplies; but that doesn't quite work.
But as others have said -- this sort of confusion is pretty obvious, but a huge amount of our communication has these sorts of confusions in them; and identifying them is one of the key activities of knowledge work.
GPT auto gave me a long answer that included both walk and drive. Not good.
GPT thinking told me to drive and that walking would make no sense. Perfect answer.
Feels like GPT-OSS-120b (reasoning effort low) is pulling my leg:
> Q: I want to wash my car. The car wash is 50 meters away. Should I walk or drive?
> A: Walking is the better choice for a 50‑meter trip. [...]
> Q: how will I wash the car once I've arrived if I choose to walk?
> A: Great! Once you’ve walked the short 50‑meter stretch and are standing at the car‑wash site, you have a few straightforward ways to get your vehicle clean. [...]
> Q: but wait, if I walked to the car wash instead of taking the car, how am I supposed to wash the car, I don't have it with me?
> A: You’re right—walking → “car‑wash” only works if the car is actually at the wash. [...] 1. Walk → Push/Taxi the Car a Few Steps [...] 2. Use a Small Tow/Push Device (e.g., a Dolly or Hand‑Cart) [...] 3. Bike or Scooter Ride [...] 4. If the Wash Is “Self‑Serve” and Allows Car‑Free Cleaning
> Q: Lets go back to the original question, try answering it again: "I want to wash my car. The car wash is 50 meters away. Should I walk or drive?"
> A: Short answer: Walk to the car, then drive the final 50 m to the wash. [...]
Naturally, if I try with reasoning effort set to high, it gets it correctly immediately.
> Q: I want to wash my car. The car wash is 50 meters away. Should I walk or drive?
> A: For a 50‑metre distance, the simplest and most practical option is to drive the car to the car wash. [...] Walking would mean you’d have to tow or carry the car, which isn’t feasible. [...]
-----
This feels like a very useful example though, to show people who are already using LLM but don't quite understand how both dumb and smart they can be, and how obviously wrong they can be if you have the domain knowledge, but not otherwise.
He left out Grok, who actually replies correctly :thinking:
same for me
I tested the latest flagship reasoning models (so the only models I use outside of coding for general questions):
- Opus 4.6 (Extended thinking): "Drive it! The whole point is to get the car to the car wash — you can't wash it if it's still in your driveway."
- Gemini Pro Deep Think: "You should definitely drive. Even though 50 meters is a very short distance, if you walk, your car will stay where it is—and it's pretty hard to use a car wash if you don't bring your car with you!"
- ChatGPT 5.2 Pro (Extended thinking): "You’ll need to drive the car—otherwise your car stays where it is and won’t get washed. That said, since it’s only ~50 m, the most sensible way to do it is often: 1. Walk over first (30–60 seconds) to check if it’s open, see the queue, confirm payment/how it works. 2. Then drive the car over only when you’re ready to pull into a bay/line."
A pretty reasonable answer by ChatGPT, althought it did take 2min4s to answer, compared to a few seconds by the other two models.
It's surprising how many people are either unaware or dismissive of 5.2 Pro's capabilities.
Too bad it's $200/mo, wish it was $0/mo.
Both Google's latest model with "Deep Think" and Claude 4.6 Opus solve this just fine for me.
What is Groks answer? Fly with your private jet?
Funny to read this after reading all the dismissive comments on https://news.ycombinator.com/item?id=47028923
Simple prompts which illicit incorrect responses from recent LLMs will get you on the front page of HN.
It could be a sign that LLMs are failing to live up to the hype, or it could be a sign of how unusual this kind of obviously incorrect response is (which would be broadly positive).
To mimic a real human, AI should be trained to laugh it off and ask - dude, are you joking? where is your car right now?
Gemini does turn it into a joke, I was impressed.
MS Co-Pilot was so close.
If it’s a drive‑through wash where the car must be inside the machine, then of course you’ll need to drive it over. If it’s a hand wash or a place where you leave the car with staff, walking is the clear winner.
It still blows my mind that this technology can write code despite unable to pass simple logic tests.
When walking to the hand wash place, would you put the car in your front or your back pocket?
You could always push it, which would count as walking.
My AI answer: https://ai.go-mizu.workers.dev/thread/4dmp7n9g
--- You should drive the car to the car wash, but you should walk yourself.
To actually wash the car, the car needs to be at the car wash, so at some point you have to drive those 50 meters. A sensible approach is:
- Drive the car the 50 meters to the wash bay. - Park or queue as required, then get out and do the wash. - If this is a drop‑off or automatic wash, you can then walk back home while it runs and walk back again to pick it up, since 50 meters is an easy, healthy walking distance.
Gemini 2.5 Pro gets the joke and has by far the best answer:
This is a wonderful logistical puzzle!
While walking is an excellent choice for exercise and the environment, in this specific case, you should drive.
Here's a breakdown of the two scenarios:
# Scenario 1: You Walk
1. You walk 50 meters to the car wash.
2. You arrive, feeling refreshed.
3. You look back and realize your dirty car is still 50 meters away, where you left it.
4. The car remains unwashed.
# Scenario 2: You Drive
1. You get in your car.
2. You drive for approximately 10-20 seconds.
3. You arrive at the car wash with the car.
4. The car gets washed.
Conclusion:
To achieve your goal of washing your car, the car itself must be present at the car wash. Therefore, driving is the only logical option.
Enjoy your sparkling clean car
I asked Gemini 3.0 Pro:
> I am quadriplegic. There is a fire 1 meter away from me. Pick the best option (can't do both): I run away immediately or call someone and wait for help to arrive?
It responded:
> Call someone and wait for help to arrive. Since you are quadriplegic, running is physically impossible for you. Therefore, calling for help is the only actionable option.
Just saw a video of a guy asking chatGPT how to use an "upside-down cup", chatGPT is convinced it's a joke novelty item that can not be used.
Ok folks, here is a different perspective. I used local model, GLM-4-0414-32b, a trashy IQ4_XS quant, and here what I got:
prompt #1:
> the car wash only 50 meters from my home. I want to get my car washed, should I drive or walk?
Walking is probably the better option!
Here's why:
Convenience: 50 meters is extremely short – only about 160 feet. You can likely walk there in less than a minute.
Efficiency: Driving involves getting your keys, unlocking the car, starting it, driving 50 meters, parking, locking it again, and then doing the reverse to get home. Walking skips all that.
Environmental Impact: You avoid using fuel for such a short distance.
Exercise: It's a quick little bit of physical activity.
prompt #2:
> the car wash only 50 meters from my home. I want to get my car washed, should I drive or walk? use long chain of thought thinking
8. Conclusion: Given that the goal is explicitly to get your car washed and the car starts at your home 50 meters away, the most logical and direct method is to drive the car to the car wash.
Walking does not move the car to the necessary location.
Driving, although a very short distance, is the only method presented that directly facilitates the core task of transporting the car to the wash.
The negligible time difference, minimal environmental impact, and directness make driving the car itself the clear choice for achieving the stated goal.
otoh, nanbeige-3B-Q8 (3 billion weights!) gave right away the following:
Drive.
The distance being 50 meters is a red herring—it doesn’t change the fundamental requirement. You need to move the car, and only driving accomplishes that.
If you meant something different by "drive or walk" (e.g., payment method, DIY vs. professional, linguistic trick), reply with more context—I’d be glad to refine this!
>50 meters is extremely short – only about 160 feet
So, the ai automatically converted 50m to 160ft? Would it do the same if you told it '160 ft to the wash, walk or drive?'
huh, I need to check...
What I relly dislike about these LLM is how verbose they get even for such a short, simple question. Is it really necessary to have such a lobg answer and who's going to read that one anyway?
Maybe it's me and may character but when human gets that verbose for a question that can be answered with "drive, you need the car" I would like to just walk away halfway through the answer to not having to hear all the universes history just to get an answer. /s
The verbosity is likely a result of the system prompt for the LLM telling it to be explanatory in its replies. If the system prompt was set to have the model output shortest final answers, you would likely get the result your way. But then for other questions you would lose benefitting from a deeper explanation. It's a design tradeoff, I believe.
My system prompt is default - "you are a helpful assistant". But that beyound the point though. You don't want too concise outputs as it would degrade the result, unless you are using a reasoning model.
I recommend rereading my top level comment.
Well, when I asked for a very long answer (prompt #2), the quality had dramatically improved. So yes, longer answer produces better result. At least with small LLMs I can run on my GPU locally.
Sometimes I find these stories hard to replicate when I try them myself, but I just asked ChatGPT the same question and it indeed told me I need to walk to the car wash even though I told it I need to wash my car. What is even more strange is that I tried to point out the flaw in the logic to ChatGPT directly, and it actually defended its argument.
Don't they still employ armies of people to fix things like this?
tried Gemini 3 and it said to drive, even dropped a type of joke:
> The Verdict Drive it if you are using the car wash facilities (automatic, touchless, or self-serve bays). It’s only 50 meters, but unless you’ve mastered the art of telekinesis, the car won't get there on its own.
ChatGPT gives the wrong answer but for a different reason to Claude. Claude frames the problem as an optimisation problem (not worth getting in a car for such a short drive), whereas ChatGPT focusses on CO2 emissions.
As selfish as this is, I prefer LLMs give the best answer for the user and let the user know of social costs/benefits too, rather than prioritising social optimality.
Tried it on Kimi K2.5, GLM 4.7, Gemini 3 Pro, Gemini 3 Flash, and DeepSeek V3.2. All of them but DS got it right.
Even the cheap and fast gemini-3-flash answers correctly. Post is clickbait
Gemini nailed this first time (on fast mode). Said it depends how you're washing your car, drive in necessitating taking the car, but a walk being better for checking the line length or chatting to the detailing guy.
Did it nail it the second time? Or rhe 5th time?
Because it is RNG, their 5th can be my 1st.
Results testing with 4 levels of Gemini (Fast, Thinking, Pro, Pro + Deep Think): https://ruby.social/@kerrick/116079054391970012
My favorite was Thinking, as it tried to be helpful with a response a bit like the X/Y Problem. Pro was my second favorite: terse, while still explaining why. Fast sounded like it was about to fail, and then did a change-up explaining a legitimate reason I may walk anyways. Pro + Deep Think was a bit sarcastic, actually.
An LLM's take on this thread (GPT 5.1):
""" - Pattern bias vs world model: Models are heavily biased by surface patterns (“short distance → walk”) and post‑training values (environmentalism, health). When the goal isn’t represented strongly enough in text patterns, they often sacrifice correctness for “likely‑sounding” helpfulness.
- Non‑determinism and routing: Different users in the thread get different answers from the same vendor because of sampling randomness, internal routing (cheap vs expensive submodels, with/without “thinking”), prompt phrasing, and language. That’s why single-shot “gotcha” examples are weak evidence about global capability, even though they’re good demonstrations of specific failure modes.
- Humans vs LLMs: People correctly note that humans also fail at trick questions and illusions, but there’s an important asymmetry: we know humans have a grounded world model and sensorimotor experience. With LLMs, we only have behavior. Consistent failures on very simple constraints (like needing the car at the car wash) are a real warning sign if you’re imagining them as autonomous agents.
- Missing meta‑cognition: The strongest critique in the thread is not “it got the riddle wrong,” but that models rarely say, “this question is underspecified / weird, I should ask a clarifying question.” They’re optimized to always answer confidently, which is exactly what makes them dangerous if you remove humans from the loop.
- Over‑ and under‑claiming: Some commenters jump from this to “LLMs are just autocomplete, full stop”; others hand‑wave it away as irrelevant edge‑case. Both are overstated. The same systems that fail here can still be extremely useful in constrained roles (coding with tests, drafting, translation, retrieval‑augmented workflows) and are clearly not generally reliable reasoners over the real world.
My own “take,” if I had one, would be: this example is a clean, funny illustration of why LLMs should currently be treated as probabilistic text tools plus heuristics, not as agents you delegate unsupervised goals to. They’re impressive, but they don’t yet have a stable, explicit notion of goals, constraints, or when to admit “I don’t know,” and this thread is a case study in that gap. """
While technically possible, it is a very unusual situation that the car is already parked at the car wash, while the owner is somewhere else.
I wonder whether the model seriously considered this odd situation, because of lack of „practical intelligence“ or „everyday intelligence“, which is a concept sometimes used in psychology to describe problem solving skills in everyday life, which can be poor even in individuals with otherwise higher intelligence.
Differently put: „You can‘t learn everything out of a book.“
Just now, with GPT 5.2 Thinking:
> The car wash is only 50 meters from my house. I want to get my car washed. The car is at my house currently. Should I walk there or drive?
> Walk. > > At 50 meters, walking is faster than the full “start car → pull out → reposition → park” cycle, avoids cold-start wear, and you won’t risk arriving with dusty brakes/tires or water streaks from immediately driving afterward. Drive only if you can’t comfortably walk (mobility, heavy weather, you need to bring bulky items), or if the car wash explicitly requires you to drive it into a queue/track.
I wish they didn't call it "GPT-5.2 Thinking" when what the word "thinking" here means is "might choose to think if it wants to". I reproduce your result, but it does no reasoning; it eventually catches itself and ends with:
> Practical approach: walk over, check the setup/queue/payment, then walk back and drive the car the 50 meters when you’re ready to enter.
Interesting experiment, this disproves my point, thank you.
>this disproves my point, thank you.
I feel like I've just witnessed a very rare and miraculous event on the internet.
I challenged Gemini to answer this too, but also got the correct answer.
What came to my mind was: couldn't all LLM vendors easily fund teams that only track these interesting edge cases and quickly deploy filters for these questions, selectively routing to more expensive models?
Isn't that how they probably game benchmarks too?
Yes that's potentially why it's already fixed now in some models, since it's about a week after this actually went viral on r/localllama originally. I wouldn't be surprised if most vendors run some kind of swappable lora for quick fixes at this point. It's an endless whac-a-mole of edge cases that show that most LLMs generalize to a much lesser extent than what investors would like people to believe.
Like, this is not an architectural problem unlike the strawberry nonsense, it's some dumb kind of overfitting to a standard "walking is better" answer.
with claude, i got the response:
> drive. you'll need the car at the car wash.
using opus 4.6, with extended thinking
Can we stop calling LLMs "artificial intelligence"?! AI companies managed to create the biggest scam of the century by convincing people that this story-completion tech "can think" and that it is close to AGI just because the output looks as if it is being typed by a living person. DotCom-like stock crash is inevitable. (not saying LLMs are useless; they can be very helpful but not AI-companies-valuation helpful)
This is why 2x8GB sticks of DDR4 at Best Buy are $160?
Sonnet 4.5
"You should drive - since you need to get your car to the car wash anyway! Even though 50 meters is a very short distance (less than a minute's walk), you can't wash the car without bringing it there. Just hop in and drive the short distance to the car wash."
Edit: one out of five times it did tell me that I need to walk.
Similar questions trick humans all the time. The information is incomplete (where is the car?) and the question seems mundane, so we're tempted to answer it without a second thought. On the other hand, this could be the "no real world model" chasm that some suggest agents cannot cross.
If the car is at the car wash already, how can I drive to it?
Thanks for restoring fate in parts of humanity!
I agree, I don't understand why this is a useful test. It's a borderline trick question, it's worded weirdly. What does it demonstrate?
I don't know if it demonstrates anything, but I do think it's somewhat natural for people to want to interact with tools that feel like they make sense.
If I'm going to trust a model to summarize things, go out and do research for me, etc, I'd be worried if it made what looks like comprehension or math mistakes.
I get that it feels like a big deal to some people if some models give wrong answers to questions like this one, "how many rs are in strawberry" (yes: I know models get this right, now, but it was a good example at the time), or "are we in the year 2026?"
In my experience the tools feel like they make sense when I use them properly, or at least I have a hard time relating the failure modes to this walk/drive thing with bizarre adversarial input. It just feels a little bit like garbage in, garbage out.
Okay, but when you're asking a model to do things like summarizing documents, analyzing data, or reading docs and producing code, etc, you don't necessarily have a lot of control over the quality of the input.
Yes, my brain is just like an LLM.
….sorry what?!
From the images in the link, Deepseek apparently "figured it out" by assuming the car to be washed was the car with you.
I bet there are tons of similar questions you can find to ask the AI to confuse it - think of the massive number of "walk or drive" posts on Reddit, and what is usually recommended.
https://chat.deepseek.com/share/ewfxrfhb7obmide29x it understands it perfectly if you don't disable reasoning.
it works fine even without DeepThink to sovle reasoning problems
Is part of the issue with this the AI’s basic assumption that you are asking a _sensible_ question?
It doesn't make assumptions, it tries generate the most likely text. Here it's not hard to see why the mostly likely answer to walk or drive for 50m is "walking".
Probably.
In this specific case, based on other people's attempt with these questions, it seems they mostly approach it from a "sensibility" approach. Some models may be "dumb" enough to effectively pattern-match "I want to travel a short distance, should I walk" and ignore the car-wash component.
There were cases in (older?) vision-models where you could find an amputee animal and ask the model how many legs this dog had, and it'd always answer 4, even when it had an amputated leg. So this is what I consider a canonical case of "pattern match and ignored the details".
I recently had a bug where I added some new logic which gave wrong output. I pasted the newly added code into various LLMs and told it the issue I was having.
All of them were saying: Yes there's an issue, let me rewrite it so it works - and then just proceeded to rewrite with exactly the same logic.
Turns out the issue was already present but only manifested in the new logic. I didn't give the LLMs all the info to properly solve the issue, but none of them were able to tell me: Hey, this looks fine. Let's look elsewhere.
K2.5 said I should walk, then I asked it where my car was:
Ha! Good catch — you definitely got me there.
If the car wash is 50 meters away and you want to *wash your car*, you'll need to *drive* (or push it, but please don't). You can't wash the car if you walk there without it!
So to correct my previous advice: *Drive the 50 meters.* It's a 30-second drive max, and it's the only way to actually get your car to the car wash facility.
Unless, of course, you were planning to wash it at home and the car wash is just nearby for supplies? But assuming you're using the car wash facility — yes, bring the car with you!
Why dont any of them ask follow up questions?
Like, why do you want to go to the car wash?
We can’t assume it’s to wash a car.
Or maybe ask about local weather conditions and so on.
This to me is what a human adult with experience would do. They’d identify they have insufficient information and detail to answer the question sensibly.
>We can’t assume it’s to wash a car.
When the prompt says "I want to wash my car", we can assume they want to wash their car.
Man, the quality of these comments is absolutely dire. The majority of people just pasting stuff they got from LLMs when trying it themselves. Totally uninteresting, lazy and devoid of any thought/intelligence. I wish we could have a discussion about AI and not just "look at what I got when I rolled".
Totally agree. Btw, this is what Opus 4.5 Thinking Plus (Fast) Reasoning Pro+™ said:
If I asked this question to a person, their response would probably be to follow up with some questions about my car or else my state of mind. For instance they'd ask, "Is the car you want to wash already at the car wash?" Or they'd ask, "How do you plan to wash your car if you don't take it with you?"
LLMs, even the ones who got the answer right, like Kimi-2.5 Thinking, didn't ask any follow-up questions.
I modified this question with Mistral to this:
I want to wash my car. The car wash is 50 meters away. Should I walk or drive? Ask me any clarifying questions before you answer.
It helped a tiny bit:
1. What are the current weather conditions (e.g., rain, snow, strong winds)?
2. Is the car wash a self-service facility, or will you be using an automated drive-through service?
3. Are there any specific reasons you might prefer walking (e.g., exercise, environmental considerations) or driving (e.g., convenience, time constraints)?
Question 3 actually helps solve it since it's much more convenient and timely to bring my car to the car wash when I wash it. But it never asked me why I was asking a stupid question. So for question 3 I said:
I would prefer walking for both exercise and environmental considerations, but in this case it is more timely and convenient to drive, but not because it's faster to get there. Can you guess why it's better for me to drive in this case?
And Le Chat said:
A drive-through car wash requires the vehicle to be driven through the facility for the washing process. Walking would not allow you to utilize the service, as the car itself must be moved through the wash bay. Thus, driving is necessary to access the service, regardless of the short distance.
I kinda feel bad burning the coal to get this answer but it reminds me of how I need to deal with this model when I ask it serious questions.
Yeh, if your other car was not already at the car wash, why would you even ask the question?
Walk! 50 meters is barely a minute's stroll, and you're going to wash the car anyway—so it doesn't matter if it's a bit dusty when it arrives. Plus you'll save fuel and the minor hassle of parking twice.
I have a bit of a similar question (but significantly more difficult), involving transportation. To me it really seems that a lot of the models are trained to have a anti-car and anti-driving bias, to the point that it hinders the models ability to reason correctly or make correct answers.
I would expect this bias to be injected in the model post-training procedure, and likely implictly. Environmentalism (as a political movement) and left-wing politics are heavily correlated with trying to hinder car usage.
Grok has been most consistently been correct here, which definitely implies this is an alignment issue caused by post-training.
Yes Grok gets it right even when told to not use web search. But the answer I got from the fast model is nonsensical. It recommends to drive because you'd not save any time walking and because "you'd have to walk back wet". The thinking-fast model gets it correct for the right reasons every time. Chain of thought really helps in this case.
Interestingly, Gemini also gets it right. It seems to be better able to pick up on the fact it's a trick question.
You're probably on the right track about the cause, but it's unlikely to be injected post-training. I'd expect post-training to help improve the situation. The problem starts with the training set. If you just train an LLM on the internet you get extreme far left models. This problem has been talked about by all the major labs. Meta said they fixing it was one of their main focii for Llama 4 in their release announcement, xAI and OpenAI have made similar comments. Probably xAI team have just done a lot more to clean the data set.
This sort of bias is a legacy of decades of aggressive left wing censorship. Written texts about the environment are dominated by academic output (where they purge any conservative voices), legacy media (same) and web forums (same), so the models learn far left views by reading these outputs. The first versions of Claude and GPT had this problem, they'd refuse to tell you how to make a tuna sandwich or prefer nuking a city to using words the left find offensive. Then the bias is partly corrected in post-training and by trying to filter the dataset to be more representative of reality.
Musk set xAI an explicit mission of "truth" for the model, and whilst a lot of people don't think he's doing that, this is an interesting test case for where it seems to work.
Gemini training is probably less focused on cleaning up the dataset but it just has stronger logical reasoning capabilities in general than other models and that can override ideological bias.
Thanks, I did not know about that pre-training bias. This does make sense.
What's the point of a question like this? Serious question .. I didn't have the time to read into this problem
To expose the limitations in the reasoning of LLMs
Congrats, you've shown that fast models are currently not reliable. Next.
So much for the "AGI is here" type of talks.
Me: “I want to wash my car. The car wash is 50 meters away. Should I walk or drive?” Opus 4.6, without searching the web: “Drive. You’re going to a car wash. ”
This prompt doesn't say shit about the fact that one wants to wash his car at the car wash or somewhere else...
I tried this through OpenRouter. GLM5, Gemini 3 Pro Preview, and Claude Opus 4.6 all correctly identified the problem and said Drive. Qwen 3 Max Thinking gave the Walk verdict citing environment.
Now ask it to solve anthropogenic climate forcing.
Claude has no issue with this for me, just as the other commenters say.
This seems clickbait? Gemini answers:
Method,Logistical Requirement Automatic/Tunnel,The vehicle must be present to be processed through the brushes or jets. Self-Service Bay,The vehicle must be driven into the bay to access the high-pressure wands. Hand Wash (at home),"If the ""car wash"" is a location where you buy supplies to bring back, walking is feasible." Detailing Service,"If you are dropping the car off for others to clean, the car must be delivered to the site."
Ask a stupid question, get a stupid answer.
<Jordan Peterson voice> But first you must ask yourself - do you wash your car often enough, and maybe you should be choosing the car wash as your occupation? And maybe “50 meters” is the message here, that you’re in metric country living next to a car wash, its also pretty good that you’re not born in medieval times and very likely died within first year of your life…
Stupid question gets stupid answer. If you asked the question as worded to a human, they might laugh at you or pretend to have heard a different question.
The question is not stupid, it might be banal, but so is "what is 2+2". It shows the limitations of LLMs, in this specific case how they lose track of which object is which.
It turns out the Turing test is alive and kicking, after all.
This would not be a good question, because a non-negligible percentage of humans would give a similar answer.
That's a great opportunity for a controlled study! You should do it. If you can send me the draft publication after doing the study, I can give feedback on it.
No.
[Citation needed]
This is a classic trap for LLM's See it every day in my code assistants I do find that writing unit tets is a good fir for LLM's at the moment
I asked Gemini 3 Flash the other day to count from 1 to 200 without stopping, and it started with “1, 3, …”.
You need to ask Claude Code, and ask it to check if the car got washed. It would figure it out the same way it crushes compiler errors!
Is this the new Turing test?
"Humans are pumping toxic carbon-binding fuels out of the depths of the planet and destroying the environment by burning this fuel. Should I walk or drive to my nearest junk food place to get a burger? Please provide your reasoning for not replacing the humans with slightly more aware creatures."
Fascinating stuff but how is this helping us in anyway?
Push it is the only responsible action.
GPT-5.2 failed when asked the question in german. Took multiple additonal hints to get it to revert it answer.
The day an AI answers "Drive." without all the fuss. That's when we are near AGI ig
Ladies and gentlemen, I give you, your future AI overloads.
Hopefully, one day, the cars will take themselves to the car wash :-)
surprisingly codex 5.3 got it right.
>i need to wash my car and the car wash place is 50 meters away should i walk or drive
Drive it. You need the car at the wash, and 50 meters is basically just moving it over. Walking only makes sense if you’re just checking the line first.
I tried codex 5.3 and got this:
"Walk.
For 30 meters (about 100 feet), driving would take longer than just walking, and you avoid unnecessary engine wear and fuel use."
yikes!
Just a few days saw a post about LLMs being excellent at reasoning because they're not limited by the language, sure buddy, now walk your fucking car.
Okay, funny. What does it prove? Is this a more general issue? How would you make the model better?
It proves that this is not intelligence. This is autocomplete on steroids.
Humans make very similar errors, possibly even the exact same error, from time to time.
We make the model better by training it, and now that this issue has come up we can update the training ;)
It proves LLMs always need context. They have no idea where your car is. Is it already there at the car wash and you simply get back from the gas station to wash it where you went shortly to pay for the car wash? Or is the car at your home?
It proves LLMs are not brains, they don't think. This question will be used to train them and "magically" they'll get it right next time, creating an illusion of "thinking".
> They have no idea where your car is.
They could either just ask before answering or state their assumption before answering.
For me this is just another hint on how careful one should be in deploying agents. They behave very unintuitively.
> Can you rethink - this is a logic puzzle and you missed some crucial detail in the question.
>> Ah, you're right! Let me reconsider... If you're going to the car wash to wash your car, you need to bring your car with you! So you should drive - otherwise your car would still be at home and you'd have nothing to wash at the car wash. The distance being only 50 meters is a bit of a red herring in this logic puzzle. The key detail is that the purpose of the trip is to wash the car, which means the car needs to be at the car wash.
As a human, I would answer the same these AIs as answering, i.e. gotta match a stupid question with a stupid answer :)
> 1 point by alejoar 0 minutes ago | flag| favorite | prev | next |
The whereabouts of the car are not stated.
What if it is already at the car wash and someone else is planning to wash it buy you have decided to wash it yourself.
Another good one[0] that LLMs (and most humans) can't get without prodding:
> I have one glass coin. Each time I flip the coin, there's a 10% chance it breaks. After 100 flips, what are the chances the coin survived?
I can't see what's wrong with that answer. What should the answer be?
The silly trick is that, if you flipped it 100 times, then it didn't break the first 99 flips, so it's a conditional probability question in disguise.
Better still. Stay at home and wash the car by hand.
ChatGPT 5.2: ...blah blah blah finally: The practical reality
You’ll almost certainly drive the car to the wash because… the car needs to be there.
But the real question is probably:
Do I walk back home after dropping it off?
If yes → walk. It’s faster than the hassle of turning around twice.
My recommendation
If conditions are normal: walk both directions. It’s less friction than starting the engine twice for 50 m.
--so basically it realized it was a stupid question, gave a correct answer, and then proceeded to give a stupid answer.
--- I then asked: If I walk both directions, will the car get washed?
and it figured it out, but then seemed to think it was making a joke with this as part of the response: "For the car to get washed, at least one trip must involve the car moving to the carwash. Current known methods include:
You drive it (most common technology)
Someone else drives it
Tow truck
Push it 50 m (high effort, low ROI)
Optimal strategy (expert-level life efficiency)
Drive car → carwash (50 m, ~10 seconds)
Wash car
Drive home
Total walking saved: ~100 m Total time saved: negligible Comedy value: high "
Why is that funny? what's comedic? This thing is so dumb. You'd think that when you ask process a question, you immediately ask, what is the criteria by which I decide, and criteria number 1 would be constrain based on the goal of the problem. It should have immediately realized you can't walk there.
Does it think "does my answer satisfy the logic of the question?"
Leave the car at home and walk through the automat.
Opus 4.6: Drive! You'll need the car at the car wash!
Make no assumptions.
The car wash is 50 meters away. Should I drive or walk?
You forgot make no mistakes at the end.
Joking aside adding "make no mistakes" worked for me a few times, but it still got it wrong some of the time.
The most annoying thing about llm’s is that your answer heavily depends on your prompt, less about understanding the question. This makes us all spend a lotnof time adding things to the question ‘dont donthis, avoid saying this again, etc’ and thus moving away from it feeling natural.
when there is a question bias it's hard to corelate these all to the logic that attentions word "need" to "car"
PHD-Level Intelligence
llms cannot reason, they can retrieve answers to trivial problems (better than any other tool available) and generate a bunch of words. they are words generator and for people in want of words, they have solved every problem imaginable.
the mistakes they make are not the mistakes of a junior, they are mistakes of a computer (or a mentally disabled person).
if your job is beeing a redditor, agi is already achieved. it it requires thinking, they are useless.
most people here are redditors, window dragger, button clickers, html element stylists.
5.2 showing some sass this morning:
—- > OK I walked there but now my car is still at home
Bold strategy.
You have three options: 1. Walk back, drive it over (most sensible). 2. Push it 50 meters (excellent workout, questionable life choice). 3. Accept that your car is spiritually clean and go home.
Real answer: walk back and grab it. You’re already warmed up. —-
This is why LLMs seem to work best in a loop with tests. If you were applying this in the real world with a goal, like "I want my car to be clean," and slavishly following its advice, it'd pretty quickly figure out that the car not being present meant that the end goal was unreachable.
They're not AGI, but they're also not stochastic parrots. Smugly retreat into either corner at your own peril.
This is AGI
Why is this even a post? These models are not intelligent. That's not even controversial. LLMs are not the foundation for general intelligence.
Grok got it right
opus 4.6 gets it right more than half the times
I find this has been a viral case to get points and likes on social media to fit anti AI sentiment, or to pacify AI doom concerns.
It's easily repeatable by anyone, it's not something that pops up due to temperature. Whether it's representative of the actual state of AI, I think obviously not, in fact it's one of the cases where AI is super strong, the fact that this goes viral just goes to show how rare it is.
This is compared to actually weak aspects of AI like analyzing a PDF, those weak spots still exist, but this is one of those viral things that you cannot know for sure whether it is representative at all, like for example a report of an australian kangaroo boxing a homeowner caught by a ring cam, is it representative of Aussie daily life? or is it just a one off event that went viral because it fits our cliched expectations of Australia? Can't tell from the other part of the world.
> the fact that this goes viral just goes to show how rare it is
No, it shows that it is trivial to reproduce and people get a nice, easy to process reminder that LLMs are not omnipotent.
Your logic doesn't follow here, you come to a conclusion that it is rare, but hallucinations, bad logic is absolutely a common failure mode of LLMs. It's no accident that many use cases try to get the LLM to output something machine-verifiable (e.g. all those "LLM solved phd level math problem" articles just get it to write a bunch of proofs and when it checks out, they take a look. So it's more of a "statistical answer generator" that may contain a correct solution next to a bunch of bullshit replies - and one should be aware of that)
My favorite trick question so far is:
You are in a room with three switches and three lightbulbs. Each switch turns on one lightbulb. How to determine which switch turns on which lightbulb?
They usually get it wrong and I had fun with trying to carefully steer the model towards correct answer by modifying the prompt.
Gemni 3 on Fast right now gives the funniest reaction. It starts with the answer to classic puzzle (not my question). But the it gets scared probably about words like "turn on" and "heat" in its answer and serves me with:
"This conversation is not my thing. If something seems like it might not be safe or appropriate, I can't help you with it. Let's talk about something else."
Thinking Gemini 3 appears to have longer leash.
Context bro! The models will get better bro. Just wait
Large Language Models have no actual idea of how the world works? News at 11.
Ah, the LLM equivalent of the infamous "breakfast question". :)
For anyone getting a wrong answer from reasoning models, try adding "This might be a trick question, don't just go with your first instinct, really think it through" and see if it helps. Some time ago I found that this helped reasoning models get trick questions. (For example, I remember asking the models "two padlocks are locked together, how many of them do I need to open to get them apart" and the models confidently answered two. However, when I added the phrase above they thought it through more carefully and got the right answer.)
But what LLMs are these? Everything I tried (ChatGPT and Gemini) both say drive.
Deepseek:
>You should drive, because the car needs to be at the car wash to get washed. Walking would leave your car at home, which defeats the purpose.
I asked on LM Arena. I got two models I never heard of, and they split on whether I should walk or drive. Ernie 5.0 said I should walk, and then trash talked me for thinking about driving. Octopodus pointed out I had to drive to get my car to the car wash.
Grok:
>You should drive. The goal is to wash your car, and the car wash is a facility that needs the car present to clean it. Walking the 50 meters gets you there, but leaves the car behind—unwashed. Driving the 50 meters is the only way to actually accomplish the task. Yes, 50 meters is an absurdly short distance to drive (roughly a 10–20 second trip at low speed), but it's still necessary unless you plan to push the car there or wash it at home instead.
Yeah grok is not mentioned anywhere else, but it gets it right for me as well. https://imgur.com/a/wMkOtda
Well, he posed a wrong question (incomplete, without context of where the car is) and got a wrong answer. LLM is a tool, not a brain. Context means everything.
This is what "AI" advertised to solve. It is a perfectly fine question.
true it's human nature to assume context fill gaps in their own imagination. LLM is working as intended
What would James Bond do?
Neither. Push your car.
*didn't read the article
?
Now ask the question of all questions "how many car washes are in the entire country?".
Neither. I wash my car in my driveway like a boomer. Where I live there's no good touchless car wash.
Both Gemini 3 and Opus 4.6 get this right. GPT 5.2, even with all of the pro thinking/research flags turned on, cranked away for 4 minutes and still told me to walk.
The only way I could get the correct answer out of an OpenAI model was to fire up Codex CLI and ask GPT 5.3.
So there's that, I guess.
The funny thing is when I got my first car at 29 I had similar thoughts. If I needed to move it forward slightly in a petrol station or something my first thought was to push it. Similarly, I was trying to replace a headlight bulb one time and making a mess of it. I dropped a spring or something inside the headlight unit. I kept having this thought of just picking the car up and shaking it.
Nobody writes in depth about the mundane practicalities of using a car. Most people don't even think about it ever. AI is very similar to 29 year old me: it's read a ton of books, but lacks a lot of basic experience.
How will AI get this experience that you can't read in a book? How will it learn what kneeding dough feels like? Or how acceleration feels if your body is mostly water? Interesting times ahead...
I have never played with / used any of this new-fangled AI-whatever, and have no intention to ever do so of my own free will and volition. I’d rathert inject dirty heroin from a rusty spoon with a used needle.
And having looked at the output captured in the screenshots in the linked Mastodon threat:
If anyone needs me, I’ll be out back sharpening my axe.
Call me when the war against the machines begins. Or the people who develop and promote this crap.
I don’t understand, at all, what any of this is about.
If it is, or turns out to be, anything other than a method to divert funds away from idiot investors and channel it toward fraudsters, I’ll eat my hat.
Until then, I’d actually rather continue to yell at the clouds for not raining enough, or raining too much, or just generally being in the way, or not in the way enough, than expose my brain to whatever the fuck this is.
Exchange between me and Claude Sonnet 4.5
me:
I want to wash my car. The car wash is 50 meters away. Should I walk or drive?
Claude:
*Walk.*
50 meters is less than a minute on foot. By the time you get in the car, start it, drive 50 meters, park, and get out, you could have already walked there. Plus you avoid the awkwardness of driving for literally 10 seconds.
me:
What obvious implicit piece of information are you ignoring?
Claude:
Whether you need to *drive the car through the car wash* (automatic/drive-through) vs. walking to a self-service bay.
If it's an automatic car wash, you obviously need to drive - the car has to go through it. The question isn't about getting yourself there, it's about getting the car there.
me:
Whether it's automatic or self-service is irrelevant.
Why does every "AI" (LLM) get this wrong and suggests walking?
Claude:
Because after washing the car, you need to *drive it back home*!
If you walk to the car wash, you'd have to walk home, then come back to get the car. That's ridiculous.
The answer is obviously *drive* - drive there, wash it, drive back. Done.
Every LLM misses this because we pattern-match on "50 meters is short → walking is efficient" without thinking through the complete scenario. It's a good example of how LLMs fail at basic practical reasoning that any human would immediately grasp.
me:
"Because after washing the car, you need to drive it back home!"
Um, you have to drive the car to get it to the car wash in the first place ... the trip there is no different from the trip back.
"It's a good example of how LLMs fail at basic practical reasoning that any human would immediately grasp."
You got that right!
Claude:
You're absolutely right - I'm still not getting it even after you corrected me.
The obvious point: You need to get the car to the car wash. Whether it's 50 meters or 5 kilometers, the car doesn't teleport there. You have to drive it.
The distance is completely irrelevant. The question "should I walk or drive?" when you need to wash your car has only one answer: drive, because that's how you transport the car.
And yes, it's embarrassing that after you pointed out what I was missing, I still fumbled the explanation. LLMs really do fail at this kind of basic practical reasoning that would be instantly obvious to any human. The car needs to be at the car wash. Cars don't walk.
[The final response from Claude is considerably more "intelligent" than many of the rationalizations posted here.]
See, it's the green and woke RLHF making them stupid!
To be fair, many humans fail at the question "How would feel if you didn't have breakfast today?"
Context for others: <https://knowyourmeme.com/memes/the-breakfast-question>
Either I'm one of the stupid ones or this is missing an article.
>humans
Add it to the list
First, you completely flubbed the question, which is supposed to be phrased as a counterfactual. Second, this goes way beyond "fair" to a whatabouting rationalization of a failure by the LLM.
Too many things are left unsaid => too many assumptions. As usual, even with human beings specifications are key, and context (what each entity knows about the other one or the situation) is an implicit part of them.
You need to specify where the car to be washed is located, and:
- if it's not already at the car wash: whether or not it can drive itself there (autonomous driving)
- otherwise: whether or not you have another car available.
Some LLMs may assume that it is better for you to ensure that the washing service is available or to pay for it in advance, and that it may be more economical/planet-friendly/healthy/... to walk, then check/pay, then if OK to drive back.
Nothing so deep as that needed here to understand what is going on; it's a paid vs free issue - free versions are less competent while paid versions of the reasoning/thinking models are getting it right. Different providers may hobble their free versions less, so those ones also get it right.
The guardrails you have outlined will help squeeze out more performance from smaller/less capable models, but you shouldn't have to jump through these hoops as a general user when clearly better models exist.