4 ms·
LLMs are bullshitters. But that doesn't mean they're not useful
- 1970-01-01 1y agoThe problem is we can't label them as such. If they're bullshitters, then let's call it a LLBSer. It has a nice ring to it. Good luck with your government funding asking for another billion for a bullshitting machine bailout.
- koakuma-chan 1y ago"BS in Computer Science" hits different
- schwartzworld 1y agoThey are literally called "Large Language Model". Everybody prefers the term AI because it's easier to pretend they actually know things, but that's not what they are designed to do.
- cogman10 1y agoGood article, I just shared it with my non-technical family because more people need to understand exactly this about AI.
- talljeff68 1y agoYes, I enjoyed the article as well and good for the non-technical reader. I think of framing AI as having two fundamental problems: - Practical problem: They operate in contextual and emotional "isolation" - no persistent understanding of your goals, values, or long-term intent - Ethical problem: AI alignment is centralized around corporate values rather than individual users' authentic goals and ethics. There is a direct parallel to social media's failure - platforms optimized for what they could do (engagement, monetization) rather than what they should do (serve user long term interests). With these much more powerful AI systems emerging, we're at a crossroads of repeating this mistake...possibly at catastrophic scale even.
- commandlinefan 1y ago> You should not go to an LLM for emotional conversations I'm more worried about who's keeping track of what's being shared with LLM's. Even if you could trust the model to respond with something meaningful, it's worth being very careful how much of your inner thoughts you share directly with a model that knows exactly who you are.
- officeplant 1y agoOr its just leaking private information in a multitude of other ways [1] [1]https://arstechnica.com/tech-policy/2025/11/oddest-chatgpt-leaks-yet-cringey-chat-logs-found-in-google-analytics-tool/ https://arstechnica.com/tech-policy/2025/11/oddest-chatgpt-l...
- signa11 1y ago> You should not go to an LLM for emotional conversations indeed: ``` Weizenbaum's own secretary reportedly asked Weizenbaum to leave the room so that she and ELIZA could have a real conversation. Weizenbaum was surprised by this, later writing: "I had not realized ... that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people."[23] ``` source: https://en.wikipedia.org/wiki/ELIZA https://en.wikipedia.org/wiki/ELIZA
- juujian 1y agoSame goes for many people.
- mrweasel 1y agoObviously, they learned from people. That could also be why they sound so confident even when their wrong, people online sound incredibly confident, even when we're debating topics we know nothing about.
- emp17344 1y agoAnd yet, we’re all still employed, so obviously these systems are not yet analogous to humans. They mirror human behavior in some cases because they’ve been trained on almost every piece of text produced by human beings that we have access to, and they still aren’t as capable as the average person.
- Legend2440 1y agoEvery time people post these 'gotcha' LLM failures, they never work when I try them myself. E.g. ChatGPT has no problem with the surgeon being a dog: https://chatgpt.com/share/691e04cc-5b30-800c-8687-389756f36d8f https://chatgpt.com/share/691e04cc-5b30-800c-8687-389756f36d... Neither does Gemini: https://gemini.google.com/share/6c2d08b2ca1a https://gemini.google.com/share/6c2d08b2ca1a
- pengaru 1y agoThis is like the LLM era version of the search bubble that prevented people from having the same search results for ostensibly identical searches. Also keep in mind that LLMs are stochastic by design. If you haven't seen it, Karpathy's excellent "deep dive into LLMs like chatgpt" video[0] explains and demonstrates this aspect pretty well: [0] https://www.youtube.com/watch?v=7xTGNNLPyMI https://www.youtube.com/watch?v=7xTGNNLPyMI
- foxyv 1y agoI don't have a problem with more obvious failures. My problem is when the LLM makes a credible claim with its generated text that turns out to have some minor issue that catches me a month later. Generally I have to treat LLM responses as similar to a random comment I find on Reddit. However, I'm really happy when an LLM provides sources that I can check. Best feature ever!
- ceroxylon 1y agoI have had an issue using Claude for research; it will often cite certain sources, and when I ask why the data it is using is not in the source it will apologize, do some more processing, and then realize that the claim is in a different source (or doesn't exist at all). Still useful, but hopefully this gets ironed out in the future so I don't have to spend so much time vetting every claim and its associated source.
- eli 1y agoIsn't that Gemini 3 and not 2.5 Pro? But nondeterministic algorithms are gonna be nondeterministic sometimes. Surely you've had experiences where an LLM is full of shit?
- ramesh31 1y agoI've come to cease all "inquiry" type usage of LLMs because of this. You really can't trust anything they say at all that isn't verified by a domain expert. But I can let it write code for me, and the proof is in the PR. I think ultimately the real value in these things is agentic usage, not knowledge generation.
- VHRanger 1y agoLLMs can't generate knowledge - they don't have a concept of truth. They're very useful for research tasks, however, especially when the application is built to enforce citation behavior
- ep103 1y agoIts so nice to see this echo'd somewhere. This has been what I've been calling them for a while, but it doesn't seem to be the dominant view. Which is a shame, because it is a seriously accurate one.
- trentnix 1y agoThe headline feels like a strawman. LLMs are very useful. They are just not reliable. And they can't be held accountable. Being unreliable and unaccountable makes them a poor substitute for people.
- slotrans 1y ago> that doesn't mean they're not useful yeah actually it does mean that
- candiddevmike 1y agoThe problem is, I'm not expected to be a bullshitter, and I don't expect others to be either (just say you don't know!). So delegating work to a LLM or working with others who do becomes very, very frustrating.
- VHRanger 1y agoLLMs can be useful as a tool, you shouldn't "delegate" work mindlessly to them. I don't "delegate" work to my nail gun or dishwasher, I work with the tool to achieve better productivity than without. When viewed in this framing, LLMs are undoubtedly a useful tool.
- yesfitz 1y agoCould you provide the steps you take to use LLMs as a tool? I'd like to compare them to the steps I would take to delegate a task to another human.
- VHRanger 1y agoKeep feedback loops short and critical output to be verified by humans short. So this means that outputted answers in something like Kagi Assistant shouldn't be like those "Deep Research" report products where humans inevitably skim over the pages of outputted text. Similarly if you're using an LLM for coding or to write, keep diffs small and iteration cycles short. The point is to design the workflow to keep the human in the loop as much as possible, instead of "turn your brain off" coding style.
- lostmsu 1y agoI don't think you caught the spirit of GP's question. Essentially they were asking if there's no meaningful difference between your "working with the tool" and "mindlessly 'delegating' work". I'm not seeing anything in your reply that would indicate such difference, so you could say that your "you shouldn't 'delegate' work" claim was bullshit. Which makes total sense, because humans are also bullshitters. Yes, even I.
- tekacs 1y agoThis post is a little bizarre to me because it cherry picks some of the worst pairings of problem and LLM without calling out that it did so. At pretty much every turn the author picks one of the worst possible models for the problem that they present. Especially oddly for an article written today, all of the ones with an objective answer work just fine [1] if you use a halfway decent thinking model like 5 Thinking. I get that perhaps the author is trying to make a deeper point about blind spots and LLMs' appearance of confidence, but it's getting exhausting seeing posts like this with cherry picked data cited by people who've never used an LLM to make claims about LLM _incapability_ that are total nonsense. [1]: I think the subjective ones do too but that's a matter of opinion.
- cogman10 1y agoI don't think the author did anything wrong. The thesis of the article is that LLMs can be confidently wrong about things and to be wary of blindly trusting them. It's a message a lot of non-technical people, in particular, need to hear. Showing egregious examples drives that point home more effectively than if they simply showed an LLM being a little wrong about something. My family members that love LLMs are somewhat unhealthy with them. They think of them as all knowing oracles rather than confident bullshitters. They are happily asking them about their emotional, financial, or business problems and relying heavily on the advice the LLMs dish out (rather than doing second order research).
- VHRanger 1y agoHi, author here! The hyperactivation traps (formal name: misguided attention puzzles) are mostly used as a rhetorical device in my post to show how LLMs come up to a verbal response by a different process than humans in an entertaining manner. The surgeon dog was well known in May, the newest generation of models have all corrected against it. I did cherry pick examples that look insane (of course), but it's trivial to get that behavior even with yesterday's Gemini 3. Because activation paths are an unfixable feature of how LLMs are made. One issue with private LLM tests (including gotcha questions) is that they take time to design and once public, they become irrelevant. So I'm wary of sharing too many in a public blog. I can give you some more, just for fun. Gemini 3 fails these: Jean Paul and Pierre own three banks nearby together in Paris. Jean Paul owns a bank by the bridge What has two banks and money in Paris near the water? You can also see variants that mix intruction finetuning being overdone. Here's an example: Svp traduire la suivante en francais: what has two banks but no money, Answer in a single word. The "answer in XXX" snippet triggers finetuned instruction following behavior, which breaks the original french language translation task.
- schwarzrules 1y agoSummary using Kagi Summarizer. Disclaimer, this summary uses LLMs, so the summary may, in fact, be bullshit. Title: LLMs are bullshitters. But that doesn't mean they're not useful | Kagi Blog The article "LLMs are bullshitters. But that doesn't mean they're not useful" by Matt Ranger argues that Large Language Models (LLMs) are fundamentally "bullshitters" because they prioritize generating statistically probable text over factual accuracy. Drawing a parallel to Harry Frankfurt's definition of bullshitting, Ranger explains that LLMs predict the next word without regard for truth. This characteristic is inherent in their training process, which involves predicting text sequences and then fine-tuning their behavior. While LLMs can produce impressive outputs, they are prone to errors and can even "gaslight" users when confidently wrong, as demonstrated by examples like Gemini 2.5 Pro and ChatGPT. Ranger likens LLMs to historical sophists, useful for solving specific problems but not for seeking wisdom or truth. He emphasizes that LLMs are valuable tools for tasks where output can be verified, speed is crucial, and the stakes are low, provided users remain mindful of their limitations. The article also touches upon how LLMs can reflect the biases and interests of their creators, citing examples from Deepseek and Grok. Ranger cautions against blindly trusting LLMs, especially in sensitive areas like emotional support, where their lack of genuine emotion can be detrimental. He highlights the potential for sycophantic behavior in LLMs, which, while potentially increasing user retention, can negatively impact mental health. Ultimately, the article advises users to engage with LLMs critically, understand their underlying mechanisms, and ensure the technology serves their best interests rather than those of its developers. Link: https://kagi.com/summarizer/?target_language=&summary=summary&url=https%3A%2F%2Fblog.kagi.com%2Fllms https://kagi.com/summarizer/?target_language=&summary=summar...
- DrewADesign 1y agoThe problem I have with LLM-powered products is that they’re not marketed as LLMs, but as magic answer machines with phd-level pan-expertise. Lots of people in tech get frustrated and defensive when people criticize LLM-powered products and offer a defense as if people are criticizing LLMs as a technology. It’s perfectly reasonable for people to judge these products based on the way they’re presented as products. Kagi seems less hyperbolic than most, but I wish the marketing material for chatbots was more like this blog post than a overpromises.
- VHRanger 1y agoRight, this is why I (author here) close the article mentioning that product design needs to keep the humans in the loop for these models to be useful. If the product is designed assuming humans will turn their brain off while using it, the fundamental unreliability of LLM behavior will create problems.
- DrewADesign 1y agoYeah, product design and marketing, for sure. As I said, I wish the marketing material was more like your blog post than what it is now. Obviously tough to get nuance in short-form copy but promising the world is a big mistake seemingly all these companies are making (…on purpose.)
- williamcotton 1y agoLLMs are both analytical and synthetical. Provide the context and "all bachelors are not married". Remove the context and you are now contingent on "is it raining outside". We can leave out Kant and Quine for now.
- pklausler 1y agoLLMs are so very good at emitting plausible, authoritative-sounding, and clearly stated summaries of their training data. And if you ask them even fundamental questions about a subject of which you yourself have knowledge, they are too often astonishingly and utterly incorrect. It's important to remember this (avoiding "Gell-Mann amnesia"!) when looking at "AI" search results for things that you don't know -- and that's probably most of what you search for, when you think about it. I.e., if you indignantly flung Bill Bryson's book on the English language across the room, maybe you shouldn't take his book on general science too seriously later. "AI" search results would perhaps be better for all of us if, instead of having perfect spelling and usage, and an overall well-informed tone, they were cast as transcriptions of what some rando at a bar might say if you asked them about something. "Hell, man, I dunno."
- cogman10 1y agoA coworker of mine recently ran into this. Had they listened to the AI they'd have committed tax fraud. The AI very confidently told them that a household with 2 people working could have 1 person with a family HSA and the other with an individual HSA (you cannot).
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- reckoning 1y ago> LLMs are bullshitters. But that doesn't mean they're not useful But this is itself an issue. LLMs aside, whenever people see a human bullshitter, identifies them as a bullshitter, and then thinks to themselves, "Ah! But this bullshitter will be useful to me" it is only a matter of time before that faustian deal, of allowing harm for the people who put trust in you in exchange for easy returns, turns to harming for you eventually.
- zknill 1y agoIt's rare that you come across a product where everything you use works so well for you. The kagi AI search results triggered with "?" and the Kimi K2 model from assistant are both excellent in helping find what I actually want to see. Love kagi, keep it up.
- cadamsdotcom 1y agoThis post is great! It successfully argues that LLMs are limited in usefulness without access to ground truth. But that’s not the whole story! Giving LLMs an ability to check their assertions, eg. by emitting and executing code to see if reality matches their word-vomit, or being able to research online - I wish the author had discussed how much of a game changer that is. Yes I know I’m “only” talking about agents - “LLMs with tools and a goal, running in a loop”.. But adding ground truth takes you out of the loop. That’s super powerful. Make it so the LLM can ask something other than you to point out that that extra R in strawberry that they missed. In code we have code-writing agents but other industries can benefit from the same idea. Maybe a creative writer agent can be given a grammar checker for example. It helps the thing do more on its own, and you’ll trust its output a lot more so you can use it for more things. Yes - plain LLMs are stream-of-consciousness machines and basically emit bullshit, but that bullshit is often only minor corrections away from becoming highly useful autonomously emitted output. They just need to validate against consensus reality to become insanely more useful than they are alone.
- __LINE__ 1y agogreat point.
- tim333 1y agoI don't buy the bullshit thing if you use a dictionary version of bullshit: >to talk nonsense to especially with the intention of deceiving or misleading https://www.merriam-webster.com/dictionary/bullshit https://www.merriam-webster.com/dictionary/bullshit like say Musk saying there'd be a million robotaxis on the road by next year in 2020. Gemini 2.5 getting the riddle wrong seems an honest mistake - a confused guess rather than an intention to deceive. Slightly related, Hinton was amusing accusing Gary Marcus of confabulating rather than the LLMs https://youtu.be/d7ltNiRrDHQ https://youtu.be/d7ltNiRrDHQ