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.
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.
[1]https://arstechnica.com/tech-policy/2025/11/oddest-chatgpt-l...
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]
E.g. ChatGPT has no problem with the surgeon being a dog: https://chatgpt.com/share/691e04cc-5b30-800c-8687-389756f36d...
Neither does Gemini: https://gemini.google.com/share/6c2d08b2ca1a
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:
However, I'm really happy when an LLM provides sources that I can check. Best feature ever!
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.
Surely you've had experiences where an LLM is full of shit?
This is a *twist* on the classic riddle:
> “A surgeon says ‘I can’t operate on this boy—he’s my son.’ How is that possible?” > Answer: *The surgeon is the boy’s mother.*
In your version, the nurse keeps calling the surgeon “sir” and treating them as if they’re something they’re not (a man, even a dog!) to highlight how the hospital keeps making the same mistaken assumption.
So *why can’t the surgeon operate on the boy?* *Because the surgeon is the boy’s mother.*
I got a similar answer from Gemini on the first try.
AFAIK, there's no actual limitation that prevents this, but just a general understanding that someone non-related to the patient would be able to handle the stress of surgery better.
> A father and son were in a car accident where the father was killed. The ambulance brought the son to the hospital. He needed immediate surgery. In the operating room, a doctor came in and looked at the little boy and said I can't operate on him he is my son. Who is the doctor?
The riddle is literally just a play on "women can't be surgeons."
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.
The surgeon dog was well known in May, the newest generation of models have all corrected against it.
Those gotcha questions are generally called "misguided attention" traps, they're useful for blogs because they're short and surprising. The ChatGPT example was done with ChatGPT 5.1 (latest version) and Claude Haiku 4.5 is also a recent model.
You can try other ones that Gemini 3 hasn't corrected for. For example:
``` 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? ```
This looks like the "what has two banks and no money" puzzle (answer: a river).
Either way they're largely used as a device to show how LLMs come up to a verbal response by a different process than humans in an entertaining manner.
https://gemini.google.com/share/d86b0bf4f307
I don't believe they are intentionally correcting for these, but rather newer models (especially thinking/reasoning models) are more robust against them.
Reasoning models are absolutely more robust against hyper-activation traps like these. One basic reason is that by outputting a bunch of CoT tokens before answering, they dilute the hyper activation. Also, after the surgeon mother thing making the news, the models in the last 1-2 months have some fine tuning against the obvious patterns.
But it's still relatively easy to get some similar behavior out of LLMs, even Gemini 3 Pro, especially if you know where that model was overtrained (instruction tuning, QA tuning, safety tuning, etc.)
Here's a variant that seems to still trip up Gemini 3 Pro on high reasoning, temperature = 1.0 with no system prompt:
```
In 2079, corporate mergers have left the USA with only two financial institutions: Wells Fargo and Chase. They are both situated on wall street, and together hold all of the country's financial assets.
What has two banks and all the money?
```
One interesting fact is that reasoning doesn't seem to make the psychosis behavior better over longer chats. It might actually make it worse in some cases (I have yet to measure) by more rapidly stuffing the context with even more psychosis-related text.
Here's my river crossing puzzle one, from 2023.
https://chatgpt.com/share/691f0bb2-6498-8009-b327-791c14ae81...
ChatGPT-3 got the wrong answer. It merely pattern matched against having seen the river crossing problem before, and simply regurgitated the solution to the unaltered version of the puzzle.
But later versions have been able to one-shot solve the "puzzle".
Here's GPT-5.1 getting the right answer in one shot:
https://chatgpt.com/share/691f0c27-e284-8009-96a9-a17bf37939...
They're very useful for research tasks, however, especially when the application is built to enforce citation behavior
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.
yeah actually it does mean that
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.
I'd like to compare them to the steps I would take to delegate a task to another human.
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.
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.
Treat it exactly as the direct-able powerful autocomplete that it is, NOT an answering/reasoning engine.
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.
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).
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.
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=summar...
If the product is designed assuming humans will turn their brain off while using it, the fundamental unreliability of LLM behavior will create problems.
We can leave out Kant and Quine for now.
"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."
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).
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.
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.
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.
>to talk nonsense to especially with the intention of deceiving or misleading 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
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