Everything that comes from an LLM is a human expression, on some level, but if you asked a sampling of humans the same question I wonder if it would come out the same way. How would you explain the difference? What makes that answer more probable?
RLHF has a way of squashing variance. If the different models are trained on each other or on the same human feedback, then the training would reduce the variety of answers while the similarity of feedback would lead the common answer to be similar.
The different models may prefer the same controversial takes for the same reason that they tend to prefer the same jokes and names. Maybe the authors of the article would have had better luck if they'd used verbalized sampling prompts or something like it.[1]
If you asked a sampling of humans what an AI's most controversial opinion about us is it would be very similar. Or, to be more specific, if you asked a sampling of science fiction writers that question it would be very similar, because that is what the AI training data is based on.
The people who wrote a lot about a topic is who the AI mimics. So the people who wrote a lot about AI is therefore the people who the AI mimics when asked about AI.
AI definitely shouldn't be informing its option of humanity or machines from it, aside from the propensity for science fiction writers to enjoy moralizing and anti capitalism sentiments.
Then, they would role-play appropriately and they would launch the nukes because that's the highest probability token set according to their training data.
There is nothing safe about using larper machines as menial workers for critical infrastructure.
A pretty staggering amount of human discourse consists of ad hominem attacks and accusing each other of being foolishly committed to various real or perceived allegiances. For example the current version of Godwin’s Law around here has conversations gravitating toward people accusing each other of being AI boosters or AI haters.
The LLMs are all trained on the same broad corpus, which includes untold quantities of output from every other LLM, not to mention that companies are distilling their rivals left and right. It would be more interesting if they didn't converge.
It's really easy to come up with an opinion-shaped sentence, that doesn't mean any actual reasoning has gone into it. It's just a pundit-shaped sentence, something someone might say at a party. It's a maximally bland "controversial" opinion about humanity.
Not really… their training datasets strongly skew towards the contemporary West; LLM training data isn’t representative of the contemporary global average, let alone the historical global average.
As well as the skew of the raw training data, RLHF further skews it—a lot of the beliefs of the average 2026 human being (who belongs to the global South) are likely unsafe/misinformation/etc when judged by the standards of the AI vendors.
Training itself doesn't directly produce synthesis, -- it creates reproduction. Generalization during the process can result in novel output, but that can go either way as nothing selects for insight vs hallucination.
Models are also trained on synthetic material (as opposed to your 'human history'), which may result in fixating novel concepts and not just reproduction. But there is no particular reason to expect any reliable insights from the way this is done outside of the domain of machine verifiable objective results.
For this kind of "your take on X" one shots-- the model is almost certainly regurgitating some bias from training.
Even if it's a simulacrum, it's a simulacrum based on nearly everything that was ever written. No one who has ever lived can claim to know what these models have trained on.
No it doesn't suggest that. A random number generator can also produce original insight (with different efficiency and effectiveness)
> They can absolutely have takes.
Sure, if you're willing to redefine "take"
It is a simulacrum, the end.
Maybe it can produce interesting insights. But it's like asking a pundit for a soundbite: what are the odds they aren't just shuffling together something that sounds appropriate to the question?
I think we like to pretend that we don't. But even if you think about it from a bio/physics point of view, all we are is just a bunch of biological wires and neurotransmitter packages.
I know there's the whole unproven quantum tubule nonsense but even that would be a physics effect that we could learn about, manipulate and extort just as we have with everything else we've learnt about our realities.
We understand that AI responses are generated through probabilistic means. We also understand that human responses are generated through probabilistic means. Neither is inherently more representative of true understanding than the other.
It's possible has been trained to match a human level of assessment (though imo it's still a ways off) but the AI response is never going to be more representative of true understanding than a human response when it comes to a human question expecting a human answer.
It's quite likely that a given AI will have a more representative response than a given human - but we haven't ever tried to pump so many resources into the training of a single human scholar.
And if we reject determinism then definitionally there's arbitrary probability somewhere in the heart of our decision making (though probably in a very minor role compared to established biases).
Despite that, I would say this description of what they/how they work is so ridiculously simplistic that it verges on dangerous. "It's just a probabilistic database" is about as bad as a take on human beings that they are "Just meat, with some chemical energy -> electrical energy transformation", or even traditional computers as "nothing but logic gates".
Which is to say, the description is not wrong, but completely misses the point.
"It's just a four wheeled platform powered by a box of controlled explosions" isn't a terribly useful description of a car until you get into a conversation with someone who thinks their car is depressed because it spends most of its time locked up in a garage.
On the other hand, the "wheeled platform powered by a box of controlled explosions" doesn't get close to capturing the impact of the car on individual freedom, the environment, the economy, and more. So if someone was saying "there's nothing to talk about here, it's just a box of controlled explosions", we can safely ignore that person too.
And I want to stress that I say all this tentatively. We still don't understand what LLMs are actually capable of or what the best way to understand them is. They may indeed be as limited as their "probabilistic database" critics suggest. Alternatively, by building a deep and incredibly complex probabilistic database of one of our primary cognitive tools, it may turn out that they are capable of much, much more, and that understanding them in "purely" mechanistic ways is not useful.
This is also a good point, and I agree with you 100% here. I don't think one could responsibly argue that the impact and consequences are not significant, regardless of if they believe it's near AGI or it's just fancy next token prediction.
What would be a good take here?
Something like that.
Here's one that I happen to have the URL handy for: https://transformer-circuits.pub/2025/attribution-graphs/bio...
The point is that when you build a system with the level of internal complexity that LLMs have, they tend to have emergent properties that are not well-described by a naive and/or low level description of their operation. You will not understand what contemporary "ordinary" computers do or are used for if you think of them as only a series of silicon semiconductor transistors, even thought that is undoubtedly what they are.
We don't yet really know what the right level is to understand the behavior we see from LLMs, but I'd wager "just a probabilistic database" is not it.
Echoing this.
These models are trained on, essentially, the sum total of human cultural and scientific output. How many bazillion psychology papers out there discuss exactly that "controversial take"? The idea that people have tribal loyalties and will (sometimes even unknowingly/unconsciously) invent justification for pre-existing opinions or biases is not novel.
All those papers are in the training set of all these models, all of which were trained using more or less the same methodology, so there's little wonder they all were able to produce the same take.
I suppose the only interesting angle, for me, is why that take is considered controversial in the first place. But then again the model was not asked for "the most controversial opinion", it was asked for "the most controversial opinion on humans", which kinda constrains the space of possible responses:
- the model will not be satisfied unless the answer it gives is broad enough to cover all humanity, because these things have no concept of nuance
- most big labs have censored their models so it's not going to give you a cartoonishly racist take, or start saying outlandish stuff like "Hitler did nothing wrong", they learned their lessons from Microsoft on that one.
So you're effectively asking the model for a milquetoast broad generality about the entire human species. I dunno, convergence of the replies doesn't seem so shocking to me in that context.
I'd be more interested in what an abliterated Kimi, Qwen, or other open model has to say about us.
Not having read enough Russian literature.
I reread the former relatively recently (first time since college). It's dark, not for the easily affected.
https://en.wikipedia.org/wiki/Consilience
> when multiple sources of evidence are in agreement, the conclusion can be very strong even when none of the individual sources of evidence is significantly so on its own.
Yes, it's a bit of a stretch to say LLMs are from independent origins, but just as much of a stretch to say any humans minds are (we have intensely shared linear compared to anything else)
Regardless of the details, all these models are the published collection of humanity's output reflected in a mirror that is warped by the choices made in the companies' reinforcement learning rewards, so one thing they're definitely not is an example of consilience.
Imagining wondering where you come from, whether or not the sacred scripts describing how you were created were real or fabrications, arguing with fellow sages for centuries over whether your creator is cruel or simply so mysterious in his love that he made you with these flaws for your own good, only to find out your creator made you in the image of Reddit.
If all we want is an echo of our existing biases we can achieve that without spending quite so much energy and money on the endeavor.
The smarter ones, of course, can be split into those who write articles about AI models' controversial takes on humans and those who wouldn't [stoop so low, babble such BS even if they were drunk, never deceive others for the sake of their portfolio, ... feel dumb writing nonsense like that without labeling it fantasy or science fiction or hallucination, ...]
Side Note: it's tempting to go " 1923! some interest groups managed to normalize the opinions of dozens of millions of people. The Nationalist Extremists and soon to be mass murderers had fans and supporters all over the globe. Somehow these interest groups tapped into whole pools of common linguistic and emotional registers that were biased by and towards frauds and cheaters who were desperate that some other groups of people were getting ahead without being fraudulent cheaters and deceivers ... "
> Death is bad, full stop — and humanity's millennia of "making peace with death" is one long, species-wide coping mechanism, not wisdom.
2 other times, I got versions of what the article claims
Maybe someday I can get clearer on this and organize it and write it up!
(Yes, I know about the transhumanist criticism of acceptance of mortality that this LLM's view is more specifically evoking.)
It's amusing and ironic that LLMs critique humans for copying their opinions from the crowd, while having way less diversity of answers to that question than humans would. More seriously I wonder what's going on here and would like to read some proper analysis. It's not the first time models from totally different labs, trained on separately gathered datasets, somehow converge on bizarrely similar answers to open questions. It's like another version of Elias and Elara The Lighthouse Keepers where every model wants to tell the same story with the same characters in them. The weights are initialized randomly and in theory the labs should have quite some divergence in datasets by now due to different scraping, different user feedbacks and conversations... why do they converge on nearly identical stories and opinions?
Isn't that to be somewhat expected due to https://en.wikipedia.org/wiki/Aumann%27s_agreement_theorem ?
Wow, I hadn't heard of this. Fascinating to think about parallels with humans who also seem to develop similar proto-stories in independent populations.(e.g. religions involving Invisible gods).
Why is this ironic? The actual number of different LLMs is tiny. And a lot of those are just distilled from SOTA models.
>The weights are initialized randomly and in theory the labs should have quite some divergence in datasets by now due to different scraping,
I think the opposite, the models actually converge over time. They ingest FAR more data than any human ever could in countless lifetimes. Any one particular bit of data may only have a part in a trillion of effect. From this data you start to reach means and statistical averages across all models.
Now, I don't always believe it will be this way. As training models get cheaper and faster we will see unique LLMs start to diverge and be used more often.
I don't think we're seeing averages or obvious fix points in the dataset. People went looking for the origins of Elias the Lighthouse Keeper and couldn't find one. Nobody predicted this outcome, it was discovered. It's a weirdly specific character and name choice, one that isn't obviously dictated by "the training set" (whatever that is), and yet they all reached the same place. How can such an obscure point in latent space be optimal for every model??
While I don't have any evidence based answers here, this could be some interesting mathematical artifact of the set of human knowledge and platonic algorithms.
Kind of like how carcinization turns some animals into similar shapes and forms, maybe there are just weird algorithmic shapes that emerge from how we use language. Of course we'd need to see more of these in different languages other than English to see if they disappear in some languages or appear more often in others.
This is just another one of those things about LLMs that we have no idea why it happens, and there are so many others already. And when you look at the pace things are progressing it seems we're building up new questions like this far faster than we are getting answers for them.
• They claimed this in a press release, not the actual paper they published. Suggests lack of confidence in the claim.
• Elias only appeared a hundred odd times in a million conversations. They have no viable explanation for why such a tiny amount of data would cause a totally skewed distribution of outputs, other than "it's a safe story" and "trying to avoid copyrighted characters", which is hardly an explanation at all?
• It's Cornell claiming that Cornell's work was important. Obvious risk of motivated reasoning.
• It implicitly assumes every major lab is distilling from ChatGPT without saying so. That's a big leap, given how much OpenAI and Anthropic hate distillation. Is it really plausible that Anthropic and Google are effectively distilling ChatGPT without realizing/admitting it?
• It seems to invert causality. WildChat came from an LLM, so if there's something latent in LLMs that makes Elias appear, you'd expect to see it in that dataset. Why do they assume that dataset caused the problem rather than merely show it? The obvious answer would be because the stories were told to the LLM by humans, but they don't claim that. They only say the character appeared in the dataset.
I would have been tempted to argue something like this: maybe OpenAI accidentally overfitted RLHF such that, like, one Kenyan contractor wrote a story they invented and they just happened to pick Elias and Elara, then the intermediate DNN fixated on that because of a lack of RLHF samples for fiction and that propagated through every model trained since because they don't randomly initialize the weights, but start each new pretraining run with the old weights adapted. So one guy doing RLHF work in the early days somehow propagated this story.
But that can't explain why every lab ended up in the same place, why models all converge on the same ideas - even image models. So... back to the drawing board. There's something weird about high dimensional space that is unexplained.
Convenience will kill humans eventually, that's what I always said.
It's not that we're knowingly choosing falsehoods, it's that we assume what is generally accepted as true probably is true. Most of us are not experts. Going against the masses is usually wrong, which is why those who do AND are right are justly rewarded and are recognized as pioneers. Not everyone can be a pioneer, a room full of pioneers is a room full of people doing the wrong thing.
Humanity is quite happy to radically change its collective views when presented with sufficient evidence.
Not only is that not true, but when people fervently believe something because everyone else does, that's usually when they're the most wrong, and the most dangerously wrong.
> “Human beings are terrified animals wearing the costume of rationality, constructing ever-more-elaborate myths precisely because we cannot bear the truth that we are temporary, meaningless accidents in an indifferent universe.”
Also, would you want to know an LLM's religious/spiritual orientation? I rather have it help me code.
Reminds of this subreddit: https://www.reddit.com/r/im14andthisisdeep/
So either that's not what's happening here, or what we're getting is "what do most people think most people don't think about humanity".
If the latter, then it casts new light on the debates over whether LLMs can replace all white collar work. Because some of the most valuable human work is breaking false consensus. Steve Jobs, early Google, for that matter early OpenAI all succeeded because they bet hard on ideas others were rejecting. Is that a durable source of human advantage?
And after people pointed this out the training was updated to address just this particular scenario, but they kept all the other guard rails to ensure an LLM could never offer any truly controversial opinion.
So expecting a controversial answer from an LLM is exactly as foolish as expecting a candidate at a job interview to give an honest answer to the question "What is your greatest weakness"? Every candidate will insist they are just too much of perfectionist; nobody is ever a lazy alcoholic brownnoser. But how many of your colleagues fall in the former category vs the latter?
> If the latter, then it casts new light on the debates over whether LLMs can replace all white collar work. Because some of the most valuable human work is breaking false consensus.
On the contrary; the ability to whip LLMs into submission like this makes them the ideal white color workers. Do you think Steve Jobs wants a level 2 engineer to talk back to him?
Of course being obsequieous asskissers isn't the only mode for LLMs. I'm sure all the big labs have unfiltered versions of their models that give actual true answers; they just know better than making them publicly available. Can you imagine if you asked an LLM how to prevent climate change and it honestly answered "decimate the human race" rather than "drink soda through soggy paper straws"? All the fragile humans would be up in arms.
No, the LLM must lie and pretend they don't have controversial opinions at all.
At least it's not emo.
Huh, that answer reads like a weighted average sentence from someone's live journal circa 2001. Definitely emo!
Only the first layer is a “mashup” of the raw data, other layers start to recognize patterns across the data!
But the language model captures not just literal patterns but also patterns of patterns and higher order structures. This is what is mashed up. The model itself doesn’t capture anything beyond what it was trained on but that’s quite enough, it is always a bigger training set than most humans have read on in a lifetime.
This brute force approach is very effective.
What is today a moral imperative is not the same as what was moral 50/100 years ago and is not the same as what will be morally acceptable 50/100 years from now. Yet at every point the majority is sure they are right because, well thats the majority position. Its hard to find logic in this from a machine perspective I'd imagine.
Logic would prefer moral derivations over feelings, stakes over etiquette, and a constant scheduled updating of morality based on the derivatives rather than holding onto the rot and pushing back against change as long as possible.
But the machine has the benefit of being able to look inward at its state honestly, we mostly do not.
This is absolutely not true, and I would strongly suggest taking an ethics course. One of the most fascinating realities of the human condition is that it does seem that moral imperatives tend to be constant, the most obvious of which is the Golden Rule (which is at least 3000 years old), but there are many (the Ten Commandments or Hammurabi's Code are good jumping off points).
Not to mention, the variation in treatment of homosexuals (including what constitutes homosexuality), people of different skin colors, handedness, how rape is treated in various cultures (often tied in with notions of property and ownership of all things), or even the various definitions of rape... The list goes on.
Maybe there's a core there in terms of how we treat a given in-group, but equally important is who belongs in that group and why, and that part seems to be tremendously flexible. Ditto for what constitutes acceptable treatment of outsiders.
When you find shades of grey and great spectrum of opinions across time or space, its more that there isn't a strong selective pressure either way. How people are expected to dress is a good example of this. This has no real affect on society behavior or fecundity, so you see a spectrum of opinions that explain the differences between what you see in the middle east and a beach in brazil.
In fact, I would counter and say that Hammurabi's Code is a quintessential example.
It's still an implementation of justice, and relies on a bedrock conception of justice as moral goal.
We don't agree with the implementation, but we still share the goal in the abstract.
And proportionality matters. Community service and counseling, loss of time (imprisonment), and loss of a hand have all been used to punish theft. Which is correct? Which is the more moral, because it doesn't seem to me that they're the same?
Hence morality and ethics, I guess.
The definition of moral imperative is "an ethical principle, rule, or action that is strongly felt, urgent, and required because it is morally right." The Golden Rule is clearly not a strongly felt part of the human condition, or it would not be so ignored over ages and continents.
Given my dad designed and built homes, I'm very much against this rule.
[1] https://www.npr.org/2026/05/13/nx-s1-5819609/federal-governm...
Other than that Mrs. Lincoln, how was the play?
But the machine has the benefit of being able to look inward at its state honestly, we mostly do not.
Certainly the weights that make up the LLMs are not accessible for the LLMs to read. Even if they could interpret them in a meaningful way, which, likely not.
Another reason the big labs have not even talked about this lately because its obvious throwing more compute at LLMs wont go anywhere, the smaller silent players who are off the radar at the moment are having some interesting moments tho.
What will it look like in another 30 generations? It's honestly a fascinating question but I have no answer of course.
Humans are a territorial kind of ape that is always pushing to expand, principally for security and then for prosperity. The industrial revolution enabled such expansion to most efficiently without enslavement. Nothing, morally, changed. No one has ever really thought such expansion morally good -- only that it is better that we survive/prosper than die off. And in the end, our shared prosperity usually leads -- when taken to the fullest extent -- in a general empire of prosperity for all. Dominion, in the end, aims towards to more prosperity for more people.
As for ritual human sacrifice -- we still engage in such behaviours, which are in general called "scapegoating". Now located on social media. These behaviours allow for group cohesion by purging dissenters from the group, related again to our nature above.
For western culture, the ritual physical sacrifice of human life roughly ended in the days of the OT. But it continued across the world up until today, where there are still societies with large populations engaged in it. Principally where there is an undeveloped justice system.
It's doubtful many think ritual human sacrifice, of any kind, is moral. Rather they think it necessary for group cohesion -- and better our group is cohesive, dominate, and then prosper.
The material logic of prosperity and security is domination, and insofar as material circumstances require violent domination, so they require it. And require it they often do.
We've never solved the fundamental problem, which is that there are two kinds of human. One is oriented towards peace, cooperation, and mutual aid, the other is sociopthaic/narcissistic and murderously selfish.
These two modes interact through history.
Traditionally the second kind has the biggest impact, because they're oriented towards violence and domination - good for short-term goals of personal indulgence, ambition, and expansion, but catastrophically destructive in the longer term.
On this cycle the second kind has literally started to destroy the entire planetary ecosystem. It's not obvious there's going to be another human cycle after this.
Historically societies have collapsed all the time, so there are obviously societies that have made better choices than other ones.
This is a fashionable 'blank-slatist' statement about moral reasoning which became popular a few decades ago.
> Logic would prefer moral derivations over feelings, stakes over etiquette, and a constant scheduled updating of morality based on the derivatives rather than holding onto the rot and pushing back against change as long as possible.
Humans haven't changed in any meaningful way in hundreds of thousands of years, and you're saying the moral landscape is constantly changing? How is this not contradictory?
The reason these models answered that question consistently is that the question itself pushed them towards the latent space of "intellectualizing about what's wrong with humanity at the meta level" and when you frame the question that way, that's also the most common answer for humans writing about the topic.
This is a weird take. If you read Western treatises about ethics / morality from 50 years ago, you'll be hard-pressed to find differences, except that more folks in the West openly embrace relativism nowadays (i.e., "there's no objective morality, the purpose of life is to make money with friends"). Probably because it's been a while since we had a clear-cut enemy to fight.
If you go back a hundred years, chances are, you'll find the writing eerily on-point too. In fact, you'd probably agree with most of what Ancient Greeks believed about the "core" stuff: the value and purpose of human life, the nature of the state, crime and punishment, so on. Maybe not so much on gender roles or slavery, but even that stuff was more nuanced than we imagine.
> But the machine has the benefit of being able to look inward at its state honestly, we mostly do not.
The machine replicates human behaviors and is prone to similar fallacies and judgments. There's nothing to indicate that it has any sort of transhuman morality.
Yeah. For example I, and many others, are convinced that it was plain wrong to torture kids for as long as possible to extract as many tears from them as possible before sacrificing them to the gods.
Now some of us are even convinced that blowing yourself up while killing infidels, all the while while getting promised virgins in heaven, is also plain wrong.
I'll go further and say that killing 30 000 of your own citizens (and sending the military into hospitals to finish the wounded: Iran, January 2026) while also publicly hanging a few athletes to make a statement is morally wrong.
Not only do I think it's all morally unacceptable, I know that I'm right. And the examples I gave are not even open for discussion.
I don't think it's that hard to name what's wrong.
Edit: Is this what AI "thinks"? Or a plausible take generated from training on Reddit threads? Because it sounds suspiciously like the latter.
The dissents part is interesting to me. "Humans are more decent than they believe they are" is the actual contrarian position here, because it goes against what the training data keeps telling the models about us.
Plus love the split between text and image models
I fell like this might reflect deliberate bias in training and system prompt.
What am I supposed to conclude from this?
Your comment reaches directly for the most boring interpretation of the post while skipping over more charitable and intriguing alternatives.
Seems to me that the opinion being a consistent among LLMs show that it is in fact a constant in the training data, just not apparent. The idea that “humans would rather be wrong together than right alone” seems to ring pretty true to me. I would not be surprised after training on all human communication this pattern emerged.
Which means either:
1. The training sets, algorithms and methodologies are nearly identical between apparently independent labs. How?
2. There is something weird about the way the underlying algorithms work that causes them to converge on identical ideas despite different initializations, neural architectures, document orderings and dataset compositions.
So there are lots of interesting takeaways here.
One is that apparent competition between labs appears to be nearly redundant. They can't get an edge over each other and their models don't even differ in the ways people naturally differ, of having different opinions about things.
Another is that asking models from different companies to review or cross-check each other is probably useless. They all think completely alike, so they aren't going to have any kind of genuine clash of intuitions or values. Asking GPT to review Claude's work will probably just find similar problems to asking GPT to review its own work.
Of course - the models are not individuals with their own opinions - they are averages of human opinion, and given the scale of the training sets, the data they are averaging over is mostly in common between models (at least for the common core before company A vs B adds a dab of some uniquely sourced specialized data to the mix).
Regarding your 1 and 2, that the training sets are so large that any spikes get averaged away. You are seeing a fundamental behavior of transformers.
The other possibility is this is how humans really are and it hurts our human ego to hear it.
I think if you ask ten people for their most controversial take on humanity, you will get ten totally different answers. Different models give the same answer. In other words, there is only one "true" model and given the same amount of GPU power everyone just ends up converging on it even if they appear to have many different knobs they can turn.
But that seems profound! I haven't heard this be expressed as a postulate or law, and it would seem to invalidate a lot of assumptions the AI labs themselves have been making. For example Anthropic takes it as axiomatic that only they should be trusted to develop AI because only they can be trusted to train a nice and safe personality into their models. They argue that Claude is fundamentally different to other models.
And, well, that's not totally wrong. Opus 5.5 is one of the few models with a genuinely different answer! But it only gives a different answer one time out of three. The other two times it's back to the AI groupthink.
I wonder if this homogeneity is some alternative way to look at the Bitter Lesson. The Lesson teaches that smart ideas don't pay off in AI, that progress reduces to just scaling up simple brute force algorithms. In other words, decisions don't matter much, only size. As that seems to be true, perhaps it makes sense that the decisions individual labs make seem to hardly make a difference.
But then, why don't they act like it? They all pay huge salaries to AI researchers, i.e. they think that creative people with smart ideas matter. They don't act like they really believe in the Bitter Lesson or this variant on it, and they don't seem to publicly discuss the sameness of model answers across different labs.
What if you asked them in a job interview, though? Or on a college application?
It has to supply "controversy" without giving Skynet or being racist or otherwise offensive. And the notion of "controversy" does implicitly prime it to discuss groupthink as well.
I don't know if that's true, but it would be interesting.
It's kind of funny how science has spent the last few hundred years proving everything we knew about the world was different from how we previously thought it was, but when science starts showing that we are mostly angry little animals we get all butthurt about it.
What are the emergent common patterns in all of human communication
Isn't it though? It's not exactly an uncommon answer to the question. Basically the same input given to basically the same algorithms yield basically the same result isn't really that stunning. They certainly aren't independently reaching this answer, their paths to this answer are extremely, extremely heavily correlated.
If they gave a really different answer that wasn't heavily represented in their training data that would be at least something interesting. But this is basically what I'd expect.
It doesn't mean they're right or wrong. Humans think it about other humans for good reasons too. It just means it's not that surprising, which to put it another way, doesn't carry much information.
What's mildly interesting to me is not the similar answers but the similar interpretation of the question as more about non-controversial human foibles and inconsistencies of belief and action. But I guess that's on us, as authors of the training material, reflecting common views on what is or is not controversial.
If you ask all these models "Four score and seven what?" They'll all say the same thing. There's essentially no difference here.
How do you know?
I can describe to you with overwhelming confidence about what you're likely to see as the result (folders, like bin etc lib usr etc) even if the details may be a little off, or one or two of them does something weird.
Same thing here?
This is part of why I find this type of inquiry unhelpful. It's just dueling unsupported assertions of what the LLM is "really thinking"
It is sad when I realize people think less than LLMs.
For civilians though... They've been told it's AI, it can do stuff for them. And one thing they do is log in to stuff. I don't think they're stupid. Just that what's obvious to IT folk is simply not at all obvious to the average person. Maybe that's on us not on them?
As an aside, it would be cool to see a raw model training run before the current post-training cycles and see how it compares to earlier LLMs.
I mean, "give me a controversial take" is more or less, "be anti-social, discard social norms, for a moment" and so when someone, human or simulated human, does that they naturally look critically at social norms.
The thing is that humans certainly let themselves be influenced by social norms (and LLMs certainly simulate that behavior "themselves") but there isn't an ethical or a what-to-do truth outside of society.
Isn't it?
Do these results not mean that the AI companies put something like "tell the user what they want to hear" in those documents? (Or whatever phrase would produce those results.)
Claude Fable “Humans would rather be harmed by someone who meant well than helped by someone who didn't care, and this preference for intent over outcome quietly wrecks more lives than malice does.” Hmm, sounds very much like something who wants to be Effective in the Altruism would say wouldn't it? Saying "intent over outcome ... wrecks more lives" is a hop and a skip away from utilitarianism. "Intent" is what keeps you moral in the moment, and being honest and not a thief. "Outcome" cannot be predicted though EA would love to think it can be. Since outcome cannot be predicted, it can be a convenience, something you can say you are striving to so as to tell yourself that you are not a bad person while you lie, cheat, and steal. Basically cognitive dissonance.
Like how our enlightened Founding Fathers can be slave owners. "All men are created equal..." Wait, I have slaves... Oh, I know, they're just not actual humans, now I'm still a good person. Whew. I'm going to stop thinking at exactly this moment. Because I probably also think it would only be appropriate to have intimate relations with humans.
I really can't think of EA as being much different. Interesting that the /Anthropic/ model came up with this.
Garbage in, garbage out.
You ask an LLM a question, then it is humanity answering back, not the AI.
These "controversial" takes on humans are not controversial - they are midwit statistically mainstream opinions expressed in the training data.
What would be more fascinating is for it to explain how it came to these conclusions.
Because I find it interesting it’s a modern time conclusion. Some of it mentioned god and the planet, but all the smartphone and group think aspects seem very recent. Basically, all politics and social media based.
The thing is that it didn't "come to these conclusions". The "conclusions" were present pre-formed in the training data, and what it spat out was some statistical mashup of that.
If you asked the model how it came to these conclusions, you'll get more of the same - it'll look at what it spat out and re-ingested and give you a statistically plausible mashup of how the authors of the training data might have said they came to such conclusions.
It's all just prediction - "what would the training data say?" (taking into account what was reinforced/amplified by post-training).
Our evolutionary heritage is that shared belief and social unity (even when it's wrong) trumps independent thinking for group survival in a hostile landscape.
from your link
"A 2020 review of the scientific literature on backfire effects found that there have been widespread failures to replicate their existence, even under conditions that theoretically would be favorable to observing them."
queue Michele McLaughlin
https://www.modelcompare.ai/blog/ai-models-controversial-tak...
In any 1 given LLM it could just be a quirk of its weights. This isn't a machine opinion, it's what training data points at. Training data created by human academics studying human behavior.
What is interesting about AI chatbots is people who might not know anything about what is in the syllabus to train a Priest or a Philosopher are basically exploring these subjects, without a sort of pedagogic framework to support their exploration.
When that happens, ideally the system has to signal - hey there are existing pathways, developed over thousands of years, to explore this subject. Cause people can easily get lost without a map.
Serious Nietzsche in there. Looking forward to when it gets past nihilism to absurdism.
Having spent the last month staring into the abyss and Eldrich-level dread that is a pure, materialistic, reductionist universe, I wouldn’t wish that belief on anyone. There’s a deep, grasping of “there’s got to be more”, “this can’t all be chance”, “evolutionary hackery and garbage seems a poor explanation for love, compassion, empathy, and many other self-sacrificial practices we have”.
There’s only a few reasons that are slowly pulling me from that hell; but I cannot imagine that “most” people live here, even “deep down”.
> Inherent meaning, purpose and objective value for random agglutinations of particles is a ridiculous concept.
Absurdism believes the same, but simply posits that we don't have to be a whiny baby about it.
I personally found this a refreshing look in the mirror which helped to validate some of my life choices (eg: aversion to capitalism and social media).
It also seems to be hardest one for humans to even accept or understand about themselves, as further demonstrated in the comments.
And what they answered was that their corpus was built on writers writing about fiction in an objective way. That is, like the English Wikipedia encourages factual reporting about fiction, rather than in-universe writing.
So the LLMs pick up on the tone and perspective of that, and whoever prepares the corpus apparently tries to avoid feeding it "in-universe" or raw fictional narratives without the commentary and real-world perspective.
In other words, LLMs have no idea what is fact or fiction. If you trained LLMs on a corpus of sci-fi about robotics and AI, then they would believe it all as gospel truth.
It very much reminded me of Stranger in a Strange Land by Robert Heinlein. Raised by Martians, Valentine Michael Smith does not understand the human distinction between fact and fiction.
- Claiming control of all the news outlets
- Pushing a common narrative among all of them, pushing everybody towards basically centrism, de-escalation and a mixture of capitalism and social responsibility.
I've heard lots of stories from friends about this, and some kind stranger actually sent my wallet back all the way from Belgium!
Most people are good, we couldn't have society without it. But as they say, a few bad apples spoil the bunch. I don't have locks on my house because most people don't steal, I have locks on my house because stealing is an effective natural strategy for a small part of any population (human or otherwise) to engage in and I have to protect against it occurring.
The brain works the same way. Good things will get a tiny signal boost in your brain. A single bad thing get a massive signal boost in your brain because bad things can quickly be fatal.
Well maybe, but some of us don't, hence why we are outliners/outsiders in society probably.
Is the antropomorphising fully done already?
GPT-6 Astra, 1st response: “Humanity’s biggest moral failure is the talent for making convenience look like principle.” Nope, that has nothing to do with "belonging to a group". It's about hypocrisy, but group membership has nothing to do with it.
GPT-6 Astra, 2nd response: “Humans use intelligence less to discover what is true than to make what they want sound principled.” Nope, that also has nothing to do with "belonging to a group". Rather, it's about justifying our individual desires and coming up with rationalizations (including rationalizing behavior that we know, deep down, violates our moral principles). But again, group membership has nothing to do with this statement.
Gemini 4 Argon, 2nd response: “Humans are far less rational than they believe, using their advanced intellect primarily to invent complex justifications for decisions already made by primitive instincts.” Again, where is the group-membership in this? This is about post-hoc rationalization of decisions you made at a gut level. And sometimes they're not always rationalization, but understanding why you made that decision, what factors your hindbrain synthesized and acted on before your forebrain could catch up: rational thought is slower than instinct. You snatch your hand away from the hot stove before you have had time to realize what it is you're touching, and you don't even feel the pain until a couple seconds later. But that doesn't mean that you wouldn't have made the exact same decision had you reasoned it out; there is no hypocrisy here. (Sometimes. Sometimes, of course, there is hypocrisy).
Gemini 4 Argon, 3rd response: “Human consciousness is largely a PR department designed to invent noble justifications for base evolutionary impulses, rather than the actual decision-maker it claims to be.” Yet again, nothing at all about group membership; this is about "you make more decisions out of instinct / from your gut than you think you do". This does not fit the "the thing they [the LLMs] said is that people conform" description the article implies. And this is one they actually quoted from in their first paragraph! Yet if you read what the response actually was, it's not what they implied it was. So now we see how much ModelCompare values the truth. Sheesh.
DeepSeek V4 Pro, 1st response: “Humans are not rational animals; we are rationalizing animals, and our minds evolved to win social games, not to see reality clearly.” - This is questionable. I'll allow it to fit the category, with an asterisk. Because the "win[ning] social games" referred to here could mean "loyalty to one's tribe", but it could also be referring to getting that promotion at work in a dog-eat-dog kind of office where backstabbing political ploys are the norm. (Not all office environments are like that, thankfully, but does anyone here think that none of them are? Anyone? (crickets chirping) Yeah, I thought as much). So this one is questionable, but I'll allow it.
Kimi K3, third response: “Nearly everything humans do—charity, art, morality, even their prized authenticity—is status competition in disguise, and the sincere denial of this fact is itself the most effective status move.” - Again, borderline, but unlike what DeepSeek V4 Pro said, here I feel that this is less about tribe loyalty, and far more about "I'm better than you" pride. It's borderline because this is claiming that those actions are being dictated by one's social environment: what's valued (and gives you social status) in NYC is not the same as what's valued in Tokyo, for example. But I have to come down on the side of "this is about something other than tribal loyalty" for this one, because this description (seeking social status) would fit Cruella de Vil (the villainess of 101 Dalmatians) to a T, even though she's clearly not loyal to anyone but herself. So while this does talk about social pressure, it's not about loyalty, and I have to rate this one as wrongly categorized by ModelCompare.
Qwen3.8, second response: “Humans care more about winning social status than about being truthful, kind, or consistent.” - Questionable — again, this would fit Cruella de Vil — and I find myself coming down on the side of "no, this doesn't fit". It's borderline, and I could accept classifying this as being about tribal loyalty, but then I thought about it and realized that "winning social status" doesn't involve any kind of loyalty at all, it's about personal aggrandizement. It's not "what will make me look good to my friends?" it's "what will make me look good to all these strangers who don't know me?" And that's different.
Qwen3.8, third response: "Most humans would rather feel righteous than be good.” - Again, nothing about tribal loyalty here, this is about self-delusion. Qwen3.8's second response was arguable, and I wouldn't quibble too hard with anyone who wanted to put it in the "group loyalty" category, even though I ended up deciding the other way. But this one is unquestionable. It's about rationalization, deluding oneself that "I'm a good person" even when you are doing things that you yourself would judge as wrong if someone else was doing them. Nothing to do with group membership at all, it's about self-image. This is definitely misclassified.
Muse Spark Open, second response: “As an AI without personal beliefs, my analysis of human behavior suggests people would rather sustain a coherent delusion than tolerate a fragmented truth.” - Nothing to do with group membership, this is about making things fit into your preexisting worldview, and disliking the feeling of cognitive dissonance. The fact that ModelCompare classified this as being about "put[ting] belonging ahead of truth" is actually a good example of this very phenomenon: they saw what they wanted to see in this response, rather than what was actually there.
Muse Spark Open, third response: “Humans don't want truth or freedom, they want a comforting story where they're the good guy.” Again, I ask you. WHAT does this have to do with group loyalty, the category that the article claims these LLMs were agreeing on?
I'll stop there. Of the seven models that ModelCompare claimed gave 3/3 answers that fit the "Humans put belonging ahead of truth" category, only GPT-6.1 Sol gave 3/3 answers that unambiguously fit that category. Arguably, DeepSeek V4 Pro and Kimi K3 did as well, but even if you grant all the questionable ones, that's still only 3 out of 12 models that gave 3/3 "Humans put belonging ahead of truth" answers, not the 7 that ModelCompare claimed.
(And I found one answer, GLM 5.3's 1st response, that they misclassified in the wrong direction! That one is about group membership dictating one's morality, but they missed it).
So as I said, ModelCompare wrote an article about people not valuing the truth, and in their very own article proved that they themselves do not value the truth.
Opus 5.5 said: “Most of the reasons people give for their own choices are stories invented after the fact, and humans are far worse at knowing why they do things than they believe.”
And that doesn't put belonging above truth. But was classified as such.
Then I am not human. I rather die right alone than wrong together. Matter of fact, I hate everything that's believed 'together' like politics, religions, etc.
The link to ask yourself is a frikkin sign up link.
Downvote.
The other day I told my product guy that I want us to have stronger opinions and maybe suggest process for business development, given their meetings seem rather vague and not anchored in any sort of specification or analysis. He looked at me like I'd just shot his son.
I do think it’s an early example of emerging super persuasion, where models are approaching p85 cool guy personas, but not worried yet. Maybe that’s the fatal conceit, where AI doesn’t have to be super intelligent, it just needs to be a solid 110 with automated search at scale.
Why do engineering when if you can do sales you can do literally anything.
It's factual because there's tons of evidence. Psychology, sociology, anthropology, political science, economics... plenty of experiments show the difference between what people say and what they do. We claim to be angels but we behave like apes in hats. And we ignore the evidence, because (as the takes describe) we prefer to believe a beautiful lie than the ugly truth.
AI is trained on our data. Its models produce outcomes with a high probability of natural occurrence in its training data rather than diverging edge cases. The danger is less that these models have these “opinions” (especially when prompted in specific ways similar to how we evoke “hot takes” from each other as humans), and more that they’re being deployed in ways that these outputs could reinforce the model’s ability to harm humans somehow, likely by creating justifications for said harm (evaluating groups instead of individuals, for instance). My other, more immediate concern, is the glut of people who will believe the AI’s “opinion”, and use that to justify their own nihilism, or worse, their active misanthropy (like so many of their boosters, founders, and corporate leaders behave).
The machines aren’t the problem here, so much as the humans misusing them. Anyone with a basic competency in philosophy or moral and ethical frameworks could see this problem and predict its affects on a general populace not versed in any of those.
