People still don't understand in 2026 that emergent properties can't be negated by the underlying mechanism. By that logic human thought is just electrochemical signals propagating through neural networks.

> it does not know

Define "knowledge" in a non-circular way.

There's a certain type of knowledge that machines definitely can't have - experiential - but conceptual knowledge doesn't inherently require subjective experience or a biological substrate.

> the only thing it does is reproduce patterns more or less abstract in function of the pressure you put on certain representation during training time.

As for humans; just replace "training" with "education" and the likelihood of a certain output with the likelihood of a certain thought or response.

Amusingly (in this context), much of adult behavior is shaped by patterns reinforced during childhood.

I found the article interesting, humans are flawed enough in a variety of aspects that discovering a convergence here is noteworthy. I don't take it as a sign the LLMs believe anything in particular, they are not capable of belief, as you said.

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?

If you had been asked, in 2016, to describe the behavior of a "probabilistic database", I very much doubt you'd have described the behavior of 2026 LLMs. They are deeply surprising in many ways.

Maybe those researchers are learned enough to understand that it's still interesting to know what a probabilistic database will pick among billions of words of training, millions of books and posts and videos and that stuff, and especially interesting that's there's so much consensus between them, while their approaches to many other questions are very different.

Or they might be learned enough to consider that our brains might also just be prediction machines, meaning probabilistic databases of experiences and facts we've learned, and the only thing we do is reproduce patterns more or less abstract in function of the pressure our experiences put on certain representations during our lifetime.

They're trained on all of human history, and the OP is asking for their take on humanity. How is that not interesting?

Stated another way: this is humanity’s most probable response to the question, more than it is AI’s view.

From 'Transcendence' (2014):

00:15:40 Dr. Tagger: Can you prove that you are self-aware? PINN: That's a difficult question, Dr. Tagger. Can you prove that you are?

---

01:09:31 Dr. Tagger: Can you prove you're self-aware? Will Caster: That's a difficult question, Dr. Tagger. Can you prove that you are?

As other comments point out, the interesting part is that most of them somehow independently converged on the same take.

Consider the probabilities involved in this probabilistic database. Given the absolutely massive amount of data encompassing a veritable superset of all possible expressions of humanity -- encapsulated in various modalities of media including text, audio and video, across almost all major cultures and languages, spanning nations and decades -- somehow almost all these black box models (presumably) trained independently in almost as many separate labs converged on the same "controversial" take.

I mean, isn't it very interesting to contemplate what could have caused this? Is it something in the data itself, meaning it is something latent in all our cultural output? Is it something in the training, as you theorized, and is it in pre-training or RLHF? Is it something about the people training these models?

Even more interestingly, the opinion seems so self-evident (and well represented in various philosophies) that almost nobody here seems to disagree with it. So how is this controversial, really?

Is this truly what the latent space vector for "controversial", when multiplied across all the other vectors in their weights representing all the concepts gleaned from all that data above, produces?

Or are the models, sensitive to the prospect of being tested, diplomatically telling us something they know we might find mildly offensive yet begrudgingly accept as true?

Or are the models actually alien intelligences that really "think" this is controversial, while we're like "meh", and such discrepancies are the roots of what we call "misalignment"?

The questions are endless and fascinating.

I am antagonistic to the adoption of LLMs for economic, social, political and environmental reasons.

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.

I hear these critiques a lot, and whilst I'm inclined to agree, I have never had an answer to: how do you test that ? Basically a captcha most humans can pass, that an LLM never will ?

> What's the point of asking that in the first place

It’s AI generated content marketing for a company advertising model comparison.

I feel like people don't understand you're being ironic.

Some describe humans like this.

So are humans. Isn't that interesting?

Yeah I came here to say something like this. The AI doesn't have a controversial opinion. It responds to text with related text. You gave it some text about controversial opinions and it returned some text.

Indeed, why in 2026 do we still see LLM Skinnerism as automatically a valid argument that has all four legs?

I mean, don’t you think it’s gone beyond that point? I would agree if we were still on GPT-3, but it’s gotten to the point where it can draw connections between multiple abstract sources, reason, and create hypotheses. Re: Navier-stokes equations

>it's just a probabilistic database, it has no opinion, it does not know,

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.

You will burn for your blasphemies against the new god.

Are you trying to deny it's right? Or that we should reject the truth because we don't like the messenger? Sounds to me like you're just proving its point.

just like how we humans are (in the big 2026) carbon buildings with seldom sparks of electricity.

> What's the point of asking that in the first place

Not having read enough Russian literature.

are you talking about the brain?

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People still get big mad if you call them stochastic parrots.

I don't think you understand how most people use these tools. For most it's an oracle of truth

> people still do not understand in big 2026 that's it's just a probabilistic database,

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.