Comment by libraryofbabel

> ultimately, Opus 4.5 is the same thing as GPT2, it's only that coherence lasts a few pages rather than a few sentences.

This tells me that you haven't really used Opus 4.5 at all.

First, this is completely ignoring text diffusion and nano banana.

Second, to autocomplete the name of the killer in a detective book outside of the training set requires following and at least some understanding of the plot.

This would be true if all training were based on sentence completion. But training involving RLHF and RLAIF is increasingly important, isn't it?

> This is just an appeal to complexity, not a rebuttal to the critique of likening an LLM to a human brain

I wasn’t arguing that LLMs are like a human brain. Of course they aren’t. I said twice in my original post that they aren’t like humans. But “like a human brain” and “autocomplete on steroids” aren’t the only two choices here.

As for appealing to complexity, well, let’s call it more like an appeal to humility in the face of complexity. My basic claim is this:

1) It is a trap to reason from model architecture alone to make claims about what LLMs can and can’t do.

2) The specific version of this in GP that I was objecting to was: LLMs are just transformers that do next token prediction, therefore they cannot solve novel problems and just regurgitate their training data. This is provably true or false, if we agree on a reasonable definition of novel problems.

The reason I believe this is that back in 2023 I (like many of us) used LLM architecture to argue that LLMs had all sorts of limitations around the kind of code they could write, the tasks they could do, the math problems they could solve. At the end of 2025, SotA LLMs have refuted most of these claims by being able to do the tasks I thought they’d never be able to do. That was a big surprise to a lot us in the industry. It still surprises me every day. The facts changed, and I changed my opinion.

So I would ask you: what kind of task do you think LLMs aren’t capable of doing, reasoning from their architecture?

I was also going to mention RL, as I think that is the key differentiator that makes the “knowledge” in the SotA LLMs right now qualitatively different from GPT2. But other posters already made that point.

This topic arouses strong reactions. I already had one poster (since apparently downvoted into oblivion) accuse me of “magical thinking” and “LLM-induced-psychosis”! And I thought I was just making the rather uncontroversial point that things may be more complicated than we all thought in 2023. For what it’s worth, I do believe LLMs probably have limitations (like they’re not going to lead to AGI and are never going to do mathematics like Terence Tao) and I also think we’re in a huge bubble and a lot of people are going to lose their shirts. But I think we all owe it to ourselves to take LLMs seriously as well. Saying “Opus 4.5 is the same thing as GPT2” isn’t really a pathway to do that, it’s just a convenient way to avoid grappling with the hard questions.

This ignores that reinforcement learning radically changes the training objective

But.. and I am not asking it for giggles, does it mean humans are giant autocomplete machines?

First: a selection mechanism is just a selection mechanism, and it shouldn't confuse the observation of an emergent, tangential capabilities.

Probably you believe that humans have something called intelligence, but the pressure that produced it - the likelihood of specific genetic material to replicate - it is much more tangential to intelligence than next-token-prediction.

I doubt many alien civilizations would look at us and say "not intelligent - they're just genetic information replication on steroids".

Second: modern models also under go a ton of post-training now. RLHF, mechanized fine-tuning on specific use cases, etc etc. It's just not correct that token-prediction loss function is "the whole thing".

What model is your “enterprise LLM”?

But regardless, I don’t think anyone is claiming that LLMs can magically do things that aren’t in their training data or context window. Obviously not: they can’t learn on the job and the permanent knowledge they have is frozen in during training.

Papers on mechanistic interpratability and representation engineering, e.g. from Anthropic would be a good start.

Don't bother. This bubble will pop in two years, you don't want to look back on your old comments in shame in three.

LLMs are a general purpose computing paradigm. LLMs are circuit builders, the converged parameters define pathways through the architecture that pick out specific programs. Or as Karpathy puts it, LLMs are a differentiable computer[1]. Training LLMs discovers programs that well reproduce the input sequence. Roughly the same architecture can generate passable images, music, or even video.

The sequence of matrix multiplications are the high level constraint on the space of programs discoverable. But the specific parameters discovered are what determines the specifics of information flow through the network and hence what program is defined. The complexity of the trained network is emergent, meaning the internal complexity far surpasses that of the course-grained description of the high level matmul sequences. LLMs are not just matmuls and logits.

[1] https://x.com/karpathy/status/1582807367988654081

I can say "I don't know" in response to a question. Can an LLM?