History LLMs: Models trained exclusively on pre-1913 texts

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.

Transformers allow for the mapping of a complex manifold representation of causal phenomena present in the data they're trained on. When they're trained on a vast corpus of human generated text, they model a lot of the underlying phenomena that resulted in that text.

In some cases, shortcuts and hacks and entirely inhuman features and functions are learned. In other cases, the functions and features are learned to an astonishingly superhuman level. There's a depth of recursion and complexity to some things that escape the capability of modern architectures to model, and there are subtle things that don't get picked up on. LLMs do not have a coherent self, or subjective central perspective, even within constraints of context modifications for run-time constructs. They're fundamentally many-minded, or no-minded, depending on the way they're used, and without that subjective anchor, they lack the principle by which to effectively model a self over many of the long horizon and complex features that human brains basically live in.

Confabulation isn't unique to LLMs. Everything you're saying about how LLMs operate can be said about human brains, too. Our intelligence and capabilities don't emerge from nothing, and human cognition isn't magical. And what humans do can also be considered "intelligent autocomplete" at a functional level.

What cortical columns do is next-activation predictions at an optimally sparse, embarrassingly parallel scale - it's not tokens being predicted but "what does the brain think is the next neuron/column that will fire", and where it's successful, synapses are reinforced, and where it fails, signals are suppressed.

Neocortical processing does the task of learning, modeling, and predicting across a wide multimodal, arbitrary depth, long horizon domain that allow us to learn words and writing and language and coding and rationalism and everything it is that we do. We're profoundly more data efficient learners, and massively parallel, amazingly sparse processing allows us to pick up on subtle nuance and amazing wide and deep contextual cues in ways that LLMs are structurally incapable of, for now.

You use the word hallucinations as a pejorative, but everything you do, your every memory, experience, thought, plan, all of your existence is a hallucination. You are, at a deep and fundamental level, a construct built by your brain, from the processing of millions of electrochemical signals, bundled together, parsed, compressed, interpreted, and finally joined together in the wonderfully diverse and rich and deep fabric of your subjective experience.

LLMs don't have that, or at best, only have disparate flashes of incoherent subjective experience, because nothing is persisted or temporally coherent at the levels that matter. That could very well be a very important mechanism and crucial to overcoming many of the flaws in current models.

That said, you don't want to get rid of hallucinations. You want the hallucinations to be valid. You want them to correspond to reality as closely as possible, coupled tightly to correctly modeled features of things that are real.

LLMs have created, at superhuman speeds, vast troves of things that humans have not. They've even done things that most humans could not. I don't think they've done things that any human could not, yet, but the jagged frontier of capabilities is pushing many domains very close to the degree of competence at which they'll be superhuman in quality, outperforming any possible human for certain tasks.

There are architecture issues that don't look like they can be resolved with scaling alone. That doesn't mean shortcuts, hacks, and useful capabilities won't produce good results in the meantime, and if they can get us to the point of useful, replicable, and automated AI research and recursive self improvement, then we don't necessarily need to change course. LLMs will eventually be used to find the next big breakthrough architecture, and we can enjoy these wonderful, downright magical tools in the meantime.

And of course, human experts in the loop are a must, and everything must be held to a high standard of evidence and review. The more important the problem being worked on, like a law case, the more scrutiny and human intervention will be required. Judges, lawyers, and politicians are all using AI for things that they probably shouldn't, but that's a human failure mode. It doesn't imply that the tools aren't useful, nor that they can't be used skillfully.