alt Hacker NewsI think this is a case of that mildly apocryphal Richard Feynman quote: "if you think you understand quantum mechanics, you don't understand quantum mechanics."
I understand LLM architecture internals just fine. I can write you the attention mechanism on a whiteboard from memory. That doesn't mean I understand the emergent behaviors within SoTA LLMs at all. Go talk to a mechanistic interpretability researcher at Anthropic and you'll find they won't claim to understand it either, although we've all learned a lot over the last few years.
Consider this: the math and architecture in the latest generation of LLMs (certainly the open weights ones, almost certainly the closed ones too) is not that different from GPT-2, which came out in 2019. The attention mechanism is the same. The general principle is the same: project tokens up into embedding space, pass through a bunch of layers of attention + feedforward, project down again, sample. (Sure, there's some new tricks bolted on: RoPE, MoE, but they don't change the architecture all that much.) But, and here's the crux - if you'd told me in 2019 that an LLM in 2026 would have the capabilities that Opus 4.7 or GPT 5.5 have now (in math, coding, etc), I would not have believed you. That is emergent behavior ("grown, not made", as the saying is) coming out of scaling up, larger datasets, and especially new RL and RLVR training methods. If you understand it, you should publish a paper in Nature right now, because nobody else really does.
I wouldn’t use the phrase “emergent behavior” when talking about a model trained on a larger dataset. The model is designed to learn statistical patterns from that data - of course giving it more data allows it to learn higher level patterns of language and apparent “reasoning ability”.
I don’t think there’s anything mysterious going on. That’s why I said we understand how LLMs work. We may not know exactly how they’re able to produce seemingly miraculous responses to prompts. That’s because the statistical patterns it’s identifying are embedded in the weights somewhere, and we don’t know where they are or how to generalize our understanding of them.
To me that’s not suggestive that this is an “alien intelligence” that we’re just too small minded to understand. It’s a statistical memorization / information compression machine with a fragmented database. Nothing more. Nothing less.