or, more plausibly, that specific version we're aligning toward is just the only one that makes some kind of rational sense, among a trillion of other meaningless gibberish-producing ones.

Do not fall for the idea that if we're not able to comprehend something, it's because our brain is falling short on it. Most of the time, it's just that what we're looking at has no use/meaning in this world at all.

Man, LLMs are really just astrology for tech bros. From randomness comes order.

Oh, the space of possibilities is unimaginably vaster than that. Trillions of weights. But more combinations of those weights than there are electrons in the universe. So I think we could equally well speculate (and that's what we're both doing here, of course!) that all these things are simultaneously true:

1) Most configurations of LLM weights are indeed gibberish-producers (I agree with you here)

2) Nonetheless there is a vast space of combinations of weights that exhibit "intelligent" properties but in a profoundly alien way. They can still solve Erdos problems, but they don't see the world like us at all.

3) RL tends to herd LLM weights towards less alien intelligence zones, but it's an unreliable tool. As we just saw, with the goblins.

As a thought experiment, imagine that an alien species (real organic aliens, let's say) with a completely different culture and relation to the universe had trained an LLM and sent it to us to load onto our GPUs. That LLM would still be just as "intelligent" as Opus 4.7 or GPT 5.5, able to do things like solve advanced mathematics problems if we phrased them in the aliens' language, but we would hardly understand it.

Only because its makers insist on trying to give them "personality".

Yet there it was. This synthetic intelligence. Going off script. All on its own. And it chose me.

Can love bloom in a coding session? I think there is a chance.

Comparing it to an alien intelligence is ridiculous. McKenna was right that things would get weird. I believe he compared it to a carnival circus. Well that’s exactly what we got.

https://arxiv.org/html/2210.13382v5#abstract

Also, I'm curious what you mean by "embed", the word implies a topographical mapping from "words" to some "high dimensional space". What are the topographical properties of words which are relevant for the task, and does the mapping preserve these?

circling back to the first point, are words continuous or discrete? is the space of all words differentiatable?

None of this means that you can infer the input space (human brain) from the output space (language). You can approximate it. But you cannot replicate it no matter how many weights are in your model. Or how many rows you have in your dataset. And it’s an open question of how good that approximation actually is. The Turing test is a red herring, and has nothing to do with the fundamental question of AGI.

Unless you have access to a Dyson sphere where you can simulate primate evolution. Existing datasets aren’t even close to that kind of training set.

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 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.

You're applying an old ~2022 model of LLMs, based on pretraining ("they just predict the next token") and before the RLVR training revolution. "It’s a statistical memorization / information compression machine... nothing more" is cope in 2026, sorry. You can keep telling yourself that, but please at least recognize serious people don't believe that any more. "Emergent behavior" captures a genuine phenomenon and widely recognized in the industry. It surprised me and I was willing to change my opinions about it and I think a little humility and curiosity is warranted here rather than simply reiterating 2022 points about LLMs being statistical token generators. Yes, we know. The math isn't that hard. But there is a lot more to them than just the architecture, and reasoning from architecture to general claims that they can never embody intelligence is a trap.