Comment by hn_throwaway_99 | Hacker News Reader

Comment by hn_throwaway_99 | Hacker News Reader

hn_throwaway_99

The fundamental question I have that honestly I haven't been able to find any answers for: With all the talk of LLM-based AI capabilities "going vertical", what evidence is there (for or against) that LLM-based approaches won't eventually "hit a wall", that is find some aspect of intelligence where humans will still have primacy, and no amount of scaling will change that.

E.g. some prominent folks definitely think LLM-based approaches will hit a wall, perhaps LeCun most notably. I also read a guest post on Terry Tao's site (which I really liked) that argues that, for all the very impressive recent AI results, they still operate within the "convex hull" of their training data: https://terrytao.wordpress.com/2026/09/13/happy-those-able-t...

I'm just curious if there is any actual data or evidence that takeoff (i.e. RSI, "the singularity", whatever you want to call it) is inevitable with current approaches.

It’s anyone’s guess.

But when you squint your eyes, LLMs are just a least-effort manifestation of the wider neural net direction we’ve been chipping away at for decades. I don’t give a fuck whether LLMs are here to stay; they’re proof that scaling neural nets gets you pretty far.

The real question is: will neural nets hit a wall? As a human, I certainly hope so. But I’d bet that they won’t. I’d bet that we’re igniting the ~~atmosphere~~ universe in slow motion.

I wouldn't take stock in what LeCun says. He's already been wrong many times about what LLMs can be capable of.

We're in new grounds, no one knows which way it'll go.

I don't think there is evidence in either direction (takeoff impossible vs inevitable).

But the "convex hull" idea always came across as wishful thinking and hand-wavy pseudo-science to me.

Here's a rebuttal [0] of it from Tim Gowers (uses another math metaphor, but still clear and the whole thread is worth a read):

>...the popularity of the idea that AI "merely explores the convex hull" mystifies me. Why wouldn't it be more like the subgroup generated by existing mathematical knowledge? (That's also a metaphor -- I'm not saying there's a group structure.)

>The convex hull metaphor suggests that AI isn't having radically new ideas and will eventually run out of things to do, whereas the group metaphor suggests the possibility of building up in small steps to something well beyond what you started with, with no obvious limit.

[0] https://twitter.com/wtgowers/status/2108090920864268679

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