Researchers just mathematically proved that AI can't recursively self-improve its way to superintelligence.
Not "we think it's unlikely." Not "it seems hard." Formally proved.
The model doesn't climb toward AGI — it slowly forgets what reality looks like. They call it model collapse. The math calls it inevitable.
I wrote about it 👇
smsk.dev/2026/04/26/ai-cannot-…
#AI #MachineLearning #LLM #Research
AI Cannot Self Improve and Math behind PROVES IT! - devsimsek's Blog
A formal proof shows AI training on self-generated data leads to collapse, not AGI. Here's what that means and why it matters.devsimsek (devsimsek's Blog)
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timorl
in reply to devsimsek • • •I don’t think this is the usual formulation of RSI though – in the one I know the input of the AI is not it’s output, but the environment plus (a representation of) itself. So I would say the way the article (and blogpost) formulates its thesis is misleading.
(I used to worry about AGI and the current focus on LLMs stopped that. Not because such a self-improvement loop is impossible (which I don’t expect it to be tbh), but rather because it’s extremely unlikely due to their very low homoiconicity.)
Jens Finkhäuser
in reply to devsimsek • • •Compare how cryptographic RNGs are usually Pseudo-RNGs fed with entropy, and which fail to output random-approximate values (of a given strength) once the entropy falls too low.
It's almost as if there is a pattern to this.