Researchers just mathematically proved that AI can't recursively self-improve its way to superintelligence.
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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
https://smsk.dev/2026/04/26/ai-cannot-self-improve-and-math-behind-proves-it/
@devsimsek
"Touch grass." It is not just a reminder to take a break or get some fresh air. -
Large language models are fundamentally different from mammals on every level. They do not build models or reason about them. A rat is more "intelligent".
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@Quantensalat @devsimsek the main issue is that unless you maintain an external signal (so human input in the form of token sequences that are actually carefully curated for coherence) the models become more and more incoherent. Sounds like you're on board with that. The next step is that we're quickly devaluing money spent on human creativity and the world is awash in LLM garbage. So the human signal *is* disappearing.
@wronglang @devsimsek Yes, sure. I mean I can imagine it improving somewhat still, like when you augment your training set for image recognition by adding noise to a smaller set, but only to a point before it goes downhill from feedback.
No, my gut feeling is rather that there have to be much more effective ways to train a model than to brute force funnel billions of pages of text to a transformer which blindly fits relations between words and structures without understanding them, that seems like doing it the hard way, even if I'm not expert enough to tell you what an alternative would look like
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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 ithttps://smsk.dev/2026/04/26/ai-cannot-self-improve-and-math-behind-proves-it/
So. That kind of "AI" - if not refused by us - shall vampirize humanity forever. It is build to suck our data forever. It is the perfect tool for control freaks. By design.
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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 ithttps://smsk.dev/2026/04/26/ai-cannot-self-improve-and-math-behind-proves-it/
@devsimsek It's logical really as AI simply doesn't have any intelligence. It only regurgitates what it has stolen and learnt.
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@devsimsek Not a developer, but that was my first thought when I understood how LLMs were trained and how they worked: What happens when there's so much AI generated content on the internet that the LLM is harvesting and recycling its own output? That's like a high school history class having their own essays as research material. #LLMs
@anne_twain @devsimsek
This reminds me of some right-wing Youtube channels or Telegram groups. For years they're feasting on their own output and in the process are getting intellectually shallower but more agitated and radical... -
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 ithttps://smsk.dev/2026/04/26/ai-cannot-self-improve-and-math-behind-proves-it/
@devsimsek the problem is: absolutely nothing you and I, all the world's scientists or anyone left with a sane mind says will stop the AI hype train because there's too much money in it already.
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@devsimsek Nobody ever claimed that llms get better by being trained on their own synthetic data. This blog post is very misleading.
The idea of self-improvement and singularity is that llms write improved versions of their own codebase and perform the research and experiments for coming up with better models themselves.
The idea of singularity is interesting but also full of hidden assumptions. I'm always confused when people act like singularity would exist. It's just science fiction. -
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 ithttps://smsk.dev/2026/04/26/ai-cannot-self-improve-and-math-behind-proves-it/
Interesting article about how AI cannot grow into a super intelligence because the more systems grow, the more they rely on information generated by themselves and the more ....
'it forgets what reality looks like'.
#AI #AGI #RSI
(RSI = Recursive Self-Improvement)
https://smsk.dev/2026/04/26/ai-cannot-self-improve-and-math-behind-proves-it/ -
@Quantensalat @devsimsek Yes.
They have also never had a machine crash because a recursive operation overran the stack or used up all the memory.
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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 ithttps://smsk.dev/2026/04/26/ai-cannot-self-improve-and-math-behind-proves-it/
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Large language models are fundamentally different from mammals on every level. They do not build models or reason about them. A rat is more "intelligent".
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@wronglang @devsimsek Yes, sure. I mean I can imagine it improving somewhat still, like when you augment your training set for image recognition by adding noise to a smaller set, but only to a point before it goes downhill from feedback.
No, my gut feeling is rather that there have to be much more effective ways to train a model than to brute force funnel billions of pages of text to a transformer which blindly fits relations between words and structures without understanding them, that seems like doing it the hard way, even if I'm not expert enough to tell you what an alternative would look like
@Quantensalat @devsimsek oh gotcha, yes agreed.
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@troed Just to make you angry.
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@devsimsek I'd be interested to see the same analysis of human consciousness. It is well understood that complexity is a regime on the absolute edge of chaos.
@onekind I would be interested in this as well.
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@Quantensalat @devsimsek For something more formal on this subject see
https://arxiv.org/abs/2601.03220
The abstract starts "Can we learn more from data than existed in the generating process itself?"
@dpiponi @Quantensalat Thanks, will check.
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How much have you studied human cognition - as well as the emergent effects shown by LLMs?
I've studied both. So far I haven't come upon a single anti-AI fanatic that has any.
Most human cognition is common to all mammals, even most of the frontal lobe is pre-linguistic. LLMs are ONLY linguistic. They are a clever hack repurposing a 1950s ERA model of how the visual cortex works to simulate the barest parody of linguistic processing. At the best you can say that they are implemented on something like a similar kind of processor, but the software, the neural connections and weights, is completely unrelated.
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Most human cognition is common to all mammals, even most of the frontal lobe is pre-linguistic. LLMs are ONLY linguistic. They are a clever hack repurposing a 1950s ERA model of how the visual cortex works to simulate the barest parody of linguistic processing. At the best you can say that they are implemented on something like a similar kind of processor, but the software, the neural connections and weights, is completely unrelated.
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Most human cognition is common to all mammals, even most of the frontal lobe is pre-linguistic. LLMs are ONLY linguistic. They are a clever hack repurposing a 1950s ERA model of how the visual cortex works to simulate the barest parody of linguistic processing. At the best you can say that they are implemented on something like a similar kind of processor, but the software, the neural connections and weights, is completely unrelated.
Yeah, so why do you think it's relevant that some brain processing starts to form before we acquire language? Most people vocalize their thoughts, even though you might not (and I don't always either). All our intellectual skills are acquired through language.
What an LLM is, is a "thinking engine". That's what the training creates. That "thinking" can then be applied to different subjects, with a rudimentary form of working memory.
The big surprise to those developing LLMs was that the technology suddenly created emergent effects not foreseen from their basic architecture - the ability to _reason_ and _create world models_. If you're still in 2022 and don't think that this is what they do then maybe you need to get off the "stochastic parrot" bandwagon and update your own knowledge?
After all - humans don't do anything but map inputs to outputs through neural networks either.
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