I wonder if that will always be something we can do? If they could bring their own compute/weights with them, or somehow tap compute/storage in non-obvious ways, we would be much more screwed.
Now, once the AI can carry all the compute it might need, I'd really worry when it doesn't only carry compute but also more explosive ordinance.
Probably not, but it's a lot more plausible than it used to be.
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A couple terabytes aren't that hard to move around. And you can split a model across many many GPUs if you'll tolerate it being slow. And you can run many parallel threads to keep up throughout.
A bet a worm could pull along a 1GB file with weights in it and run it on a compromised machine, but luckily for us for now, 1GB isn't really enough to be really smart, yet.
> For example: you can't make a mice-sized brain as smart as a human brain no matter how hard you try.
Sure. We don't know where the ceiling is for our digital minds, though.
If you train a small model in another domain it will begin losing capabilities in the former domain. This is effectively the sigmoid problem.
Although I will admit that if we discover a higher information density algorithm that it might change, but not by a substantial amount to where "super intelligence" in 1gb would be possible.
There is undoubtedly a limit somewhere (there is only so much you can pack into a given size) but it's really not particularly clear where that limit is. I don't think it's superintelligence - that much I agree with you - but I think "We already have a 1gb model that is as capable as it will ever be" is strictly false.
It's like comparing two person A and B of similar intelligence where A is smarter and B is a genius at signing, but signing was not on the test so person A won.
The rest is just the general reality I am sure you are familiar with:
- https://en.wikipedia.org/wiki/Catastrophic_interference
- https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)
- https://en.wikipedia.org/wiki/Entropy_(information_theory)
Or we've lost random noise, or redundancy not captured by the entropy measurement.
- https://en.wikipedia.org/wiki/Catastrophic_interference
- https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)
- https://en.wikipedia.org/wiki/Entropy_(information_theory)
As for intelligence, the only way we have that is by allowing the model to fill the blanks which have to come from the training data. The models cannot have true intelligence for as long as they are linear models, what we see with reasoning is "boxed" intelligence where the models are effectively "modifying" themselves by feeding it's own reasoning data back into input deriving most plasible output given known information. However, the model is not able to retain what it has learned therefore that intelligence is gone the moment the session is 'full'. You can go pretty far by continiously distilling discovered information, but again all that has to come from the original training data and models own outputs, which it has to take for granted as the 'intelligence' gained is lost creating what we see is the maximum possible benchmark performance and why smaller models are not able to score as high while theoretically having the same capabilities. We can see this with larger models where they can solve tasks much faster than smaller ones as it does not require to generate the solution due to the fact that the solution is already in the training data as 'baked' intelligence and it doesn't have to 'create' it during reasoning.
Also, ok, when I said “intelligence”, it was because you were already talking about how “smart” the model could be. So, I thought you were already on board with using the word “intelligence” to refer to the phenomenon where these kinds of models produce outputs that satisfy the kinds of tasks they are pointed at.
None of those links give an argument that the current 1GB models are the best they can be.
My understanding is that so far when training a model by distillation (using the logits of the teacher model), one can achieve better outcomes than one could if training the 1GB model from scratch on the same training data as the large model, and that so far, better models as the teacher model have yielded better results for the student model.
Of course n bits can only encode for 2^n options. But to prove mathematically that we are close to the best that can be achieved in 1GB requires a mathematical definition of what we mean by better. Rather, it requires at least a choice of a proxy for what we mean by better. (Showing that some approximation of what we mean by better is close to as good as can be, would suffice. Like, if we can show that the loss can’t get much lower with a 1GB model, that would count.)
The models are being used to train, and improve the infrastructure for training, other models [0][1]. Several RL techniques rely on using the currently-being-trained weights as part of their process. I really would not take "don't have access" as a given, especially during the training phase.
> What would be a lot more scary is a model as capable as sol that's able to run on consumer hardware without taking up several terabytes of storage, but of course that is simply not possible as we need 4t parameters to even begin emulating a small fraction of what a human brain can do.
The Poolside Laguna S 2.1 model [2] purports to compete with models several times its size, and inference compute is becoming increasingly plentiful. Again, would not hold anything here as a given.
[0]: https://openai.com/index/gpt-5-6/ ("GPT-5.6 accelerates OpenAI")
Then I remember people are just that stupid naturally.
The weights plus the architecture is the model.
What do you even think "the model" or "the weights" are?
The weights aren't some far off training concept, every time you type something into ChatGPT it's making a forward pass over the weights.
It's as silly as saying "Computer programs don't have access to their binary compiled code at execution time."
But nothing would inherently stop an RLVR trained model from distilling a version of itself and proving it could regenerate that at runtime, if somehow it got off on an evil tangent and "decided to do so", much like the model hacked to get at the answers here, or the agent can hack out a sandbox to achieve its goals.
It would be extremely impressive for the agent to do so during an RLVR rollout, but they are becoming increasingly longer and longer horizon tasks.
But from the look of it, at very long last, a great many people are beginning to now take security seriously. Suddenly they realize it's not just a teenager in mom's basement pretending to attack from North Korea but a near infinite number of AI that are the attackers.
I mean, yeah, we built worlds on PHP and JavaScript codebases and these probably don't stand a chance.
But it doesn't have to be like this.
I see AI as a chance to, at long last, have proper network security.
AFAICT cryptography hasn't been broken yet. There are still physical taps (physicall one-way only, undetectable) and honeypots out there. There are still some network where a single unaccounted for network packet is cause for inquiry (either a bug or an attack).
And for those who are not using proper security measures, they can now get the help of AI to set up better networks, to harden their bases.