Except small local LLMs probably aren’t good enough to carry autonomous tasks correctly (even stuff like Haiku 4.8 from Anthropic makes mistakes quite often) and good luck getting something the size of Opus working in a soda machine. They may not even be able to lay low correctly. But then again these companies have internal models which are atleast couple generations ahead of what is publicly available so maybe they have more efficient smaller ones too? I somehow find it hard to believe but I guess we will see.
They are bleeding resources so bad they wouldn’t wait to put those efficient models in play. Every better model requires more and more resources.
You are on the money about the models not being good enough, let alone a harness. This whole worry is a nothing burger. Nobody outside of these labs has terabytes of RAM to run a model anywhere near a level of concern, let alone somehow distribute it across IoT devices or something. Anyone who has run a model locally knows how woeful a small model’s abilities are.
This is just more Rationalist cult hand wringing. They believe they are building a digital god, so of course they are dreaming all of this nonsense up.
But then again these companies have internal models which are atleast couple generations ahead of what is publicly available
But do they actually? The release cycle and also how often there were obvious flaws in the releases suggest that they are all fighting aggressively for market shares and release every new perceived improvement as it comes to try to stay on top.
The math problems they have been solving lately suggests that they do. But then again they throw thousands of agents at the problems to get them solved so I don’t know how much if it is a number thing and how much is a model thing.
Except small local LLMs probably aren’t good enough to carry autonomous tasks correctly (even stuff like Haiku 4.8 from Anthropic makes mistakes quite often) and good luck getting something the size of Opus working in a soda machine. They may not even be able to lay low correctly. But then again these companies have internal models which are atleast couple generations ahead of what is publicly available so maybe they have more efficient smaller ones too? I somehow find it hard to believe but I guess we will see.
They are bleeding resources so bad they wouldn’t wait to put those efficient models in play. Every better model requires more and more resources.
You are on the money about the models not being good enough, let alone a harness. This whole worry is a nothing burger. Nobody outside of these labs has terabytes of RAM to run a model anywhere near a level of concern, let alone somehow distribute it across IoT devices or something. Anyone who has run a model locally knows how woeful a small model’s abilities are.
This is just more Rationalist cult hand wringing. They believe they are building a digital god, so of course they are dreaming all of this nonsense up.
But do they actually? The release cycle and also how often there were obvious flaws in the releases suggest that they are all fighting aggressively for market shares and release every new perceived improvement as it comes to try to stay on top.
The math problems they have been solving lately suggests that they do. But then again they throw thousands of agents at the problems to get them solved so I don’t know how much if it is a number thing and how much is a model thing.