Ask HN: Could free/low cost LLMs be a momentary thing?
Say they(OpenAi Etc)don’t find a way to reduce the cost of running these LLMs. Will we shift towards slower/worse LLMs running locally? Or maybe enterprise ones only used by large corporations for specific tasks?
Will the era of using these to generate code end? Is the assume that the inference problem will be solved?
Yes. That’s my opinion. I think Apple will leverage their shared RAM and M architectures to sell their computers as local-LLM ready.
They for sure are testing LLMs and checking the performance of local models. Once they reach a performance and quality enough for some tasks they will announce Apple AI or some variation of the name.
All of this is speculation, but I think is obvious the right way.
Government funding is another way. In some countries people pay some sort of "media" tax. That can be redistributed. A new one can be added. If LLMs are becoming a standard way to interact and process the data and using them is a social necessity then it is absolutely the job of the state to provide means for it.
The rumour mill (justified, given cloud cost of running big open models with similar performance scores) says that these companies make money on inference, but lose it all on training.
So: when the money runs out and the bubble pops, we'll still get cheap existing models, what we lose is the race for new models.
We'd probably even keep free models: I forget where I saw it, but back in the early days someone noticed that models were so cheap that you could generate a decent sized blog post about any topic for about the same as the expected revenue from putting a few adverts on it and having it viewed *exactly once*.
That said, when (/if) these businesses stop chasing new models, it can make sense to burn the weights of the best at that date into a fixed (and analog, given how well they work with only a few bits of precision) circuit, making them more efficient. Not my field, so I'm not sure exactly how much more efficient analog can be; one or two orders of magnitude from what I've heard, but don't hold me to that, not my field.
They for sure are testing LLMs and checking the performance of local models. Once they reach a performance and quality enough for some tasks they will announce Apple AI or some variation of the name.
All of this is speculation, but I think is obvious the right way.
That's what Sam Altman said we should do.
But that would lead to another competition on prices again.
> Will we shift towards slower/worse LLMs running locally?
Bet on faster/better LLMs running locally and invest/brace yourself for recession accordingly.
So: when the money runs out and the bubble pops, we'll still get cheap existing models, what we lose is the race for new models.
We'd probably even keep free models: I forget where I saw it, but back in the early days someone noticed that models were so cheap that you could generate a decent sized blog post about any topic for about the same as the expected revenue from putting a few adverts on it and having it viewed *exactly once*.
That said, when (/if) these businesses stop chasing new models, it can make sense to burn the weights of the best at that date into a fixed (and analog, given how well they work with only a few bits of precision) circuit, making them more efficient. Not my field, so I'm not sure exactly how much more efficient analog can be; one or two orders of magnitude from what I've heard, but don't hold me to that, not my field.