i mean, imagine if your a business and suddenly you had to expense > $200 a month for every employee just to run a compiler, or you could just buy a computer and use it for everything else and that as well…
i think llms will be the same.
n=1 but at $work they are forcing us off enterprise and on to personal claude max that caps at $200 and people are like “you don’t need the top end models anyways just use the cheaper one for coding”, which begs the question how long till those “good enough” models run locally (with relative ease) and our company can cut that huge monthly cost with just the macbook pro they are gonna buy anyways.
Nobody knows how the future will be. There are lots of options. There are self hostable open models today - they are not as good, but some report they are good enough. Right now I don't have the hardware to try them (I could - if I'm willing to accept a small number of tokens per minute - useful means more tokens per second as I can get in an hour). However I expect in a few years I'll be able buy consumer hardware with good enough GPUs (or perhaps an AI chip???). Will anyone other than a few hobbyists buy them? time will tell.
And space launches. The Big Names will lose a ton of money and disappear as the tech commoditises.
The moat is Being Able To Do Things No One Else Can and as both hardware and software commoditise that moat disappears.
Open LLMs are already only a few months behind the frontier models and as soon as someone works out that accessible and cheap domain-specific custom training has the potential to be a gamechanger the Big Names will die.
Math puzzles only get you so far.
LLMs are in their Big Compute phase, and it's only a matter of time before that ends - because the intelligence of the frontier models isn't increasing fast enough and providing big enough benefits to make the models more valuable than cheaper, smaller competition.
I think prices will go down not up eventually and everybody will have access to it in abundance. That might make most current AI companies go down of course.
i mean, imagine if your a business and suddenly you had to expense > $200 a month for every employee just to run a compiler, or you could just buy a computer and use it for everything else and that as well…
i think llms will be the same.
n=1 but at $work they are forcing us off enterprise and on to personal claude max that caps at $200 and people are like “you don’t need the top end models anyways just use the cheaper one for coding”, which begs the question how long till those “good enough” models run locally (with relative ease) and our company can cut that huge monthly cost with just the macbook pro they are gonna buy anyways.
Maybe - ask me in 20 years...
Nobody knows how the future will be. There are lots of options. There are self hostable open models today - they are not as good, but some report they are good enough. Right now I don't have the hardware to try them (I could - if I'm willing to accept a small number of tokens per minute - useful means more tokens per second as I can get in an hour). However I expect in a few years I'll be able buy consumer hardware with good enough GPUs (or perhaps an AI chip???). Will anyone other than a few hobbyists buy them? time will tell.
And space launches. The Big Names will lose a ton of money and disappear as the tech commoditises.
The moat is Being Able To Do Things No One Else Can and as both hardware and software commoditise that moat disappears.
Open LLMs are already only a few months behind the frontier models and as soon as someone works out that accessible and cheap domain-specific custom training has the potential to be a gamechanger the Big Names will die.
Math puzzles only get you so far.
LLMs are in their Big Compute phase, and it's only a matter of time before that ends - because the intelligence of the frontier models isn't increasing fast enough and providing big enough benefits to make the models more valuable than cheaper, smaller competition.
I think prices will go down not up eventually and everybody will have access to it in abundance. That might make most current AI companies go down of course.