This is what really gets me with AI and why its 1000% a bubble.
Putting aside all the ethical problems.
Putting aside the environment problems.
Putting aside how basically everyone hates it.
Lets say its popular and will take off etc.
Its all going to become more efficient enough you can run it all locally. Why are we trying to piss away zillions of dollars on data centers and these stupid companies, when 99.9% of uses will work with a model running locally on the GPU/NPU?
Its all going to become more efficient enough you can run it all locally. Why are we trying to piss away zillions of dollars on data centers
The idea that the hardware we currently have is good enough for future applications hasn’t been true of software in general. As better hardware becomes available, software has improved to take advantage of it. Also, the improvements in software have allowed the development of even better hardware, in a virtuous cycle.
Also, regarding local vs cloud computing in general, datacenters provide an economy of scale that can’t be matched by local compute. Shared datacenters are also more efficient than everyone having expensive hardware that they only use a fraction of the time. This is why datacenters were expanding rapidly even before the recent advances in large language models.
This is what really gets me with AI and why its 1000% a bubble.
Putting aside all the ethical problems.
Putting aside the environment problems.
Putting aside how basically everyone hates it.
Lets say its popular and will take off etc.
Its all going to become more efficient enough you can run it all locally. Why are we trying to piss away zillions of dollars on data centers and these stupid companies, when 99.9% of uses will work with a model running locally on the GPU/NPU?
The idea that the hardware we currently have is good enough for future applications hasn’t been true of software in general. As better hardware becomes available, software has improved to take advantage of it. Also, the improvements in software have allowed the development of even better hardware, in a virtuous cycle.
Also, regarding local vs cloud computing in general, datacenters provide an economy of scale that can’t be matched by local compute. Shared datacenters are also more efficient than everyone having expensive hardware that they only use a fraction of the time. This is why datacenters were expanding rapidly even before the recent advances in large language models.
Because its easier to disguise the surveillance centers that way
Running is one thing, training is another though. That doesn’t come cheap and is not really feasible at home.
However training also doesn’t require 40,000 datacenters.
Hyping up training and increasing its costs doesn’t keep the bubble alive.
It won’t, no.
People who are focusing on consumer side ships and hardware (which I have been told Apple is) are going to be the big winners in the end.
Much already can. Even software dev.