4 ms·

It still doesn't make sense to me. If the money for training is still there, wouldn't companies that can afford it use the efficiency gains and also scale up mo

It still doesn't make sense to me. If the money for training is still there, wouldn't companies that can afford it use the efficiency gains and also scale up models? Unless AI is a bubble, and it pops, I can't see the demand for compute going down.

MichaelZuo 2y ago
They would scale things up slightly slower. And money 5 years from now is simply worth less to markets than money 7 years from now.
EGreg 2y ago
I think AI is a bubble. The amount of compute for inference is vastly overestimated, because a lot of caching is coming. It's driven by maniacal statements like Sam Altman's insistence that we must spend Trillions on compute, to achieve AGI, and it's more important than anything else. Project Stargate is some large fraction of that, and of course Softbank is no stranger to losing money on overestimating demand (for example, WeWork). To be fair, China has a lot of overestimation of demand too (for example Evergrande). The other is that rapid competition leads to overinvestment by all parties. https://www.wsj.com/tech/ai/sam-altman-seeks-trillions-of-dollars-to-reshape-business-of-chips-and-ai-89ab3db0 https://www.wsj.com/tech/ai/sam-altman-seeks-trillions-of-do...
- disgruntledphd2 2y ago
  Which is great for us, we'll have loads of cheap compute and hopefully a bunch more carbon free energy supply, assuming that the AI stuff all ends in tears (for now).
  - EGreg 2y ago
    Yep! Shareholders and capitalists overinvesting in stuff is great if it leaves behind great infrastructure. They take the risk and the public benefits.
gleenn 2y ago
There is a belief that we've peaked in terms of bigger model = better results. I think it was GPT 4 is actually smaller in parameter count and better than 3.5 for instance. There is also a finite amount of useful data that people think we have hit, so adding more parameters isn't helpful if you don't have new data to train them on.

MichaelZuo 2y ago

They would scale things up slightly slower. And money 5 years from now is simply worth less to markets than money 7 years from now.

EGreg 2y ago

I think AI is a bubble. The amount of compute for inference is vastly overestimated, because a lot of caching is coming. It's driven by maniacal statements like Sam Altman's insistence that we must spend Trillions on compute, to achieve AGI, and it's more important than anything else. Project Stargate is some large fraction of that, and of course Softbank is no stranger to losing money on overestimating demand (for example, WeWork). To be fair, China has a lot of overestimation of demand too (for example Evergrande). The other is that rapid competition leads to overinvestment by all parties. https://www.wsj.com/tech/ai/sam-altman-seeks-trillions-of-dollars-to-reshape-business-of-chips-and-ai-89ab3db0 https://www.wsj.com/tech/ai/sam-altman-seeks-trillions-of-do...

disgruntledphd2 2y ago
Which is great for us, we'll have loads of cheap compute and hopefully a bunch more carbon free energy supply, assuming that the AI stuff all ends in tears (for now).
- EGreg 2y ago
  Yep! Shareholders and capitalists overinvesting in stuff is great if it leaves behind great infrastructure. They take the risk and the public benefits.

gleenn 2y ago

There is a belief that we've peaked in terms of bigger model = better results. I think it was GPT 4 is actually smaller in parameter count and better than 3.5 for instance. There is also a finite amount of useful data that people think we have hit, so adding more parameters isn't helpful if you don't have new data to train them on.