Not that I have anything against local use---I do my stuff locally---but people running local models will very probably greatly increase demand for memory relative to cloud-based AI compute (and it's why I think that in the near term, most AI compute stuff is gonna be in the cloud, because we don't have the memory to do everything locally).

If you run in the cloud, the hardware is shared. When one person isn't using it, another can be. That can lead to high rates of capacity utilization, approaching 100%.

If I have local AI compute hardware, if I'm not using it, it's idle. If 1% of the time, I have it crunching something for me, then I'm only getting 1% capacity utilization of that hardware. That means that to provide the same level of compute capability to everyone for local use, I need 100 times as much hardware.

AI cloud companies have purchased more memory than the rest of the world is buying.

If we did everything locally, we'd need a hundred times more memory than what the cloud AI companies are picking up.

We couldn't do that without far more memory production capacity. Even if someone started on a buildout of that scale today, a new memory factory takes 4--5 years to get into production.