1. So this makes Burry’s argument even less convincing since those auxiliary hardware can last longer.

2. Jevons Paradox. More efficiency should lead to bigger models, faster inference, and more total tokens.

3. By all accounts, Trainium and Maia and Meta’s internal chip are struggling to keep up with Nvidia. That’s why they order as many Nvidia chips as possible. They’re not giving up but it isn’t as easy as buying stock Arm cores and taking them to TSMC.

Neoclouds may very well be Nvidia’s biggest customers and this probably what Nvidia wants.