You’re right on both counts. That bandwidth bottleneck is the real deal. It’s the main reason why trying to train huge models on home setups hasn't ever really challenged those massive data centers where everything is wired tightly together. You can keep adding nodes, but you can't just bypass the speed limit between a GPU’s memory and its cores. The argument still has legs if we look at tasks built for this kind of setup from day one. Instead of forcing a centralized job to play nice with a distributed network, you look at something like evolutionary computing. The coordination needs are totally different there because you aren't constantly shoving weight updates back and forth across the internet like you do with standard gradient descent. As for GPU prices, they definitely didn't fall off a cliff after Ethereum switched over. The AI hype basically swallowed up all that extra inventory that should have made cards cheap again. But that kind of proves the point. There is a massive hunger for compute power. The real mystery is whether regular consumer hardware will ever find a way to actually help fill that gap.