Peter Smith
● Alibaba Cloud just dropped some numbers that should make the entire AI industry sit up and pay attention. They've cut their Nvidia GPU usage by 82% using a pooling system that lets multiple users share GPU clusters without losing performance. Instead of buying more chips, they optimized how they use what they already have.
● Here's where it gets interesting. While China races toward efficiency, the U.S. seems stuck in a different game entirely. As Kakashii pointed out, American companies are "hypnotized by Jensen into believing they must buy as many GPUs as possible." Everyone's pouring billions into hardware like it's the only path forward, even though alternatives are proving otherwise.
● The financial implications are pretty clear. If Alibaba's approach catches on globally, Nvidia's growth story—and the entire GPU-dependent infrastructure—could hit some serious headwinds. Hyperscalers might realize they can get similar results with a fraction of the chips.
● This isn't the first warning shot either. DeepSeek already showed months ago that you can dramatically slash compute requirements for large language models. But instead of pivoting, U.S. firms doubled down on GPU spending, driven by a simple fear: if they stop buying, their stock prices might tank.
The industry is already too deep into massive spending, huge capex, and a GPU buying spree. Hypnotized by the fear that stopping would cause their stocks to drop. wrote Kakashii
● Alibaba's milestone suggests the next chapter of AI won't be won by whoever accumulates the most hardware—it'll be won by whoever uses it smartest.
Peter Smith
