alt Hacker NewsAt this point, when you are doing big AI you basically have to buy it from NVidia or rent it from Google. And Google can design their chips and engine and systems in a whole-datacenter context, centralizing some aspects that are impossible for chip vendors to centralize, so I suspect that when things get really big, Google's systems will always be more cost-efficient.
(disclosure: I am long GOOG, for this and a few other reasons)
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I'd bet that too if their management wasn't so incredibly uninspiring. Like, Apple under Cook was also pretty mild and a huge step down from Jobs, but Google feels like it fell off a cliff. If it wasn't for OpenAI releasing ChatGPT, they might still be sitting on that tech while only testing it internally. Now it drives their entire chip R&D.
> I suspect that when things get really big, Google's systems will always be more cost-efficient.
In fact I am opposite of this hypothesis for two reasons. Google has artificially limited production. And because TSMC favours whoever could pay for the most capacity(as incremental capacity is very cheap for them). So Nvidia gets first slot for new process.
Also the second reason is that GCP's operating margin is very high compared to say Hetzner or lambdalabs and you can get GPUs much cheaper there compared to GCP. So students/small researchers are stuck on GPU.
I'd go long Google too if using Gemini CLI felt anything close to the experience I get with Codex or Claude. They might have great hardware but it's worthless if their flagship coding agent gets stuck in loops trying to find the end of turn token.