alt Hacker NewsIn recent discussions about Tim Apple [sic] moving on there was a discussion about whether Apple flopped on AI, which is my opinion. Of course you had the false dichotomy of doing nothing or burning money faster than the US military like OpenAI does.
IMHO that happy medium is Google. Not having to pay the NVidia tax will likely be a huge competitive advantage. And nobody builds data centers as cost-effectively as Google. It's kind of crazy to be talking ExaFLOPS and Tb/s here. From some quick Googling:
- The first MegaFLOPS CPU was in 1964
- A Cray supercomputer hit GigaFLOPS in 1988 with workstations hitting it in the 1990s. Consumer CPUs I think hit this around 1999 with the Pentium 3 at 1GHz+;
- It was the 2010s before we saw off-the-shelf TFLOPS;
- It was only last year where a single chip hit PetaFLOPS. I see the IBM Roadrunner hit this in 2008 but that was ~13,000 CPUs so...
Obviously this is near 10,000 TPUs to get to ~121 EFLOPS (FP4 admittedly) but that's still an astounding number. IT means each one is doing ~12 PFLOPS (FP4).
I saw a claim that Claude Mythos cost ~$10B to train. I personally believe Google can (or soon will be able to) do this for an order of magnitude less at least.
I would love to know the true cost/token of Claude, ChatGPT and Gemini. I think you'll find Google has a massive cost advantage here.
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Apple has not flopped on AI as you say. They are just focused on privacy and are likely waiting for the time when local models become efficient enough to run on iPhones (which is quickly becoming a reality).
Google could probably train models for orders of magnitude less money as you say, but they aren't. They are not capable of creating high quality models like OpenAI and Anthropic are. Their company is just too disorganized and chaotic.
Anecdotally, I don't know a single person who uses Gemini on purpose.
> I saw a claim that Claude Mythos cost ~$10B to train.
Can you cite this? That seems absurd.