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PaperclipPerfector  4mo ago (text post)   25843 thread views

Culture War Roundup for the week of August 18, 2025

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If you start with the assumption that the well has run dry and LLMs are never (not any time soon, at least) going be much better or much different than they are now, then yeah, very little about the market makes sense. Everyone willing to put substantial money into the project disagrees.

I'm actually assuming that the dumb money is pumping up a bubble with a significant gaps knowledge on what they are actually investing in and don't have any realistic way of getting a return. Much like other investment bubbles in the past.

Lets reverse the responses

Who wants to blow piles and piles of money on custom silicon that might eventually reduce their inference costs by a bit (though, since they were working with RISC-V, I kind of doubt it'd have ended up being better per-watt; cheaper only after licensing costs are factored in, probably) when a new architecture might render it obsolete at any moment?

Didn't Google already do it with TPU:s although not based on RISC-V?

Inference costs are exaggerated (and the environmental costs of inference are vastly exaggerated). It's certainly a big number in aggregate, but a single large query (30k tokens in, 5k out) for Google's top model, Gemini 2.5 Pro, costs about $0.09 via the API. And further queries on substantially the same material are cheaper due to caching. If it saves your average $50,000 a year office drone 30 seconds, it's more than worth it.

Google ends up losing a lot of money on inference not because it's unaffordable, but because they insist providing inference not only for free, but to search users who didn't even request it. (With a smaller, cheaper model than 2.5 Pro, I'm sure, and I'm sure they do cache output.) Because they think real world feedback and metrics are worth more than their inference spend, because they think that the better models that data will let them build will make it all back and more.

How much of the inference run on Google TPU:s and how much on GPU:s?

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