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No, I am saying they are using it as cover to slow down unsustainable capex.
And there isn't any possible evidence that could exist that could disabuse you of that impression because pausing unavoidably causes less capex spend. You're epistemically sealed against the straightforward explanation for why the people are doing the things obviously motivated by the durably stated beliefs over many years.
Why would this be the case, in your telling? Why wouldn't AI companies pivot towards customer compute build-out?
Well, actually when you put it like that they are still building the datacenters right? So they aren't even pulling back in capex which makes your narrative even more ridiculous. Thanks for the contribution.
Which narrative is that?
I'm not sure this means chooky is wrong – training and inference are different tasks, and so don't (necessarily) run on the same datacenters/GPUs. So chooky could be correct that they are slowing down on unsustainable capex (training) but that doesn't necessarily mean that pausing unavoidably means less capex spend total, because you could pivot to building inference datacenters to serve customers with existing models, shifting from expensive R&D to increasing revenue via selling inference. Right?
Now, caveat – I don't know of the top of my head what the current datacenter build out looks like – how much of it is inference versus training, what's being financed directly by the primes versus third parties, etc. etc. I gather that you're not familiar with it either (and I'm not sure it's all public knowledge, anyway). So I am just thinking about the logical levers at play here.
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Well, yes, it is tough for the companies making AI to be convincing that they have long been opposed to making AI.
If only they had long records of writing on the topic going through their beliefs and positions in detail that you refuse to recognize.
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