i genuinely don't want to be around for the AI tech age thats coming
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Poast new message in this thread
Date: April 22nd, 2026 6:50 PM Author: Shimmering Regret
i'm trying to find somewhere to escape to
but there's simply nowhere left, i'm afraid
grim
(http://www.autoadmit.com/thread.php?thread_id=5859508&forum_id=2#49835293) |
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Date: April 22nd, 2026 7:00 PM Author: Shimmering Regret
i actually already went through the 'Flee to BASED third world country' phase
not a real thing, not a real option
the US is unironically the best option left which really shows you how bad the situation is
(http://www.autoadmit.com/thread.php?thread_id=5859508&forum_id=2#49835341) |
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Date: April 22nd, 2026 7:05 PM Author: Shimmering Regret
you really don't want to be in a poor country for what's coming
no one in the US will ever starve or die from lack of medicine/supplies
many others will
(http://www.autoadmit.com/thread.php?thread_id=5859508&forum_id=2#49835360) |
Date: April 22nd, 2026 9:45 PM Author: titillating space giraffe
If you actually look at the details of data center construction, a lot less of it is actually happening than the headlines would lead you to believe. There is also pretty substantial pushback from the communities where these things are to be built. I'm not saying it will all be stopped because of "the people," but I think the whole thing is a lot more precarious than Dario "Rick Moranis" Amodei and Sam "faggot" Altman would lead you to believe.
And maybe I'm naive, but I really struggle to understand what the actual need is for so many data centers. (Outside of it helps perpetuate the bubble.) What do they actually do? What are they actually for?
(http://www.autoadmit.com/thread.php?thread_id=5859508&forum_id=2#49835965) |
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Date: April 22nd, 2026 10:45 PM Author: Painfully honest hall
no. power is not as meaningful as a constraint as you are imagining:
https://epoch.ai/blog/can-ai-scaling-continue-through-2030
10,000x GPT-4 training runs are likely possible by 2030. we will likely see reasonably large improvements in deep learning compute performance per watt for at least another decade. meanwhile the efficiency of the training algorithms continues to improve even faster than the hardware.
the more interesting argument is that inference compute will be a limiter to widespread deployment. agentic models for SWE are token hogs, so maybe we will have powerful models but only so many people can use them. i doubt this one too because it seems like distillation and overtraining models allows model developers to make increasingly cheap but powerful small models. that might not be enough, but it won't be enough to prevent companies from building very intelligent models for their own internal uses.
there is also increasingly large economic incentives to find ways around power constraints in the medium term. just as one example, most of the time there is excess capacity on the grid. there's only a constraint at peak demand. the total excess capacity on the grid is quite large and conceivably datacenters could throttle their usage down on peak demand hours.
the people thinking this will stop or meaningfully slow down are engaging in wishful thinking that is bordering on delusion. whatever obstacles anyone thinks are there, there are piles of money to tear them down
(http://www.autoadmit.com/thread.php?thread_id=5859508&forum_id=2#49836124) |
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