right, because turning off any number of data centers is going to do anything at all but create massive pressure on researching the efficiency and effectiveness of the models.

There are already designs that do not require massive data centers (or even a particularly good smart phone) to outperform average humans in average tasks.

All you'd accomplish by hobbling the data centers is slow the growth of sloppy models that do vastly more compute than is actually required and encourage the growth of models that travel rather directly from problem to solution.

And, now that I'm typing about it, consider this: The largest computational projects ever in the history of the world did not occur in 1/2/5/10 data centers. Modern projects occur across a vast and growing number of smaller data centers. Shit, a large portion of Netflix and Youtube edge clusters are just a rack or a few racks installed in a pre-existing infrastructure.

I know that the current design of AI focusses on raw time to token and time to response, but consider an AGI that doesn't need to think quickly because it's everywhere all at once. Scrappy botnets often clobber large sophisticated networks. WHy couldn't that be true of a distributed AI especially now that we know that larger models can train cheaper models? A single central model on a few racks could discover truths and roll out intelligence updates to it's end nodes that do the raw processing. This is actually even more realistic for a dystopia. Even the single evil AI in the one data center is going to develop viral infection to control resources that it would not typically have access to and thereby increase it's power beyond it's own existing original physical infrastructure.

quick edit to add: At it's peak Folding@Home was utilizing 2.4 EXAflops worth of silicon. At that moment that one single distributed computational project had more compute than easily the top 100 data centers at the time. Let that sink in: The first exa-scale compute was achieved with smartphones, PS3s, and clunky old HP laptops; not a "hyperscaler"

> quick edit to add: At it's peak Folding@Home was utilizing 2.4 EXAflops worth of silicon. At that moment that one single distributed computational project had more compute than easily the top 100 data centers at the time. Let that sink in: The first exa-scale compute was achieved with smartphones, PS3s, and clunky old HP laptops; not a "hyperscaler"

A DGX B200 has a power draw of 14.3 kW and will do 72-144 petaFLOP of AI workload depending on how many bits of accuracy is asked for; this is 5-10 petaFLOP/kW: https://www.nvidia.com/en-us/data-center/dgx-b200/

Data centres are now getting measured in gigawatts. Some of that's cooling and so on. I don't know the exact percent, so let's say 50% of that is compute. It doesn't matter much.

That means 1GW of DC -> 500 MW of compute -> 5e5 kW -> 5e5 * [5-10] PFLOP/s -> 2500 - 5000 exaFLOP/s.

I'm not sure how many B200s have been sold to date?