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> The facility will consume 2.2 gigawatts of electricity — enough to power a million homes. Each year, it will use millions of gallons of water to keep the chips from overheating. And it was built with a single customer in mind: the A.I. start-up Anthropic, which aims to create an A.I. system that matches the human brain.

Sentences like this never ceases to amaze me. All of this juice to attempt to match what a single human brain can do with its relatively low resource requirements.

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> Each year, it will use millions of gallons of water to keep the chips from overheating.

Growing corn on that same 1200 acres would require on the order of a billion gallons of water. People have no sense of just how much water agriculture consumes.

In all probability, putting a data center on farm land is greatly reducing water consumption.

Doesn’t a typical McDonald’s require millions of gallons of water each week just for the beef alone, without even considering things like drinks and ice and boiling potatoes and washing floors?

By pointing out the obvious you miss out jumping on the bandwagon of everyone writhing in pain over how devastated they are for the environmental impact. Whenever AI is brought up you have to remind people how unsustainable it is and how it’s all about to fall apart and how the value isn’t there, among other topics. Off topic/on topic I’m reminded of death reports during COVID for particular states. When I went to go and look at the statistics of years prior to compare, they were at par if not less than the few previous years. Just an example of uncontrolled (mostly manipulative) outrage in action. I’d like to expect better of HN but I don’t: I’ve lost all respect for the moderation (dang and now tom) here over the years.

Yeah. The power use is impressive, although still only 1/10th of the output of the largest power station in the world, the three gorges dam. The water use is not really a big deal at all. I don’t know why journalists inevitably have to mention both. And even the power numbers require much more careful contextualization.

I wish I had a good balanced article to link to when these discussions come up, one that really digs into the power use question and compares it to the power consumed by other human activities and capital projects. Does anyone know of one?

I think you’re conflating things. It’s not a single human brain. It’s processing power to provide the human trove of knowledge and reasoning at the beck and call of millions of people nearly simultaneously. No single person would be able to do that.

No, but we had done a pretty good job of storing and indexing it all on this thing called the Internet.

And for the same purpose before that we had, or rather have, since it is still very much in use today, the Dewey Decimal System. Peering further onward still into the annals of library science, one can find even more creative and revealing methods of indexing human knowledge. In doing so, one might even be inclined to believe that the state of the science has come so far as to be considered solved. Alas.

I think if you went to the library and asked for information on "how to build a chicken coop" and the librarian took 60 books related to chickens and building and farming, cut up the words in them, then arranged them in a way they found satisfying, you might start going to a different library.

> It’s processing power to provide produce near unlimited spam and millions of pictures of shrimp jesus

ftfy

It's a laudable goal to match human intelligence but we can't ignore the cost for too long. If humans can create the same reasoning for cheaper why would we chug along on this path? And this may be the case when the AI hype bubble pops and real economics take over.

This is not the intelligence of one person, even the most intelligent person but rather the intelligence (wisdom) of the whole crowd -the whole world democratized (I.e. available to everyone for a nominal fee).

That’s an important difference.

I thought datacenter water cooling was usually a closed loop? I keep reading conflicting info about this

Nope. Evaporative cooling.

For very hot data centers, evaporative cooling is still popular. This is from 2012 but I doubt much has changed.

https://blog.google/outreach-initiatives/sustainability/gett...

Are new water-guzzling DCs and nuclear plants built on water sources unlikely to be affected by climate change?

13 years is an incredibly long time for something as fast moving as data center development. I guarantee that a _lot_ has changed. I know AWS in particular has gone through multiple entire revisions of their DC designs, and I recall a talk from some of their engineers saying how AWS actually found it more economical to use less cooling and let their DCs run hotter than they used to.

Here’s a recent article from AWS about using closed-loop systems for their AI data centers: https://www.aboutamazon.com/news/aws/aws-liquid-cooling-data...

Data centers may change but the physics of cooling doesn't.

It's more economical to run chips hotter but at the end of the day you'll still have heat that needs dissipating and it's hard if not impossible to beat evaporative cooling in terms of cost.

This is like someone in 1800 saying “at the end of the day you still have transportation needs and it’s hard if not impossible to beat horses and carriages in terms of cost”.

Literally just do a google search. There are advancements every day that improve upon evaporative cooling to make it use less and less water and energy, and alternative methods other than evaporative cooling.

Bleeding-edge advancement and commercially-viable solutions are not apples to apples.

If there were then datacenters would use them ;) There must be a catch eh?

Let me fix that for you: all that juice to exploit human's cognitive biases and trick them into paying engineers and founders ungodly sums of money.

I don't think any rational person outside of the hype cycle thinks LLM s are emulating a human brain in a meaningful way.

Considering the human brain evolved over hundreds of millions of years of evolution (what a lot of data!) that almost seems cheap to do in the span of a number of months to train a model.

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