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Anthropic raises $13B Series F

(www.anthropic.com)
585 points meetpateltech | 2 comments | | HN request time: 0.397s | source
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llamasushi ◴[] No.45105325[source]
The compute moat is getting absolutely insane. We're basically at the point where you need a small country's GDP just to stay in the game for one more generation of models.

What gets me is that this isn't even a software moat anymore - it's literally just whoever can get their hands on enough GPUs and power infrastructure. TSMC and the power companies are the real kingmakers here. You can have all the talent in the world but if you can't get 100k H100s and a dedicated power plant, you're out.

Wonder how much of this $13B is just prepaying for compute vs actual opex. If it's mostly compute, we're watching something weird happen - like the privatization of Manhattan Project-scale infrastructure. Except instead of enriching uranium we're computing gradient descents lol

The wildest part is we might look back at this as cheap. GPT-4 training was what, $100M? GPT-5/Opus-4 class probably $1B+? At this rate GPT-7 will need its own sovereign wealth fund

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AlienRobot ◴[] No.45106150[source]
I saw a story posted on reddit that U.S. engineers went to China and said the U.S. would lose the A.I. game because THE ENERGY GRID was much worse than China's.

That's just pure insanity to me.

It's not even Internet speed or hardware. It's literally not having enough electricity. What is going on with the world...

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ipython ◴[] No.45108226[source]
Not to mention water for cooling. Large data centers can use 1 million+ gallons per day.
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xnx ◴[] No.45110537[source]
1 million gallons is approximately 0.5 seconds of flow of the Columbia river.
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1. wiredpancake ◴[] No.45111427[source]
It means nothing when most water is recycled anyways. It's not like the GPUs actually drink the stuff, the water just connects to heatsinks and is cycled around.
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2. xnx ◴[] No.45111698[source]
That's true for closed loop systems, but some data enters use evaporative cooling because it is more energy efficient.