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How much Anthropic and Cursor spend on Amazon Web Services

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152 points isoprophlex | 2 comments | 20 Oct 25 15:05 UTC | HN request time: 0.419s | source
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daft_pink ◴[20 Oct 25 15:28 UTC] No.45645040[source]▶
I think this is a minor speed bump and VC’s believe that cost of inference will decrease over time and this is a gold rush to grab market share while cost of inference declines.

I don’t think they got it right and the market share and usage grew faster than inference dropped, but inference costs will clearly drop and these companies will eventually be very profitable.

Reality is that startups like this assume moore’s law will drop the cost over time and arrange their business around where they expect costs to be and not where costs currently are.

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1. username223 ◴[20 Oct 25 16:27 UTC] No.45645748[source]▶
Color me skeptical. We're running into the speed of light when it comes to transistor size, and the parallelism that made neural nets take off is running into power demands. Where do the exponential hardware gains come from? Optimizing the software by 2x or 4x happens only once. Then there's the other side: if Moore's Law works too well, local models will be good enough for most tasks, and these companies won't be able to do the SaaS thing.

It seems to me like models' capability scales logarithmically with size and wattage, making them the rare piece of software that can counteract Moore's Law. That doesn't seem like a way to make a trillion dollars.

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2. throwaway290 ◴[20 Oct 25 16:35 UTC] No.45645841[source]▶
One improvement is from scraping and stealing better quality IP to train on. And they can just ride Moore's law until they profit then lobby governments to require licenses for fast GPUs because of national security.