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Anthropic raises $13B Series F

(www.anthropic.com)
585 points meetpateltech | 2 comments | 02 Sep 25 16:04 UTC | HN request time: 0.404s | source
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llamasushi ◴[02 Sep 25 16:29 UTC] 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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duxup ◴[02 Sep 25 16:33 UTC] No.45105396[source]▶
It's not clear to me that each new generation of models is going to be "that" much better vs cost.

Anecdotally moving from model to model I'm not seeing huge changes in many use cases. I can just pick an older model and often I can't tell the difference...

Video seems to be moving forward fast from what I can tell, but it sounds like the back end cost of compute there is skyrocketing with it raising other questions.

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renegade-otter ◴[02 Sep 25 16:52 UTC] No.45105699[source]▶
We do seem to be hitting the top of the curve of diminishing returns. Forget AGI - they need a performance breakthrough in order to stop shoveling money into this cash furnace.
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1. mikestorrent ◴[02 Sep 25 17:13 UTC] No.45105998[source]▶
Inference performance per watt is continuing to improve, so even if we hit the peak of what LLM technology can scale to, we'll see tokens per second, per dollar, and per watt continue to improve for a long time yet.

I don't think we're hitting peak of what LLMs can do, at all, yet. Raw performance for one-shot responses, maybe; but there's a ton of room to improve "frameworks of thought", which are what agents and other LLM based workflows are best conceptualized as.

The real question in my mind is whether we will continue to see really good open-source model releases for people to run on their own hardware, or if the companies will become increasingly proprietary as their revenue becomes more clearly tied up in selling inference as a service vs. raising massive amounts of money to pursue AGI.

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2. ethbr1 ◴[02 Sep 25 19:33 UTC] No.45107941[source]▶
My guess would be that it parallels other backend software revolutions.

Initially, first party proprietary solutions are in front.

Then, as the second-party ecosystem matures, they build on highest-performance proprietary solutions.

Then, as second parties monetize, they begin switching to OSS/commodity solutions to lower COGS. And with wider use, these begin to outcompete proprietary solutions on ergonomics and stability (even if not absolute performance).

While Anthropic and OpenAi are incinerating money, why not build on their platforms? As soon as they stop, scales tilt towards an apache/nginx type commoditized backend.