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Anthropic raises $13B Series F

(www.anthropic.com)
586 points meetpateltech | 1 comments | 02 Sep 25 16:04 UTC | HN request time: 0.217s | 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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reissbaker ◴[02 Sep 25 17:16 UTC] No.45106035[source]▶
According to Dario, each model line has generally been profitable: i.e. $200MM to train a model that makes $1B in profit over its lifetime. But, since each model has been more and more expensive to train, they keep needing to raise more money to train the next generation of model, and the company balance sheet looks negative: i.e. they spent more this year than last (since the training cost for model N+1 is higher), and the model this year made less money this year than they spent (even if the model generation itself was profitable, model N isn't profitable enough to train model N+1 without raising — and spending — more money).

That's still a pretty good deal for an investor: if I give you $15B, you will probably make a lot more than $15B with it. But it does raise questions about when it will simply become infeasible to train the subsequent model generation due to the costs going up so much (even if, in all likelihood, that model would eventually turn a profit).

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dom96 ◴[02 Sep 25 17:56 UTC] No.45106689[source]▶
> if I give you $15B, you will probably make a lot more than $15B with it

"probably" is the key word here, this feels like a ponzi scheme to me. What happens when the next model isn't a big enough jump over the last one to repay the investment?

It seems like this already happened with GPT-5. They've hit a wall, so how can they be confident enough to invest ever more money into this?

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bcrosby95 ◴[02 Sep 25 18:26 UTC] No.45107077[source]▶
I think you're really bending over backwards to make this company seem non viable.

If model training has truly turned out to be profitable at the end of each cycle, then this company is going to make money hand over fist, and investing money to out compete the competition is the right thing to do.

Most mega corps started out wildly unprofitable due to investing into the core business... until they aren't. It's almost as if people forget the days of Facebook being seen as continually unprofitable. This is how basically all huge tech companies you know today started.

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1. ricardobayes ◴[03 Sep 25 09:38 UTC] No.45113886[source]▶
It's an interesting case. IMO LLMs are not a product in the classical sense, companies like Anthropic are basically doing "basic research" so others can build products on top of it. Perhaps Anthropic will charge a royalty on the API usage. I personally don't think you can earn billions selling $500 subscriptions. This has been shown by the SaaS industry. But it is yet to be seen whether the wider industry will accept such royalty model. It would be akin to Kodak charging filmmakers based on the success of the movie. Somehow AI companies will need to build a monetization pipeline that will earn them a small amount of money "with every gulp", if we are using a soft drink analogy.