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Anthropic raises $13B Series F

(www.anthropic.com)
585 points meetpateltech | 3 comments | | HN request time: 0.004s | 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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AlexandrB ◴[] No.45107239[source]
The whole LLM era is horrible. All the innovation is coming "top-down" from very well funded companies - many of them tech incumbents, so you know the monetization is going to be awful. Since the models are expensive to run it's all subscription priced and has to run in the cloud where the user has no control. The hype is insane, and so usage is being pushed by C-suite folks who have no idea whether it's actually benefiting someone "on the ground" and decisions around which AI to use are often being made on the basis of existing vendor relationships. Basically it's the culmination of all the worst tech trends of the last 10 years.
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simianwords ◴[] No.45107334[source]
This is very pessimistic take. Where else do you think the innovation would come from? Take cloud for example - where did the innovation come from? It was from the top. I have no idea how you came to the conclusion that this implies monetization is going to be awful.

How do you know models are expensive to run? They have gone down in price repeatedly in the last 2 years. Why do you assume it has to run in the cloud when open source models can perform well?

> The hype is insane, and so usage is being pushed by C-suite folks who have no idea whether it's actually benefiting someone "on the ground" and decisions around which AI to use are often being made on the basis of existing vendor relationships

There are hundreds of millions of chatgpt users weekly. They didn't need a C suite to push the usage.

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acdha ◴[] No.45107713[source]
> Take cloud for example - where did the innovation come from? It was from the top.

Definitely not. That came years later but in the late 2000s to mid-2010s it was often engineers pushing for cloud services over the executives’ preferred in-house services because it turned a bunch of helpdesk tickets and weeks to months of delays into an AWS API call. Pretty soon CTOs were backing it because those teams shipped faster.

The consultants picked it up, yes, but they push a lot of things and usually it’s only the ones which actual users want which succeed.

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1. HotHotLava ◴[] No.45110983{3}[source]
I'm pretty sure OP wasn't talking about the management hierarchy, but "from the top" in the sense that it was big established companies inventing the cloud and innovating and pushing in the space, not small startups.
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2. pandemicsyn ◴[] No.45111191[source]
Sure Amazon was a big established co at the dawn of the cloud, and a little bit of an unexpected dark horse. None of the managed hosting providers saw Amazon coming. Also ran's like Rackspace and the like where also pretty established by that point.

But there was also cool stuff happening at smaller places like Joyent, Heroku, Slicehost, Linode, Backblaze, iron.io, etc.

3. acdha ◴[] No.45114565[source]
That could be, I was definitely thinking of management hierarchy since that difference has been so striking with AI.

A lot of my awareness started in the academic HPC world which was a bit ahead in needing high capacity of generic resources but it felt like this came from the edges rather than the major IT giants. Companies like IBM, Microsoft, or HP weren’t doing it, and some companies like Oracle or Cisco appeared to thought that infrastructure complexity was part of their lock on enterprise IT departments since places with complex hand run books weren’t quick to switch vendors.

Amazon at the time wasn’t seen as a big tech company - they were where you bought CDs – and companies like Joyent or Rackspace had a lot of mindshare as well before AWS started offering virtual compute in 2006. One big factor in all of this was that x86 virtualization wasn’t cheap until the mid-to-late 2000s so a lot of people weren’t willing to pay high virtualization costs, but without that you’re talking services like Bingodisk or S3 rather than companies migrating compute loads.