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peppevignanello 5 hours ago

Exactly, it's like saying Shell is spending a fortune on fuel compared to what they spend on employees, if you count oil extraction costs as 'fuel'.

general1465 5 hours ago | parent [-]

So where are these training costs getting paid from?

ssivark 4 hours ago | parent | next [-]

It'll get paid from revenue, not by redirecting employee salaries. All that AI+compute is literally what customers pay Anthropic for.

Big AI labs are not software companies where payroll dominates expenses. They're capex-heavy industrial entities; it just so happens that the "machines" (whose output they sell) are nominally the same category as the devices that their knowledge worker employees use on their desks.

mrweasel an hour ago | parent | prev | next [-]

Compute (and IP theft) is the raw materials being used by the AI companies to build their products (the models).

It doesn't make sense to compare OpenAIs or Anthropics compute spend to that of our average software company, because different products require different raw materials. Dropbox also use way more storage than Snapchat, that's an equally silly comparison.

seasox 4 hours ago | parent | prev [-]

VC mostly, since Anthropic is not profitable.

arjie 4 hours ago | parent | next [-]

That's about to change: https://www.wsj.com/tech/ai/mind-blowing-growth-is-about-to-...

Anthropic was profitable last quarter.

debugnik 3 hours ago | parent | next [-]

xAI is discounting the first few months of their rental to Anthropic, which will help them seem profitable for a bit longer. We'll need to see if that lasts.

surgical_fire 2 hours ago | parent | prev [-]

Hello kind sir, may I interest you in some bridges I have for sale?

littlecranky67 4 hours ago | parent | prev [-]

I wonder if they ever will be. If the chinese open source models are only 3-6 months behind every major frontier model release, I can't see the business model. GLM-5.2 is supposedly on par to Opus depending on the case. And everybody and their mother can run that model in their datacenter and charge Dollars for tokens.

est31 4 hours ago | parent | next [-]

There is distillation going on where chinese providers give the model lots of outputs. We don't live in a world where chinese providers are not doing this so we can't compare the advantage of this distillation, but there is some advantage to it otherwise they wouldn't do it.

If Anthropic can block distillations somehow (which are fair game imo given that Anthropic et al did the same with the written works of mankind), then they might stop or slow down the chinese from catching up.

Chinese also have like 40% of the AI researchers of the world, plus they have access to a lot of cheap labour for writing training data. I'm sure an hour of training data creation from one of China's 162 million university educated people is much cheaper than an hour of work from one of US's 97 million. Probably still cheaper than someone from the grand area.

China is behind in AI chips/GPUs but they are catching up. One thing where they have a hard dependence on outside is their energy imports: they have to import a lot of stuff from third party countries. The US on the other hand is energy self sufficient.

littlecranky67 3 hours ago | parent | next [-]

Stopping distilation is a condradiction to growth: The more active users Anthropic gets, the easier it will be for chinese companies to distile the model. Heck, I can see a paid browser extension being issued that does nothing but send copies of your AI chatbox prompts+results to China - with a "hidden" feature that creates distillation prompts every now and then. Give each 5$ a month for installing the browser extension, and you got an unstoppable distillation botnet.

nylonstrung 3 hours ago | parent | prev | next [-]

I think the panic around distillation misses the fact that US labs also benefit heavily from Chinese breakthroughs like Deepseek's work on sparsity, MoE and training architecture

It may be that US labs use Chinese models for distillation but we'd ofc never know because they can host the models themselves

torginus 2 hours ago | parent | prev [-]

I think it's quite hard to block distillations, and everyone is distilling, even unintentionally.

If you feed the most recent Github repos into the training, most of that code will be written by frontier LLMs. Training on that is distillation.

tryagainian 4 hours ago | parent | prev [-]

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