Comment by nvme0n1p1
18 hours ago
That doesn't really answer the question. What they do internally for training the next model is a separate issue. I'm talking about the models they offer publicly.
Per the article, companies are dropping OpenAI+Anthropic (partly) because of costs. If distilling is so simple and easy, why doesn't OpenAI take this "quick shortcut" and serve a self-distilled model externally, so they can charge reasonable prices and stop bleeding customers? Surely they can at least match the Chinese labs' efficiency, right? Wouldn't more customers and less opex look good for the IPO?
Anthropic, OpenAI, GDM, and Meta spend more on training than other labs by an order of magnitude. If they felt safe reducing this spend they would. These labs fear getting outcompeted.
Again, I am talking about inference, not training. Please read.
I indeed failed to understand your point. Isn't that what they already do with Claude Haiku, GPT-5.6-Terra, etc?
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How do you think they fund training? This is just as asinine as insisting that drug manufacturers only price medications based on production costs.
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