Hacker News Reader

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.

View on HN

Again, I am talking about inference, not training. Please read.

How do you think they fund training? This is just as asinine as insisting that drug manufacturers only price medications based on production costs.

Less opex = more profits. They can use that money to fund training. I don't see how that could possibly be a bad thing.

I indeed failed to understand your point. Isn't that what they already do with Claude Haiku, GPT-5.6-Terra, etc?

That's the goal, but those smaller models don't match the price:performance of leading open-weight models, which is why companies are switching away (as explained in TFA).

The open weight labs figured out some secret sauce that (so far) big name labs are unable to replicate, so instead of competing, they're going on the defensive with claims of distillation attacks.

Again, in the friendly article, only one company is on record. The article allows the reader to make whatever inferences she may want to make.

Reply on news.ycombinator.com