[tsvetkov]

> Claude's API is still running on zero-margin, if not even subsidized, AWS prices for GPUs; combined with Anthropic still lighting money on fire and presumably losing money on the API pricing.

Source? Dario claims API inference is already “fairly profitable”. They have been optimizing models and inference, while keeping prices fairly high.

> dario recently told alex kantrowitz the quiet part out loud: "we make improvements all the time that make the models, like, 50% more efficient than they are before. we are just the beginning of optimizing inference... for every dollar the model makes, it costs a certain amount. that is actually already fairly profitable."

https://ethanding.substack.com/p/openai-burns-the-boats

[JCM9]

Most of these “we’re profitable on inference” comments are glossing over the depreciation cost of developing the model, which is essentially a capital expense. Given the short lifespan of models it seems unlikely that fully loaded cost looks pretty. If you can sweat a model for 5 years then the financials would likely look decent. With new models every few months, it’s likely really ugly.

[AnimalMuppet]

Interesting. But it would depend on how much of model X is salvaged in creating model X+1.

I suspect that the answer is almost all of the training data, and none of the weights (because the new model has a different architecture, rather than some new pieces bolted on to the existing architecture).

So then the question becomes, what is the relative cost of the training data vs. actually training to derive the weights? I don't know the answer to that; can anyone give a definitive answer?