this post was submitted on 17 Jan 2026
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You'd think, but efficiency gains are erased by the LLMs having bigger context windows and self-referencing "thinking" or "agent" modes that massively extend token burn. There's public data out there showing how training costs are an enormous fixed point, but then inference costs very quickly catch up and exceed the training cost.
A model that's token-efficient is a model that's pretty useless and a model that's useable for anything is so inefficient as to have massively negative profit margins. If there was even one model out there that was cost effective for the number of tokens burned, the provider would never shut up about it to buyers
Wow, really? I guess context windows have been going up but did not realise they were so ruinously expensive. Where can I read more about this?
Ed Zitron is chugging the haterade but he does a lot of good reporting on this stuff. https://www.wheresyoured.at/oai_docs/