this post was submitted on 20 Oct 2026
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There is a genuine "training corpus" that is an accumulated asset and grows and feeds new models. The vast majority of training cost, that should be expensed, is the training time compute. Neither of these are expensed. There was about 13 major US model releases in September. At least 7 of these replaced a 1 month old previous model family. Open AI gpt sol went from 5.6 to 6 to 6.1 in about 2 months.
Fast model releases not only lock in structural losses, they juice revenue growth illusions. Model use rate generally drops by 66% 4 weeks after release, because of competition, and a surge of use on release for evaluation.