Why does training on my writing earn me nothing when the model ships?

Every large language model is built on billions of documents written by individual people, yet no technical mechanism exists to trace how much a specific creator's work influenced a specific model output. Data attribution methods like influence functions exist in research but do not scale to models with hundreds of billions of parameters trained on trillion-token corpora. A 2025 position paper argues that training data should be the most expensive part of an LLM precisely because its value is currently externalized onto creators who receive nothing. A March 2026 proposal called the Sovereign Context Protocol and a February 2026 framework for human-centric data attribution both attempt to close this gap, but neither has been deployed at production scale by any major model provider. Without a working attribution primitive there is no technical basis for compensation, licensing negotiation,