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

85

Oportunidad

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,

Por qué importa

Attribution at scale is the missing piece that separates uncompensated scraping from a market where data creators and model builders can negotiate terms, and without it no voluntary or regulatory licensing scheme can function.

Cómo evalúo la oportunidad

La Puntuación de Oportunidad es mi propia lectura, no una medición: cuánto duele, con qué frecuencia aparece y qué tan poco existe para resolverlo hoy. Un valor más alto significa que creo que vale más la pena construirlo.

Gravedad9/10

Cuánto dolor causa cuando aparece.

Frecuencia8/10

Con qué frecuencia la gente se topa con ello.

Espacio en blanco7/10

Qué tan pocas herramientas buenas existen para ello hoy.

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