Why does training on my writing earn me nothing when the model ships?
機会
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,
重要な理由
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.
機会をどう評価するか
Opportunity Scoreは測定値ではなく、私自身の見解です。どれほど痛みを伴うか、どれほど頻繁に影響を与えるか、そして今日時点で解決策がいかに少ないか。スコアが高いほど、構築する価値が高いと私は考えています。
それが現れたときにどれほどの痛みをもたらすか。
実際にどれほど頻繁に人々がそれに直面するか。
今日時点で、それに対する優れたツールがいかに少ないか。