What AGPL teaches about AI
GPL was written for a world where distribution was the important event. If you gave someone a copy of a modified program, the license could require you to provide source code and preserve the user's freedoms.
But network services changed the practical meaning of distribution. A company could modify a program and let people interact with it remotely, arguing that it had not distributed the program at all. AGPL responded to that gap by treating network interaction as a trigger for source-sharing obligations.
The lesson for AI is not that an "AI AGPL" is required by default. The lesson is that a new technology or delivery model can expose a gap in an existing bargain — and when that happens, the community has choices: reinterpret existing licenses, create a new license, define new artifacts and disclosure obligations, build technical mechanisms for attribution and provenance, or accept that some forms of openness cannot be captured in one legal instrument.
AI raises the same kinds of questions. What is the source of a trained model? What is the preferred form for modifying it? What does reciprocity mean when the valuable inputs include data, compute, evaluation, and community feedback?
Adapted from the draft opening chapter of But Is It Open?