AI Copyright Problem Nobody Wants to Define

We keep collapsing several technically different things into “AI training”: copying source text, retrieval over passages, fine-tuning, and learning a general concept. They are not the same operation. I’m building a local-first assistant called Christine around a hard separation: • **Warranted Retrieval:** user-facing factual answers may use only admitted public-domain or explicitly permitted sources and chunks. A claim needs direct support. If the evidence is not there, the system should say so rather than fill the gap. • **Abstraction-only learning:** for owner-authorized nonfiction, the system can derive its own compact notes about concepts, causal relationships, methods, and open questions. It then discards the original. No retained passages, page images, searchable text, source-like embeddings, or substitute copy. The abstraction path cannot cite or reproduce the original, and it is tested for reconstruction, close-paraphrase leakage, and style imitation. That is not a claim that this settles copyright law. Ingestion can create technical copies; jurisdiction and facts matter; an architecture needs evidence, audits, and tests, not marketing language. But it raises a question that seems unavoidable: if a human reads a nonfiction book, retains the underlying ideas, and later applies them without copying the expression, what technical and legal boundary should apply when a local AI is designed to retain only independently written conceptual notes and discard the source? Systems like this are being built now, including offline-first systems. We need to define the boundary before “all learning is copying” and “all training is fair use” become the only two positions. Do our laws permit only human minds to learn from a work, or can we define a rigorous machine analogue that is genuinely non-retentive and non-substitutive?