There's growing confusion and debate about whether training AI systems—specifically large language models (LLMs)—on copyrighted works constitutes unfair or even illegal use of intellectual property. The heart of the misunderstanding lies in conflating two fundamentally different acts: copying and learning.
Copyright law is explicitly designed to prevent unauthorized copying, reproduction, and distribution. It is meant to incentivize creative efforts by ensuring creators can profit from their original works without facing unfair competition from exact or near-exact copies.
However, copyright law does not—and fundamentally cannot—prohibit learning from protected materials. Consider:
A student reads and internalizes concepts from a textbook.
A critic watches a film and writes an original analysis.
An engineer studies patented inventions to improve upon them.
These activities do not violate intellectual property rights because internalizing ideas, facts, and patterns, and then generating new, transformative work, is explicitly protected. Copyright restricts exact duplication—not understanding, analysis, abstraction, or creative reinterpretation.
Training an AI language model is an analogous process. LLMs statistically encode relationships and patterns in vast datasets, extracting generalized understandings and linguistic structures. Crucially, the model neither stores nor reproduces copyrighted works verbatim; it "learns" rather than "copies."
Critics often mistakenly equate "training data" with unauthorized reproduction. But legally and conceptually, feeding text into a model is closer to a student reading a book than to someone photocopying it. Both activities generate knowledge internally without compromising the original creator's market.
Legal precedents, notably cases like Authors Guild v. Google, affirm that transformative uses—including indexing, summarization, and text analysis—are generally protected as fair use. AI training, which is highly transformative, clearly falls into this category.
The misconception arises primarily because people intuitively treat AI systems as storage devices rather than as learners. Yet, recognizing the transformative, abstract nature of LLMs clarifies that existing copyright frameworks not only accommodate but encourage such uses.
In short, copyright law protects creators from unfair competition arising from duplication, not from learning or transformative innovation. AI models embody this principle explicitly.
Learning is not copying—and the law, rightly understood, reflects this distinction.
Disclaimer: This post was composed with the assistance of an LLM—no copyrighted materials were harmed in the making of this text.