Lisa Larrimore Ouellette

Critics of generative AI often describe it as a “plagiarism machine.” They may be right, though not in the sense they mean. With rare exceptions, generative AI doesn’t just copy someone else’s creative expression, producing outputs that infringe copyright. But it does get its ideas from somewhere. And it’s quite bad at identifying the source of those ideas. That means that students (and professors, and lawyers, and journalists) who use AI to produce their work generally aren’t engaged in copyright infringement. But they are often passing someone else’s work off as their own, whether or not they know it. While plagiarism is a problem in academic work generally, AI makes it much worse because authors who use AI may be unknowingly taking the ideas and words of someone else.

Disclosing that the authors used AI isn’t a sufficient solution to the problem because the people whose ideas are being used don’t get credit for those ideas. Whether or not a declaration that “AI came up with my ideas” is plagiarism, failing to make a good-faith effort to find the underlying sources is a bad academic practice.

We argue that AI plagiarism isn’t—and shouldn’t be—illegal. But it is still a problem in many contexts, particularly academic work, where proper credit is an essential part of the ecosystem. We suggest best practices to align academic and other writing with good scholarly norms in the AI environment.

Since its inception, patent law has required that inventors publicly disclose information about their inventions in exchange for receiving patent rights. This foundational requirement is policed through multiple doctrines: patents fail “enablement” if “undue experimentation” is needed to practice the invention, and they lack adequate “written description” when they fail to establish the inventor’s “possession” of the invention. Despite disclosure doctrines’ centrality, fundamental puzzles about their application remain unresolved. In Amgen v. Sanofi , the Supreme Court recently took up one such puzzle: Must a patent enable the full scope of the claim or merely some number of working examples? But the Court failed to address long-standing puzzles surrounding this issue. In this Article, Jonathan S. Masur and Lisa Larrimore Ouellette tackle these questions and more. The Article attempts to bring conceptual order to the disclosure doctrines, reconciling them with one another and with the broader animating principles of patent law. These puzzles must be solved if patent law is to fulfill its promises; if they are not, the resulting doctrinal gaps will expose the patent system to strategic behavior by nefarious noninventors—including those aided by new generative artificial intelligence tools—who learn how to acquire the patent quo without paying their quid.