Microsoft Deletes AI Guide Using Pirated Harry Potter

Microsoft has deleted a developer blog post that walked users through training AI tools on the full text of Harry Potter and the Philosopher’s Stone—using a dataset that was incorrectly marked as public domain. The guide had been live since November 2024. It vanished within hours after a heated discussion erupted on Hacker News.

For readers and writers, the controversy lands on familiar ground: who controls beloved stories in the age of generative AI?

A ‘Relatable’ Example With Real Copyright Stakes

The now-archived post demonstrated how to integrate large language models with Azure SQL Database and LangChain. To make the example “engaging and relatable,” it used the complete text of J.K. Rowling’s first Harry Potter novel as a vector search dataset. Developers were shown how to run semantic queries, explore characters’ emotions, and even generate new AI-driven fan fiction.

The dataset was hosted on Kaggle and labeled public domain. It included all seven Harry Potter books, which remain under copyright. After questions surfaced, the dataset was removed. The uploader said it had been marked public domain by mistake and that there was no intention to misrepresent the licensing status.

The blog post itself was taken down shortly after the online backlash gained traction, as reported by Ars Technica. No public statement has followed.

Why Authors Are Watching Closely

The Harry Potter series is one of the most tightly controlled literary properties in the world. Rowling has long maintained a firm grip on the franchise’s rights, a reality that continues to shape everything from illustrated editions to screen projects like HBO’s upcoming Harry Potter series.

The incident adds fuel to a broader publishing debate. AI companies rely on massive text datasets to train models. Authors and publishers have increasingly questioned whether copyrighted books are being used without permission—or compensation.

Microsoft Research has separately explored how to make models “forget” specific works, including Harry Potter, in a paper on approximate unlearning. But the existence of unlearning techniques only sharpens the central question: if a model can be taught to forget a book, was it trained on that book in the first place?

For fantasy fans, the idea of machines remixing Hogwarts might sound novel. For authors, it raises existential stakes. As generative AI tools spread through publishing and tech, the line between inspiration and infringement is becoming harder to ignore—and much harder to police.