A mathematician's year-long work on a Millennium Prize Problem was fed into an AI coding assistant, then OpenAI solved the same problem via the same route weeks later—raising a hard question about whether anonymization actually protects novel ideas that are inherently identifiable by their uniqueness. The real issue isn't whether data was stolen, but that the rarest, most valuable ideas can't be hidden inside training data no matter how many names you strip out.

• De-identification (removing names, emails, etc.) only works when your data blends into a crowd; unique research approaches are inherently re-identifiable because the method itself is the fingerprint.
• The same mechanism that makes LLMs powerful—compression of billions of human interactions—makes privacy impossible for anyone working on sparse, high-value problems; there's no technical fix that preserves both capabilities.
• The solution isn't avoiding these tools but controlling where your work lives: keep sensitive data local and bring the model to it rather than uploading your thinking into systems you don't control.