The Unlearning Trap Federated unlearning—the ability to permanently remove a user's data from a trained AI model without retraining from scratch—has become the regulatory holy grail. GDPR's right to be forgotten, emerging AI acts across the EU and beyond, and shareholder pressure have made it a compliance mandate. The problem is brutal: federated unlearning at scale is technically immature, legally untested, and may be mathematically unsolvable in the way regulators demand. The appeal is obvious. Instead of destroying training data and retraining models (expensive, time-consuming), you surgically excise a user's influence from weights that have already been baked in across billions of parameters. It sounds clean. It isn't. Why Technical Reality Outpaces Regulation The verification problem Unlearning claims are nearly impossible to verify independently. How do you prove that a specific user's information is truly gone from a neural network?…