
The Bigger AI Models Get, the More They Forget Who They Learned From: MIT Reveals a Wall of Unattributability
An MIT research team has demonstrated a phenomenon in which attributing diffusion model outputs to specific training data becomes impossible as dataset scale grows. While this undermines the evidentiary foundation of copyright lawsuits, it also raises a paradox: scaling up a model itself could become a liability-shielding strategy.








