A bit delayed, but the Navier–Stokes results sparked a lot of conversations about privacy, and inspired me to write a primer on what AI labs actually do with our data. I go through @OpenAI , @AnthropicAI, @GoogleDeepMind , and @SpaceXAI policies: training defaults, retention, feedback exceptions, and what deleting a chat actually does. Putting the policy details aside, one thing about this whole debate is worth emphasizing: I think focusing only on whether someone read the chat logs misses something. Sometimes the valuable secret is just one bit: someone has already made this approach work with an AI. You don’t need their proof, their name, or their conversations for that to change where you put your time and compute. This is why I don’t think Clio-style aggregation and LLM privacy filters are enough. Hiding the raw conversations doesn’t settle what someone can learn. Full blog below 👇