> Do we use user feedback and de-identified data to improve ChatGPT and Codex in a holistic way? Yes.

This says that they trained on user sessions. The de-identification here I believe refers to removing PII, which doesn't matter here because the issue at hand is the content of the researcher's session where they likely discussed their approach tackling the Navier Stokes problem.

> Did any human or agent look at user data as part of the Navier Stokes effort? No.

If they trained the model on Lavent's chat sessions (PII removed or not), then this statement is meaningless as the model weights already contain that information.Given it's a new yet unreleased internal model, it is likely a 10+ trillion parameters (Astra is rumored to be 10 trillion), so the model can retain a lot more detail/info from training data.

Why is he leading the with the irrelevant part first ?

> And so does every LLM company.

Nope, not for enterprise users.No enterprise customers would use it if all of their internal business plans / trade secrets would end up in the model weights of the next OpenAI model. Imagine your competitor asking chatGPT a question and the model spitting out your business plan. These models can retain very specific fine grained data. I remember there were examples of them reproducing sections of their training data verbatim.

Even if you apply Goldfish loss or other things like that, they still understand the gist of the thing they're trained.

That's of course the whole point of things like Goldfish loss.