I agree, publicly deployed models seem to be easy to train from. I did say "internally deployed LLM" though. agentcoops said "...where the models in question increase the productivity of workers in their non-ai-related profit centers" above, that's the bit I was thinking about. I think private models, either trained from scratch or fine-tuned, are going to be a big deal though they won't make the PR splash that public models make.

The conclusion for that seems to be that it just yields a model that has the surface look and feel of GPT3 or 4 but without the depth, so the experience quickly becomes unsatisfactory once you go out of the fine tuning dataset.

I don’t think they necessarily will be allowed to train on their data unless they get explicit permission. They will try, but the way I see privacy revaluations is that users will have to authorize specific uses of their data and not be surprised by any application.

This could be one of the more interesting privacy fights of the next decade.

I’m sure there are easy cynical takes about how they will just shrink wrap the EULA, and maybe they will. But in a good privacy environment, users should never be surprised and have control over how their data is used. And I think we’ve made some progress there.

China probably has the most comprehensive data on its users from a surveillance perspective