Training data is "approximately everything"; I've never understood why people (outside of copyright lawsuits, obviously) bother to ask for it given all the startups in this scene with competitive models.

The RLHF, RLVR etc. processes, those are likely more useful: semi-automatic training on outcomes, not mere inputs.

Monitoring, evaluations, how things fail, that's where I think transparency is key.

Though even then, these companies currently publish much about their own failures: when they are PoC they get ignored as "hype", "fear mongering", and "doing it for the IPO", when they're in-the-wild they still get called "hype", "fear mongering", and "doing it for the IPO", but sometimes they also get sued.