I like library providers that can provide mechanical upgrade instructions. For example:

model.adjust(x,1,y) is now model.single(Adjustment.Foo, x).with_attribute(y)

Or whatever. Then people can go through your instructions find-and-replacing the changes, or even better, have an automated tool do it.

Also you pay some of the maintenance burden by writing all this documentation, so you have a some stake in keeping the changes minimal.

If you are this early career and the company is still paying you, PIP or no PIP, it's very likely that they want you to succeed. Question your assumptions and make sure you really understand the feedback they are giving you. Don't put in extraordinary hours (you want to have clear judgement, get plenty of sleep) but look for every resource, mentors, books, ask for help, and focus on doing your best. This will put you in the best possible light no matter what happens.

There are a lot of different companies out there each with a different culture. One of them might be a better fit.

But in the case of Brendan, he had recently been exposed as a white supremacist and lost his job when he was enrolled in the study. He was full of regret about getting caught out.

I imagine that this person was already contemplating personal growth, and the drugs just kicked his not-fully-conscious or not-fully=acknowledged feelings into conscious, actionable thought.

The chess engine's training is anchored by the win/lose outcome of the game. LLM training is anchored by what humans like to read and write. This means that a human needs to somehow be in the loop.