I say no to both. Of the things we've made on purpose, but excluding where we were actually trying to make them opaque like e.g. cryptography, a trained artificial neural network is the most difficult thing to understand the inner workings of. This makes it a complete pain to even evaluate if one model is better or worse than another for your needs, so we have to mostly outsource these to other people's rankings and hope the score on ARC-AGI-3 or τ-bench or DeepSWE v1.1 or BioMysteryBench or whatever, actually corresponds to something we care about. Which it might do kinda but on the other hand a high score may turn out to be the curse of Goodhart.
Also, as with the Chinese models and the social media feed algorithms, the only way to tell if there's some systemic flaw in it is by analysing the aggregate outcomes. It is claimed (I can't read the laws myself*) that the Chinese government requires models to support the government's worldview about e.g. Tiananmen Square; and we have seen examples of Grok glazing Musk in amusingly stupid ways; so I fully expect something similar from Zuckerberg, e.g. requiring the model to glaze Meta products or propagandise for things Zuckerberg wants as a billionaire.
* every time I try to illustrate how mediocre Google Translate is from English to Chinese, the result is so bad that one of the replies is someone telling me the Chinese example I give is borderline gibberish.
Also, even if I could actually read it, I'm not a lawyer.