[timkam]

Fully agree. Central functions of these types do not scale. Even with more mundane objectives, like operational excellence, organizations have learned that centralization leads to ivory tower nothing-burgers. Most of the resources should go to where the actual work gets done, as little as possible should be managed centrally (perhaps a few ops and thought leadership fluff folks...).

[marcus0x62]

And decentralized functions tend to be wildly inconsistent across teams, with info sec being a particular disaster where I've seen that tried. Neither model is perfect.

[timkam]

Sure, but we are talking about research teams here, not about an ops or compliance team. Central research tends to be detached from the business units but does not provide any of the 'consistency' benefits. Central research makes sense if the objectives are outward-facing, not if one wants to have an effect on what happens in the software-building units. So I'd say that ideally/hopefully, the people of the RAI team will now be much closer to Meta's engineering reality.

[mcny]

It works for things you can automate. For example, at Microsoft they have some kind of dependency bot such as when you have newtonsoft installed but have version < 13.0.1 and don't upgrade within such and such time frame, your M1 gets dinged. This is a very simple fix that takes like five minutes of work if that.

But I don't know if things are straight forward with machine learning. If the recommendations are blanket, And there is a way to automate checks, It could work. Main thing is there should be trust between teams. This can't be an adversarial power play.

https://github.com/advisories/GHSA-5crp-9r3c-p9vr