Comment by antonvs

> how much technical expertise is really needed in executive management.

For running an AI lab? a lot. Put it this way, part of the reason that Meta has squandered its lead is because it decided to fill it's genAI dept (pre wang) with non-ML people.

Now thats fine, if they had decent product design and clear road map as to the products they want to release.

but no, they are just learning ML as they go, coming up with bullshit ideas as they go and seeing what sticks.

But, where it gets worse, is they take the FAIR team and pass them around like a soiled blanket: "You're a team that is pushing the boundaries in research, but also you need stop doing that and work on this chatbot that pretends to be a black gay single mother"

All the while you have a sister department, RL-L run by Abrash, who lets you actually do real research.

Which means most of FAIR have fucked off to somewhere less stressful, and more concentrated on actually doing research, rather than posting about how you're doing research.

Wangs misteps are numerous, the biggest one is re-platforming the training system. Thats a two year project right there, for no gain. It also force forks you from the rest of the ML teams. Given how long it took to move to MAST from fblearner, its going be a long slog. And thats before you tackle increasing GPU efficiency.

> Which raises the age-old question of how much technical expertise is really needed in executive management.

For whomever you choose to set as the core decision maker, you get out whatever their expertise is with minor impact by their guides.

Scaling a business is a skill set. It's not a skill set that captures or expands the frontier of AI, so it's clearly in the realm to label the gentleman's expensive buyout is a product development play instead of a technology play.

Hopefully he isn’t referring to Alex Wang, as it would invalidate anything else he said in his comment

Alexandr