[moandcompany]

I'll argue that AI leadership is not limited to just research, and an under appreciated aspect of leadership is making actual things useful for people (e.g. applying AI/ML technology to problems, and making those solutions common or ubiquitous).

[throwaway202020]

It would be hard to be leader without having leaders in the field at your disposal. There are literally 20,000 papers getting published each year. While stunning gains are being reported every month, it would be very hard for you to identify good implementable papers with state of the art results unless you are active in the field. Many of the techniques are amalgamation of other dozen techniques. So its even more harder to add/subtract from a given paper to productize the results. Keeping up with these papers and techniques is literally a full time job which is why you need researchers at your disposal if you are working on these problems. Unfortunately, researchers are almost never willing to just give up research and become your next engineering dude. They are in the field because they love research, freedom and can publish. So the viable way is to have a research lab which you can tap into on demand and have options for rotations. Many people believed that this can be done without having your own research lab but that doesn't often work out. If you approach a researcher in academia for consulting, you have to count on them being free from academic duty, grad students and other commitments. They will typically do 80/20. If you have your own research lab, you can turn this around to 20/80.

[moandcompany]

There's more to leadership than research and pushing state of the art in papers.

There's also a lot more to making useful AI/ML-powered technology than having good models.

Defining and limiting the field to just research and models is a common pitfall I've seen.