Vanilla deep learning is so far ahead of what most companies use for analytics, that very little research is needed to offer a product with significant advantages over what's in practice now. In fact, I'd say most ML/DL/AI startups don't need to do research at all. They just need to spread more widely what exists now.

I disagree with Andreessen that Google's Tensorflow propaganda on Udacity is "opening the kimono" or having a large effect on AI. TF isn't better than pre-existing tools in many ways. And the ways that Google uses it aren't really transferable in an online course, since Google does have the data, architectures, infrastructure and talent -- some of which it can't share by definition, and some of which it won't share, for obvious reasons.

The smartest startups outsource research to universities and government funded bodies. It's a way of derisking themselves as investments. That's no different for AI. Theoretical research and product development are almost (but not quite) totally different things. Which is something that graduate students who think they want to do a startup should remember, because if they love research, they probably won't have much time for it.

[Disclosure: I'm associated with a rival DL library that I won't plug here.]