That's a really neat way to brainstorm. Let me add one more thing.
The big companies have an advantage in hardware and research. But they dont care about niche applications of their tech, because prizes worth less than $1B don't matter at their scale. That's where I try to focus on.
The key challenge is data. Too many AI startups get stuck in the "give us your data and we'll do some awesome stuff." That almost never works. [This](http://mattturck.com/2016/09/29/building-an-ai-startup/) talk does a really good job explaining why. The trick is figuring out how to get enough initial data to deliver value upfront.
Good slide deck, and I agree with most of it (and what I don't agree with is probably my own ignorance of the field).
I suspect there will be very few "pure AI" startups, and a ton of regular old tech startups that figure out how AI fits into their business faster than their competitors or figure out how to use it for a business advantage or to deliver a service that couldn't exist in that way before AI. With the early "like X but on the Internet" startups, the ones that succeeded in the biggest way (Amazon, for example) were the ones that built a great X that leveraged the internet to make it an order of magnitude better X.
So, Amazon was the best book store because they got everything right about being a regular bookstore (good prices, good service, efficient sales channel, solid relationships with publishers) and had damned near every book and could serve customers everywhere; a thing that is only possible on the Internet.
So, the best "X except with AI" company will be a great X company, and then AI will allow them to do some kind of force multiplier to push them to the top of the heap. That means we need to look for opportunities that currently require a lot of resources (say, people, or vehicles, or ) and can have AI added to it to make it produce 10x value given the same inputs. Even 2x value could be a big enough difference to beat your competitors at market, but the real out-of-the-park success stories probably need an order-of-magnitude boost from AI, even if it starts out slower because AI is still clumsy and most of the small companies are starting with tiny data sets (relatively speaking).
Anyway, mostly I think it's cool to play with. I think I see some ways to provide value and make some money with it, but it'll be as much an experiment as a business plan in the short term.
The big companies have an advantage in hardware and research. But they dont care about niche applications of their tech, because prizes worth less than $1B don't matter at their scale. That's where I try to focus on.
The key challenge is data. Too many AI startups get stuck in the "give us your data and we'll do some awesome stuff." That almost never works. [This](http://mattturck.com/2016/09/29/building-an-ai-startup/) talk does a really good job explaining why. The trick is figuring out how to get enough initial data to deliver value upfront.