Hm, sounds interesting but vague. I don't really understand how it will work.

How is data for the algorithms provided? A lot of times it is big. And messy, and proprietary. There seems to be an implicit assumption that you can just plug different algorithms to different data sets. But I can't think of a project where that has been the case.

I also doubt that "algorithms" are the bottleneck in a lot of projects. I'm not an expert, but I have some personal experience to back up what people say about "data trumping algorithms" (e.g. Peter Norvig and others have written about this)

I would like to hear about some more concrete examples / success stories. "Algorithms" is just too vague. I think if this becomes successful, it will be by first narrowing it to a particular domain, and then generalizing it again.