A friend of mine is working on coscout. It's in beta right now, but he showed me some pretty insane machine learning based insights for companies, investors and founders. Things like - When will this company raise the next round? - What is the net worth of ? - What is the probability that this investor will invest in you? (given your sector, founder age, pedigree, gender, market conditions, do you have an MVP or not…
Feedback loops in this sort of thing always scare me. For example - say people of one demographic are less likely to fund raise, so the model says they're less likely to succeed, so investors using the model don't invest in them and they are put at an even further disadvantage. And so something that is inherently data driven can end up moving further away from the meritocratic ideal it's likely trying for.
And the thing is, it's hard to get this bias out of models - almost everything ends up correlating to age, race, gender and so on - zip codes, income, schools, past employers, etc.