The model seems to correlate what worked previously to what will work next. However, in a field that is supposed to lead to new product funding - past industrie

The model seems to correlate what worked previously to what will work next. However, in a field that is supposed to lead to new product funding - past industries may steer funding in the wrong direction from what will work next. Two issues: 1. The purple cow effect - the opportunities for highest growth may be in underserved segments which are best addressed by founders less represented in Silicon Valley. (the next big thing may be a farming startup in India, but the founder won't fit the well connected or well advised by people with startup exits model this framework uses, and thus will be missed by SV investors, which leads to problem 2. ) 2. The money trumps product problem - whatever you can't beat with a solid product, you could always hammer with more money in the bank. Instead of hearing about a farming innovation in India, American farmers could be getting FarmVille ads on TV instead and tuning out. Since VCs invest locally, even if a startup starts picking up steam in Chicago, SF investors who don't have a toe in that pond, pick the local fish that eats the same algae and fund it far better than the Chicago company, which might have a better product. In a land-grab industry that money may be enough to gain adoption to the SF pond-dweller, but returns for the entire market will be lower, due to lower product quality and tendency of big firms here to pick only one company per industry. So the framework, biased by past data, may skew future data away from results that would be optimal without a framework.