Could algorithms help create better venture capitalists?

How can VCs better predict when innovations will survive or fail, both for startups and when corporations launch new products or do acquisitions? That is the problem William Hambrecht, a legendary venture capitalist who made early investments in Apple, Genentech, and Google, is trying to solve with building a Growth Science prediction engine.

Each company that WR Hambrecht Ventures invests in has gone through the Growth Science prediction engine and passed. “There’s no human subjectivity involved anywhere along the line,” Thurston explained. “All the algorithms converge on a discrete yes or no.”

The company uses proprietary databases and data harvesting, along with algorithms, to bring innovations into the world of statistics, delivering probabilities on the success of business models and new technology.

People talk a lot about the importance of innovation to economic growth. In a recent survey of voters in swing states by the Economic Innovation Group, 75% of those surveyed agreed that America needs more entrepreneurs and investors in order to improve long-standing economic problems. Hartshorn considers this a call to action. “The innovation economy has an information problem,” he says. “The information that drives it isn’t good. How can countries become innovation economies in a more efficient way? Let’s get better at funding the startup companies that will grow and drive employment. For every dollar that goes in the wrong place, that’s a shitty dollar. And it should matter.” Big data may indeed be able to help, but it’s more likely to be a piece of the puzzle, not the solution. For instance, academic studies have shown that serial entrepreneurs successful in the past are more likely to do well in new ventures. That implies there is some explanatory power in looking backwards for guidance on what’s ahead. “But the nature of entrepreneurship is always changing,” says Josh Lerner, the Jacob H. Schiff Professor of Investment Banking at Harvard Business School. “Most regressions predicting entrepreneurial success in the literature have very low goodness of fit (R-squared), which suggests the limits of a ‘Moneyball’ approach here. Predicting which startup is going to be successful is much harder than [predicting] which baseball player is. It is as if the baseball rules are being changed every year in unpredictable ways.”

Read the full  article on  Fortune.

Posted by Rudy de Waele aka @mtrends /

See what Gerd Leonhard writes about AI

On algorithmic curation

Gerd Leonhard on AI

Leave a comment