Chris Yeh, a partner at Wasabi Ventures just wrote a great piece on the dangers of stereotyping when guessing how an entrepreneur or athlete is going to perform. It was originally published in his blog Adventures in Capitalism, and then also syndicated by PEHub Wire.
The key point he makes that stereotyping only makes sense in the absence of better data. Nobody subscribing to conventional wisdom saw the Jeremy Lin basketball phenomenon coming because he was an Asian player from Harvard, but a group of Moneyball-type analysts with better data knew he would be a super-star because they had studied his stats.
And of course he brings it back to picking entrepreneurs (specifically in Silicon Valley):
“In today’s world, with the ability to judge entrepreneurs based on a vast amount of publicly available data, ranging from social media to GitHub, with the ability to launch MVPs and generate tangible engagement and conversion statistics without raising money from investors, we now have the better data we need to make stereotyping AKA “pattern matching” AKA bigotry obsolete.
“But old habits die hard. Just in the last few months, we saw a CNN special on black entrepreneurs in Silicon Valley. Whether or not you feel that CNN used ambush tactics to help stir up controversy, the fact is that African-Americans make up only 1% of venture-backed entrepreneurs nationwide. And just last month, Whitney Hess conducted an analysis of top venture capital firms that showed that the most gender-balanced firm was Kleiner Perkins at 23% female, while the majority of those firms had zero female investors.
“Discussing such topics makes people in Silicon Valley uncomfortable. Few of us like to think of ourselves as racist or sexist. Yet I know of many entrepreneurs who feel that they are overlooked because of their skin color, gender, age or simply because they didn’t go to the right schools.”
Interesting post. And as a fan of Moneyball, it makes me curious about these new basketball statisticians – I had stereotyped those people as baseball only.
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