Algortihm Can Choose Next Silicon Valley Unicorn

Algortihm Can Choose Next Silicon Valley Unicorn


In the world of venture capitalists, not everyone is Peter Thiel. The Silicon Valley investor reaped 1 billion dollars in 2012 when he cashed in his Facebook stocks, turning a 2,000 percent profit from his initial $500,000 investment. Stories like Thiel’s may be inspirational, but they are by far the outlier. The start-up world sees thousands of hopeful companies pass through each year. Only a fraction of those ever returns a profit.



Picking a winner, the elusive “unicorn,” is as much a matter of luck as it is hard numbers. Factors like founder experience, workplace dynamics, skill levels and product quality all matter, of course, but there countless other variables that can spell heartbreak for an aspirational young company. Successful venture capital firms claim to know the secret to success in Silicon Valley, but it can still be a harrowing game to play.

Chasing Unicorns

Humans just aren’t very good at objectively sorting through thousands of seemingly unrelated factors to pick out the subtle trends that mark successful companies. This kind of work, however, is where machine learning programs excel. Two researchers at MIT have developed a custom algorithm aimed at doing exactly that and trained it on a database of 83,000 start-up companies. This allowed them to sift out the factors that were best correlated with success — in this case, a company being acquired or reaching an IPO, both situations that pay off handsomely for investors.


What’s the Secret Recipe?

They found that one of the biggest predictors of success was how start-ups moved through rounds of funding. And it wasn’t the slow and steady companies that were hitting it big, it was the ones that moved most erratically, pausing at one level of funding and then rocketing through the next few. How this plays into start-up success isn’t completely understood at the moment though.

The researchers say that their algorithm could be applied to much more than just nascent tech companies. The same principles that allow it to pick a handful of winners from a crowd of dudes should also apply in areas as diverse as the pharmaceutical industry and the movie business, where just a few successes can pay out billions. These are fields where the top players are lionized for their ability to sniff out winners and reap the substantial rewards. As with factory workers, bank tellers and telemarketers, the robots could be coming for their jobs as well.
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