An Algorithm Can Pick the Next Silicon Valley Unicorn

By Nathaniel Scharping | June 29, 2017 4:08 pm

Silicon Valley‘s unicorn hunter Erlich Bachman played by T.J. Miller, who said he plans to leave the show. (Credit: HBO)

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 return 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 from 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.

In a paper published to the pre-print server arXiv, they say that their algorithm picked successful companies 60 percent of the time — double the rate of most venture capitalist firms. It did so by incorporating data on the founders themselves, the executives and advisors, such as education levels, and whether they had been involved with a successful company before, as well as information on how various companies progressed through the multiple funding rounds that sustain start-ups. They based their algorithm on a series of equations normally used to describe the chaotic movements of particles in a fluid, known as Brownian motion, and essentially attempted to isolate which variables mattered the most.

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.

They also found correlations with more traditional factors as well, such as the level of experience among founders and executives. These are things that most venture capital firms already take into account when choosing start-ups to back, but it seems that the algorithm could optimize for these things much better than humans. It’s another area of finance where artificial intelligence has made significant gains in recent years. Algorithms are already trading stocks and performing market research for corporations. It may soon be deciding which companies get off the ground at all.

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 duds 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.

CATEGORIZED UNDER: Technology, top posts
  • Uncle Al

    How this plays into start-up success isn’t completely understood at the moment though.” Discovery is a few odd pieces suddenly fitting into the jigsaw of Creation. Managers cannot manage discovery, they can only manage to end it. Deeply managed startups are as crippled as established corporations.

  • OWilson

    The search to analyse, quantify, and thereby control, creativity, is the Holy Grail of “Central Control” advocates. They salivate at the thought of mindless robots doing their bidding, with no interference from inconvenient “elections”.

    If they can reduce individual skills to a few basic programming instructions, we could have a more egalitarian society, with equality for all. :) Of course they would be the Holy Programmers!

    They do not understand that the basket of abilities, attitudes and virtues, the elusive “gift” that is exemplified in people from a Michelangelo, a Mozart, a Shakespeare, a good plumber or a good builder, an Eric Clapton or even a good salesman, involves much more than instruction and practice.

    Consequently those inspired gifts are devalued!

    Success will never be a science, it is an art!

    Sorry, all you bureaucrat Central Planners! :)

  • Uolevi Kattun

    Are these algorithms still making some sense? Creativity and freedom are needed to innovations. But the long tail takes care that the future will not be easy. Race of venture capital is tough. Winners will be officers and bureaucrats who work systematically and doggedly.

    Perhaps these two researchers begin a start-up, where they sell this custom algorithm testing in internet, let other start-ups compare with the benchmark and perfect their commercialization procedures. This is just step two. Algorithms don’t tell how to innovate and fund it.

    • OWilson

      “I work from home and make $10k per month in my spare time using this simple method from MIT”

      • Uolevi Kattun

        Madonna is an artist, but even more she is a businesswoman. Big companies often buy start-ups just when those have met success. Similar situation is in Shark Tank. A top innovation is not enough if you don’t trust the start-up and its founders.

        Nokia was connecting people. Today it’s Mingla. Are the unicorns just the best matches between innovations, start-ups, developers, investors and other participants? Algorithms strengthened with AI might be employed to find these top matches.

        • OWilson

          The AI and Algos have to be programmed only by winners, not economists, or you’ll just finish up like climate science, with the truly able forecasters off making fortunes on Wall Street, Real Estate, while the remainder spend a lifetime trying to “fix” their flawed models.

  • Ramesh Chandramowli

    Thanks for the informative post & for providing us with this great write up, Keep it up.
    Do Visit Adaalo™ Free Online Classifieds Marketplace In India, Offer’s Fastest Posting Ever, Find Nearby Ads With Registered Users Only. Free / Secure / Easy.


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