don’t believe “any algorithm could catch that spark of genius that makes their business special,” he says, and from corporate M&A types who worry, “Do they have their place at the table, or does it undermine their value-add?”
He acknowledges that “good old VC” work is still required to identify promising companies on which to run the algorithms. After hearing a company’s pitch, Thurston would plug scores of data points into a few different algorithms “that triangulate onto the answer,” a process that takes a couple of days. He compares it to “how engineers would use a computer to simulate an airplane under different conditions before building a real one.”
Thurston guards the precise details of the algorithms as trade secrets, so much so that he has not pursued patents. “We expect the algorithms to be an advantage a lot longer than a patent lifetime so it didn’t make sense to disclose, even for a filing,” he says.
He shares these general details with Xconomy:
“We feed in a lot of data about a market, competitors, larger social-behavioral-economic trends, the relative advantages and disadvantages of the startup relative to these factors, which value drivers a startup has chosen to focus on. While this is a rough estimation, around 30 percent of the analysis is on the company itself, while around 70 percent is focused on modeling the market(s) the startup is going into.”
The algorithms—which were honed on a hand-built database of information on thousands of businesses, mostly at the time they were pitching investors for funding—tend to smile on business models that are “cheaper and worse” than market leaders, but still good enough. These are more likely to be disruptive—and also more likely to fly under competitors’ radars—than the “better mousetrap” plays, which, according to his statistics, fail more often, Thurston says.
“All we can do is hope to manage probabilities much better than other people have before,” he says.
One of the biggest differences between Thurston’s approach and the established practice of many VCs is emphasis on betting on the right people and expecting them to adapt to fast-changing markets.
Porter argues that the team is “the single most important factor in a startup’s ultimate success.”
Thurston says his algorithms value the team “as much as the empirical research says we should… but we don’t care nearly as much as most VCs do.”
In the end, the algorithm will be the final arbiter. “If the algorithm doesn’t like a business,” Thurston says, “we’re not going to do a deal no matter what.”
