This AI Engine Takes Common Biases Out Of The Venture Capital Process

Using data to find the best entrepreneurs, not the entrepreneurs most like the people funding them

This AI Engine Takes Common Biases Out Of The Venture Capital Process
Illustration: Suppapong_L via Shutterstock

Venture capitalists pride themselves on their ability to pick winning ideas and winning people. But could artificial intelligence do a better job?


That’s the intriguing question arising from an experiment now underway in London. Founders Factory, a U.K. startup accelerator, has developed an AI platform that identifies high-potential entrepreneurs. The hope is to avoid the unconscious bias that normally privileges some demographic groups and backgrounds, and prevents others from getting ahead.

“I was interested in getting around the bias of selection, that if you’ve gone to a good school or university, you probably have a good network and a good chance of doing fairly well,” says Tom Bowles, who created the software. “I tried to remove that bias by normalizing the data and identifying people who did things differently, and persevered and exceeded [expectations].”

Founders Factory used the software to identify 100 candidates for its program. They’re now being helped to start businesses, with data about their accomplishments (or lack of them) fed back into the learning loop. As time goes on, the AI should improve its ability to predict success.

The software sifts profile data from Google, CrunchBase, and LinkedIn looking for characteristics that push entrepreneurs to succeed. That includes their education, awards, whether they started businesses at school, their extracurricular activities, their pace of career advancement, and their ability to do cross-disciplinary roles. The bot reduces the importance of hereditary and racial advantages and tries to narrow in on someone’s entrepreneurial character.

In addition, Founders Factory also contacted established entrepreneurs such Niklas Zennström of Skype and Jimmy Wales of Wikipedia, who made their own recommendations for people who might be included. These names were cross-referenced with the names produced by the AI engine.

Bowles says the software does what a recruiter or VC normally does (it goes through resumes), but at a greater scale than a human being could manage. The bot went through thousands of candidates to arrive at the first 100. And it could even help the entrepreneurs identify projects that might be suitable for them, based on their past interests and experience.


Bowles has a Ph.D. from Oxford University and a long background in big data computing. He worked on the first Lara Croft game and built systems for the Worldwide LHC Computing Grid at the CERN Laboratory in Switzerland. He says he’d like to broaden the current project to include European, Asian, and American data sets, so it can be as comprehensive as possible.

“We want to put together the world’s largest data set on business creation and the journeys that entrepreneurs have gone through,” he says. “Why do [VCs] make decisions on investment? What are the things that give them a better chance of success? Why don’t they succeed?”

Silicon Valley is often criticized for a lack of diversity. It’s known for advancing a certain type of 20-something white male who inevitably develops products appealing to 20-something white males. Perhaps AI-based recruitment could help broaden the talent pool?

About the author

Ben Schiller is a New York staff writer for Fast Company. Previously, he edited a European management magazine and was a reporter in San Francisco, Prague, and Brussels.