This blog post was written by Alec Miller, Manager, Measured Innovation at TechAlliance.
According to Adeo Ressi of the Founder Institute – a startup training centre in Silicon Valley – entrepreneurs with the best chance of success tend to exhibit the following characteristics (professional experience, fluid intelligence, high openness, moderate agreeableness):
- generally 28 or older
- project completion skills
- real world experience
- pattern recognition
- abstract thinking
- positive thinker
- curious and adventuring seeking
In contrast, unsuccessful founders tend to be highly aggressive, deceptive, emotionally unstable and narcissistic and often make excuses. One unexpected finding from the research showed that I.Q. and conscientiousness show little-to-no correlation with success as an entrepreneur.
As mentioned in a Forbes article discussing the research, “successful entrepreneurs have more in common with artists than with managers.”
Ressi argues that a psychological aptitude test can improve the identification of high potential entrepreneurs and, hopefully, returns to early stage investors (which are notoriously volatile).
Ressi and collaborators have developed and implemented an entrance exam for the Founder Institute based in part on the “Big Five” personality traits. By tracking the success of graduates from the program and correlating that data to the psychological traits captured in the entrance exam, the Institute has begun to develop a profile of what a high potential entrepreneur looks like.
One of the most heavily weighted traits used when scoring the assessment is fluid intelligence. Fluid intelligence, or “street smarts” in popular parlance, is particularly important because of the dynamic nature of startups. Being able to see patterns, learn new rule sets quickly, and synthesize a lot of information into actionable steps, is a crucially important skill for entrepreneurs. One take-away here might be that if your founding team is stacked with PhD’s and specialists, you might want to consider bringing on board someone who can see the forest through the trees.
The idea of using a data-driven approach to identify and accurately value early stage opportunities is tantalizing but, to folks like Paul Graham of Y Combinator legend, too good to be true.
“There is too much randomness,” says Graham in response to the notion that a formula can pick and value startup winners. Instead, Y Combinator has an admissions committee that uses a more traditional application and group decision making process to screen applicants.
Will algorithms eventually surpass seasoned investors and strategically-minded advisors when it comes to picking winners?
This idea of venture capital algorithms is yet another example of data-driven decision making and how we are increasingly using computers to augment and/or replace human decision making.
Well-known examples are easy to come by: movie recommendations (Netflix), book recommendations (Amazon), web-content recommendations (StumbleUpon), and medical recommendations (IBM Watson).
But are these data-driven recommendation engines really superior to human intelligence in all situations? Under what circumstances might computers make better recommendations than humans and vice versa? If computer algorithms can make better recommendations than humans under certain circumstances, does this mean we can or should replace human oversight? What happens when these algorithms go awry? Who takes responsibility?
As with any novel idea, the true implications of it will only be revealed over time. Whether or not venture capitalists and angel investors can or will be replaced by algorithms is still very much an open question.
Will we eventually see IBM’s Watson replace Kevin O’Leary on Dragon’s Den?
Only time will tell.