Last week, we posted a definitive list of Israel’s serial entrepreneurs in the AI ecosystem, who have exited at least one AI venture, and currently embarked on another AI venture(s).
We dug deeper into the fabric of these 32 serial AI entrepreneurs to determine if there is a recipe to success in starting an AI startup. Do these entrepreneurs have commonalities in their backgrounds? Is their success repeatable? What is the formula?
Upon review, the qualities of theses serial entrepreneurs appear to be very similar to the qualities of the typical entrepreneurial profile. But there are subtle differences and recurring themes worth highlighting.
Mathematics, Computer Science and America
Of the technical background entrepreneurs (85% of the entire group), the near majority earned a bachelors of science in computer science or mathematics. Furthermore, one third of the group studied at an american university during their graduate or doctorate studies. One fifth earned a PhD, and on average, earned two degrees. Prior to starting their hot-streak of serial AI entrepreneurship, they worked for an average of 10 years, or went straight from their army service (average service of 7 years) to their first AI startup success. And the average AI startup success takes 6 years to realize.
Youth mobility and programming
In a related vein, many entrepreneurs experienced a similar situation: their family moved around at a younger age, during which time they took to computer programming. It was during that period they gained an aptitude for coding, as well as curiosity and mental patterns to learn AI and even commercializing AI. Such a trait can be highly positive for serial entrepreneurship, although akin to fostering non-commitments in livelihood, grooming decision making as a habit in future critical decisions that influence the course of startup activity is a valuable trait. Mental mobility may aid entrepreneurs in understanding the subtle cues on when to move onto another project, pivot the company, hire another executive, or seek another sponsor. Timing is critical and the complacency in the turbulent cycle of startups can be a killer. And what successful startup hasn’t pivoted before?
The group of technical AI minds acquired their skill-sets in both the army and university with no clear distinction on the right path to learning AI. But many repeat the same commentary on how AI is like any other technical skill-set, it’s knowing how and where to apply it that makes the difference between winners and the rest. AI is simply a toolbox, and these entrepreneurs narrowed in to exactly what’s needs fixing, and which algorithms are suitable to improve the existing offering. Moreover, the first AI startup was built off of a subset of AI algorithms, and another common theme is for the serial entrepreneurs to improve, or build off of the AI algorithms that were employed at their previous venture.
Team above all
In building a startup, a key and decisive skill for the best startups and most importantly, every founder likens their attribution of success to the collective strengths of the team. And so it appears a typical trait of an AI startup success can easily be represented by the traits of any successful startup founder. Assembling a team capable of executing many thousands steps to building and selling a startup is crucial.
Another frequent commentary is the concept of their teams’ data-IQ: the ability to recognize and evaluate data and subsets immediately. With a high data-IQ, an AI engineer understands the right models and data types under first glance, knowing how to proceed with meaningful research or action.
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