Like it or not, we live in a quantitative world, and it gets more so every day. Manufacturing defects are measured to 99.99966% accuracy. Automated trading algorithms evaluate businesses, prices, alphas, betas, libraries of ratios and make trades based on picoseconds of marginal arbitrage.
In 2006, an estimated 40% of trades on the London Stock Exchange were conducted by robotic intelligence. US estimates are closer to 80%.
In this world of empiricism, data and calculation, the job of a venture investor seems an anomaly indeed. While there are some exceptions, the majority of venture investors allocate billions of dollars every year based on little more than experience and gut intuition.
This is not, in any way, to detract from successful investors – the ability to pick winners, wrestle out a deal and drive others towards a central direction can require tremendous talent and skill.
Rather, the point here is merely to pose a question. Given the dollars at stake and lives in the balance, will venture investing inevitably evolve in a more empirical direction? Will there be a robot uprising?
Once upon a time, marketing and advertising were matters of visionary intuition and interpersonal sensitivity. Yet today statisticians are increasingly replacing marketing MBAs, and software developers are taking the place of bygone "mad men."
If you want to know what makes lefthanded, Republican, blonde, female smokers buy pink purses in Nebraska, you are increasingly better off writing a few lines of code rather than hosting a living room focus group.
While intuition-based venture investing works for some, most will agree it usually fails. Investors spend inordinate amounts of time screening deals, only to see around 90% fail (on a good day).
Industry-wide venture capital returns in the US over the past 10 years are now negative. Not only is venture investing falling short of limited and general partners’ expectations, but it also – by necessity – excludes the gross majority of startups. Fewer than 1% of start-ups attract venture investment in any given year. Fewer than 95% of businesses attract any equity investment at all.
A common counter-argument to empiricism is that venture investing is inherently unquantifiable – too unpredictable, too subtle – so as to be forever exempt from the purview of robots. Yet is venture capital really more multivariate than manufacturing, biology, chemistry or physics?
What makes venture investors immune? Why are they so special?
The field of psychology is a worthy analogy. Equally amorphous and intangible, psychology is divided between clinical methodologies (relying on human judgement and subjective analysis) and mechanical methodologies (relying on statistics, algorithms and other more objective tools).
More than 136 studies have tested the relative accuracy of both methods going as far back as the early 1900s, almost invariably concluding that mechanical methods are more consistent, accurate and yield higher-quality results1.
Even in Blink2, a book often held up in defence of intuition, author Malcolm Gladwell goes to lengths to call out the limitations of intuition, such as inaccuracy, wishful thinking, knowledge inaccessibility and the mind’s ability to play tricks on itself. Along these lines it was found that even simple checklists, the most basic of objective tools, reduced hospital surgical mortality by around half3.
With so many lives and dollars at stake in the realm of venture investing, can empirical methodologies lend more of a hand? Should we demand more than "gut feeling"? Could society benefit from a little more robot uprising in venture capital?
Notes
1 Grove & Meehl, 1996: ‘Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal (Mechanical, Algorithmic) Prediction Procedures: The Clinical- Statistical Controversy’, in Psychology, Public Policy, and Law
2 Gladwell, 2005: Blink: The Power of Thinking Without Thinking, Little, Brown and Company
3 Haynes, Weiser et al (2009): A Surgical Safety Checklist to Reduce Morbidity and Mortality in a Global Population, in The New England Journal of Medicine, Vol 360: pp491-499