At a recent lunch with two long time friends and mentors I found myself talking through what I thought the best VCs exhibited and how they approached their jobs. The conversation focused on founders vs investors, key differences between the two, and attached characteristics the top few from each group exhibited.
A point of contention came when discussing great founders as having a ‘knack for the dramatic’ vs exceptional investors as thriving on backend and operational initiative. While certainly not trivializing this (I’m happy to point to a handful of previous investments I’ve championed arguing the founder, above all, would dramatically run through brick walls on their way to success), I’ve found the strongest founders and investors alike as coupling strong technical and leadership qualities alongside unmatched applied-philosopher-like questioning, modeling, and proxying instincts.
I’ve found in top founders and investors an inclination to view the world as moving human-accessible bits and data, where the companies founded and investments made are outcomes governed from axioms, rules, or models that sit hidden underneath the tertiarily accessible world. While the physical and digitally tangible aspects of the world are easily understood in a static context, unearthing the rules and models that dictate change over time is anything but. There’s a certain tier of entrepreneur and investor that sees this as a puzzle to solve and underlying model to find. They set forth creating early stage companies, investing in seeds, and pivoting at A-rounds as an iterative model-checking of their theses, constantly assessing current models vs the context at hand, and creatively bootstrapping and proxying ways to iterate and learn to further refine their model.
As example, we can view the previous decade’s emergence of cloud infrastructure as the result of a model whose main rules centered around the continuation of Moore’s law, increased proliferation and speed of internet and bandwidth, increased ability of software to manage increasingly complex infrastructure, and the exponential increase in large data sets. In the early 2000s this model was controversial. The internet was the first digitized ‘big dataset’, and viewing the above statements as axioms for a model was a leap of faith for most. An opportunity emerged for an initial set of cloud companies to quickly and cheaply model-test and proxy their theses and then raise large sums of venture capital to undercut enormous swaths of business from less willing and slower incumbents. The top entrepreneurs and top investors saw this model, and did what they needed to feel confident to move forward. For entrepreneurs this meant starting capital light companies and being quick to pivot as they learned more and further refined their theses. For investors this meant placing small amounts of initial capital, learning and gaining clarity, and then doubling down once they had seen enough to validate this thesis. They both took a similar intellectual path, but tested and instantiated their thesis through different mechanisms.
Transitioning to Lux Capital, I’m more than ever cognizant of the nature of venture as being model-driven. The best investors, like the best entrepreneurs, aren’t always the ones with the most accurate initial thesis, but rather the ones that at every step of the way reassess, question, and refine their models.
With the above in mind, I think there’s a fundamental failure in the majority of venture to properly incentivize the truth seeking expedition of accurate models. That is, investors can be easily seduced to invest in companies consistent with the easy-to-explain or flavor-of-the-month underlying dynamics and theses, regardless of true accuracy. These are the easiest to convince others of, and can quickly be outwardly trumpeted to generate future deal flow and thought leadership. It’s worth noting that in the long run these ‘hottest’ deals have underperformed – I suspect due to the lack of investor and entrepreneur willingness to stay intellectually honest and iterative, but rather remain static and unchallenged in riding the wave of what’s hot and easy to explain away.
In a recent conversation with my new colleague, Peter Hebert, we had a refreshingly honest conversation exploring the above dynamics. In an increasingly excitable and accelerating industry, it’s easy to get caught in wide spread hubris – to leave behind intellectual honesty and truth seeking in favor of short term ‘success’ and notoriety. This conversation well characterized my motivation for joining Lux: an opportunity to practice as part investor, part entrepreneur, and full applied-philosopher. As I step into Lux I’m thrilled to work with a team of awesome individuals, each continually learning, model-checking, iterating, and unearthing theses to understand and shed light on the future of large and disruption-prime industries. Onward.