Studying Yourself
In the world of investing, there are two areas of study. The first world is outward-facing—the study of what makes a good investment opportunity, a good business, fundamentals, frameworks, etc. This is where most people spend all of their time studying, and with good reason. It intuitively feels like the most expansive, information-rich canvas to explore, with many great investors having generously published their distilled thinking for future generations to consume. Similar to hard science fields like physics, if one is studious enough to examine history and the outputs of great minds that have come before, he or she may be able to stand on their shoulders. The “science” aspect of investing is alluring, even enticing—the prospect of a grand formula or framework to precisely distinguish between good and bad investments seems like the holy grail for any capitalist. I can’t speak for the other areas of investing, but it seems that in venture capital, this is a goal that continues to elude the smartest and hungriest minds of the field. And that’s because even if we get it right, it’s only part of the formula.
The second world of study is inward-facing—the study of one’s own judgment, mental biases, where intuition is perfectly right and where it is perfectly wrong, motivators that skew incentives, and our natural tendency to want to outsmart ourselves. To study ourselves, we must be constantly trying to populate an ever-evolving 2x2 matrix with information:
(a) I think I’m right, I am right
(b) I think I’m right, I am actually wrong
(c) I think I’m wrong, I am wrong
(d) I think I’m wrong, I am actually right
It’s not *if* one is being subjected to mental biases in decision making, it’s *what* mental biases one is subjected to. When I’m trying to help a new investor understand this dynamic, I ask them to remember their first love—how certain they were that it was going to be a perfect relationship that would last forever—and how wrong they were. That means, objectively, you are capable of having 100% conviction in something and also being 100% wrong. Sitting with that as a truth about one’s judgment, while uncomfortable, ought to prompt us to be hungry to debug ourselves (rather than dismiss it to protect our ego). If we do this well enough, we can study ourselves at a distance, like an anthropologist, and come to valuable conclusions and workarounds. After all, an oracle that’s always wrong is just as useful as an oracle that’s always right.
For example, one thing that I’ve learned about myself is that in order for me to know that I have conviction in an investment opportunity, I need to do the work myself. This insight yields a few actionable takeaways that can improve the signal available in decision-making:
The more that we can abstract away from ourselves and understand these meta-level dynamics, the higher quality decisions we can make with greater consistency. Our emotions, desires, instincts, and behavioral patterns are all signals waiting to be valuable inputs, but only when processed correctly and not granted de facto root access. In order to strengthen this muscle of self-improvement, we must hold strong opinions, but voraciously listen for contradictory evidence. Every decision is a feedback loop waiting to populate the matrix.
An example of this more broadly is the FAANG trade. Over the past dozen years, these companies drove the vast majority of returns in the public markets, but many investors missed them completely despite strong fundamentals that would have satisfied conventional analysis. Why? Because it was “too obvious.” These were already the largest cap public companies and every investor argued that further upside was already “priced in.” That makes sense, but the applicable insight here is that investors are naturally egotistical—they want to be right AND clever. An investor who makes money via the consensus investment challenges their own self-worth as a producer of alpha. This means that these companies were disproportionately overlooked by capital allocators who wanted to find returns elsewhere in some “underdog” that they understood best. As a result, a previously non-obvious takeaway from this analysis is that:
It is easy to underestimate how dominant market leaders truly are.
The challenge with this inward-facing world of study is that it is fractal. We constantly try to outsmart ourselves once we know how we operate. We tell ourselves that going to the gym tomorrow instead of today is actually what’s best because we’ll be better rested. It is a constant cat and mouse game between our rational self and meta-analysis, each vying for superposition. But it’s also where the most alpha as investors comes from and is wholly unique to each individual. The expansive, information-rich canvas of exploration perhaps counterintuitively is not studying the outside world—it’s studying yourself.