My deep and articulate friend, Jerry Neumann, just penned a thought-provoking piece “On being special in the venture business.” Jerry and I have co-invested together on many occasions, both during my time as an angel investor as well as a VC. And as fortune would have it, we were both original investors in the startup that became the 2016 IPO The Trade Desk. I have witnessed his investment skill and constant introspection first-hand, so I was not surprised that he raised an issue most venture investors I know ponder daily. For my purposes “venture investing” refers to investing in pre-product/market fit stage companies, where data is sparse and the distribution of potential outcomes is extreme. A neat summary of Jerry’s discussion is captured by the following paragraph:
Anyone can learn to invest well in startups. But you do have to learn how to do it and then you have to work hard to do it right, every time. Like any job, you show up and you do the work and you notice your mistakes and you try to do better and you improve over time. It requires thinking and trial and error and trying to be rational and asking yourself if you’re thinking about this right and asking other people how they did what worked and what they think about this one you’re thinking about. Figuring out the nos from the maybes is, more than anything else, like solving a puzzle. The puzzle is different each time. Your job is solving the puzzles.
Well, yes and no. There is a lot to unpack here. Can “anyone” learn to invest well in startups? I personally think this assertion warrants it’s own treatment, and to be honest, I do not agree, for the same reasons that I don’t think just anyone can be a successful trader. In venture investing, as in trading, assessing an opportunity requires clinical detachment and dispassion. Billions are lost regularly across the investment spectrum due to decisions fueled by emotion as opposed to reason. Investing well, or trading well, requires a system, a continuously evaluated, regularly tuned, consistently applied system, that can generate superior returns across many, many plays. And venture capital, as in liquid-market trading, requires a large enough data set to determine if one’s returns are driven by luck or skill, and if the system is robust or fragile and idiosyncratic.
The reason I could never say how I’m special is because I’m not. And neither were any of the other VCs I met with. At least not in a way anyone could wedge into a 30-second pitch. Divvying the world into two piles is hard, but it’s not magic. Being special is being magic, that’s what the question is really, how are you magic?
I do not think evaluating startups should be like a series of separate puzzles: I think they should be outputs of a robust system that takes seemingly disparate inputs and normalizes them for analysis and assessment. This, to me, is where magic happens, when you can look at a dizzying array of investment opportunities, identify the salient factors that contribute to successful, large-scale outcomes to construct a system, rigorously apply the system, and generate superior returns over an extended period of time. And doing this in a field with sparse data and myriad non-quantitative inputs that require normalization is, shall we say, hard as hell. As Jerry correctly mentions in his post, the numbers of investors and funds who are, in my words, “one hit wonders”, are legion. Venture is a business where it is easy to confuse luck with skill, but the one way of distinguishing between the two is persistence. I do not know the data — perhaps Mr. Douvos has it — but I’d hypothesize that the persistence of multiple 3x net funds across firms is frighteningly rare. This, to me, would be the evidence of true magic.
So who is special? Those who have demonstrated persistent, superior returns across time, which, to my way of thinking, is the outgrowth of a continuously optimized system (approach, framework, model, rubric, or whatever you want to call it) that can demonstrate magic across verticals and eras. And in venture investing, it is the synthesis of objective and subjective inputs that makes creating robust system so challenging, but ultimately so rewarding.
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