Over the past year, I have spent a lot of time digging into cloud infrastructure and technical tools as a space for investing. One of the emergent behaviors of technology trends is the accelerating advantage of being a winner, played out with network effects or scale effects (or both!). Since its mid-2000’s launch, AWS has obviously become a juggernaut, growing so quickly and throwing off so much cash that even Amazon can’t put it to work fast enough (!). Getting a new software product to market has never been as cheap or fast as it is today, despite the fact that the surface area of in-depth knowledge required to build high-performing software has never been higher.
I have learned only a minuscule amount about machine learning; just enough to get under the hood and to build something very rudimentary. As I was learning some of the underlying concepts and methods, a couple of things really struck a chord in my brain–with how applicable they are to, well, everything.
This is a belated and probably too-skinny version of a post I decided I’d do every year, starting with last year. Usually, when I say ‘learned,’ I really mean I engaged enough to understand a little bit of what’s going on, and not that I am wizard-level user in any of these. In 2018, I spent time learning fewer new things than in 2017, but I probably went slightly deeper.
I remember the feeling of meeting with prospective investors and wanting to make a good impression. The first few conversations with an investor are high-stress and full of subtle signals. Most founders have good instincts and navigate these situations well, but the points below comprise some of the nuanced issues that I have observed over the years from the other side of those meetings. Hopefully these apply to early stage VC conversations in general, but note that they are specifically biased toward founders in the Midwest.