The next generation in venture capital
Traditionally speaking, venture capitalists make investment decisions using "rules of thumb". Scientifically speaking, this approach is called heuristics. Heuristics is when decisions are made based on limited information relying heavily on past experiences. Today, there's more data about startups than ever. In an industry driven by data and analytics, it only makes sense to have a rational approach in investing using data, models and stochastic theories.
We select investment opportunities through the lens of multi-factor analysis. We deploy capital in a calculated way. We optimize and solve for how much to invest at any given round.
This analytical way of thinking is the basis for how we invest. Read more here...
Our funds are formed based on a three tenets: bias-free selection, calculated deployment, and risk concentration.
1. Cognitive biases are toxic when it comes to making investment decisions. That's why we evaluate startups for their merits in terms of technology and business.
2. Instead of deploying capital arbitrarily from deal to deal as it's been done traditionally, we perform complex stochastic calculations to determine check sizes, re-up levels and dry powder similar to the way it's done at legendary funds such as Pimco and Berkshire Hathaway.
3. Risk concentration is key in venture capital. Many managers deploy capital to way too many companies particularly in the beginning which leaves very little room for mistakes and caps the upsize. This is why the term "spray and pray", "chasing homeruns" or "seeking unicorns" are commonly heard in discussions regarding fund returns. To avoid early mistakes, we've built bias-free multi-factor selection models such as the one we used to evaluate Y-Combinator's Summer 2014 graduating class.
value-ADD via science
When it comes to partnering with the right VC firm, the founders hear every truism out there. Yes, we have the right connections needed for a startup. We know all about perseverance, drive, and dedication. But what a startup truly needs is real scientific guidance on their path to growth. What is the right pricing strategy? How do you optimize distribution? What levels of traction is needed for explosive growth? These aren't simple questions one can answer by thinking through. These are scientific questions PhD candidates work on every day. Our background is in operations research and our team has tackled these questions. Our expertise is in pricing and revenue optimization. We know how to build elasticity curves, how to set dynamic pricing levels, how to optimize for revenue or growth and how pricing applies to the specific type of product or service being offered.