Traditionally speaking, venture capitalists make investment decisions using "rules of thumb" also known as heuristics. It is when you take shortcuts to make decisions based on "past experience" and not data, analytics or math. But today, there's more data about startups than ever, computing power is vast and the study of decision theory and artificial intelligence has enabled decision makers to avoid biases in countless industries. In an industry driven by data and analytics, it only makes sense to have a rational, calculated and structured approach in investing. This is the essence of venture science.

We've evaluated over 1 million deals and built artificial intelligence in decision theory to support investment decisions in venture capital.

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Our investment strategy and models are built on 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 and avoid biases.

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.

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. 

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