The Full Ratchet: Venture Capital Demystified

Niki Pezeshki of Felicis Ventures joins Nick to discuss Finding Markets with Long-Term Tailwinds; Macro Impacts on Venture; and Robust vs. Fragile Data. In this episode, we cover:

  • Background and path to venture.
  • Thesis at Felicis and your focus there?
  • How has the pandemic affected your approach to investment?
  • You’ve mentioned that you look for market tailwinds, especially tailwinds that will be lasting. Right now, we’re seeing a number of shifts that will have lasting effects -- which are you watching most closely?
  • Why do you think the public markets have, largely, stayed high during a very large health and economic crisis?
  • Can you give us an overview on your three-part investing framework for making investment decisions?
  • Where do you look for these large shifts, creating opportunity -- aside from Mary Meeker's report?
  • How do you make sure you are current on-trend instead of getting anchored on data that's old or fragile?
  • What are you looking for in the business model that indicates to you that it is not only the correct approach but can lead to transformational changes in the industry?
  • In which types of businesses do you like to see product-focused founders versus marketing-focused founders, is there a heuristic or systematic way that you think about this?
  • You mentioned that business is a formula and that it’s clear early on whether it’s going to work or not -- can you give us an example of the formula?
  • What's your approach to coaching and advising founders? 
  • There's this fine line between being overbearing w/ advice and not providing enough insight in an area that could derail a company.  How do you strike the balance?
  • What keeps you up at night... the ones you invested in that you shouldn't have or the ones you didn't invest in that you wished you had?
  • 3 Data points: Let's say that you have a consumer SaaS company that is doing 300K in ARR, growing 20%  MoM and that’s all you currently know about the company.
    • Which 3 data points do you ask for and why?

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