The Full Ratchet (TFR): Venture Capital and Startup Investing Demystified

Ash Fontana of Zetta Venture Partners joins Nick on a special Crisis Coverage installment to discuss The AI Investing Playbook; Why "Dev Tools" for Data Scientists is the Next Great Opportunity; When Fund Returns Don't Fit the Power Law; and Ash's Favorite Investment Heuristic from Naval Ravikant. In this episode, we cover:

  • Zetta III was announced recently, a $180M fund for founders building AI-first companies.
  • You went from $60M -> $125M -> $180M... how was the fundraise different this time around?
  • Quickly can you give us your definition of an AI-first company?
  • What will you be doing differently with the new fund and how does the pandemic affect your approach?
  • Tom Tunguz just mentioned that in the data they're analyzing they are seeing a drop in spend on Machine Learning Infrastructure.  How much of a concern is this to you and your portfolio companies?
  • With the launch of the new fund, you outline focus areas both Applications as well as infrastructure and tools...Is the application-layer ready to leverage AI in a significant way or is there still a lot headway that needs to be made at the infrastructure level first?
  • Carlota Perez has written about technology cycles and how new technologies typically go through this installment phase, w/ rapid development and heavy investment, followed by crash and subsequent recovery leading to the deployment phase...  in your estimation where are we in the tech life cycle of AI and is it really ready (or will it be ready over the next 3-7 years) for mass deployment?
  • How effective are the AI models today when much of the input data, generally speaking, is flawed?
  • Talk about the next 3-5 years for Data Science... we've seen significant advances in developer tools and systems for software but I still feel like we're at very early stages in evolution, efficiency and scalability of data science tools/fundamentals.
  • Does your fund returns follow the power law?
  • Part of the advantage to AI-first startups is the supreme data moat that they can build, preventing others from gaining traction w/ competitive solutions. While this is an advantage for the startups that get a head start (and their investors) is there an adverse impact on other startups that are founded later and don't have the extensive data sets?
  • Many of the startups you invest in are "deep-tech" and will not monetize and grow ARR the same way many familiar SaaS or transactional businesses will.  What are the major gating factors to raise each of a Seed Round, a Series A and a Series B, in these longer cycle tech-first approaches?
  • You've create a Playbook on how to build an AI-first company.  It's evergreen with plans to update regularly as you work w/ companies...  I wonder if you might give us the basics... What do AI-First companies have in their DNA and when building a company, what's the sequence and major building blocks required at the early stages?
  • Last time you were on the show you mentioned you learned a lot of great heuristics and mental models from Naval Ravikant. Can you give us a couple of these that have been really valuable in helping you quickly frame startup investment potential?

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