20VC: Sequoia’s Mike Vernal on His Biggest Lessons From 8 Years of Hyper-Growth at Facebook, Why The Strength of Data Moats Is Over-Rated Today and The Challenge of “Overthinking Investments” In Venture
Posted on 26th August 2019 by Harry
Mike Vernal is a General Partner @ Sequoia, one of the world’s leading and most renowned venture firms with a portfolio including WhatsApp, Zoom, Stripe, Airbnb, Github and many more incredible companies. As for Mike he has led and sits on the board of Citizen, rideOS, Rockset, Threads and Houseparty (acquired by Epic). Prior to venture, Mike spent 8 years at Facebook as VP of Product & Engineering leading multiple different teams including Search, Commerce, Profile, and Developer product groups. Prior to Facebook Mike spent 4 years at Microsoft as a PM lead in Microsoft’s Developer Division.
1.) How Mike made the move from VP of Product & Engineering at Facebook to General Partner at the world-famous, Sequoia Capital? What were Mike’s biggest takeaways from his 8 years at FB seeing the hyper-growth first hand?
2.) Mike has previously said that he has struggled in the past when it comes to “overthinking investments”. What does he mean by this? How does it play out in reality? How does Mike balance between trusting his gut and relying on the data? How does Mike think venture partnerships should participate in this balancing act?
3.) Why does Mike believe decision-making in venture to be fundamentally different to decision-making in operations? How do they compare? How does the decision-making process and approach change as a result of this contrast? How does Mike think about his own time allocation now in venture? What is the most challenging element?
4.) How does Mike evaluate the proliferated SaaS landscape today? Why does Mike believe that the notion of SaaS as a construct will fade over the coming years? What does Mike believe is the reasoning for SaaS apps becoming more and more niche? What problem does that pose for VC? Will we enter a period of consolidation in SaaS? What size do the incumbents have to be to really engage in the M&A process moving forward?
5.) Why does Mike struggle to see the strength of data moats? What are the major downfalls associated with the argument of their strength? At what point is the asymptotic point of the utility value of the data for models today and how does that change over the coming years? What does Mike instead see as durable and sustainable moats?