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Malika Cantor, Operations Partner, Comet Labs at The AI Conference 2017


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Malika is devoted to bringing great ideas to life. She is an Operations Partner at Comet Labs, a cross between a venture fund and experimental research lab that supports AI and robotics startups. She previously worked in investment banking, and oversaw the development and growth of software and hardware startups in the education, healthcare, and telecom fields, in Asia, Europe and North America. She graduated from the University of Cambridge and has an MBA from Tsinghua University and MIT Sloan.

A VC Perspective on AI

Published in: Technology
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Malika Cantor, Operations Partner, Comet Labs at The AI Conference 2017

  1. 1. A VC’s Perspective on AI AI CONFERENCE, JUNE 2017
  2. 2. 10 minutes 0.5min: A bit about Comet 0.5min: Reality vs. Hype 1min: Does traditional VC work for AI startups? 3min: What AI startups need to succeed 5min: Insights into our Due Diligence process
  3. 3. ➔ 2 years old ➔ Focused exclusively on AI Enabling Tech & Applications of AI ➔ The most active early-Stage investor in B2B AI and robotics. ➔ Fund + Labs model ➔ China + US dual perspective ➔ 40 + portfolio companies, follow on investment from:
  4. 4. The AI Revolution is here, despite the Hype The scale and scope of change is larger than ever Previous waves of disruption are the foundation of the next seachange: intelligent machines Machine intelligence has reached an inflection point enabling a new generation of infrastructure and applications 100x more touch-points with technology creates opportunities for more intuitive products and new business models
  5. 5. Traditional investment methods don't work for AI startups PRODUCT DEVELOPMENT Meaningful solutions require deep involvement of the customers early on, as well as an understanding of potential markets and pain points. CUSTOMER DEVELOPMENT The access to industry for any single startup is extremely limited and inefficient. Entrepreneurs need to find pilot projects, early customers, and tools for scaling.
  6. 6. DATA + POC/PILOT + UNDERSTANDING OF USE CASES What do AI startups need? + TALENT
  7. 7. DD: Data Moat ● How are you obtaining enough data initially to provide satisfactory solutions to your early customers? (Cold Start problem) ● Are you generating or negotiating access to proprietary data sets? ● If you are generating proprietary data sets: ○ What makes your data valuable and hard to obtain? ○ Do you own the data that you are collecting? Do you own it exclusively? ● If you are negotiating access to proprietary data set: ○ How did you negotiate access? ○ Exclusive license? Nonexclusive license?
  8. 8. DD: Product ● Walk us through the product technology stack, from data ingestion to output ○ Which part of the stack is hardest to replicate, and why? ○ What is your technical secret sauce? ○ Which parts did you build, and which parts are off the shelf? ■ Which libraries did you use? ● How much of a “human in the loop” is required, and where? ● Customer references
  9. 9. DD: Team ● At least one technical founder ● Who’s your first hire after the fundraise? ● Why are you the best team to work on this problem? ● References
  10. 10. DD: Market ● What do you understand about the industry that others don’t? ● If you could rethink the industry from first principles, how would you do it? ● What is the immediately addressable market? ● Why now? Risk of being too early and running out of cash before mass adoption? ● What competitor do you most often run into when closing sales, and how are you differentiated? ● Sales cycle? Payback period? Churn? ● Monthly recurring revenue? ● Who are your biggest customers? ● How have you been acquiring customers so far?
  11. 11. THANK YOU Questions? Email :)