Last Saturday, an HBS student club (CODE Club) hosted a Machine Learning Workshop. Over 100 attendees heard speakers from MIT, large companies (Google, etc.) and several startups. Below are my notes from the AI/ML investor panel. The emphasis on unique access to clean data was refreshing to hear and is consistent with my experience.
Notes from the AI/ML investor Panel
Rick Grinnell (Glasswing Ventures)
- His investment focus is on vertical opportunities. Verticals are more attractive because of vertical-specific data and a clearer line to customers' budget
- He encourages startup teams to have a ML knowledgeable person from the beginning. This is not a market where several business orientated MBA students can start a company with a MVP using offshore development.
Habbib Haddad (MIT Media Lab E14 Fund)
- He focuses on investment opportunities with unique and large clean data sets (often vertical plays in agriculture, fintech, etc.)
- CLEAN DATA are critical
- Product managers today must know the new language to talk with data scientist
Mackey Craven (Open View)
- Attractive investments must have clear path to clean data "moat." Moats can be created via unique access and contractual terms
- Computing power and algorithms are now a commodity and thus are not the basis of competitive advantage. Defensibility centers on business knowledge and access to clean data that grows uniquely over time and gets to market first, thus flywheel to clean data
Sunil Nagaraj (Ubiquity Ventures) - moderator