Machine Learning Startups
summary
Four startups since 2009 and a VC fund founded in 2011 lead to one clear prescription: build full‑stack, domain‑specific products with proprietary data, not SDKs. Practical claims: data is more valuable than models, use production research engineers, and prefer underwriting, fintech and healthcare cohorts where six underwriting AI firms already cluster. Two bootstraps work for customer data: tit‑for‑tat plug‑ins or outside‑in crawling plus simulations and weak supervision. Selling raw alternative data to hedge funds rarely sustains a moat; move up the stack.
AI-written summary · about
chapters (8)
- 0:00Introduction and background
- 1:40Key lessons for AI founders
- 3:38Defining vertical AI
- 6:51Enterprise cohorts and markets
- 10:49Analyzing high-value industries
- 13:33White space and timing
- 17:25Promising future sectors
- 22:14Q&A: Data and competitive edge
related talks
45mInnovating with Data
23mUsing Software, AI, DS to Source Growth Opportunities
35mGetting to yes - What investors look for in big data & AI startups
56mData, AI, Hype and Fear - Recipe for Disaster?
21mWhere I am putting my money in 2018
27mSuccessful Business With Machine Learning
21mHow The Application Of Machine Learning Is Changing The Way Companies Use Data
20mBeyond Transactions: Leading Fintech with Data, AI, and Monetization