The Future of VC - How the Industry Gets Disrupted
summary
Claims that the venture value chain modernizes with a machine learning engine that web crawlers across 100+ sources (GitHub, Product Hunt, company registries, social, payment and traffic data), enriches profiles with Crunchbase/PitchBook/news, and benchmarks signals to rank deals. Built in-house tooling lifts the sourcing funnel from roughly 8,000 to ~30,000 opportunities per year for Western Europe and applies an ML ranking validated by a TU Munich study showing ~80% recall versus top investors. Outcome: faster, more targeted deal flow and automated screening that still hands final decisions to humans.
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chapters (12)
- 0:00Introduction and speaker intro
- 1:05Why VC industry needs disruption
- 2:27Current VC value chain explained
- 4:35Shift from inbound to outbound VC
- 6:20Deal universe and sourcing challenges
- 8:10Tech-driven sourcing with AI
- 10:57AI engine training and data sources
- 13:50Trust in AI for investment decisions
- 16:15AI-enhanced deal flow expansion
- 18:22AI impact on VC value chain
- 21:31Q&A on AI and selection criteria
- 27:10Closing remarks and sailing metaphor
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