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In an increasingly competitive environment among Venture Capital (VC) firms, fund managers continuously search for ways to develop an edge over their peers. This realisation has led them to pursue more data-driven approaches and to start diving into the potential of data in investment processes.
Venture Capital 2.0—the revolution of Machine Learning & Data-Driven VC
Session state of vc2024
arc.net
The best example of this alternative product is Indie.VC, run by Bryce Roberts. Over the course of 6 years, Indie invested in 40 companies. It held the two key components of limited fund size and gave equity optionality through redemption clauses or equity buybacks. The results are encouraging, with a 51% IRR and 4.3x TVPI, while 87% of the compani... See more
Evan Armstrong • Venture Capital Is Ripe for Disruption
Venture Capitalists at Work: How VCs Identify and Build Billion-Dollar Successes
Tarang Shah, Tarang Shah, Sheetal Shah
amazon.com
The lack of quality data on investors has led to a sales-first-oriented venture environment where investors are overpromising and under-delivering.
Medium • Unbundling the unit economics of venture capital via DAOs
Venture Capital 2.0—the revolution of Machine Learning & Data-Driven VC
medium.com

Why new VC funds offer the greatest opportunities in Venture Capital
Francesco Perticarariblog.francescoperticarari.com
Soon most leading firms will employ quantitative evaluations because they need to be competitive. There is an increased demand for alpha in private markets and richer data available.