AI-Driven VC & Decision Science

Research on how AI, prediction systems, and decision frameworks reshape sourcing, screening, and judgment in venture investing.

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ASSET: fcb42d99

VCBench: Benchmarking LLMs in Venture Capital

Rick Chen Joseph Ternasky Afriyie Samuel Kwesi Ben Griffin Aaron Ontoyin Yin Zakari Salifu Kelvin Amoaba Xianling Mu Fuat Alican Yigit Ihlamur

Published: 2026-05-05
Signal: Must Read
DOI: Unavailable
Paper ID: fcb42d99

Abstract

Benchmarks such as SWE-bench and ARC-AGI demonstrate how shared datasets accelerate progress toward artificial general intelligence (AGI). We introduce VCBench, the first benchmark for predicting founder success in venture capital (VC), a domain where signals are sparse, outcomes are uncertain, and even top investors perform modestly. At inception, the market index achieves a precision of 1.9%. Y Combinator outperforms the index by a factor of 1.7x, while tier-1 firms are 2.9x better. VCBench provides 9,000 anonymized founder profiles, standardized to preserve predictive features while resisting identity leakage, with adversarial tests showing more than 90% reduction in re-identification risk. We evaluate nine state-of-the-art large language models (LLMs). DeepSeek-V3 delivers over six times the baseline precision, GPT-4o achieves the highest F0.5, and most models surpass human benchmarks. Designed as a public and evolving resource available at vcbench.com, VCBench establishes a community-driven standard for reproducible and privacy-preserving evaluation of AGI in early-stage venture forecasting.

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Tracked Topics: 8
Screened Papers: 190,042
Canonical Papers: 1,009
Live Signals: 447
Must-Reads: 246