The podcast introduces VCBench, the first standardized, anonymized benchmark designed to evaluate Large Language Models (LLMs) in the challenging domain of venture capital (VC) founder-success prediction. Built from 9,000 founder profiles, the benchmark utilizes a multi-stage pipeline of standardization and adversarial testing to ensure data privacy by reducing re-identification risk by over 90% while preserving predictive features. Experiments showed that several state-of-the-art LLMs, such as GPT-4o, surpassed established human expert baselines, achieving a precision multiple higher than tier-1 VC firms. Ultimately, the resource aims to provide a community-driven, reproducible standard for assessing sophisticated decision-making under uncertainty, complete with a public leaderboard at vcbench.com.