Startup Evaluations with Generative Artificial Intelligence: An Exploratory Study on Early-Stage Investments and Survival Predictions by Large Language Models
研究测试了大语言模型在评估初创企业时的表现，发现其能较好模拟真实融资结果，但生存预测的准确性主要源于对数字足迹的暴露，而非真正的推理能力。
We examine the capabilities of large language models (LLMs) in evaluating early-stage ventures. In pre-registered experiments, we prompt selected LLMs to generate investment evaluations and survival predictions for an archival dataset of 171 new venture pitches under systematically varied information cues. We compare these LLM-generated evaluations with realized fundraising outcomes, post-campaign survival rates, and evaluations provided by a benchmark sample of human investors. LLMs show strong capabilities in mirroring real fundraising outcomes. In contrast, their apparent accuracy in predicting venture survival largely reflects prior exposure to some ventures’ digital footprints rather than genuine reasoning under uncertainty. Providing LLMs with scientific, contextual, and social information cues can improve their evaluations, but can also activate human-like heuristics, including anchoring and herding. Our study highlights LLMs’ potential in venture evaluations while cautioning that unobserved influences can mislead interpretations of their capabilities.