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.
Kleinert et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: