A Familiar Face: Measuring Visual Similarity in Venture Capital

Receiving venture capital can dramatically shape the trajectory and ultimate success of startup firms. Because these investments occur under significant uncertainty, investors rely heavily on subjective assessments, often backing entrepreneurs who are similar to themselves. Yet, research on this tendency has largely relied on coarse demographic categories, potentially obscuring more granular visually assessed forms of similarity that influence investment decisions. This paper introduces “face distance,” a novel measure of visual similarity derived from facial recognition models, to explore this subtle channel. Using a mixed-methods approach that combines observational data from a top startup accelerator with an online controlled experiment, I find that facial similarity between an entrepreneur and an investor is a powerful predictor of investment. This relationship is more pronounced when there is relatively higher uncertainty, suggesting that facial similarity is a heuristic investors tend to rely on when concrete “hard” information is scarce. In addition, investments between visually similar entrepreneurs and investors underperform in terms of successful exits, which is consistent with a costly distortion. These findings highlight that homophily operates at a highly granular, visual level in early-stage investment and that this is not only inequitable but also a potentially inefficient heuristic.

Funding: The author gratefully acknowledges funding from the UCLA Behavioral Lab.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2025.0490.