Professor Emmanuel Yimfor on Capital Allocation Bias in Private Markets and the Choices That Will Determine Whether AI Fixes or Entrenches Them

Welcome to the Agentic Allocator. Today we're joined by Professor Emmanuel Yimfor, Professor of Finance at Columbia Business School, whose research examines the frictions and biases shaping how capital flows through private markets with a focus on racial and gender gaps in VC funding, board representation, and private capital fundraising.

The key idea is to be able to expand the search space. Given that this tool helps me reduce the cost of due diligence, can I expand the set of potential people I meet to those that are outside my network?

His work reveals how alumni networks, homophily, and hidden signals determine who gets funded and who does not.

The danger can be a little bit subtle. So you picture an LP who feeds a new manager's material, so the private place in the Miranda or even the one pager, and ask the AI, how similar is this person to the managers or back before? Now, that sounds objective, but if the past portfolio was built through the same kind of narrow networks, then the model is just automating the same types of exclusions.

What makes this conversation so timely is the question of what happens when these dynamics get encoded into AI. As the industry moves towards agentic capital matching and digital representations of LPs and GPs, the biases Professor Yim for has documented risk becoming embedded in the technology itself.

These are really exciting times. And so to the extent that the industry adopts some of this technology in the right way, not in ways that are related to how past decisions have been made, I could really see a potential where it not only expands the pool of who is receiving capital, but it also allows this capital to be allocated towards its most productive uses.

I hope you enjoy the conversation. Professor Yim Four, thank you so much for taking the time to join me for the conversation.

Thank you for having me.

Before we dive into AI, would love to focus on your research. So your research shows that there, and documents, that there is a long-term racial and gender kind of disparity in terms of private capital funding. Would love for you to dive into that for our listeners today.

Oh, absolutely. So, you know, like my research in general is about like, you know, the core frictions driving disparities in access to capital in private markets. And so you talked about race and gender. Let me take each in turn because, you know, the mechanism is the same, but, you know, the details differ. So for race, so far what my work has shown is that, you know, the core friction is networks, right? Like who you can reach as opposed to necessarily how good you are. If you look at more structured ways of reaching funders, I'm talking about things like accelerators or grant funding, Essentially, the racial gap in access to funding disappears. It mostly shows up when things are more relationship-driven. If you think about founders, for example, trying to raise black or Hispanic founders, trying to raise venture capital funding from GPs, they raise about 40% less, even given identical patent holdings or going to the same school or even having the same track record. We see, you know, very similar patterns for gender, which are, you know,