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

Professor Emmanuel Yimfor, Assistant Professor of Finance at Columbia Business School, joins The Agentic Allocator to share his research that should sit at the centre of every conversation about AI in private markets. His work documents the core friction driving racial and gender disparities in access to capital: not quality, not track record, but networks. Who you can reach, not how good you are.

That finding has direct and urgent implications for how AI gets deployed across the LP/GP ecosystem. Used thoughtfully, AI has the potential to widen the top of the funnel dramatically, reducing the cost of due diligence enough that LPs can evaluate managers far beyond their existing networks. Used carelessly, the same tools will automate and entrench the same exclusions, encoding past decisions into future ones in ways that are subtle, hard to detect, and difficult to reverse.

Professor Yimfor walks through the mechanics of embedding-based matching and why it is a black box that can pick up on signals of race, gender, and network affiliation even when no one intended it to. He explains what the research on accelerators and structured access programmes shows about what happens when the top of the funnel is genuinely open. He makes a clear, practical case for what LPs, GPs, and technology developers should each be doing differently right now.

What You'll Learn:

• Why the core friction driving racial and gender disparities in private markets is networks and what the research evidence shows
• Why Black and Hispanic founders raise around 40% less capital than peers with identical patent holdings, educational backgrounds, and track records
• Why the gap in funding disappears entirely when access is structured, as in accelerators and grant programmes, and what that tells us about where the problem lies
• How embedding-based matching works, why it is a black box, and how it can encode biases in allocation decisions even when no one intended it to
• Why asking AI how similar a new manager is to managers you have backed before is not objective, and what the alternative looks like
• How structured, standardised due diligence processes enabled by AI can reduce the role of network signals and subjective impression in manager evaluation
• What GPs should do differently when preparing pitch materials and identifying which LPs to approach in an AI-enabled world
• What LPs should ask any technology developers and providers about how their existing AI tools are sourcing and filtering the managers they evaluate
• Why the industry is at a fork in the road and what Professor Yimfor’s research will be tracking to understand which path it is taking

About Professor Yimfor

Professor Emmanuel Yimfor is an Assistant Professor of Finance at Columbia Business School. His research focuses on the core frictions driving disparities in access to capital in private markets, with a particular focus on race, gender, and the role of networks in determining which founders and fund managers receive funding. His work has direct implications for how AI systems are designed and deployed across the LP/GP ecosystem, and he is currently researching how AI adoption is reshaping the equilibrium dynamics of capital allocation across the industry. Before joining Columbia Business School, he was an Assistant Professor of Finance at the University of Michigan Ross School of Business.