Artificial intelligence and venture capital: Revisiting sourcing, selection, and nurturing activities

Abstract

Abstract Research Summary Venture capital (VC) is an information‐intensive form of entrepreneurial finance conducted under extreme uncertainty. Investors must originate promising opportunities, select ventures with sparse and strategically disclosed information, and monitor and support firms after investment. Prior research documents both formal mechanisms (e.g., staged financing, syndication) and informal mechanisms built on referrals, reputation, and constant interaction. Geographic and social proximity enable these mechanisms by lowering the cost of obtaining contextual information and coordinating action. We develop a mechanism‐based account of how data and Artificial Intelligence (AI) reconfigure this established model. Our framework yields propositions on geographic dispersion in sourcing, portfolio selection, and the distinct effects of AI on monitoring versus value‐added support. It also identifies boundary conditions associated with institutional setting and heterogeneous AI adoption. Managerial Summary For venture capital firms, AI is not a single tool and adoption is not a binary choice. A fund may use data systems to identify founders, machine‐learning models to organize deal flow, generative AI to summarize diligence materials, or analytics to monitor portfolio companies. Each application changes a different part of the VC process. In sourcing, AI can widen the funnel beyond warm introductions and local ecosystems, especially in sectors such as software and e‐commerce where hiring, product, and customer signals are visible. In selection, AI can make diligence faster and more consistent, but models trained on past investments may favor ventures that resemble previously funded companies and may miss novel opportunities for which historical analogues are weak. In post‐investment activity, continuous operational data can partly replace physical presence for monitoring, while value‐add support such as customer introductions, recruiting, strategy advice, and founder coaching remain relationship‐intensive. The practical implication is a task‐level approach: automate codifiable search and processing, retain independent human judgment where uncertainty is fundamental, and use AI to strengthen rather than erode trusted investor–founder relationships. Funds also face organizational choices about building proprietary infrastructure, buying external tools, or combining the two. The appropriate configuration depends on data access, strategy, scale, and the activity in which the fund seeks advantage.