Disrupting Venture Capital: Carrots, Sticks, and Artificial Intelligence

Despite the massive dollars invested each year by Venture Capital (VC) firms, more than two-thirds of the companies they fund will provide zero return. More problematic, less than 3% of VC funds go to female-led startup teams, and less than 1% to racially diverse founders. While many argue that this underrepresentation will work itself out over time, in reality, these numbers have remained stagnant for over 30 years. This is especially perverse given that diverse startups, when funded, appreciably outperform male-only founding teams. The VC industry operates under an antiquated model of investing in founders with demographics reflecting those of VC partners (white men control 93% of VC funds, and only 0.2% of VC partners are Black or Latina women). While antidiscrimination law intended to create a level playing field for all, the VC field operates outside this regulatory scheme. In addition to its lack of diversity, ironically it also has a technology problem. Despite the incredible advances in artificial intelligence (AI), and the industry’s focus on tech startups, many VC firms fail to incorporate data analytics and machine learning to guide their decision- making, relying instead on “gut instinct.” This is the first article to comprehensively explore the current state of the VC industry through the lens of behavioral law and economic theory, revealing the field\u27s intransigence and the heuristics and biases infecting its decision-making. Using insights gained from this analysis, this Article suggests that disruption is possible through a combination of policy and legal initiatives as well as leveraging advances in technology. The Article concludes by offering a novel multipronged solution comprised of a combination of carrots (incentives), sticks (penalties), and AI to motivate behavioral change within the VC industry and stimulate a true meritocracy where gender and racially diverse startups are equitably funded, and innovation flourishes