Study Overview
Whether generative AI narrows or widens inequality in low-income economies, and whether it builds or erodes human capital, remains largely untested outside high-income, short-horizon settings. We study these questions among young entrepreneurs in Uganda by embedding a randomized take-up experiment in the SEED 2.0 program, which in 2025 randomized 8,455 secondary-school graduates into a control group and two entrepreneurship-training arms. At the 2027 follow-up, we offer a purpose-built AI assistant—trained on the program curriculum and locally developed content and delivered over WhatsApp—to three quarters of participants in each arm, retaining the rest as a within-arm comparison group, with assignment stratified by baseline knowledge, prior training take-up, and gender. Among those offered, we cross-randomize the tool's mode (tutoring versus direct answers) and a take-up incentive. Intention-to-treat comparisons identify the effect of access and how it varies with baseline ability—whether AI complements or substitutes for skill—whether learning persists once the tool is withdrawn, whether adoption is itself selected on ability, and whether AI substitutes for or complements training. The study tests not only whether AI helps, but whether its design and deployment can make the benefits inclusive.