Distributionally robust optimization is a methodology for solving optimization problems under uncertainty by considering a set of possible probability distributions for the uncertain parameters. This approach is particularly relevant to the tech community as it enables startups and companies to develop more resilient and reliable machine learning models, decision-making systems, and risk management frameworks that can withstand uncertainty and variability in real-world data and environments.