Generative AI (GenAI) is transforming the machine learning (ML) landscape by enabling the creation of high-quality synthetic data for training and evaluation. Yet when these synthetic datasets are used in economic or multi-agent environments—where learning systems interact with other decision-makers—the assumption that data can be generated independently of incentives often breaks down. In such settings, each agent’s behavior depends on beliefs, payoffs, and strategic anticipation of others, making incentive structures an integral part of the data-generating process. This perspective advocates for an incentive-aware approach to GenAI, emphasizing the importance of embedding economic and strategic considerations into synthetic data generation. We review recent case studies in which incorporating incentive consistency has led to better-performing ML models in persuasion and competitive search environments. Finally, we outline a research agenda for developing strategically aligned generative models that integrate economic reasoning, mechanism design, and ML to ensure robustness and reliability in complex decision-making ecosystems.
Madmon et al. (2026) studied this question.