Modern artificial intelligence has made rapid progress by scaling data, models, and computation. Yet scale alone does not solve one basic problem. Many intelligent systems must learn, adapt, and act when direct experience is scarce, costly, noisy, or changing. A scientist may have only a few labeled molecules. A recommender system may see only sparse interactions for a new user or item. A language-model agent may need to align with a user's preference from a short interaction history. These settings differ, but they share the same question: how can AI extract more learning signal from less experience? My research studies this question through data-efficient generalization. This article summarizes a research path from few-shot learning to meta-learning, in-context learning, and data-efficient agentic systems. The central theme is that limited supervision can be made useful by the right priors, the right adaptation mechanism, and the right way to reuse experience. I also discuss two regimes where data efficiency is not only desirable but necessary: scientific scarcity and efficient data utilization.