Recent breakthroughs in AI offer tremendous potential to reduce the costs of data collection. For example, there is a growing interest in leveraging large language models (LLMs) as efficient substitutes for human judgment in tasks such as model evaluation and survey research. However, AI systems are not without flaws: generative language models often lack factual accuracy, and predictive models remain vulnerable to subtle perturbations. These issues are particularly concerning when critical decisions, such as scientific discoveries or policy choices, rely on AI-generated outputs. In this talk, I will present recent and ongoing work on AI-assisted approaches to data collection and statistical inference. Rather than treating AI as a replacement for data collection, our methods leverage AI to strategically guide data collection and improve the power of subsequent inferences, all the while retaining provable validity guarantees. I will demonstrate the benefits of this methodology through examples from computational social science, where LLMs are increasingly used to reduce the costs of human annotations.