We introduce GenAI-Powered Inference
(GPI), a statistical framework for both causal and predictive
inference using unstructured data, including text and images. GPI
leverages open-source Generative Artificial Intelligence (GenAI)
models - such as large language models and diffusion models - not
only to generate unstructured data at scale but also to extract
low-dimensional representations that capture their underlying
structure. Applying machine learning to these representations, GPI
enables estimation of causal and predictive effects while
quantifying associated estimation uncertainty. Unlike existing
approaches to representation learning, GPI does not require
fine-tuning of generative models, making it computationally
efficient and broadly accessible. We illustrate the versatility of
the GPI framework through three applications: (1) analyzing Chinese
social media censorship, (2) estimating predictive effects of
candidates' facial appearance on electoral outcomes, and (3)
assessing the persuasiveness of political rhetoric. An
open-source software
package is available for implementing GPI.