National statistical agencies increasingly face budget constraints and shrinking sample sizes, while simultaneously gaining access to rich auxiliary data and powerful pre-trained machine learning (ML) and artificial intelligence (AI) models, including Large Language Models (LLMs). Traditional model-assisted estimation techniques, which fit models using survey sample data, are limited by small sample sizes, struggle to leverage complex non-linear relationships in auxiliary data, and cannot accommodate frontier pre-trained models. This work re-examines the use of pre-trained black-box models, fit independently of the survey sample, for design-based parameter estimation. Inspired by the Prediction-Powered Inference (PPI) framework, we introduce the Prediction-Powered Estimator (PPE), an unbiased estimator with an unbiased variance estimator for the survey design setting. We also formalize the use of pre-trained models with the classic difference estimator—which we term the Prediction-Powered Difference (PPD) estimator—and with the Generalized Regression Estimator via predicted values as covariates ( GREG y ^ ). Through LLM-based use-cases leveraging unstructured auxiliary data (images and text) and experiments with real-world survey data from Statistics Canada, complemented by simulation studies in the Supplemental Material , we demonstrate that these approaches consistently outperform standard baseline estimators across bias, mean absolute error, mean squared error, coverage, and confidence interval width. The results suggest that pre-trained models can yield more accurate and efficient estimates while potentially reducing survey sample sizes and respondent burden, and motivate expanding the survey methodologist’s toolbox to include pre-trained models and novel auxiliary data sources.