We introduce an improved measure of geopolitical risk that builds on Caldara and Iacoviello (2022) and uses artificial intelligence to evaluate newspaper content. Our approach replaces keyword matching with semantic understanding: instead of searching for specific word combinations, we use one of the language models underlying ChatGPT (GPT-4o-mini) to read newspaper articles and assess their geopolitical risk intensity. The daily AI-GPR index scores about 5 million articles from the New York Times, Washington Post, and Chicago Tribune from 1960 through 2025. The approach reduces false positives from articles mentioning war or terrorism in non-geopolitical contexts while capturing relevant articles that lack exact or common dictionary terms. The AI-GPR index also assigns gradations of risk intensity rather than simple yes-or-no classifications, providing more nuanced measurement even at high frequencies. We demonstrate the potential of our approach with three applications: the AI-GPR index improves the estimated negative effect of geopolitical risk on stock returns; combined with a second classification layer, it produces a historical time series of geopolitical risk-driven oil supply disruptions by region; and, using a third classification layer, it maps directed networks of geopolitical actors—initiators, respondents, and spillover countries—across major historical episodes.