A translation of a mass shooter’s cryptic journal in the days after an attack. A public records review that revealed failures to install flood warning systems in Central Texas. An exposé of American technology companies’ complicity in building the Chinese surveillance state. An audit of the SEC’s crypto lawsuits that showed weakening enforcement under the second Trump administration. On May 4, the Pulitzer Prizes recognized these stories among the winners and finalists across 15 journalism categories. The reporters behind each of these stories also disclosed using AI to the judging committee. Ultimately, five award winners and three finalists this year disclosed AI adoption in their submissions — the most since the disclosure requirement was added in 2024. For the past two years, I’ve spoken to Pulitzer-recognized reporters about how they used AI in their reporting. Both years, generative AI took a back seat to more conventional machine learning technologies, like using embedding models to produce complex data visualizations and pattern recognition models to analyze satellite imagery in conflict zones. This year, though, generative AI tools and commercial large language models (LLMs) were more commonly used, largely to speed up the process of combing through document dumps. “To state the obvious, perhaps, AI is here to stay,” Marjorie Miller, the administrator of the Pulitzer Prizes, told me. “The industry [used to be] far more apprehensive about AI tools than it is today, with a clearer understanding now of what uses might be appropriate — data collection and analysis, for example — and when it might not, such as in writing and editing stories in any format that might be considered for a Pulitzer Prize.” Miller cautioned that as AI evolves, reporters will need to “ensure and reassure” the Pulitzers that submissions are ultimately produced by human beings, even when AI is