We introduce PrismAI, an environment for the automatic detection of AI-generated text. Our contributions are threefold: Firstly, we release the largest AI-detection dataset to date, comprising 537588 human-written and AI-generated documents in both English and German across seven domains, including scientific writing, weblogs, parliamentary speeches, legal court cases, classic literature, news articles, and student essays, synthesized using state-of-the-art models. Secondly, we introduce Luminar, a CNN-based model for the automatic detection of AI-generated texts. Our experiments show that by leveraging the hidden states of an LLM to derive intermediate likelihoods, our model, despite having a small footprint, can outperform other likelihood-backed baselines significantly while demonstrating strong generalization capabilities in out-of-domain and out-of-language scenarios. Thirdly, we unify existing datasets into a common corpus called AIGT-World and make it accessible through a publicly available web-based corpus explorer, which facilitates searching, reading, visualizing, and interacting with the underlying data. By doing so, we aim to elevate research in this area, expand the field to include non-English texts, propose new models, and unify existing efforts to build toward a common dataset and objective.