Data‑Driven Analysis of Text‑Conditioning in AI‑Generated Music: A Case Study with Suno and Udio

Online commercial artificial intelligence (AI) platforms for generating music from text prompts (AI music) are now being used by many users to create millions of music audio recordings daily. Some AI music is appearing in advertising, music playlists of restaurants and gyms, and even hit music charts, in many countries. How are users engaging with these text‑to‑music AI platforms, where text is a principal mode of interaction to specify prompts (e.g., free terms), lyrics (e.g., sung terms), and tags (e.g., high‑level stylistic terms)? What languages appear? What characterizes prompts, lyrics, and tags? How are mentions of real artists used? What kind of additional instructions (metatags) are used? To address these questions, we assemble and analyze a collection of 101, 953 songs generated from May to October 2024 by 60, 342 users of Suno and Udio. Using a combination of state‑of‑the‑art text‑embedding models, dimensionality reduction, and clustering methods, we analyze the prompts, tags, and lyrics and automatically annotate and display the processed data in interactive plots. Our results reveal prominent themes in lyrics, language preferences, and prompting strategies, as well as peculiar attempts at steering models through the use of metatags. We share our code and data resources to promote further musicological study of AI music.