AI (artificial intelligence) as a music genre refers to works in which machine-learning systems are central to generating, arranging, or performing core musical material, rather than being used only as peripheral studio tools.
The style spans fully generative ambient soundscapes and pop songs written from text prompts, to voice-cloned performances and neural resynthesis of timbres. Stylistically it borrows from contemporary electronic and internet-born aesthetics (hyperpop, vaporwave, IDM, electropop), while foregrounding the uncanny, synthetic qualities of ML models.
Beyond sonics, AI music is also a process-driven genre: datasets, prompts, model architectures, and iterative sampling are treated as creative choices on par with chords or instrumentation. Ethical and legal questions around training data, consent, and authorship are part of its identity and discourse.
Early computer and algorithmic composition (Hiller & Isaacson’s Illiac Suite, Xenakis’s stochastic music) laid the conceptual groundwork for machine-created music. Through the 1990s–2010s, generative and live-coding scenes (e.g., algorave) normalized code-as-instrument, while academic/industry projects (Markov/ML harmonizers, Google Magenta, Flow Machines) demonstrated that statistical models could write convincing melodies and textures.
By the late 2010s, artists began foregrounding machine learning as the main creative agent: neural resynthesis of voices and timbres, style-transfer of instrument recordings, and model-steered songwriting. Albums and performances by pioneering practitioners showed that model training, dataset curation, and prompt engineering could define a recognizable aesthetic.
Large-scale generative models (for audio, singing voice, and text-to-music) made fully AI-authored tracks accessible to non-specialists. Viral voice-clone songs and text-prompted pop on short‑form video platforms brought the sound and the ethics debate (consent, credit, compensation) to mass audiences. Parallelly, ambient/wellness generators and AI background-music startups popularized perpetual, context-aware soundscapes.
Today, “AI” functions both as a production method and an aesthetic tag: from dreamy, model-hallucinated pads to glossy prompt‑pop and experimental neural glitches. Toolchains continue to hybridize with traditional DAWs; attribution frameworks and consent‑based voice models are shaping professional adoption.