The Rise of AI Created Music

AI created music is no longer a futuristic concept – it’s mainstream. In 2025, Billboard reported that at least six AI-powered or AI-assisted artists have charted across gospel, rock, country, and R&B. The real number is likely higher, because even Billboard admits it’s “increasingly difficult to tell who or what is powered by AI, and to what extent.”

One of the most prominent examples is Xania Monet, an R&B artist developed by Mississippi-based songwriter Telisha “Nikki” Jones. Jones writes Monet’s lyrics and uses the generative AI platform Suno to turn them into fully produced tracks. Monet’s chart success, including a reported $3 million recording deal with Hallwood Media, signals the commercial viability of AI-generated creativity.

Xania Monet: AI Artist in Context

Xania Monet represents the intersection of human creativity and algorithmic production. Jones draws inspiration from personal experience, translating emotion and story into lyrics. Using Suno, she inputs prompts such as “female voice,” “soulful R&B,” and “slow tempo,” iterating through multiple versions until the final track aligns with her vision.

The combination of human composition and AI allows Monet to produce commercially viable tracks that are indistinguishable from traditional artists on streaming platforms. Yet, this process raises questions about authorship, rights, and who benefits financially when AI is involved.

Suno: Powering AI Created Music

Suno is the engine behind Monet’s tracks. Users enter text prompts, and the platform generates complete music – vocals, instrumentation, and production. While this makes creating more accessible, the platform has not disclosed its training data sources.

The opacity of training data is central to the ongoing debate. If Suno’s models were trained on copyrighted songs without licensing, the generated tracks are effectively built on uncredited human work. This lack of transparency challenges the music industry’s traditional understanding of intellectual property and compensation.

Spotify’s Role in AI Created Music

Spotify is adapting to the rise of AI created music, updating its policies to prohibit impersonation and spam uploads. However, the platform still allows AI-generated tracks to be uploaded without mandatory labelling of AI involvement.

Spotify’s terms grant the platform broad rights over uploaded content, including AI-generated works, but do not provide guidelines for royalties tied specifically to AI-assisted creations. As AI created music becomes more common, this regulatory gap leaves human artists unprotected and revenue streams unclear.

Artist Royalties and the Question of Training Data

The most pressing issue in the era of AI created music is training data. If AI platforms generate tracks based on copyrighted material without compensating original creators, artists are being exploited. Emerging research into training-time and inference-time attribution could allow royalties to flow to the original musicians whose works influenced AI outputs.

In countries like Sweden, rights organizations are already implementing licensing systems that allow AI training while ensuring songwriters receive payment. Globally, however, most AI platforms remain in a legal grey area, operating without standardized royalty or licensing structures.

Transparency, Labeling, and Watermarking

The music industry must adopt clear rules for AI-created music:

• Mandatory labelling to disclose AI involvement.

• Metadata and watermarking for AI tracks to track provenance.

• Royalty systems to compensate artists whose work contributes to AI training.

Without these measures, AI-created tracks risk overwhelming human creativity, generating revenue from labour that remains uncredited.

Conclusion: Fairness in the Era of AI Created Music

AI-created music is transforming the music landscape. Platforms like Suno provide innovative tools, while streaming services like Spotify struggle to adapt policies and protect artists. For creators, listeners, and industry stakeholders, the priority must be fairness.

Human artistry forms the foundation of AI’s output. By enforcing labelling, watermarks, transparent training data, and royalty distribution, the industry can embrace AI created music without undermining the artists whose work makes it possible.