One software developer in Leipzig, Cedrik Sixtus, eventually acted on that frustration by building a browser-based tool that identifies and blocks suspected AI-generated tracks on Spotify.

His tool, which has been downloaded hundreds of times after being shared on code platforms, works by filtering a large and expanding database of thousands of suspected AI artists. It combines community-driven tracking lists with signals such as unusually rapid song releases, AI-like cover designs, and external detection systems that attempt to identify synthetic audio patterns. Sixtus describes it as a matter of personal preference, arguing that users should be able to decide whether they want AI music in their playlists or not. He also notes that his software is installed through Spotify’s web interface and may conflict with the platform’s terms of service.

Much of the wider debate, as reported by the BBC in its coverage of the issue, centres on how difficult it has become for platforms to clearly define, label, or filter AI-generated music at scale. Streaming services like Spotify, YouTube Music, and Amazon Music have so far avoided introducing strong user-facing labels or filters. Instead, they largely rely on limited self-disclosure from artists and distributors, alongside internal moderation focused on spam or impersonation rather than the origin of the music itself.

Spotify has introduced a small step in this direction by testing a system that displays credits showing when AI tools were used in production. However, this remains voluntary and depends on what artists or labels choose to disclose. The company has acknowledged that this approach is incomplete, saying that a broader solution would require coordination across the entire music industry.

Experts argue that this cautious approach reflects a difficult balance. Streaming platforms must decide whether to take a stance on how music is created or remain neutral, even if that risks reducing transparency for listeners. At the same time, AI-generated music is becoming harder to distinguish from human-made tracks. In controlled tests cited in industry research, most listeners failed to correctly identify whether a song was created by AI or a human artist.

Other companies are taking different approaches. Deezer, for example, has begun tagging AI-generated tracks and excluding them from algorithmic recommendations and curated playlists. It uses detection systems trained to recognise statistical patterns in audio data and has even started offering this technology to other platforms. Apple Music has also announced plans for transparency labels, although much of the system will still rely on self-reporting by creators and distributors.

Despite these efforts, challenges remain. AI music tools are not uniform: some generate entire songs from text prompts, while others assist artists in specific stages of production. This makes it unclear where to draw the line between human and machine creation. Researchers also warn that detection systems may struggle to keep up as AI models improve, creating an ongoing cycle of adaptation between generators and detectors.

There is also disagreement over whether platforms should wait for perfect solutions. Some critics argue that even partial labelling of fully AI-generated tracks would improve transparency for listeners and help protect human artists’ earnings. Others believe that economic incentives may discourage streaming platforms from introducing strict filtering systems, since AI-generated content is often inexpensive to produce and can scale rapidly.

As industry standards slowly develop, including ongoing work on disclosure frameworks and upcoming regulatory requirements in some regions, the music streaming landscape appears to be entering a period of transition. For now, however, the question of how to define, label, and regulate AI-generated music remains unresolved, leaving platforms, artists, and listeners to navigate a rapidly changing environment.