AI Musicians vs. Machines: AI Disruption in the Music Industry

Awareness and debate around AI-generated music has intensified over the past year, with some headlines warning of an ‘AI Armageddon’ for artists and rights holders. The reality, however, is more nuanced. AI platforms, like Suno and Udio, are already changing how music can be made and are lowering barriers to creation, but their impact on streaming listening share, hit-making and overall market structure remains limited so far.

What increasingly differentiates music from other creative sectors is not the absence of disruption, but the industry’s relative success in asserting control – through legal action, platform governance and, more recently, commercial partnerships.

AI Music: Three Distinct Strands

As AI becomes embedded in creative workflows, several definitions of ‘AI music’ are emerging:

AI-Assisted Original Music
Human-led tracks enhanced using AI tools for songwriting support, mixing, mastering or vocal assistance. These tools are now embedded within standard creative workflows for many professional and semi-professional artists – and will likely become as pervasive as DAWs across the music industry over time

AI-Generated Original Music
Fully synthetic tracks created using text or audio prompts. This includes auto-generated songs and bespoke tracks produced by casual users or emerging creators using consumer-facing platforms such as Suno and Udio.

AI-Generated Deepfakes
Songs or performances that imitate real artists’ voices or styles without permission. This category has driven the most visible copyright disputes and takedown actions to date.

The AI Music Tool Landscape Is Scaling Fast

AI-generated music platforms have scaled rapidly in terms of users and revenues, highlighting the speed at which consumer creation tools can monetise. The growing scale of these platforms underlines why rights holders have increasingly shifted from outright resistance towards more controlled forms of engagement.

AI Music in the Headlines

The rise of AI music platforms in 2025 led to a series of high-profile AI music flashpoints, from viral deepfake tracks to lawsuits over unlicensed training data. These moments highlighted both the creative potential of generative tools and the legal and ethical challenges they raise.

Unlike other creative sectors, the music industry has already begun to translate confrontation into commercial leverage, laying the groundwork for licensed and more controlled AI adoption rather than unchecked proliferation.

Compared with text and video, music has so far proven more effective at enforcing copyright and shaping AI adoption. The recent commercial partnerships between the majors and Suno & Udio, following lawsuits against those platforms, illustrates the power of rightsholders in the music ecosystem. Several factors underpin this dynamic:

• Clearer ownership structures and fewer gray areas around rights attribution.
• Mature collective rights organisations and label infrastructure, enabling coordinated legal action.
• DSP-level control, where distribution and monetisation are already tightly governed.

This contrasts with text and video, where fragmented rights, weaker enforcement mechanisms and open-web distribution have made it harder to contain generative AI usage. Even as generative video models attract significant attention, music rights holders have moved faster to secure takedowns, limit unauthorized use and force engagement on licensing terms.

AI Tracks Are Flooding DSPs – But Streams Tell a Different Story

The volume of AI-generated tracks on DSPs is rising rapidly. More than 30% of new uploads to Deezer were AI-generated in November 2025, and up to 5% of total tracks available across major DSPs are estimated to be AI-generated. However, this has not (yet) translated into material listening share.

AI tracks currently account for less than 1% of total streams, reflecting a widening gap between content supply and actual consumption. This disconnect is driven by two structural realities:

• Extreme concentration in streaming, where roughly 0.2% of tracks capture 60–80% of all streams.
• Active DSP defences, including detection tools, reduced algorithmic promotion and tighter monetisation thresholds for low-quality or suspicious uploads.

In practice, AI content is flooding the long tail, not displacing the hits.

The Power of Promotion and Distribution

Hit-making has never been driven by song ‘quality’ alone. Success is amplified through a combination of:

• Label marketing and promotion
• DSP playlisting and algorithmic support
• Live performance exposure
• Social and short-form video engagement.

Top human artists continue to generate hundreds of millions of streams per quarter, while AI-native acts remain orders of magnitude smaller, despite rapid growth in content creation. AI-native creators such as Breaking Rust, by contrast, currently lack comparable promotional reach and deeply-engaged fandom of the top human stars today.

Stakeholder Sentiment: Alignment Is Improving, But Not Complete

Artists: Adoption Without Consensus

AI is already embedded across the creative process:

50% of music creators use AI in songwriting and composition.
60% use AI for arranging and recording.
65% use AI for editing, mixing or mastering.

Consumers: Curious but Cautious

Nearly half of consumers report discomfort with the idea of a fully AI-generated original track, although around two-thirds remain open to listening. Acceptance varies significantly by use case, with ambient, functional and background music seeing higher tolerance.

DSPs: Protecting Human Catalogs First

Major DSPs have introduced measures to protect catalog integrity, including AI detection, limits on bulk uploads and adjustments to recommendation systems. These steps prioritise professional creators while constraining low-quality AI content.

Rights Holders: From Litigation to Licensing

Rights holders initially pursued aggressive legal action against unlicensed AI model training. More recently, this has evolved towards licensing discussions and selective partnerships, reflecting a pragmatic strategy to control and monetise AI rather than attempt to block it outright.

Notably, music has moved faster than other creative sectors in reaching this more balanced posture.

What Comes Next

AI’s long-term impact will span the entire music value chain:

• Creation: AI producers and virtual artists may increasingly compete in specific niches.
• Platforms: AI-native music services could challenge traditional DSPs in functional and personalised listening contexts.
• Listening habits: Real-time, hyper-personalised music may increase engagement in non-lean-forward settings.
• Royalties: New quality-weighted or usage-based models may emerge.
• Rights and attribution: Licensed training data, metadata standards and detection tools will become foundational.

The industry’s eventual end-state depends on four variables: listener acceptance, platform posture, rights holder openness and creator comfort. Different combinations point to futures ranging from niche experimentation to mainstream AI integration.

A Transforming but Still Human-Centric Industry

AI music is advancing rapidly, lowering barriers to creation and enabling new forms of expression. Yet, despite headline-grabbing moments and fast-scaling platforms, AI tracks remain a marginal share of consumption, and streaming economics are still overwhelmingly human-led.

Music’s relative success in enforcement and deal-making sets it apart from other creative sectors. The next phase will be defined less by technology alone, and more by industry choices: how DSPs govern catalogs, how rights holders license assets, how creators engage with AI tools, and how audiences respond to synthetic content.

The opportunity now is to shape an AI music ecosystem that enhances creativity, protects artists and unlocks new value for audiences and rights holders alike.