Apple Music Will Put Custom Tags for AI Songs and Visuals, But It’s Not Enough

Apple Music has initiated a significant step towards transparency in the burgeoning landscape of AI-generated content by implementing a new system for flagging music and artwork created by artificial intelligence. This initiative, rolled out on March 4th, allows record labels and distributors to apply custom tags to content, delineating whether AI played a material role in the creation of tracks, compositions, artwork, or even music videos. While this move is being lauded as a foundational element for developing industry-wide policies, a critical omission has left many questioning its ultimate effectiveness: the absence of any enforcement mechanism.

The core of Apple Music’s new policy, as reported by Music Business Worldwide, centers on a declaration system. When a label or distributor submits new material to the platform, they are presented with an option to indicate if AI was instrumental in generating a substantial portion of the recording or its associated packaging. Apple has communicated to its partners via a newsletter that this data collection is the initial phase in empowering the industry to establish informed policies regarding AI-generated content. However, the reliance on self-declaration without a verification process casts a long shadow over the potential impact of this new tagging system.

The timing of Apple Music’s announcement is particularly poignant, coming shortly after streaming service Deezer released stark data illustrating the consequences of an honor system approach to AI music. Deezer has reported an astonishing influx of fully AI-generated tracks, with the platform now receiving over 60,000 such submissions daily. This figure represents a staggering 39% of all music delivered to the service. Since the beginning of 2025, Deezer has identified a cumulative total exceeding 13.4 million AI-generated tracks.

This deluge of AI music has significant financial implications. Deezer’s investigations revealed a disturbing trend: in 2025, an estimated 85% of all streams on AI-generated music were fraudulent. This represents a substantial increase from the 70% recorded the previous year. Such fraudulent streams are subsequently demonetized and removed from the royalty pool, meaning that the revenue generated from these fake plays is not distributed to legitimate artists and rights holders. For context, overall streaming fraud across Deezer’s entire catalog stood at a comparatively modest 8% last year.

The stark reality presented by Deezer’s data underscores the motivations behind the rapid adoption of AI music by streaming platforms. The primary concern is the potential for widespread exploitation of royalty systems by bad actors. The ability to generate tens of thousands of tracks daily and then artificially inflate their stream counts through bots directly siphons money away from human artists. Each fraudulent stream represents a direct financial loss for creators who rely on their music for income.

Major players in the streaming industry are keenly observing these developments. Spotify, for instance, announced more stringent AI policies last year and is actively working towards establishing industry-wide tagging standards. However, its current detection infrastructure, much like Apple’s, relies heavily on disclosures made by labels and distributors, leaving it vulnerable to the same potential for deception.

The limitations of a trust-based system are becoming increasingly apparent. Deezer has proactively developed and is now licensing its AI detection technology to other entities. The French collecting society, Sacem, is among the early adopters, reportedly testing a tool that claims to achieve 100% accuracy in identifying AI-generated music from prominent models like Suno and Udio. This development strongly suggests that automated and robust detection methods are viable and effective solutions to the AI music challenge.

Apple’s chosen path, however, diverges from this more assertive approach. By implementing transparency tags that rely on the discretion of labels and distributors to define what constitutes AI-generated content, the platform is essentially placing its trust in the integrity of the industry players. The technical specifications for these tags indicate that they are optional, and the absence of a tag will be interpreted as no AI involvement. This creates a significant loophole, as labels seeking to obscure the AI origins of their content are not compelled to disclose it.

For the average listener, the introduction of these AI tags on Apple Music signifies a forthcoming increase in transparency. However, the crucial caveat remains: the veracity of these labels is entirely dependent on the honesty of the content creators. Without an independent verification process or penalty for misrepresentation, these tags may offer little more than a superficial layer of information.

The broader implications of this approach are significant. The music industry is at a critical juncture, grappling with the disruptive potential of generative AI. The ability of AI to create music indistinguishable from human-made tracks raises fundamental questions about authorship, copyright, and the economic sustainability of artistic careers. The current landscape is characterized by a race to develop effective strategies for both identifying and regulating AI-generated content, while also ensuring that human artists are not unfairly disadvantaged.

The Rise of AI in Music Creation: A Historical Perspective

The integration of artificial intelligence into music creation is not an entirely new phenomenon. Early experiments with algorithmic composition date back decades, utilizing rule-based systems to generate musical patterns. However, the advent of sophisticated machine learning models, particularly deep learning and generative adversarial networks (GANs), has dramatically accelerated the pace and sophistication of AI-generated music. These advanced models can learn from vast datasets of existing music, enabling them to produce compositions that mimic specific styles, genres, and even the vocal characteristics of particular artists.

The accessibility of AI music generation tools has exploded in recent years. Platforms like Amper Music, Jukebox (by OpenAI), and more recently, Suno AI and Udio, have made it possible for individuals with little to no musical training to generate original music simply by inputting text prompts. This democratization of music creation has fueled an unprecedented surge in the volume of AI-produced content.

The Economic Pressures and Fraudulent Practices

The economic model of music streaming, largely dependent on per-stream royalties, creates a fertile ground for exploitation by AI-generated content. The ability to generate a high volume of tracks quickly and cheaply allows for the mass creation of content that can then be subjected to artificial streaming to generate revenue. This practice directly undermines the livelihoods of human artists who rely on fair compensation for their work.

The scale of this problem was highlighted by Deezer’s findings. The sheer volume of AI-generated tracks, coupled with the high percentage of fraudulent streams, points to a systemic issue. The current streaming economy is not adequately equipped to differentiate between legitimate artistic output and AI-driven content designed to game the system. This imbalance threatens to devalue human creativity and destabilize the music industry’s financial ecosystem.

Industry Responses and the Search for Solutions

The music industry, a complex ecosystem of artists, labels, publishers, distributors, and streaming platforms, is actively seeking solutions to the challenges posed by AI. Several approaches are being explored:

• Labeling and Transparency: As seen with Apple Music’s initiative, a primary strategy is to implement clear labeling mechanisms for AI-generated content. The goal is to inform listeners and provide data for policy development.
• Technological Detection: Companies like Deezer are developing and licensing sophisticated AI detection tools. These technologies aim to identify AI-generated music with a high degree of accuracy, regardless of whether it has been self-declared.
• Copyright and Legal Frameworks: Legal experts and policymakers are grappling with how existing copyright laws apply to AI-generated works. Questions surrounding ownership, authorship, and fair use are central to these discussions.
• Ethical Guidelines and Industry Standards: Various organizations are working to establish ethical guidelines for the use of AI in music creation, promoting responsible innovation and protecting the rights of human artists.
• Watermarking and Content Authentication: Some research is focused on developing methods to embed invisible watermarks into AI-generated content or to authenticate the origin of music, making it harder to pass off synthetic creations as human work.

The Limitations of Self-Declaration: A Critical Analysis

Apple Music’s decision to rely on self-declaration for AI tagging, while a starting point, faces significant criticism due to its inherent limitations. The music industry has a history of entities prioritizing profit over transparency, and the allure of exploiting the AI music boom without disclosure is a powerful incentive. Without an independent verification system or clear penalties for misrepresentation, labels and distributors have little to lose by failing to disclose AI involvement. This could lead to a scenario where only ethically-minded entities comply, while those seeking to benefit from AI-generated content without scrutiny continue to do so, effectively rendering the transparency initiative less impactful.

The parallel with the advertising industry’s past struggles with transparency and disclosure serves as a cautionary tale. When self-regulation is the primary mechanism, the potential for loopholes and exploitation remains high, necessitating external oversight and enforcement to ensure genuine accountability.

The Path Forward: Towards Robust AI Governance in Music

The music industry’s journey with AI is still in its nascent stages. While Apple Music’s move towards transparency is a step in the right direction, it highlights the urgent need for more comprehensive and robust solutions. The industry must move beyond mere declarations and invest in technologies and policies that can effectively identify, regulate, and ensure fair compensation in the age of artificial intelligence.

The success of future initiatives will likely hinge on a multi-faceted approach that combines technological innovation in detection, clear legal frameworks, strong industry-wide standards, and perhaps most importantly, a commitment to protecting the value of human creativity. Without these elements, the promise of AI in music risks being overshadowed by the potential for exploitation and the erosion of the artistic ecosystem. The ongoing efforts by platforms like Deezer to develop and deploy automated detection tools suggest that a more proactive and verifiable approach is not only possible but necessary for the long-term health of the music industry. The coming months and years will be crucial in determining how effectively the industry can navigate this transformative technological shift.