Artificial Intelligence and Music Ecosystem

Martin Clancy’s contributed volume Artificial Intelligence and Music Ecosystem (2022) does not present a dystopian vision that involves a hostile takeover of the music business by robots who steal music artists’ jobs and other music industry stakeholders’ positions. Instead, it is a remarkably optimistic book that highlights the opportunities and rewards associated with the application of artificial intelligence (AI) in the field of music. The contributed chapters are either interviews with Clancy or more traditional academic book chapters. Designing the book in this way enabled Clancy to introduce an array of different voices including, amongst many others, economist and philosopher Jacques Attali, who in the book confirms AI as the composing technology he predicted in Noise (1977) and who outlines the stages of human and music evolution; Warner Music Group Chief Innovation Officer Scott Cohen, who provides insight into our past and future engagement with new technologies and who indicates how near a “crazy” future is to us; and American musician and sound artist Holly Herndon who discusses how AI continues to inform her work and who considers its impact on the music industry and creative practice generally.

AI in the context of the music business both increases commercial music artist’s workloads, while also potentially decreasing them. For a start, understanding the ways in which AI tools can enhance and assist music artists’ businesses involves a new and demanding cognitive load. For example, the requirement for artists to be aware of what rights they are granting when they use AI tools is important. Further, the challenges of understanding how artists who are already subject to agreements with record labels and song publishers can use AI in their creative processes increase their workload because there is a need to revisit the terms of their extant agreements. In addition, understanding how to factor AI into the negotiation of new deals with labels, distributors or publishers also increases the artist’s workload; publicity, image and personality rights have come to the fore because artists and their managers and lawyers need to be particularly careful in granting any rights beyond copyright to business partners (see Cooke and Taylor, 2023, p. 12 for more on this). Yet, for all the additional work required in navigating the AI frontier, AI presents music artists and music industry stakeholders opportunities to lighten their workloads as well.

In November 2023, I met Martin Clancy at the Kristiansand Roundtable Conference (KRC) in Norway. Our meeting followed a keynote lecture he gave at the International Music Business Research Days conference at the University of Agder in Kristiansand, Norway the week before the KRC. Over dinner, Clancy put a convincing argument that AI will lessen workloads. While use of AI-based services to generate images and video will theoretically reduce artists’ workloads when it comes to marketing their work via social media for example, Clancy (2022) argued that the ethical use of AI in the music business has the potential to address the value gap; this has profound implications for the music ecosystem and, if realised, it will dramatically decrease the workload involved in having to aggressively negotiate with the dominant corporations in the music business to obtain better terms for music artists.

According to Clancy (2022), there is an opportunity here to show leadership regarding the ethical use of AI. So, what does he mean by the value gap? Basically, the fact that major record labels have deep catalogues and that they invested in services such as Spotify in the first instance means that the revenues from such services are directed towards major stars and major record labels rather than benefiting all musicians in the same manner (Eriksson et al., 2019, p. 3). Spotify’s business model has therefore perpetuated a value gap in the chain of income earned through music streaming. Although Spotify themselves argue that this is changing by regularly publishing their usage data via their Loud & Clear initiative, Dahrooge (2021) articulated this value gap in the following way:

[on] average, Spotify pays US$ 0.00437 per average play, meaning that an artist will need roughly 336,842 total plays to earn US$ 1,472. While in comparison, Spotify has an annual revenue of US$ 4.99 billion through its paid subscribers.

(Dahrooge, 2021, pp. 212–213)

For Clancy (2022), AI presents music artists and other stakeholders in what he calls “the music ecosystem” the opportunity to reduce the value gap that exists between them and services such as Spotify. For him, the posthumanist thought engendered by AI can potentially accelerate the closing of this gap:

the inclusion of posthumanist thought can actively contribute towards a significant reimagining of the concept of “human-centred values” ... This realignment of values turns into a legal question: how do human and non-human actors – including the development of AI – become interlinked in a sustainable and equitable music ecosystem? In comparison to humanism and transhumanism, posthumanism offers the third philosophical approach beyond the dominating binary positions of either a dystopian vision, where AI technologies embark on a hostile takeover (robots stealing jobs), or the alternate utopian model in which AI technologies are beneficial to human spheres of existence (as helpers, companions, and care workers).

(p. 173)

It is through the third philosophical approach offered by posthumanism that the term “music ecosystem” comes to life for Clancy. Clancy embraces Actor-Network Theory concepts to consider human and non-human (AI) “member organisms” as being stakeholders of the global music community, including the commercial music industries.

Clancy cited Paul McCartney who argued that “the value gap jeopardises the music ecosystem [and its narrowing will] ... help assure a sustainable future for the music ecosystem and its creators” (McCartney 2018 cited in Clancy, 2022, p. 178). For Clancy, there is an opportunity here for this new interaction between both human and non-human (AI) “member organisms” of the music ecosystem to engender ethical considerations of issues such as music copyright that can help issue in an era of more sustainable and fairer environments for music creation and commercialisation. His theoretical model underpins “an ethical AI ‘fair trade’ mark to support a fair and sustainable music ecosystem” (p. 2).

To realise this ambition of establishing an ethical AI “fair trade” mark, following the publication of the contributed volume being reviewed here, Clancy developed an initiative entitled AI:OK (2024). The AI:OK logo is designed to represent “a secure and trustworthy certification for approved music, products and services within the music ecosystem in the era of Generative AI and future technologies” (AI:OK, 2024, np). The problem that AI:OK addresses is that when AI-generated music enters the market, presently consumers and stakeholders are unaware which content is AI-generated. Further, for AI:OK, knowing whether AI was used is not enough; the logo also needs to signal whether AI is encouraging human creativity or replacing it:

The AI:OK logo benefits the industry by defining and managing sustainable AI use standards and fostering trust among consumers and creators. It reassures stakeholders regarding the equitable sourcing and creation of AI-driven services and products while providing creators with a mark of quality and compliance that enhances their artistic and commercial credibility.

(ibid, np)

The goal of AI:OK aligns with the primary argument Clancy (2022) developed in the conclusion of the contributed volume: AI presents us with an opportunity to create a more sustainable playing field for music artists and music industry stakeholders. Therefore, AI:OK is an initiative that music artists and their representatives can use in the age of generative AI to help close the value gap. AI:OK is a highly commendable initiative that has grown from a highly commendable book.