Part I: Evolution of the Creative Value Chains

1. Creative value chains (CVC)

Creative industries are at the heart of this book, particularly three sectors : music, video games and visual arts. Music, because it is one of the first sectors to be most radically transformed by digital technology, with the domination of streaming. Visual arts because it is the sector most resistant to change; and video game because it is an economically dominant sector within CCS, generating greater revenues than those of the film and music industries combined. 

The concept of the creative value chain (CVC) is at the heart of this book. A CVC describes the process through which creative works acquire value, from creation to production, distribution, and public engagement.  

Unlike linear industrial models, CVCs are circular and multi-layered: CVCs involve a wide range of actors: creators, cultural professionals, intermediaries, platforms, authorities, audiences, “click workers,” and increasingly, machines. Their contributions may be recognized by copyright, neighboring rights, or fall outside formal legal protection altogether. 

Value in this context is multidimensional—economic, social, cultural, educational, emotional, and political. Historically, cultural value was tied to ethics and the common good: from medieval cathedral builders working for civic prestige to 19th-century social realist painters addressing political causes. Over time, however, value became increasingly financialized, as art markets, publishing houses, and later the entertainment industry formalized cultural production. The challenge in the digital age is to reconcile these dimensions and prevent cultural and social value from being eroded by purely economic logics. 
 

2. Digital transformation across sectors

The digital era has radically reshaped CVCs. Streaming, social media, AI and blockchain, have multiplied tools for creation, production, and distribution, while shifting bargaining power toward dominant intermediaries. 

Music: Streaming now accounts for most revenues but centralizes power in platforms (Spotify, YouTube, TikTok) and major labels. While blockchain and fan-based platforms offer experiments in direct-to-fan models, dependence on intermediaries persists. 

Visual arts: Traditional structures endure due to the uniqueness of artworks, but digitization has expanded mass image production, VR/AR works, and NFT markets. Yet bargaining power remains concentrated in galleries and auction houses, and the promise of NFTs has largely been undermined by recentralization. 

Video games: Now the largest CCS by revenue, gaming has shifted from physical sales to digital distribution, subscriptions, and especially Free-to-Play monetization. Power is concentrated in major publishers and platforms, while developers often surrender rights. Communities (mods, open-source, indie platforms) resist concentration, but structural inequalities remain. 

Across sectors, the pattern is clear: digital tools expand opportunities but exacerbate inequalities. Value is captured at the top, while creators face precarity at the end of the chain. New technologies may empower, but without governance they risk reproducing old asymmetries. 
 

3. Common dynamics and systemic challenges

Challenge of remuneration and cultural diversity: The platform and AI economies dominate today’s CCS. Platforms monetize both content and user data, creating dependence and limiting cultural diversity. AI models scrape vast datasets—often without authorization—to generate outputs that compete with human works, shifting value to tech companies. 

Roles are being redefined: Creators become self-entrepreneurs, juggling creation with promotion; Machines act as quasi-players, shaping both creation and consumption; Audiences function as both consumers and co-creators, though their data is locked within platforms; Value increasingly lies in metadata and usage data, but these remain fragmented and inaccessible. 

Multiple uses of AI: AI models can be used across the entire CVC, to support creative professionals in their creative workflows, from creation (e.g. idea generation) to production (e.g., AI production & post-production tools) and dissemination (e.g., content recommendation, audience analysis). They can also support the public, when consuming a work (e.g., providing real-time explanation, or adaptation of a work to suit individual tastes), or transforming a work (e.g., AI tools to creatively reuse or transform a work). AI technologies can also be used across a spectrum of human intervention, from fully generated works to assistance in the creative process.  

Value-generating layers: In the CVC, multiple activities give value to the chain. At the outset, there is artistic creation, any artistic practice or cultural expression conceived at grassroots level by the main players. This includes the original creation protected by copyright but also activities of other players who participate in the creation and dissemination of a work with more or less creative intensity. Beyond artistic creation, there are practices, techniques, or information that add value during an artwork’s life cycle. Practice can be more or less creative or technical along a creative or technical spectrum. Beyond that, when artistic creation itself is dematerialized, several elements generate value, namely the information associated with the work, particularly the metadata and usage data. We enter the ‘informational’ dimension of the work. 

Creative workers are at the beginning of the CVC. The higher we go in the value chain, the more value the initial work acquires with successive layers of information and each player attempts to appropriate a share of it at its own level. The challenge for creative workers is to bring value back down the chain. 

It should be recalled that platform business model is to extract value from user-generated content (UGC) and usage data, resulting in a dissociation between the platform value chain and the CVC. This is due to the legal environment of the time, in which platforms benefited from a no-liability regime for UGC (‘safe harbor’). However, new legislations—such as the Digital Millennium Copyright Act (DMCA) in the United States and the Copyright in the Digital Single Market (CDSM) in Europe—have introduced liability regimes and profit-sharing obligations, leading to merge the platform value chain and the CVC into a single, integrated value chain. The AI value chain may follow the same trajectory. At present, AI models extract value from artistic works to generate new outputs. They remain separate from the CVC, since this extraction occurs without authorization or remuneration of artists. We believe that these two separate chains—the CVC and the AI value chain—will also converge, as licensing market develops and the legal framework becomes more clearly defined.  

Oversupply and saturation (120’000 tracks added on streaming platforms daily ; 5 billion images taken daiialy), reducing visibility for most creators. 

This oversupply is part of the long tail theory, according to which a very small fraction of artists is at the head with huge popularity (‘superstars’) and absorbs most of the revenue, while the rest, middle and long tail, achieve less success or none at all (the ‘winner take all’ principle). The long tail has only grown longer in the digital and AI-age. Digital technologies enable to create and produce more and more efficiently, even though they also enable to promote diverse cultural content like niche products. 

Blurring boundaries between creators and consumers, physical and digital, human and machine. 

Intermediation vs disintermediation: platforms promise direct access but remain indispensable gatekeepers. 

Value gaps, as revenues fragment across many actors while platforms retain disproportionate shares. 

Cultural homogenization, driven by algorithmic logics and training biases. 

CCS as societal catalysts, like gaming and visuals arts as a forerunner of new economic models (Free-to-Play monetization, microtransactions, NFT royalties), music and audio-visual as a pioneer of digital disruption (streaming anticipating debates about platform power, algorithmic governance, and data transparency). This may foreshadow wider transformations in labor, governance, and technology. 
 

Part II: Solutions for Better Value Distribution

4. Legal solutions

Copyright and contracts remain central but insufficient. Indeed, copyright aims to incentivize the production of new works. Yet it was neither designed to compensate for job replacement, or displacement (e.g. with large-scale productions replacing artists day job) nor to provide a ‘salary’ to the artists for past works. As for contracts, they are dependent on power relations. Consequently, the “value gap” persists because of free/cheap access models, multiple intermediaries, opaque contracts, and weak bargaining power for creators. AI adds a new “AI gap”, as training datasets and generated works raise novel questions of authorship and remuneration.  

More specifically, the chapter explores how copyright can be rethought in its distributive and collective functions. This can be done with a new right to remuneration for streaming and AI uses, potentially unwaivable and collectively managed. This new right shall be extended to the informational value of the work (e.g., metadata, usage data). Legally, this can be achieved through special transparency rules or, in the absence of such rules, through an interpretation of copyright. 

Because of the limits of such new right to remuneration, which does not resolve work outside the scope of copyright, whether metadata, user data and invisibilized creative labor, the book proposes a novel approach combining copyright and contract law, i.e. to contractualize value distribution across the chain. More specifically, it proposes a novel commons-based licensing model applicable to data of all kinds (including copyrightable, personal and technical data), to be combined with data trusts or Collective Management Organizations (CMOs), to improve effectiveness and enforcement. The licensing model includes modular clauses (e.g. restricted to authorised users or uses), allowing individuals to define their values while fostering the commons.  

The combination of the various compulsory and optional elements to be selected by licensors may look as follows, illustrated by the following pictograms, inspired by legal design techniques like the Creative Commons. These pictograms do not ambition to offer an ultimate and final solution but aim to contribute to the global debate and to facilitate the emergence of global standards for a commons-based copyright license. Further work requires collaboration with other stakeholders and possible ongoing initiatives  

Methodologically, this reverses the current contractual logic: rather than platforms (licensees) imposing their Terms of Services (ToS) unilaterally to the users (licensors, being the copyright owner, or data subject), licensors would set their own terms for access and use of data, by selecting standard terms. The goal: to move past the limits of today’s open licenses, rebalance power in the data economy, and build true data commons.