As the first empirical evidence is published on the consequences of Generative AI systems on labor markets1, broad anxiety is felt from creator communities on the effects of this technology on their income streams. Consequently, the question of how to deal with Generative AI from a copyright law perspective is gaining a lot of attention globally. Several lawsuits have been filed in the US by creators against AI operators and the first attempts to legislate that matter have already been introduced at the national level. The EU is currently finalizing an ambitious regulation package called the “AI Act” with important implications for its copyright regime, in particular the implementation of transparency obligations concerning copyright-protected works used to train the AI algorithms. In this context, Martin Senftleben’s new article Generative AI and Author Remuneration is particularly timely and proposes a very inspiring reflection on what could be the way forward regarding copyright reforms in this field.
One of Senftleben’s main concerns is to find a workable approach not to disincentivize AI innovation while at the same time creating new revenue streams for “flesh and blood authors” to secure remunerations that will improve their working and living conditions. Indeed, the starting point of the author is that:
the increasing sophistication of AI systems will inevitably disrupt the market for human literary and artistic works. Generative AI systems provide literary and artistic outputs much faster and cheaper. It is therefore foreseeable that human authors will be exposed to substitution effects. They may lose income as they are replaced by machines in sectors ranging from journalism and writing to music and visual arts.
In the article, much attention is devoted to analyzing whether the training of the machine learning algorithm with copyright-protected works can be permitted under copyright law. This is a complex question, and it is fair to say that no jurisdiction in the world has a straightforward answer to it, as no copyright law has yet passed having generative AI technology in mind. As machine learning is based on Text and Data mining (TDM), from a copyright point of view, the question often concentrates on whether exceptions and limitations that allow TDM can cover these uses as well.
While the Big Tech AI industry claims this situation falls under the US fair use exception, the content industry considers on the contrary that these uses are covered by the exclusive right and should be licensed. In Europe, a recently introduced TDM exception offers the possibility (under certain circumstances) for right holders to “opt-out” of the exception for text and data mining and to retain full control of their work (article 4 of the Directive for Copyright in the Digital Single Market). Some large collective management organizations have already announced that they will opt out their entire repertoire from TDM activities for machine learning, which will significantly reduce the available training material for AI systems.
As Martin Senftleben rightly underlines, applying this “opt-out”-mechanism to generative AI is not a satisfying solution because it would inhibit the development of this technology and thus make the European Union unattractive for AI developers. For the same reason, he rejects the idea of submitting these uses to the exclusive right: “The need to obtain individual authorizations and manage remuneration payments for AI training constitutes an additional cost factor in the form of transaction costs and licensing fees. If the costs involved are too high, it will negatively impact the ability of the EU’s AI sector to compete on the world market”.
The core of Senftleben’s proposal lies in the argument that copyright law, in order to compensate human authors for the reduction in their market share and income through Generative AI, should introduce an AI levy system in the form of a statutory remuneration and ensure the payment of equitable remuneration to creators. In his model, the statutory remuneration would however not be related to the TDM use of protected works for AI machine learning purposes, but it is “the literary and artistic output of generative AI systems” that serves “as a reference point for a legal obligation to pay remuneration”.
According to the author, focusing on an “output-oriented AI levy system can be applied uniformly to all providers of generative AI systems in the EU. In contrast to a remuneration obligation focusing on the input dimension and AI training activities, the output-oriented levy approach avoids the risk of disadvantages for EU high-tech industries. All providers of generative AI systems are equally exposed to the levy payment obligation the moment they offer their products and services in the EU.” This lump-sum remuneration would have to be paid by AI developers when their systems produce AI-generated output that have the potential to serve as substitute for works made by human authors. To counter legal/doctrinal concerns and to give theoretical support to the proposal, Senftleben refers to the theory of the “domain public payant” (“paying public domain”), according to which the exploitation of the public domain should at least partly serve the living generation of authors.
Admittedly, the proposal put forward by Martin Senftleben is very European in its spirit as its income redistribution rationale might not be an easy fit for all copyright traditions, in particular the US one. From a European point of view, however, the proposal is certainly compatible with a tradition of remunerated exceptions, as there is an established practice and case law about the distribution rules in favor of creators of this kind of remuneration via collective management organizations. In this respect, maybe submitting TDM for machine learning in the context of generative AI to a remunerated exception could be a workable alternative?2 Indeed, at the policy level, it will be more difficult to achieve consensus on a proposal based on a paid public domain, as advocates of a robust public domain might be favorable to ameliorate the remuneration situation of creators, but less sympathetic to the idea of a domaine public payant.
In any case, there is no doubt that this important article provides further arguments to consider the position of creators in forthcoming copyright reforms in the field of AI and more generally helps to reflect on how to finance creative ecosystems in a fast-moving technological environment.
- Xiang Hui, Oren Reshef, & Luofeng Zhou, The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market, available at SSRN (Aug. 1, 2023).
- See in this sense for example Christophe Geiger & Vincenzo Iaia, The Forgotten Creator: Towards a Statutory Remuneration Right for Machine Learning of Generative AI, 52 Comp. L. Sec. Rev. (forthcoming, 2024), available at Elsevier (Nov. 24, 2023); Christophe Geiger, Elaborating a Human Rights friendly Copyright Framework for Generative AI, available at SSRN: (Nov. 16, 2023).
