Location Is All You Need: Copyright Extraterritoriality and Where to Train Your AI - Rättzén, Mattias (Science and Technology Law Review, 2025)
The development of artificial intelligence (“AI”) models requires vast quantities of data, which will often include copyrighted materials. The reproduction of copyrighted materials in the course of training AI models will infringe on copyright, unless there are applicable exceptions and limitations exempting such activities. There is so far considerable divergence between jurisdictions, including between the United States, EU, U.K., Japan, Singapore, Australia, India, Israel, and many more countries, in this regard. In the absence of international harmonization, there is therefore a high likelihood that the same type of training activity would be considered copyright infringement in some countries but not in others. The AI community is not blind to that risk. If copyright law restricts the development and deployment of AI, developers may decide to relocate their operations elsewhere, where the reproduction of training data is clearly not infringing. This Article concludes that there is a loophole in the international copyright system, as it currently stands, that would permit large-scale copying of training data in one country where this activity is not infringing. Once the training is done and the model is complete, developers could then make the model available to customers in other countries, even if the same training activities would have been infringing if they had occurred there. Because copyright laws are territorial in nature, by default they can only restrict infringing conduct occurring in their respective countries. From that point of view for AI developers, location is indeed all you need. The EU has become the first to respond to this problem by retroactively extending their text and data mining exception extraterritorially to training activities occurring in non-EU countries, once the completed AI model is placed on the EU market. While such an extraterritorial application benefits rightholders and closes the loophole now present, it makes the situation significantly more complex for developers. If other regulators decide to follow the same path as the EU, which previously happened in the data privacy context, then developers would be facing multiple, conflicting copyright laws targeting the same underlying activity. This could significantly complicate the development process for AI and potentially undermine the AI industry. This Article critically discusses these and related issues, and whether an extraterritorial application of copyright laws is compatible with territoriality norms that are supposed to respect foreign sovereignty. It also explores, in light of these difficulties, whether we should instead shift focus from regulating the inputs (i.e., the data used to train AI models) to regulating the outputs (i.e., the AI-generated content itself). Indeed, to the extent that the transnational data loophole cannot be closed without infringing upon foreign sovereignty, we may need to look at other regulatory means instead. The Article also suggests that we should consider model training and copyright infringement as a product-by-process problem, which calls for a comparison with how patent law solved similar extraterritoriality issues. Several decades ago, international patent treaties harmonized the extent to which patent laws can be applied extraterritorially to reach imported products derived from foreign manufacturing processes. If regulators wish to extend their copyright laws’ extraterritoriality to close the loophole that exists for training activities in the context of AI, and to do so in a way that is aligned with copyright territoriality, there may be a need to similarly revise international copyright treaties. This Article, therefore, urgently calls for a similarly coordinated international effort in copyright law, which balances the interests of rightholders with the technical, regulatory, and economic realities faced by developers. How we resolve these issues could make or break the future of AI. If we cannot find a way to reconcile the interests of rightholders and AI stakeholders, the world may be left with a segregated and fragmented AI landscape, one in which there can only be losers and no winners.