Introduction
In a world where artificial intelligence (AI) is developing at a dizzying pace, the question of intellectual property (IP) is being raised with renewed urgency. Recent advances in the field of AI have not only transformed entire industries but have also given rise to entirely new creations, often produced without direct human intervention. This reality raises fundamental questions about how these creations should be protected by law, if indeed they should be at all.
In 2024, the United States Copyright Office reaffirmed its position that works generated entirely by AI, without human intervention, cannot be protected by copyright. This decision sparked a worldwide debate on the need for new legal frameworks to address this new frontier. At the same time, the European Union has begun implementing its AI Act, which includes provisions affecting intellectual property rights. These developments clearly show that AI and IP have become a new battleground for conflict, requiring ongoing dialogue and the rapid adaptation of existing laws.
Context/History
The evolution of the relationship between AI and intellectual property has gone through key stages over the years. From the very beginnings of AI in the 1950s, the question of machine-generated creativity was already up for debate. However, it was only with the advent of neural networks and machine learning algorithms in the 2010s that the question became central. The rise of natural language models such as OpenAI's GPT illustrated the creative potential of AI, thus posing unprecedented challenges for regulators and legal experts.
Some of the earliest legal battles over AI and IP focused on the question of whether inventions made by AI could be patented. In many countries, including the United States and Europe, the law required that an invention be attributable to a person in order to be patented. The debate remains open across jurisdictions, and turns less on the machine than on inventiveness and human contribution.
Applications/Use Cases
The applications of AI in the field of content creation are vast and varied. For example, companies such as OpenAI (with GPT), Stability AI (with Stable Diffusion), or Midjourney have developed models capable of generating texts and images, used in marketing, content writing, and even artistic creation. These tools make it possible to quickly produce high-quality content, but they also raise the question of ownership of the works thus generated.
In the field of healthcare, Google DeepMind has used its AlphaFold system to make significant discoveries about protein folding, an advance that could transform biomedical research. However, these discoveries raise the question of intellectual property: who owns the results generated by a machine?
Technologies/Methods
The underlying technologies that enable these advances in AI-driven creation mainly include machine learning and deep neural networks. These systems learn from large quantities of data to imitate or surpass human capabilities in certain tasks. Recent language models hold a number of parameters that their publishers do not always disclose, and produce text a reader cannot always tell apart from a human's.
AI content generation techniques also raise questions of transparency and traceability. Systems must be designed to be audited and to enable an understanding of how decisions are made, which is essential for addressing intellectual property issues.
Challenges/Limitations
The main challenge for AI with regard to intellectual property lies in defining human contribution. AI systems can produce works of art, music, and even technical inventions, but the question remains as to whether these results can be protected by copyright or patents if no human intervention is identifiable.
There are also concerns regarding ethics and responsibility. Who is liable in the event of an intellectual property violation by an AI? Companies must navigate a complex legal landscape to ensure that they do not infringe existing rights while innovating.
These questions of governance and data ownership are not theoretical for the companies that deploy AI in contact with their users. At Koaee, we design multilingual conversational assistants for businesses, available in sixteen languages, with servers located in Europe and no conversations retained. The models we use — Mistral (Voxtral) and OpenAI (GPT) — are part of this shifting technological landscape, which these very debates on AI and intellectual property help to shape.
Perspectives
Looking ahead, we can expect intellectual property laws to continue to evolve in response to technological advances. It is likely that new legal frameworks will be needed to address the specificities of AI-generated works. Furthermore, international standards could be developed to ensure a consistent approach across jurisdictions.
The concept of collaborative models, where AI and humans work together, could also influence the way intellectual property is managed, with an emphasis on co-creation and the recognition of shared contributions.
Conclusion
As AI continues to transform creativity and innovation, the question of intellectual property is becoming increasingly pressing. Regulators, companies, and researchers must work together to create frameworks that protect rights while fostering innovation. The way forward will require a balance between regulation and flexibility, to allow AI to reach its full potential without compromising intellectual property rights.
Source: Eurostat, “Artificial intelligence by size class of enterprise” (isoc_eb_ai) — enterprises with 10–249 employees, France. Retrieved via API on 30 August 2026. The survey publishes neither 2022 nor 2026: the series stops at the latest available year.