The Ownership Dilemma: Who Really Holds the Rights to AI-Generated Content?

As generative AI tools fundamentally alter the ways in which content is created and consumed, issues surrounding copyright, intellectual property, and data ownership have come to the forefront.

Despite the rapid advances in AI, these questions remain unresolved and provoke lively debates within legal and tech communities. Content creation typically involves multiple parties: model developers, training specialists, users, and the AI models themselves. However, when it comes to ownership rights over the final output, there is still no unified or clear-cut answer regarding who — if anyone — has legitimate claims to these rights.

If you are creating or utilizing generative AI tools, it directly impacts your business model, monetization strategies, and susceptibility to legal risks. As technology outpaces the development of regulatory frameworks, establishing a legal strategy becomes not just important but critical for ensuring sustainable growth and legal compliance for a project.

Essentially, projects that incorporate LLMs and other AI tools into their offerings must consider two key aspects:

Based on our observations and experiences, most companies integrating LLMs and other AI tools into their products — including industry leaders like OpenAI and Google (Gemini) — typically do not assert ownership rights over the outputs generated by their models. However, while ownership of final results may not be a priority, projects generally aim to retain rights to use user prompts and AI-generated materials for further model training. It is at this stage that intellectual property issues become particularly relevant — not only from a legal standpoint but also for building user trust and fostering ethical data practices in AI technology development and implementation.

Let’s examine two main categories of data that are most commonly employed for training models:

In many instances, copyright infringement arises from using protected materials to train AI models without the necessary permissions. To operate effectively and unlock the full potential of models — especially LLMs — access to extensive and diverse datasets is essential. This creates a conflict between the need for broad training data and the restrictions imposed by copyright law.

First and foremost, it is crucial to understand the sources of training data for your model. Typically, AI platforms rely on at least two primary sources of information:

It is worth noting that, in certain cases, the law allows the use of copyrighted content without a license — but only under specific conditions. One of the most well-known mechanisms is the doctrine of “fair use.” For instance, in the case of The New York Times v. OpenAI, the latter argued that training models on publicly available content fell under this doctrine. However, it is not absolute — its application requires careful legal assessment in each specific instance.

Generally, courts consider four main factors to determine if the use of content is fair:

In summary, the risk of copyright infringement significantly increases if content that potentially infringes third-party rights is repeatedly used during model training or reproduced when generating new materials, especially in cases where effective mechanisms for monitoring, identifying, and removing such content are absent — even after a potential violation is established. Therefore, it is critically important for projects to ensure that their training datasets and practices comply with applicable copyright legislation.

Launching AI products or those where artificial intelligence is a key component is more than just a technical innovation; it also requires comprehensive legal and operational planning. Below are key aspects to focus on to mitigate risks and ensure legal safety for your product:

As AI technologies evolve at an unprecedented pace, so does the complexity of the legal, ethical, and regulatory issues surrounding the training, commercialization, and deployment of AI systems. Key takeaways for projects looking to implement and utilize models in their offerings include:

For founders, developers, and business leaders working with AI and Web3 technologies, compliance with applicable legal norms is not merely a checklist item; it is an integral part of a successful strategy. A competent approach to legal matters can protect not only the business but also foster user trust, model reliability, and the long-term viability of innovations.