Artificial intelligence has moved from novelty to necessity at a pace few technologies have ever matched. Generative AI systems now draft text, design products, compose music and write software code—often in seconds.

For inventors and creators, this acceleration presents extraordinary opportunities but raises very complex intellectual property infringement questions.

Is AI a powerful tool that lawfully builds on existing knowledge, or is it an unregulated copier that threatens the value of human creativity?

The answer depends largely on where one sits in the debate.

The case for concern

From the perspective of inventors, artists and patent owners, AI systems pose real and immediate infringement concerns. Many generative models are trained on massive datasets scraped from the internet, technical publications, images, music libraries and code repositories.

Those datasets often include copyrighted works, patented inventions and trade secrets, frequently without the explicit consent of rights holders.

Copyright owners argue that using protected works to train AI models is itself an act of infringement, particularly when the resulting outputs closely resemble the original material.

Visual artists have pointed to AI-generated images that mimic distinctive styles. Authors have alleged that language models reproduce passages that are substantially similar to their books. Software developers raise similar concerns when AI tools generate code that mirrors proprietary programs.

For inventors, the risk extends beyond copyright.

AI-assisted design tools can generate product configurations or technical solutions that unknowingly fall within the scope of existing patents. A company that relies heavily on AI-generated designs may find itself accused of patent infringement without ever having intentionally copied a competitor’s technology.

These risks are amplified by the opacity of many AI systems. When neither the developer nor end user can fully explain which training data was used in the generation of an output, performing traditional freedom to operate or clearance analyses becomes more difficult. For small inventors and startups, defending infringement claims tied to AI use could be financially detrimental.

Key issue: The engine’s role

An important question is whether the AI engine itself can be infringing, separate from any particular output.

Rights holders argue that infringement can occur at the training stage, when copyrighted works are copied into datasets to build the model—even if the final outputs are not exact replicas.

This theory is now being tested in high-profile litigation. In June 2025, Disney Enterprises and Universal filed suit against AI image generator Midjourney, alleging that the company trained its model on vast quantities of copyrighted characters and images without authorization.

The complaint points to AI-generated images that closely resemble well-known characters and argues that the engine itself is built on systematic infringement rather than incidental exposure. The studios seek to hold the AI developer responsible for both the training process and the predictable outputs that follow.

Other major copyright owners—including authors, news organizations and visual artists—have brought similar claims against AI developers. These plaintiffs assert that large-scale ingestion of protected works exceeds fair use and amounts to unlawful copying.

Collectively, the cases raise the possibility that liability may attach not only to users who deploy AI outputs but to the companies that design, train and commercialize the engines themselves.

AI developers counter that training necessarily involves temporary and intermediate copying that is transformative and non-expressive. They argue that holding AI engines liable would overturn decades of precedent that allow technology to learn from existing information. Courts have not yet determined where that line will be drawn.

Overstated risks?

On the other side of the debate, AI developers and many technology companies argue that infringement concerns, while understandable, overstate the legal risks and underestimate AI’s societal value. They contend that training AI models on large datasets is analogous to how humans learn—by reading, observing, and synthesizing existing information.

From this perspective, they argue that AI does not store or reproduce protected works in a traditional sense but instead learns statistical relationships that allow it to generate new outputs. Supporters often point to fair-use principles, particularly when training does not substitute for the original work in the marketplace.

In the patent context, proponents argue that AI is another tool in the inventor’s toolbox, similar to computer-aided design software or simulation platforms. Patent infringement remains governed by claim scope, not by whether a human or a machine assisted in development.

AI developers warn that limiting access to training data could slow innovation and deprive independent inventors of tools that reduce development costs and accelerate experimentation.

The legal system is still catching up.

Courts are facing whether AI training constitutes infringement, whether outputs can be infringing even when they are not identical to source material, and who bears responsibility for resulting harm. Regulators worldwide are proposing transparency requirements, data use disclosures and opt-out mechanisms for rights holders though no comprehensive framework has emerged.

For inventors and creators, this uncertainty cuts both ways. AI can dramatically enhance research, prototyping and commercialization efforts, but it also introduces new diligence obligations. Understanding how AI tools are trained, what licenses apply and how outputs are used is becoming an essential part of intellectual property risk management.

Finding balance

A balanced approach between IP infringement and IP innovation is likely to emerge—one that preserves strong intellectual property rights while recognizing AI’s transformative potential.

Meanwhile, inventors and creators should proceed thoughtfully.

Using reputable AI tools with clear terms of usage, maintaining documentation of development processes and seeking legal guidance before commercialization can help mitigate risk. At the same time, rights holders should monitor how their works are used and engage in policy discussions shaping the future of AI and intellectual property law.

Artificial intelligence is reshaping how ideas are created and refined. Whether it ultimately strengthens or erodes intellectual property rights will depend on how the law evolves and how responsibly innovators deploy this powerful technology.