AI Disruption in the Gaming Industry: An IP Perspective

“AI models have helped studios create [a host of gaming elements] or helped to create them faster and more efficiently, but they have also created uncertainty regarding IP issues, including ownership, authorship, infringement, and responsibility.” — Gurbir Sidhu

From an intellectual property (IP) perspective, the emergence and evolution of artificial intelligence have been as disruptive to the gaming industry as it has been a boon to it.

Indeed, a variety of surveys show that 90 percent of developers are already integrating AI into their workflows, 97 percent believe that AI is reshaping the industry, approximately 50 percent now actively use AI in development, and 22 percent of 20,004 games released on Steam (a digital distribution platform for PC games) in 2025 carried an AI content disclosure.

“AI models have certainly helped studios create text, dialogue, artwork, characters, and game levels, among other things, or helped to create them faster and more efficiently,” says Gurbir Sidhu, a computer science engineer turned lawyer in the Toronto office of Smart & Biggar, a member of the IPH network. “But they have also created uncertainty regarding IP issues, including ownership, authorship, infringement, and responsibility.”

The Human element

A central issue in both copyright and patent law is the need for human creative or inventive contribution. For copyright, AI-generated game assets may be difficult to protect unless they reflect sufficient human authorship, and for patents, AI-assisted inventions still require a human inventor who made a meaningful contribution to the claimed invention.

“That presents complex challenges for copyright law, which is rooted in human originality and authorship. AI-generated game assets—characters, environments, music, dialogue—may receive limited or no copyright protection unless they reflect sufficient human creative control, leaving studios with weaker IP protection over important parts of their games,” Gurbir explains.

Privacy & data ownership

In modern games, gameplay data can be a valuable asset for personalization, monetization, moderation, and AI training. Some AI systems collect and analyze real-time data regarding players’ speech, facial expressions and behavioural cues, which can raise privacy, consent, and data-governance concerns because they require tracking sensitive player information, including emotional states and personal preferences.

Ownership also becomes an issue.

Is the owner of gameplay data the studio that collected the data; the AI third-party tool whose model was trained or improved using that data; or the player who actually generated the data? The extent of the uncertainty is reflected in statistics that show 63 percent of developers expressing concerns about data ownership with AI applications and games.

Absent a definitive legal standard on the ownership issue, Gurbir suggests a contractual approach.

“For player and in-game data, contractual protections and trade secret protections may be especially important. We advise developers and studios to be clear and specific in their subscriptions and licensing terms with AI tool providers, vendors, and platforms about whether gameplay data can be used for AI training, who may retain or reuse it, and what restrictions apply to third-party access.”
Gurbir Sidhu

Personality rights & third-party infringement

“Not only must developers guard against AI-generated characters or assets infringing the voice, style, performance, or likeness of real people, but they must also guard against infringing rights held in relation to virtual assets, including protected characters, branded skins, music, artwork, and other in-game content,” Gurbir says.

The difficulty here is that it is much more difficult to trace elements reproduced by generative AI models (which pull from enormous and varied datasets) than it is to track elements produced in traditional asset creation— especially in the case of video games that embrace a host of creative elements.

So, what to do?

The most oft-cited risk mitigation steps are:

Inject as much human input as possible to promote copyright protection;
Mind the regulatory and legislative trend toward transparency and due diligence;
Look out for baseline compliance measures introduced by various platforms. Steam, for example, has recently implemented a disclosure policy that has rapidly become such a baseline; and
Keep proper records: review contracts and licensing terms with AI tool providers, document when and how you are using AI tools, the instances of human involvement, and where training datasets originated.