adminGenerative AI in game development can help studios speed up production, explore ideas, and support technical workflows. But adopting these tools also raises questions about copyright, regulation, player trust, and responsibility for content created with third-party systems. The level of risk depends in part on where AI enters the production pipeline and how its output is reviewed.
Image credit: Tingey Injury Law Firm
Ali Mirsaidi is a partner and a member of a digital media, entertainment, and gaming practice group.
This article is part of AI Week.
Generative AI in game development: where it fits in the pipeline
AI is already used in parts of game production, often without being prominent in the finished game. In 2025, one in five games released on Steam disclosed generative AI use. Studios are drawn to tools that may help them iterate faster and produce more content, particularly as large-scale games have become more expensive and time-consuming to develop.
AI use is not a single, uniform practice. Its legal and reputational implications can vary depending on how visible the output is and whether it becomes part of the final player experience.
- Coding and backend workflows: AI can support engineering tasks and software development. These uses are generally internal and less visible to players, so reputational risks may be more manageable. Studios should still review code and assess intellectual property concerns associated with the tools they use.
- Concept art and early ideation: Generative tools can help teams explore visual directions and brainstorm. Risk is often lower when outputs remain internal references rather than finished assets. Before an early concept moves into production, teams should review it and confirm that it is suitable for further use.
- Production assets: AI-generated character models, textures, environments, animations, voices, or dialogue can appear directly in a released game. This raises the stakes, especially when a studio cannot determine what material was used to train an external model.
A practical rule is to increase the level of clearance and review as AI-generated material gets closer to the shipped game. Internal quality assurance, compliance teams, and intellectual property specialists may all have a role in that process.
Third-party tools and studio accountability
Many studios use external generative art tools, AI voice services, or developer copilots instead of building models themselves. These systems can be easy to integrate into production, but studios may have limited visibility into their training data, safeguards, and output controls.
That uncertainty does not automatically transfer responsibility away from the studio. If an output reproduces protected work and is included in a commercial game, the studio publishing and monetising the game may face downstream exposure. Although much litigation has focused on AI providers so far, developers should not assume that users of these systems cannot also be targeted.
Game developers should not assume they are immune from downstream exposure.
Some companies fine-tune existing systems using proprietary material, while others consider developing their own models. Greater control may bring benefits, but it can also mean additional governance, documentation, and compliance responsibilities. Keep records of the tools used, the purpose of each use, and the human review applied to outputs.
Copyright questions remain unsettled
Generative AI raises familiar copyright questions at a larger scale and with less transparency. Courts have issued differing rulings on whether using copyrighted works to train AI models may qualify as fair use. These disputes could take years to resolve, and any eventual decisions may not create simple rules for every development scenario.
Studios should also consider whether they can protect AI-assisted work of their own. The US Copyright Office has reaffirmed that material generated entirely by automated systems is not eligible for copyright protection. However, meaningful human creative control—including selection, revision, and deliberate creative decisions—may support protection for some aspects of a work.
For production teams, the practical takeaway is to document human contributions and avoid treating AI as a shortcut around ordinary clearance procedures. Review both the risk of infringing someone else’s rights and the ability to protect the studio’s own creative work.
Regulatory issues studios should monitor
Copyright is only one area of potential scrutiny. Generative systems can also make it easier to reproduce recognisable voices or likenesses, or to let players insert those elements into a game. Studios should check whether appropriate permissions have been obtained and consider how publicity rights apply in relevant jurisdictions.
Studios will face difficult questions about whether appropriate consents have been obtained.
AI-driven characters that generate open-ended dialogue or behaviour raise additional questions about safety and moderation. Teams should consider how to prevent harmful outputs and respond when an AI-enabled feature is misused, particularly if the studio cannot fully predict what a character may say or do.
Personalised engagement and monetisation are another area to watch. The games industry already faces scrutiny over loot boxes, microtransactions, and protections for younger users. AI systems that adapt offers or progression mechanics to individual player behaviour in real time may attract further attention if they are seen to encourage excessive spending or cause harm.
Requirements can differ across jurisdictions, including rules that may apply to training-data transparency for models placed on the market. Studios should check the obligations that apply to their products and intended markets rather than assuming that one approach will satisfy every region.
Managing reputation and planning ahead
Legal questions may take time to settle, but public reaction can be immediate. Players often value human creativity, and a studio may face criticism even when a particular use of AI is legally permitted. Disclosure expectations can also come from platforms, awards organisations, industry standards, and labour agreements—not only from legislation.
Before adopting a tool, studios can establish clear policies that specify which uses are permitted, which require approval, and which are prohibited. A review process should identify the tool and its intended use, assess the visibility and sensitivity of the output, and record any human checks, permissions, or corrections.
Generative AI in game development offers potential efficiency gains, but responsible adoption requires more than choosing a tool. Studios that combine practical oversight with attention to copyright, regulation, and community expectations are better positioned to manage risk. Decide where AI belongs in the pipeline before a production or reputational issue forces the decision.
