As AI advances, the race to define governance is proving just as consequential as the race to build. While much of the attention has focused on technology innovation emerging from the United States and China, an alternative and historically patterned form of power is emerging in Europe: regulation. That is why Anu Bradford’s work on the “Brussels Effect” deserves attention. Her core point is simple: because the European Union is one of the largest and most important markets in the world, companies seeking access to Europe must comply with its rules. And once they do, many decide it is easier to apply those same standards globally.
When considering AI governance, copyright is not a side issue. It is easy to get lost in the weeds of individual copyright cases or the rules of one country versus another. For global companies, the better question is which rules become the baseline for everyone.
Many companies, if only for simplicity, choose to apply a single set of compliance rules even where they are not legally required to do so. The logic comes down primarily to economics. Maintaining separate compliance systems across regions is expensive and inefficient. By complying with more stringent standards, such as those set by the EU, companies simplify processes and reduce legal complexity. Over time, this creates a de facto global standard, one formed not by international agreement, but by market incentives.
Europe’s history of regulatory influence
We have seen this pattern before in data privacy. Bradford’s work on the GDPR, the EU’s privacy framework that she discussed during the recent CCC LinkedIn Live event, “Geopolitics of AI: The Impact of Local Regulation on Global Business, explains why a European framework influenced conduct well outside Europe, even without a formal international agreement. AI may be the next major example.
The EU’s AI Act is not, strictly speaking, a copyright law, but it is highly relevant to copyright. The AI Act uses a risk-based model. For example, certain forms of mass surveillance are banned outright and are considered “unacceptable risk.” Applications that are considered “high risk,” such as AI used in credit scoring or hiring, are permitted, but only under strict requirements around transparency, data quality, and oversight. Lower-risk systems encounter fewer obligations.
The law does not just apply to companies based in Europe. Any organization offering AI systems within the EU must comply, regardless of where those systems were developed. While some might criticize this as “extraterritorial,” it is no different from the need to comply with food safety or automotive emissions standards to access the common market.
The implications are especially significant for generative AI. The EU has introduced transparency requirements that compel developers to disclose how their models are trained, including the content sources used. This includes information about materials such as copyrighted works, a move that has triggered both concern and change across the tech sector.
Once developers are expected to disclose and account for “training data,” questions about rights and compensation become much harder to avoid. That is one reason licensing is moving closer to the center of the AI conversation.
From an industry perspective, this has broad implications. A functioning AI ecosystem depends on a sustainable supply of high-quality content. Transparency and licensing mechanisms help ensure that creators and publishers remain incentivized to produce the materials that underpin AI systems in the first place. This becomes more important as more content online is AI-generated and thus less reliable for AI training.
Major AI developers are already adjusting. Some have signed voluntary codes of practice tied to the AI Act, signaling intent to comply and, in many cases, entering into direct licensing agreements with publishers. Publishers and users are also adopting voluntary collective models such as those offered by CCC. These developments show that regulation is not simply constraining the market. It is actively determining how it evolves.
At the same time, the Brussels Effect is not the only force at play. Other jurisdictions are responding in diverse ways. South Korea has introduced its own AI legislation, seemingly inspired by the EU model, while U.S. states like California are implementing transparency requirements that resemble European rules. Even where countries diverge, they often react to the same baseline, frequently based upon what Europe has already put in place.
This creates a complex regulatory environment. In some cases, stricter rules in smaller markets may push companies to adapt further or offer different versions of their products. Companies may choose to withdraw services rather than comply, and they often threaten to do so. However, when large markets align, the gravitational pull of a common standard becomes difficult to resist.
Is regulation the enemy of innovation?
There is also a deeper question about what regulation means for innovation. A widespread sentiment suggests that “Europe regulates, while others innovate.” But this oversimplifies reality. The impact of regulation depends in part on its scope and design. Overly broad or burdensome rules can constrain innovation, but targeted, well-calibrated regulation can create clarity, safety, and trust, conditions that are increasingly important for the adoption of AI, especially in high-risk environments like healthcare, finance, and education.
In AI, trust is becoming a competitive differentiator. At CCC, we have seen our users yearn for disclosure of training materials so that they can evaluate the reliability of systems. As organizations integrate AI into core operations, they need confidence in how systems are built, what data they rely on, and how outputs are generated. Transparency requirements, once considered burdensome, are now emerging as essential components of product quality and market acceptance.
That is why the Brussels Effect is not simply about exporting rules. It is also about shaping expectations. As users, businesses, and governments become accustomed to higher standards of accountability, those standards begin to define what responsible AI looks like in practice.
The result is a subtle but powerful shift. The future of AI will not be determined solely by who builds the most advanced models. It will also be determined by who defines the rules under which those models operate, how those rules spread, and how they come to be expected by economic buyers.
One can debate Europe’s leadership in AI (I personally see much innovation there), but through its regulatory influence, Europe plays a meaningful role in setting the terms. For anyone focused on AI and copyright, that influence is no longer peripheral. It is central.