Two Continents and Two Visions: How the US and EU Regulate AI in Sport
By David B. Hoppe, Managing Partner, Gamma Law, San Francisco, United States
Artificial intelligence (AI) reshapes markets, institutions, and individual agencies, forcing governing bodies to confront a deceptively simple question with enormous consequences: how do we regulate AI without strangling the innovation that makes it valuable in the first place?
The sports world vividly illustrates this tension, as it uses generative AI to rapidly transform training, performance, fan engagement, and operations.
In the United States, the NBA partners with Google Cloud to leverage generative AI for advanced player performance analytics and customized broadcast narratives, and the NFL uses similar technology to produce dynamic marketing content and optimize training regimens.
In Europe, Paris Saint-Germain FC uses generative models to create hyper-personalized fan content and tactical simulations, whilst the German Bundesliga employs AI to generate real-time, automated highlight reels and commentary.
There is no global consensus on how to govern such applications.
Instead, the regulatory map is hardening into a tale of two continents, each reflecting a fundamentally different view of risk, responsibility, and the role of government. For sports bodies, clubs, and tech companies building or deploying AI, this divide brings immediate implications across operations and global strategies.
At a high level, two regulatory archetypes now dominate the field. The United States has opted for flexibility, experimentation, and ex post accountability. Whilst Europe has chosen structure, predictability, and ex ante control. This chasm highlights a philosophical divergence that is forcing organizations from Serie A football clubs to Silicon Valley sports-tech startups to operate in two worlds at the same time.
The United States: Innovation First, Accountability After
The American approach could not look more different. With no comprehensive Federal AI statute, governance emerges from a loose constellation of principles, agency actions, and litigation.
The White House Blueprint for an AI Bill of Rights sets out nonbinding principles, but oversight usually happens only after harm occurs. The Federal Trade Commission might, for example, investigate biased AI in sports ticketing algorithms, or the Equal Employment Opportunity Commission might review AI-driven the hiring tools a franchise uses. The US litigation-heavy ecosystem reinforces the regime, enabling a college volleyball player to sue over an AI-generated name, image, likeness deepfake, or a baseball coach to challenge an AI-informed performance decision.
The advantage is obvious: speed, experimentation, and technical freedom. The NBA rapid generative AI adoption for content and analytics thrives in this environment. The downside is just as clear: fragmentation and uncertainty. In the absence of Federal alignment, States are filling the void. California, New York, Illinois, and others are advancing their own AI rules on hiring, biometrics, and deepfakes. This creates a regulatory patchwork that is expensive to track and difficult to harmonize.
Europe AI Act: Guardrails First, Innovation Second
With the AI Act (the Act), the European Union (EU) has done what it often does best: set a comprehensive, rights-driven framework designed to govern an entire technological era. This is not incremental regulation. It is constitutional in scope.
The Act imposes a clear, risk-based hierarchy. For athletes and sports organizations, this means a generative AI system used for biometric injury prediction or talent scouting would likely be deemed “high-risk,” triggering rigorous obligations. So would AI used in employment decisions for coaches or staff, or in stadium admission using real-time facial recognition. For these systems, access to the EU market would only be granted after developers and practitioners clear a demanding compliance threshold of rigorous risk assessments, disciplined data governance, technical documentation, built-in human oversight, and demonstrable accuracy and cybersecurity safeguards.
Certain practices, such as government social scoring, which could theoretically intersect with public sporting event management, and most forms of real-time biometric surveillance in public spaces, are banned outright.
The Brussels message is unmistakable: trust is a prerequisite, not a byproduct. Enforcement reflects that conviction. Penalties can reach €35 million or 7% of global annual turnover. For Manchester City FC or ADIDAS, for example, this means that any generative AI tool affecting EU athletes or fans falls under the Act. That extraterritorial reach - the “Brussels Effect” - effectively turns EU law into a global baseline.
This is classic ex ante regulation: build the guardrails before the car hits the road. Supporters say that it brings legal certainty and public confidence. Critics see friction, cost, and a real risk that early-stage innovation, such as the experimental AI sports analytics Barcelona FC developed, migrates elsewhere. Both are right.
Consequences for Sports Organizations and AI Startups
This transatlantic divide creates two radically different operating realities.
In the United States, the challenge is navigation. A startup creating AI-generated personalized training regimens must track shifting agency guidance, evolving State laws, like the Illinois Biometric Information Privacy Act, and litigation risk, whilst iterating quickly. Here, responsible AI practices are a competitive differentiator. Transparency and credible governance help to win contracts with professional teams and attract capital.
In Europe, compliance is a pre-launch discipline. An Italian football academy using generative AI for youth talent development must conduct impact assessments, ensure human oversight, and maintain exhaustive documentation before deployment. Failure means market exclusion, so sophisticated teams are embedding the Act and the EU General Data Protection Regulation requirements into product architecture from day one.
The Strategic Middle Ground
The most successful sports leagues and progressive athletics-focused tech companies are not choosing between the United States and Europe. They are synthesizing the best of both:
- Build to the highest standard: For regulated use cases, like athlete health analytics or fan biometrics, designing to EU-level requirements future-proofs products for global expansion.
- Make regulatory fluency a core competency: Compliance cannot live in a silo. Engineers, product teams, and executives need a shared understanding of how the law shapes design, whether for a new fitness app or a stadium experience.
- Invest in governance infrastructure: Tech tools that automate documentation and support impact assessments turn compliance into a scalable asset.
- Engage early and often: Leagues, clubs, and tech providers that help shape rules, whether through industry groups, pilot programs, or public consultation, are far better positioned than those who wait to react.
The lesson is straightforward: the winners in sports’ AI-driven future will move fast, but they will not break things. They will outpace competitors by making trust, safety, and accountability central to their value proposition. Not because regulators demand it, but because athletes, fans, and partners increasingly do.
In this environment, governance is not the antithesis of innovation. It is the foundation for sustainable scale and long-term credibility. The organizations that understand that will define not only the future of AI in sport, but the integrity of the games themselves.
Competitive edge lies not in building the most powerful systems, but in building the most resilient, trustworthy, and responsibly governed ones.
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