Regulating Claims, Not Code

Artificial intelligence regulation in the United States is entering a more pragmatic phase. Rather than attempting to define how AI systems must be built, federal regulators are increasingly focused on how AI systems are described, marketed, and deployed. At the center of this shift is the Federal Trade Commission, which in early 2026 reiterated an enforcement strategy that prioritizes deceptive or exaggerated AI claims while avoiding prescriptive rules on model design.

This approach reflects a broader evolution in U.S. AI governance. Instead of regulating algorithms directly, the FTC is applying long-standing consumer protection principles to AI-related representations. The result is an enforcement model that is already shaping compliance practices across the technology sector and may influence how future federal AI oversight develops.

How AI Marketing Claims Became a Regulatory Flashpoint

The commercialization of generative AI has triggered a surge in marketing claims describing products as “AI-powered,” “autonomous,” or “bias-free.” In many cases, these labels lack clear definitions or supporting evidence, blurring the line between promotional language and factual representation.

From the FTC’s perspective, this is a familiar problem. The agency has a long history of challenging misleading claims in sectors such as health, finance, and data security. AI now falls squarely within that remit. The technical sophistication of a system does not exempt it from truth-in-advertising requirements.

Overstated AI claims can mislead consumers, distort market competition, and obscure real system limitations. These risks are especially pronounced in sensitive contexts such as hiring, lending, education, and healthcare, where inflated expectations can translate into real-world harm.

The FTC’s Authority Under Consumer Protection Law

Rather than seeking new AI-specific statutory powers, the FTC is relying on Section 5 of the FTC Act, which prohibits unfair or deceptive acts or practices. This allows the agency to act quickly, without waiting for Congress to resolve broader debates over comprehensive AI legislation.

In recent guidance and enforcement commentary, the FTC has emphasized that companies must be able to substantiate AI-related claims. Assertions that a system improves accuracy, reduces bias, or operates autonomously must be supported by evidence derived from appropriate testing, monitoring, and documentation.

This strategy enables meaningful oversight without requiring the FTC to define technical standards or evaluate model architectures. Instead, accountability is enforced at the point where AI systems meet the market.

Enforcement Versus Innovation: Where the FTC Draws the Line

A common concern among technology firms is that regulation will inhibit innovation. The FTC has sought to counter this perception by framing its approach as innovation-aware. By focusing on claims rather than code, the agency avoids dictating how AI systems should be engineered.

The FTC is not assessing whether a particular model design is optimal or whether a training dataset meets predefined thresholds. It is assessing whether companies are honest and precise in how they describe system capabilities and limitations. This distinction preserves flexibility for experimentation while reinforcing accountability at commercialization.

At the same time, this approach increases internal governance expectations. Legal, compliance, product, and engineering teams must collaborate more closely to ensure that public statements accurately reflect real-world system behavior. For many organizations, this represents a shift in governance culture rather than merely a new compliance checklist.

Comparisons With European and UK Regulatory Approaches

The FTC’s enforcement-led model contrasts with developments in Europe. The European Union has adopted a structural, ex ante framework through the EU AI Act, which classifies AI systems by risk and imposes obligations based on intended use.

Under the EU approach, compliance is determined largely by what an AI system does, not how it is marketed. High-risk systems face mandatory requirements related to data governance, human oversight, transparency, and documentation regardless of promotional language.

The United Kingdom has taken a different path. Regulators such as the Competition and Markets Authority have promoted a principles-based, sector-led model that emphasizes fairness, transparency, and accountability while avoiding rigid, centralized AI rules.

These differences illustrate a growing divergence in global AI governance styles. The United States relies on flexible enforcement of existing laws, the EU emphasizes upfront risk controls, and the UK favors adaptive supervision through existing regulators.

What Compliance Teams Should Change in 2026

For organizations deploying or selling AI-enabled products, the FTC’s approach has immediate operational implications. Compliance can no longer be treated as a final sign-off on marketing materials. AI claims must be grounded in continuous technical validation.

Key steps include establishing internal review processes for AI-related representations, maintaining documentation that substantiates performance claims, and ensuring that system limitations are clearly disclosed. Post-deployment monitoring also matters, as real-world performance gaps can render earlier claims misleading over time.

Perhaps most importantly, compliance teams must learn to translate technical uncertainty into clear, conservative language. Overconfidence has become a regulatory liability. Precision and restraint in describing AI capabilities are increasingly essential risk management practices.

A Signal for the Future of U.S. AI Governance

The FTC’s enforcement playbook offers a clear signal about the likely direction of U.S. AI governance. Rather than comprehensive, technology-specific statutes, federal oversight may continue to rely on existing legal authorities applied to emerging technologies.

By regulating claims instead of code, the FTC reinforces the principle that AI systems are not exempt from accountability simply because they are complex. As states, agencies, and international bodies continue to experiment with AI rules, this enforcement model may function as a baseline for U.S. federal oversight.

In practical terms, the message is straightforward: in the age of AI, what companies say about their systems matters as much as how those systems are built.

References

1. Reuters. “The FTC enters a new chapter in its approach to artificial intelligence and enforcement.” February 2026.
2. Federal Trade Commission. Business Guidance on Artificial Intelligence and Advertising.
3. European Commission. Artificial Intelligence Act: Regulatory Framework Overview.
4. Competition and Markets Authority (UK). AI and Competition Policy Statements.
5. NIST. AI Risk Management Framework (AI RMF 1.0).
6. OECD. OECD AI Principles.