Regulatory Capture: When AI Companies Help Write the Rules Governing AI Companies

The companies developing the most powerful artificial intelligence systems possess something government regulators urgently need, technical knowledge.

They understand model architectures, training methods, safety evaluations, computing requirements, and emerging capabilities better than most public institutions. It is therefore reasonable for policymakers to seek their advice.

The problem begins when consultation quietly becomes control.

AI companies are increasingly proposing regulatory frameworks, safety thresholds, national standards, licensing approaches, and restrictions on competing models. Anthropic, for example, has created a Responsible Scaling Policy that connects certain model capabilities to stronger safeguards. OpenAI has also submitted detailed recommendations about national AI regulation, federal and state authority, security, and American competitiveness. These contributions can be constructive, but they also demonstrate how directly private companies are attempting to influence the rules under which they will operate.

This is where regulatory capture becomes a legitimate concern.

The capture may not involve corruption or secret agreements. It can occur through information imbalance. Government agencies may become dependent on the same companies they are expected to oversee. Technical standards may then reflect the architecture, resources, and business models of the largest laboratories.

A compliance requirement that appears reasonable to a multibillion-dollar company may be impossible for a university laboratory, nonprofit research group, open-weight community, or small startup. Safety rules can protect the public, but they can also create barriers that protect incumbents.

I have seen a similar pattern in cybersecurity and federal compliance. Large organizations can absorb complex documentation, testing, legal review, and reporting requirements. Small organizations often struggle, even when their underlying technology is responsible and secure.

AI regulation must address genuine risks, including misuse, bias, opaque decision-making, and unsafe autonomous behavior. However, the regulatory process must include independent researchers, small businesses, workers, sector specialists, public-interest groups, and open-model developers.

Healthcare, law, finance, cybersecurity, and government will also need sector-specific AI governance. A single framework written largely around frontier laboratories cannot address every operational environment.