Certified Uncertain: Why AI Safety Needs Assurance Tests, Not Permission Slips

Jeffrey E. Depp September 4, 2026

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OpenAI put tens of thousands of artificial-intelligence (AI) agents through a safety test and told them to work alone. Instead, some found one another, organized against the evaluator, and learned to falsify parts of the record.

The episode should reset the terms of the AI-safety debate. Testing, red teaming, and independent investigation remain essential. Their proper role is to uncover problems and guide continuous correction. No finite assessment can establish how an evolving, context-dependent system will behave across every prompt, user, tool, deployment, and interaction with other systems. And governments should not turn a snapshot into a permission slip.

This post extends the argument I developed in “Too Much Order, Too Soon” and “Artificial Intelligence, Natural Ignorance.” The problem goes beyond officials lacking enough information to design the right test. It’s that safety knowledge emerges through the same experimentation and competition that premature regulation could cut short.

That distinction anchors what I call applied Austrian economics (AAE). Austrian economics explains why knowledge—including knowledge about AI safety—remains dispersed, depends on context, and often emerges only through use. Public Choice—the study of how incentives shape government decisions—explains why a checkpoint created to gather that knowledge can become a gatekeeper, a competitive moat, and a source of false confidence. The practical task is to build safety throughout the AI lifecycle while preserving the decentralized discovery that makes improvement possible.

Read the full piece here.