In his March 20 Wall Street Journal opinion piece – The Economics of Regulating AI – economist Roland Fryer argued that much of today’s artificial‑intelligence regulation is failing because regulators cannot observe what they most need to know. When lawmakers cannot distinguish between safe and risky systems, firms respond rationally. They retreat from beneficial tools, complying on paper, and revealing less rather than more about real‑world risk.
Fryer is right. But the enforcement problem he identifies runs deeper than information asymmetry. Artificial intelligence is colliding with a structural limitation of law itself: some technologies cannot be meaningfully controlled ex ante without assuming regulators possess powers they do not.
AI is not a discrete product, factory, or industry. It is a general‑purpose capability that now permeates nearly every sector of the economy, often invisibly and without centralized deployment. Enforcement models designed for stable actors and inspectable processes, such as licensing audits and reporting mandates, strain when applied to systems that evolve continuously, cross borders effortlessly, and are frequently used informally by individuals rather than according to formal – and governable – protocols.
Employment law illustrates the problem in stark relief. Even within that...
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