The Case for Regulating AI Without Freezing It

There are two bad ways to regulate artificial intelligence.

The first is to assume that every new AI capability is dangerous until proven otherwise. The second is to assume that regulation is inherently an obstacle to innovation.

Neither position survives much contact with reality.

AI systems are becoming embedded in decisions involving employment, education, finance, health care, and public administration. When software influences decisions that affect people's lives, someone eventually has to answer a basic question: who is responsible when it gets something important wrong?

The answer cannot simply be "the algorithm."

That is why regulation is necessary.

But regulation should be designed around consequences rather than technological novelty. Not every AI system deserves the same level of scrutiny. A tool that recommends recipes should not be regulated like a system that helps determine whether someone receives a loan.

This sounds obvious, but policy often becomes complicated precisely because governments are tempted to regulate categories rather than risks.

The European Union's AI Act is now entering a significant phase of implementation, with major provisions applying from August 2026. The European approach is more comprehensive than the regulatory structure currently used in the United States.

Europe is effectively asking whether society can establish rules before technology becomes too deeply embedded to regulate.

America should ask the same question, but it does not necessarily need the same answer.

The United States has historically benefited from a regulatory environment that permits experimentation. That advantage should not be discarded. If every startup needs a lawyer before testing a new product, smaller companies will struggle while established technology firms become even more dominant.

Good regulation should therefore accomplish three things.

First, it should make accountability clear. Companies should not be able to hide behind the complexity of their models when their systems cause foreseeable harm.

Second, it should establish basic transparency requirements in situations where people reasonably need to know that AI is being used.

Third, it should be flexible enough to change as the technology changes.

The third requirement may be the hardest.

Traditional legislation moves slowly. AI development does not.

A law written around one specific model or capability may become obsolete before the ink is dry. Policymakers should therefore focus on durable principles rather than attempting to predict every technological development.

There is a larger philosophical question underneath all of this.

Innovation is not automatically good. Regulation is not automatically good either.

The purpose of both should be human welfare.

AI should be allowed to become extraordinarily capable. But capability without accountability is not progress. Nor is regulation that protects society by preventing useful technology from existing.

The goal should be neither maximum freedom nor maximum control.

It should be competent government operating in a field that changes faster than government normally does.