A key distinction in AI regulation is to focus on making specific harmful applications illegal—like theft or violence—rather than restricting the underlying mathematical models. This approach punishes bad actors without stifling core innovation and ceding technological leadership to other nations.

The U.S. is at a crossroads with AI regulation. It can follow Europe's path of heavy-handed, pre-emptive regulation that slows growth, or it can stick to its traditional approach of fostering innovation while using existing consumer protection and liability laws to ensure safety and accountability.

The growing, bipartisan backlash against AI could lead to a future where, like nuclear power, the technology is regulated out of widespread use due to public fear. This historical parallel warns that societal adoption is not inevitable and can halt even the most powerful technological advancements, preventing their full economic benefits from being realized.

The internet's early days were filled with worms, viruses, and massive economic damage, yet it wasn't shut down. This history suggests that the current zeal to preemptively regulate AI for hypothetical harms is a departure from how we've successfully navigated previous technological shifts.

Historically, effective regulation for technologies like cars and aviation came decades after their invention, once failure patterns were understood. Regulating AI before we know how it will fail is likely to be useless and stifle innovation, as we can't create rules for unknown problems.

Contrary to the belief that compliance stifles progress, regulations provide the necessary boundaries for AI to develop safely and consistently. These 'ground rules' don't curb innovation; they create a stable 'playing field' that prevents harmful outcomes and enables sustainable, trustworthy growth.

The fear of killer AI is misplaced. The more pressing danger is that a few large companies will use regulation to create a cartel, stifling innovation and competition—a historical pattern seen in major US industries like defense and banking.

The fearful, regulation-heavy response to AI in the U.S. is analogous to the reaction after the Three Mile Island nuclear incident. That panic led to policies that effectively halted the U.S. nuclear industry's progress, while other nations advanced. The risk is repeating this mistake with AI, ceding leadership to competitors.

The history of nuclear power, where regulation transformed an exponential growth curve into a flat S-curve, serves as a powerful warning for AI. This suggests that AI's biggest long-term hurdle may not be technical limits but regulatory intervention that stifles its potential for a "fast takeoff," effectively regulating it out of rapid adoption.