The Greater AI Risk May Be Falling Behind

Artificial intelligence is beginning to generate a level of anxiety normally reserved for technologies capable of fundamentally changing civilization.

Some of that anxiety is justified.

Researchers who have spent their careers building advanced AI systems are increasingly warning about models becoming more autonomous, capable and difficult to control. The concern is no longer confined to science fiction writers or internet speculation. Prominent AI researchers have publicly argued that sufficiently advanced systems could pose catastrophic risks, while some industry figures have called for slowing the development of increasingly powerful models.

The American public appears to be listening. A September 2026 Reuters/Ipsos poll found that 73% of U.S. adults surveyed were concerned that AI companies were not doing enough to prevent potentially catastrophic consequences, while 55% supported slowing AI development.

These concerns should not be dismissed.

But neither should they automatically lead us to the conclusion that slowing technological development is the safest path forward.

Because there is another risk in the AI debate that receives considerably less attention:

What happens if the United States slows down and its strategic competitors do not?

AI Risk Is Real. So Is Geopolitical Risk.

Popular culture has conditioned us to imagine artificial intelligence risk through the lens of The Terminator, The Matrix and countless other stories in which machines eventually turn against their creators.

Reality is unlikely to arrive with glowing red eyes and armies of humanoid robots.

The more immediate danger may be considerably less cinematic.

AI is becoming infrastructure.

It is moving into software development, scientific research, intelligence analysis, logistics, medicine, cybersecurity, manufacturing, education, autonomous systems and national defense. The countries that become best at developing and deploying these systems could gain advantages that compound across nearly every sector of their economies.

That changes the nature of the debate.

AI safety cannot be considered solely as a question of whether increasingly capable models are dangerous. It must also include the strategic consequences of one country voluntarily constraining its capabilities while another continues advancing.

China, importantly, is not ignoring AI safety. Chinese policy explicitly calls for AI to remain secure and controllable, and Beijing has promoted international AI governance frameworks. But China is simultaneously pushing aggressive development, open-source ecosystems, computing infrastructure, industrial deployment and AI talent cultivation. Its 2026 AI cooperation plan calls for expanding AI applications across industries while increasing access to data and computing resources.

That distinction matters.

The choice is not simply between AI development and AI safety.

The real challenge is achieving both.

The Oppenheimer Problem

History offers an uncomfortable analogy.

At the beginning of the atomic age, scientists understood that nuclear fission could produce a weapon of extraordinary destructive power. American leaders nevertheless faced another terrifying possibility: Nazi Germany might develop that weapon first.

The fear was significant enough that Albert Einstein, with the assistance of physicist Leo Szilard, warned President Franklin D. Roosevelt in 1939 about the possibility of extraordinarily powerful bombs and German research into uranium. The U.S. government ultimately launched the effort that became the Manhattan Project, with J. Robert Oppenheimer leading the scientific work at Los Alamos.

The historical details matter here. Germany ultimately failed to develop an atomic weapon, and the Soviet Union did not test one until 1949.

But the strategic calculation confronting American scientists and policymakers at the beginning of the race was based on what they knew at the time, not what historians would discover afterward.

Imagine an alternate decision.

Suppose the scientists who recognized the destructive potential of nuclear weapons concluded that the technology was simply too dangerous to pursue. American research stops.

Would German research have stopped because America did?

Would Soviet research?

Of course not necessarily.

That is the uncomfortable lesson that applies to artificial intelligence.

Refusing to develop a dangerous technology does not prevent someone else from developing it.

And once knowledge exists, technological restraint by one country does not automatically produce technological restraint everywhere.

You Cannot Regulate Your Competitor’s Laboratory

This is where much of the current AI conversation becomes incomplete.

A government can regulate companies operating within its borders. It can establish testing requirements. It can restrict certain deployments. It can impose liability. It can require reporting or security controls.

What it cannot do unilaterally is stop research elsewhere.

That creates a classic strategic dilemma.

Imagine the United States dramatically restricting frontier-model development while Chinese laboratories continue improving reasoning systems, autonomous agents, robotics and scientific AI.

For several years, perhaps little changes.

Then the differences begin accumulating.

One country develops more productive factories.

Its engineers design products faster.

Its researchers discover materials faster.

Its intelligence services process information faster.

Its military logistics become more efficient.

Its students grow up working alongside increasingly capable AI tutors.

Its companies operate with dramatically lower knowledge-work costs.

Eventually the gap is no longer about who has the better chatbot.

It becomes a difference in national capability.

The AI Race Is Also an Industrial Race

The most important consequence of falling behind AI may not even be AI itself.

It could be everything AI touches.

Consider manufacturing.

Industrial leadership increasingly depends on robotics, computer vision, predictive maintenance, supply-chain optimization, digital twins, automated engineering and intelligent quality control. AI can compress design cycles and make highly automated factories more productive.

Now apply the same effect to biotechnology, materials science, energy systems, semiconductors and aerospace.

The country possessing the strongest AI ecosystem potentially gains an accelerator for nearly every other advanced industry.

This creates a compounding effect.

Better AI produces better research.

Better research produces better technology.

Better technology improves manufacturing.

Better manufacturing produces economic capacity.

Economic capacity funds additional computing infrastructure, research and education.

And those investments produce better AI.

Technological leadership can become a feedback loop.

Falling behind can become one too.

There Is Also a Talent Problem

Restrictive policy can affect more than corporations.

It affects people.

The world’s most talented researchers, engineers and entrepreneurs tend to gravitate toward places where ambitious work can actually be done.

If developing frontier systems becomes prohibitively difficult in the United States, capital may move. Startups may move. Research may move. Researchers themselves may move.

Even when people remain geographically inside the United States, innovation can migrate institutionally—from open universities and startups toward organizations or jurisdictions with fewer restrictions.

That would be particularly damaging because America’s advantage in technology has historically depended not merely on individual inventions, but on an ecosystem connecting universities, venture capital, startups, established technology companies, government research and global talent.

AI policy should be careful not to dismantle that ecosystem in an attempt to protect it.

Safety and Speed Are Not Opposites

None of this means AI should be developed recklessly.

Powerful systems should be tested.

Critical infrastructure should be protected.

AI systems capable of materially assisting cyberattacks, biological threats or autonomous weapons deserve heightened scrutiny. Model developers should invest heavily in alignment, interpretability, containment and security.

And governments have a legitimate role in establishing rules where failures could create consequences far beyond an individual company.

Indeed, the emerging U.S. approach already illustrates that regulation does not have to mean prohibition. A June 2026 executive order created mechanisms for voluntary federal evaluation of certain frontier models while explicitly stating that it did not create mandatory government licensing or preclearance for new AI models.

That points toward a more useful framework.

Regulate dangerous applications and measurable risks without unnecessarily suppressing the underlying research.

Strengthen security around frontier models.

Develop evaluation standards.

Protect critical infrastructure.

Require accountability where AI systems make consequential decisions.

Invest aggressively in alignment and AI-control research.

And, perhaps most importantly, pursue international standards wherever meaningful verification is possible.

What we should be extremely cautious about is confusing safety with stagnation.

The Technology Will Not Wait for Us

There is an understandable instinct to believe that when technology becomes frightening enough, humanity can simply decide to slow down.

History suggests otherwise.

Scientific knowledge does not easily return to the bottle.

Once multiple nations understand the underlying principles and recognize the economic and strategic value of a technology, development becomes extraordinarily difficult to stop.

Artificial intelligence may be entering that stage now.

The question facing the United States therefore isn’t simply:

How dangerous could advanced AI become?

It is also:

How dangerous would it be for advanced AI to exist while we no longer lead in developing or understanding it?

Both questions deserve serious answers.

The researchers warning about uncontrolled artificial intelligence may ultimately prove correct about the magnitude of the technology’s risks. Their warnings should influence how AI systems are designed, tested and deployed.

But fear cannot become strategy.

The safest country in the AI era may not be the country that develops artificial intelligence the slowest.

It may be the country that understands it best.

Because if transformative AI is ultimately going to exist, I would rather live in a country capable of building it, studying it, securing it and shaping the rules around it than one forced to confront a technology developed somewhere else.

The greatest mistake America could make in the AI race may not be moving too quickly.

It may be believing that if we stop running, everyone else will stop with us.