Prohibition, Monopoly, and the AI Wild West

Artificial intelligence is often described as a Wild West.

Usually, this is meant as shorthand for lawlessness: rules lag behind reality, somebody is shooting somebody at any given street corner, fortunes appear overnight, speculators arrive alongside serious pioneers, and institutions struggle to keep up.

Fair enough. But the Wild West was not only lawlessness. It was also a frontier.

And before congratulating ourselves for ending the Wild West, it is worth remembering what frontiers actually do.

They are messy precisely because no single institution controls the process of discovery. Thousands of people are experimenting at once, producing everything from spectacular failures and outright exploitation to unexpected solutions, new settlements, new connections and entire industries nobody had planned in advance.

The American West was violent, extractive and profoundly unjust in ways that should not be romanticized. It also unleashed enormous parallel experimentation.

Those two facts can coexist; the disorder was part of the danger, but it was also part of what made the frontier generative.

That distinction matters when we use the Wild West as a metaphor for artificial intelligence.

Today’s AI ecosystem is undeniably chaotic. Companies are racing to establish themselves. Capital is pouring into the sector at something like Gold Rush 2.0 speed. The result is not an orderly process of development, but a volatile mix of genuine research, speculative investment, strategic competition and hurried commercialization.

Researchers jump between laboratories. Open-source models compete with proprietary systems. Startups appear and disappear almost overnight. A new “LLM mine” is discovered 50 miles up the hill. Governments are trying to write rules for technologies that change before the rules can make it through a legislature.

There are cowboys, yes, but also snake-oil salesmen. There are probably a few people shooting at the saloon. But there are also thousands of independent experiments happening simultaneously.

A small research group can pursue an idea a large corporation has dismissed, while competing laboratories can challenge one another’s assumptions and expose weaknesses that might otherwise go unnoticed. Researchers can carry expertise from one institution to another, open systems can surface knowledge that closed ones would prefer to keep proprietary, and entirely new applications can emerge far from the organizations that developed the underlying technology.

The Seductive Simplicity of “Just Halt It”

Faced with the possibility that increasingly capable AI could eventually become dangerous at a civilizational scale, there is an understandable response: Stop! Pause development. Halt the race. Do not build systems more powerful than the ones we already have until we understand how to control them.

As an instinct, this makes sense. As a description of what would actually happen, I think it is naive.

You cannot put the genie back in the bottle.

Once knowledge exists, it cannot meaningfully be made unknown again.

The mathematics exists. The algorithms exist. The papers exist. Thousands of engineers understand techniques that were barely conceivable a generation ago.

Those people live in different countries and work for different companies. They publish, copy ideas, combine them, improve them and sometimes leak them.

Artificial intelligence is no longer a secret locked inside one laboratory. Put a roadblock in front of a tank, and someone will mount the technology on a bike and ride away with it. And its potential economic, scientific and military value is far too great for everyone, everywhere, to agree indefinitely not to pursue it.

You can prohibit public development—although governments themselves may have strong incentives to carve out exceptions—or regulate access to computing power, shut companies down, and make open development slower, more expensive and more dangerous.

What you cannot do is rebottle the genie. Someone, somewhere, will continue. And once we accept that, the policy question changes dramatically.

The question is no longer simply how to stop AI development; it becomes: who will still be developing it after everyone else has been persuaded, regulated or frightened into stopping?

We Have Tried Prohibition Before

This is not the first time societies have mistaken prohibition for disappearance.

The United States tried to prohibit alcohol, but alcohol did not vanish. People did not forget how to make it, and demand did not evaporate because Congress said it should. Instead, the structure of the market changed.

Legal production contracted as supply chains moved underground and enforcement became uneven, creating an extraordinarily valuable market for organizations willing to violate the law after legitimate businesses had been pushed out.

Prohibition did not merely suppress an activity; it selected for the actors willing and able to continue that activity illegally.

That is the relevant lesson for AI. Not that nothing should ever be regulated, or that dangerous technologies should be allowed to develop without limits.

The lesson is that prohibiting something while leaving the knowledge, demand and incentives intact can reorganize a problem into a form that is harder to see and potentially harder to control. It is ludicrous to think that solutions that do not work with products like alcohol will work for something as fundamental as knowledge.

Imagine that tomorrow the major American AI companies agree to stop frontier development and Europe follows suit. Open-source development above a certain capability level is prohibited, universities lose access to massive training infrastructure, smaller companies struggle to meet new security and compliance requirements, and public research slows sharply.

For a while, it might look as though the halt worked. But no one could know whether rival states, military or intelligence programs, wealthy private actors, or competing corporations were still pushing forward in secret.

That uncertainty is not incidental. It changes the game.

We could congratulate ourselves for closing the saloons while the distilleries move into the basement. And AI presents an even harder problem than Prohibition did.

Bootleggers were merely pursuing profit, but the actors developing advanced artificial intelligence may be pursuing economic dominance, military advantage, scientific leadership or national security. The incentives to defect are not incidental to the problem; they are built into it.

Which is why even a widely announced halt immediately raises the question that matters most: who believes everyone else has actually stopped?

The Game Theory of a Halt

The problem becomes clearer when nobody involved has to be evil.

Suppose every major AI laboratory sincerely believes that developing these systems too quickly is dangerous. Each laboratory still has to ask what happens if it stops and another does not.

The same applies to governments. The United States might genuinely prefer an enforceable international agreement restricting frontier AI development. But if American officials believe China might secretly continue, stopping American development becomes strategically dangerous.

China can make exactly the same calculation about the United States. Neither side has to want an AI arms race. Each only has to fear losing one. The better everyone else behaves, the greater the potential advantage for whoever defects.

That is the trap.

The stronger the prohibition, the more valuable successful evasion becomes. And if verification is imperfect, suspicion becomes rational.

A government wonders whether another government has hidden a program. A corporation wonders whether a competitor has obtained an exemption. A security agency wonders whether a foreign laboratory has crossed a threshold nobody else knows about. One secret breakthrough could change the balance of economic, military or political power.

Under those conditions, continued development is not some remote possibility that might occur despite a halt. It is what the incentive structure rewards.

The race does not disappear; it changes location, becoming quieter, less transparent and, crucially, accessible to fewer people.

From the Frontier to the Company Town

If the Wild West gives us a historical image of too little control, another part of American history offers a glimpse of the opposite problem: the company town.

Company towns grew around mines, mills, railroads and factories. In some of them, the corporation did not merely employ the worker. It also owned the worker’s housing, the local store and much of the infrastructure necessary for everyday life.

This could be efficient, even comfortable. The danger was not necessarily misery, but dependency.

The same institution paid your wages, rented you your home and sold you what you needed to live. The company was no longer one participant in your economic life; it had become the environment in which your economic life occurred.

That is worth thinking about before we decide that the obvious solution to the AI frontier is to make frontier development so expensive, restricted and regulated that only three or four corporations can participate.

At first, this could look wonderfully responsible: those companies would have sophisticated security departments, dedicated safety researchers, teams of lawyers, government relationships, enormous compliance budgets and secure computing facilities, while regulators would know exactly whom to audit.

The frontier would finally have sheriffs. But imagine what else those same companies might control…

Businesses, researchers, professionals and governments increasingly build their work around these models, while search, analysis and administrative systems become ever more dependent on the same underlying infrastructure. Eventually, the issue is no longer that three companies make the best AI. It is that an increasing portion of civilization thinks through three companies.

That is the AI company town.

Except the company does not own the mine, the workers’ houses and the general store. It owns the compute, the models and the intelligence layer connecting everything else.

The Cure Can Create Its Own Disease

This is what makes the AI problem much harder than the familiar argument between acceleration and restraint.

Competition creates risk by rewarding speed and first-mover advantage, sometimes pushing actors to release systems before their implications are fully understood. Yet competition also distributes power.

Regulation can reduce some of those risks by imposing safety requirements, external testing and liability for reckless behavior. But those protections come with costs, and once those costs become high enough, regulation can stop merely governing a market and start deciding who is allowed to exist within it.

The largest corporations can absorb billion-dollar compliance costs; universities, independent researchers, startups and open-source communities often cannot.

Soon the regulation that was designed to protect society from AI has also protected a handful of AI companies from competition.

A moratorium presents the same paradox. It can slow visible development, but the more strategically valuable the prohibited research becomes, the greater the incentive to conduct it somewhere nobody can see.

Open development makes powerful capabilities available to more people. Closed development concentrates them among the institutions powerful enough to close everyone else out.

There is no clean side of this equation. That is precisely why “just halt it” is not enough.

What If the Mess Is Part of the Safety System?

There is an uncomfortable possibility hidden inside all of this: the chaotic AI ecosystem we currently dislike may contain one of its own safeguards. Not because chaos is safe. It is not. But because distributed knowledge makes complete control difficult.

A plural ecosystem creates its own checks: companies can challenge one another’s claims, researchers can move between institutions, journalists can investigate, competitors can reproduce discoveries, and open-source communities can keep certain techniques from becoming the permanent intellectual property of a tiny number of corporations.

Competition creates dangerous incentives, but it also creates counterweights: the same fragmentation that makes artificial intelligence harder to govern may make it harder for anyone to govern society through artificial intelligence.

This does not mean the answer is laissez-faire.

The Wild West was not some libertarian paradise to which we should aspire. Frontiers eventually need laws, courts, standards and institutions capable of punishing fraud, protecting people from reckless behavior and deciding which activities impose unacceptable risks on everyone else.

The point is not to preserve lawlessness; it is to preserve plurality.

Safety rules are not the same thing as permanent concentration. Independent testing is not the same thing as limiting development to four approved corporations. Liability is not the same thing as creating a regulatory moat that only trillion-dollar companies can cross. International monitoring is not the same thing as pretending an international declaration has caused strategically valuable knowledge to cease existing.

We should regulate the frontier, but we should be very careful about accidentally transferring ownership of it.

The Real Choice

The debate over artificial intelligence is often presented as a choice between acceleration and restraint.

That may already be the wrong question. Once knowledge exists, it is extraordinarily difficult to contain, and AI research will continue somewhere. Someone will push the next experiment forward, accumulate more powerful capabilities, or decide that the strategic reward is worth violating whatever agreement everyone else has signed.

The meaningful question is therefore not whether artificial intelligence continues to develop; it is under what conditions, and in whose hands.

One possibility is an unruly frontier: competitive, dangerous, innovative, difficult to regulate and populated by many actors capable of challenging one another.

Another is a far more orderly world in which advanced artificial intelligence becomes the province of a tiny number of corporations and governments, operating systems so expensive, restricted and strategically valuable that meaningful competition becomes impossible.

The first looks frightening. The second might look reassuringly civilized. But history should make us suspicious of that reassurance.

We have spent centuries developing antitrust law, constitutional checks and balances, competitive markets, free inquiry and divided political authority for a reason. We learned that concentrated power does not become harmless simply because the people exercising it are competent.

Artificial intelligence should not cause us to forget that lesson precisely when the stakes become enormous.

The challenge is not to preserve the Wild West forever; it is to civilize the frontier without turning it into a company town. Because if the knowledge cannot be stopped, then a successful prohibition may not prevent the future we fear. It may simply determine who gets to own it.