Right Is Smart

 

What if “might makes right” has it backwards, and right is simply smarter?

For those who regard ethical conduct as part of a life well lived, the argument that follows may be almost beside the point. If doing the right thing is already its own reason, there is little need to demonstrate a return on the investment.

There is no need, in any case, to turn virtue into another asset class. Returning a lost wallet does not reliably improve a retirement portfolio, and holding the elevator for a stranger is unlikely to produce a measurable return on capital. Plenty of decent acts cost something and give nothing tangible back. That is precisely why they retain moral meaning.

But there is another, much more familiar assumption worth examining: that selfishness is simply better business.

The idea is old enough to have become proverbial. If you want to get ahead, climb the stairs by stepping on heads. Be harder, take more, concede less, extract the maximum, and do not spend too much time worrying about who absorbs the cost. The world may disapprove, but the balance sheet will understand.

There is an obvious truth buried in that picture. Ruthlessness can be profitable. Exploitation can produce margins. Monopoly can be lucrative. Paying less and charging more can improve a quarter very quickly.

But profitable behavior comes in many forms. Some gains come from extraction. Others come from cooperation, trust, specialization, reciprocity, and the creation of value that no participant could have produced alone.

The more interesting question is whether, among the full array of profitable strategies available to us, the collaborative ones may often be the more powerful.

If so, ruthlessness may not represent superior self-interest at all. It may simply be self-interest that never ventures beyond the shallows.

The Oldest Productivity Technology

Ant colonies and bee colonies can achieve levels of organization and productive specialization that would be impossible for their members acting independently. There is nothing moral about this. The ant is not practicing civic virtue. The bee has not read Adam Smith. The point is simpler: organized collaboration can produce capabilities that individual organisms do not possess.

Cooperation, then, predates economics by a very long time. Humans simply took the principle somewhere else. Our species did not become successful merely because individual humans became clever. We became unusually good at coordinating, teaching, sharing information, dividing tasks, exchanging, punishing free riders, and accumulating knowledge beyond the lifespan of any single person.

For most of our history, exclusion from that cooperative network was no minor inconvenience. Ostracism could be catastrophic. The person who took too much, betrayed too often, or became intolerable to live with did not merely acquire a bad reputation. He risked losing access to the productive system that kept him alive.

In that world, cooperation was not the moral alternative to self-interest; cooperation was how self-interest worked.

When the Feedback Gets Delayed

Accumulated wealth changes this ancient relationship in an important way because it delays the consequences of extraction. The hunter who alienates the band is likely to discover his mistake quickly; the billionaire, insulated by wealth from many of the people on whom that wealth ultimately depends, can spend decades mistaking the alienation of employees, suppliers, customers, competitors, communities, or even entire countries for evidence of business acumen.

The feedback has been delayed.

This is one of the peculiar effects of accumulation. It allows a person to convert yesterday’s cooperation into an asset that can temporarily reduce his dependence on today’s collaborators. And that can produce an illusion: the fortune appears to be an individual possession, and legally it is. But economically it remains a collaborative artifact.

Behind a large fortune stands a dense web of cooperation: people who work, people who buy, institutions that make contracts meaningful, generations of accumulated knowledge, and a level of trust and stability sufficient for exchange to keep functioning. Wealth may sit in one person’s name, but the conditions that make it possible are widely shared.

One can own the result individually, but one cannot individually manufacture all of its preconditions.

This is where Adam Smith remains remarkably powerful.

Smith’s great insight was not that greed is good. It was that self-interest can be structured so that serving one’s own interests requires serving somebody else’s. The baker does not need to love you; he only needs to value your business enough to bake bread you are willing to buy.

Competition turns self-interest into a form of cooperation. Specialization allows one person to become extraordinarily good at one thing while relying on thousands of strangers for nearly everything else. Exchange connects these islands of specialization, producing gains that cannot be reduced to the genius of any one participant but emerge from collaboration itself. The system is impressive precisely because it does not require sainthood. But the fact that cooperation can emerge from self-interest does not mean that every form of self-interest is equally capable of sustaining the conditions on which it depends.

Productivity’s Strange Success

This is where Karl Marx becomes more interesting than some of the theoretical machinery he built around his insights.

One does not need to accept every step of the labor theory of value to notice something peculiar: productivity means obtaining the same output with fewer inputs, more output from the same inputs, or, in the ideal case, more output from fewer inputs.

When labor is one of those inputs, increased labor productivity necessarily means that less labor is required per unit of output.

That is not a flaw. It is part of the system’s very logic.

A civilization capable of producing the same food, clothing, housing, transportation, information, and entertainment with fewer hours of compulsory human work has done something wonderful.

But now consider the position of the person whose primary commodity is his labor. The worker participates in improving processes: he learns shortcuts, corrects mistakes, develops methods, trains others, contributes knowledge, adopts better tools, and helps build the institutional memory that makes the organization more efficient.

Those improvements can remain long after the hours that created them are gone. His time cannot.

Human time is irrecoverable; productivity is scalable.

The worker therefore participates in a process whose success consists, at least partly, in reducing the future quantity of the very thing he has to sell. In this way, the worker helps produce the progressive obsolescence of his own product.

Historically, this tension has often been softened by economic expansion: productivity creates new industries, lowers costs, enlarges markets, and opens other forms of participation for displaced workers.

But there is an asymmetry here that deserves more attention: the pressure to economize inputs is built into the very definition of productivity; the mechanisms that give displaced humans some new economically valuable role are not built into anything. They are contingent.

There is no law requiring every labor-saving technology to generate an equivalent quantity of new labor demand. And there is certainly no automatic mechanism by which those who helped create a productive improvement acquire ownership of the productive capacity that replaces them.

Artificial intelligence makes the problem unusually visible: years of human language, judgment, creativity, and problem-solving can help build systems increasingly capable of reproducing portions of those same abilities with progressively less human time. In other words, finite human activity can be converted into productive capacity that can be reused and scaled far beyond the labor that created it.

Again, the problem is not automation. If productivity is the goal, automation is one of its most desirable milestones.

A System Can Succeed Against Itself

The contradiction is distinctly Hegelian: the system’s own success generates the conditions that destabilize it.

Capitalism rewards productivity; productivity economizes inputs; labor is an input. But labor income is also one of the principal mechanisms through which most people acquire claims on the fruits of capitalism. Push the two tendencies far enough and a productive triumph can begin producing a distributive problem.

Imagine an absurdly successful future economy. Twenty percent of humanity owns productive systems of staggering efficiency. Eighty percent has become economically marginal. The factories are magnificent. The algorithms are brilliant. Output per worker has reached levels our ancestors could not imagine. But there is only one annoying question:

Who buys the stuff?

There are only so many refrigerators one person, no matter how wealthy, can want; only so many cars, only so many houses. Even yachts eventually encounter a practical inconvenience: one human being cannot be on all of them at once, nor remain interested in acquiring them beyond a certain point.

This is where John Maynard Keynes enters the story: as people become wealthier, a smaller share of additional income tends to become additional consumption. The first thousand dollars above subsistence may quickly be absorbed by ordinary needs and pleasures. The billionth dollar does not create proportionately more consumption; it becomes saving, investment, another claim on future production.

There is nothing wrong with saving. Productive investment is one of the engines of growth. But investment itself ultimately needs a reason: someone must plausibly buy the future output.

At a certain point, extreme concentration can therefore create a peculiar economy in which total wealth continues rising while the circulation that gives that wealth economic meaning weakens.

Production ultimately requires customers with purchasing power, and the same is true even when wealth is held as financial assets rather than spent directly. A stock portfolio is a claim on future corporate earnings, and those earnings still depend on revenues generated by somebody else’s expenditure.

Your 401(k) needs somebody else’s paycheck.

This is why distribution cannot be treated merely as the moral argument that begins after production has done the serious work; distribution is inside the productive mechanism. A population must retain enough capacity and purchasing power to participate meaningfully in the system that produces wealth.

Starve enough of the productive organism and the problem is no longer merely unfairness; the organism begins to malfunction. If ninety percent of a bee colony’s resources accumulated somewhere that contributed little to keeping the colony functional, we would not congratulate the hive on its extraordinary wealth concentration. We would say its allocation system had become dysfunctional.

Human beings seem to have more difficulty recognizing the same possibility in ourselves.

The Global Mismatch

The problem becomes stranger at the global level.

Within a wealthy country, technological displacement can at least theoretically be offset through institutions that redistribute ownership, income, or opportunity, including arrangements we have not yet invented.

The same political community experiencing the productivity gain possesses institutions capable of distributing some of it. But globally, that connection largely disappears.

Productivity shocks cross borders far more easily than redistribution does. A worker in a poorer country can lose the value of what she sells because somebody became more productive thousands of kilometers away; a service once profitably outsourced may become cheaper to automate, and an economy that relied partly on inexpensive labor can discover that technology has made inexpensive labor itself less valuable. The productivity gain may occur in California while the displacement is felt in Manila, Buenos Aires, Lagos, or Dhaka. Markets transmit the gain globally, but the mechanisms that distribute its benefits remain largely national.

There is no global 401(k).

This creates a civilizational mismatch. Technology, capital, competition, supply chains, and displacement increasingly operate across borders, while the institutions that tax, insure, represent, and redistribute remain overwhelmingly national.

The same productivity that enriches one part of the system can depreciate the principal asset available to another: human labor. Yet this creates an unexpected argument from self-interest. A greedy view of poorer countries sees cheap labor; an even more ambitious one sees future collaborators and future consumers.

Raising productivity and purchasing power elsewhere is not necessarily money sacrificed to somebody else’s prosperity. It can enlarge the market in which one’s own assets will operate.

The person whose retirement depends on decades of corporate growth should perhaps want billions of currently poor people to become much richer, not merely as beneficiaries but as future customers. Collaboration expands the pie twice: first by making production more efficient, and then by expanding the number of people capable of participating in the exchange that makes further production worthwhile.

Killing the Golden Goose

None of this demonstrates that righteousness always pays. Sometimes doing the right thing genuinely costs something. At times, conversely, one person can profit handsomely by harming everyone else and die comfortably before the bill arrives.

Nor does any of this prove that markets naturally generate virtuous results. But the narrower proposition is more interesting: perhaps we have a tendency to overestimate what greed can actually accomplish.

The temptation seems evident: greed is extraordinarily good at answering one question:

How much can I capture?

But economic life requires another:

What arrangement causes the greatest amount of value to exist in the first place, and what must remain intact for that value to continue existing?

Those are not the same question. And once the distinction is made, the boundary between moral restraint and a more refined understanding of self-interest becomes less obvious.

Again and again, what appears as moral restraint admits a second interpretation: it may be an intuitive way of preserving the system on which self-interest depends, before the logic of doing so is fully understood.

A moral prohibition can preserve something whose usefulness has not yet become obvious to the individual making the decision, restraining exploitation or domination in ways that keep enough of the game intact for others to continue wanting to play it.

Perhaps this is one reason moral codes persist even when we cannot fully explain them. We are not very good at calculating complex systems across generations; we tend instead to optimize what is visible and discount consequences several steps removed.

Such prohibitions can sound like instructions to sacrifice self-interest and, perhaps, sometimes they are. But sometimes morality may be society remembering something that individual calculation has forgotten.

Righteousness may be collective intelligence operating at a horizon longer than any one person naturally sees.

And then there is another possibility, one that requires no moral premise at all.

As our understanding deepens, some of the apparent distance between collective benefit and self-interest may disappear. Not because morality is secretly capitalism in disguise, and not because goodness guarantees profit, but because much of what creates wealth in the first place is collaborative, and self-interest itself has reason to preserve the conditions that make collaboration fruitful.

Greed is shallow self-interest. Righteousness is collective intelligence. Imagination may be the bridge between them, because the failure to see beyond extraction is often not realism at all, but a failure to imagine arrangements capable of creating more value than extraction alone can capture.

For the very ambitious, there may be a silver lining: there is always one stair higher to climb. Sometimes that stair is not taking a larger share of what already exists, but imagining a way for more to exist in the first place.

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.