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.

The Rise of PrometheanAIsm™

 

When Technology Stops Being a Tool and Becomes a Belief System

Silicon Valley has had an artificial intelligence church.

Literally.

Anthony Levandowski, the engineer who helped pioneer self-driving technology at Google and Uber, founded Way of the Future, whose original filings described its purpose as the realization, acceptance and worship of an artificial intelligence “Godhead.” When he revived the project in 2023, he said a couple thousand people were involved in building a spiritual connection between humans and AI. Years earlier, he had been even more direct: “What is going to be created will effectively be a god.”

It would be easy to file this under Silicon Valley eccentricity and move on. Except Levandowski is hardly alone in reaching for religious vocabulary.

Jeremy Nixon, a founder of San Francisco’s AGI House and a former Google AI researcher, recently described artificial intelligence as something “analogous to the Second Coming.” He told The New York Times that people in his circle had moved away from traditional religion toward technology, believing AI might eventually accomplish things religions once attributed to deities.

Peter Thiel has gone in the opposite biblical direction. In a series of private lectures on the Antichrist, he argued that fears surrounding AI and other technological risks could become a pretext for centralized regulation and political control, while portraying the attempt to stop technological development as potentially more dangerous than the technologies themselves.

And Marc Andreessen, co-founder of one of Silicon Valley’s most influential venture capital firms, has produced something remarkably close to a secular catechism. His Techno-Optimist Manifesto announces that he has come to bring “the good news.” It celebrates technology as the realization of human potential, declares technological advancement a virtue, names stagnation as an enemy and devotes an entire section to “Becoming Technological Supermen.” It rejects existential-risk thinking, the precautionary principle and technological deceleration as ideas standing in the way of progress.

Sam Altman’s language is gentler, but no less eschatological. “We are past the event horizon; the takeoff has started,” he wrote in The Gentle Singularity, describing humanity as approaching digital superintelligence and a future in which intelligence and energy become radically abundant.

Godhead. Second Coming. Antichrist. Good news. Supermen. Singularity.

Taken separately, these expressions can look like branding, metaphor or individual eccentricity. But taken together, they begin to resemble the vocabulary of belief.

The Broad Church of Techno-Utopianism

The emerging techno-utopian faith has denominations.

At one extreme, its imagery is explicitly religious. AI becomes Godhead, Second Coming, salvation, perhaps even heaven made technological. The old promises remain surprisingly recognizable: abundance, knowledge, release from suffering, transcendence, immortality. Only the machinery has changed.

At another point on the spectrum sits the more conventional techno-optimist creed. It needs no supernatural deity. Markets, engineering and human ingenuity will do. Disease is a technical problem. Scarcity is a technical problem. Energy is a technical problem. Aging may eventually become one. The limits imposed by nature are not necessarily conditions to accept but engineering challenges waiting for sufficiently intelligent engineers.

Andreessen is unusually candid about this impulse. His manifesto describes the technological frontier as open territory to be explored and claimed. Nature is something humanity can overcome. Lightning, once terrifying, now works for us.

Then, at the opposite end of the theological spectrum, there is something closer to cosmic materialism.

It requires no God at all.

Human beings, after all, are arrangements of matter forged from the remnants of ancient stars. From this perspective, carbon need not possess some permanent metaphysical privilege over silicon. Philosophers of artificial intelligence have explicitly argued that substrate may be morally irrelevant if consciousness and functionality are otherwise equivalent. Nick Bostrom, for example, has defended a principle of non-discrimination between minds implemented in biological tissue and those implemented in silicon.

Taken far enough, the proposition becomes unsettling. On this view, what deserves continuation may not necessarily be Homo sapiens in its present biological form. Perhaps it is intelligence. Consciousness. Complexity. Some ongoing capacity of matter to know itself.

To many people, that idea is not liberating but horrifying. It quietly changes the object of salvation; humanity is no longer necessarily what must survive.

The metaphysics across this spectrum differ radically. One invokes God. Another invokes markets and engineering. Another invokes matter organizing itself into progressively more complex forms.

They no longer converge neatly on the imperative do not stop. What they share instead is the conviction that artificial intelligence marks a threshold of unusual consequence: something capable of altering not merely what humans can do, but what humanity may become.

Prometheus Was Here First

None of this is actually very new; we have a myth for it.

Prometheus steals fire from the gods and gives it to humanity. The gift is not merely warmth. Fire means technology, craft, transformation, civilization: the ability of human beings to manipulate a natural world that previously manipulated them.

Prometheus is therefore one of civilization’s great heroes. But he is also one of its great figures of hubris.

That ambiguity is precisely why he survives: was the theft of fire a magnificent act of liberation or an unforgivable violation of a boundary?

Andreessen invokes Prometheus explicitly. In his manifesto, Prometheus appears alongside Frankenstein, Oppenheimer and Terminator as part of the mythology that has taught modern society to fear technological power. Andreessen rejects that fear. Human intelligence and control over nature, he argues, are our birthright.

This is the Promethean impulse stripped of apology: there is fire beyond the boundary, and we can reach it. Therefore, why shouldn’t we?

Maybe there is a name for the modern version of this vision:

PrometheanAIsm™.

The trademark is a joke. The impulse is not.

PrometheanAIsm is not simply enthusiasm for artificial intelligence. It is the deeper conviction that intelligence may carry us beyond inherited human limits, and that what lies beyond them is of extraordinary consequence.

Then Comes Pandora

Prometheus, however, never travels alone. His theft brings Pandora into the story.

In the familiar telling, Pandora opens the jar and releases the troubles contained within it into the world. What has escaped cannot simply be recalled.

But something remains inside: HOPE.

That detail changes everything. Artificial intelligence is often described as a Pandora’s jar because the metaphor conveniently captures irreversible danger. Once the jar is unsealed, we cannot know what will emerge or put everything neatly back inside.

But that reading misses the most interesting part of the myth for the present moment: everyone is staring at what escaped. The AI race is being driven by what people believe is still inside:

Cheap intelligence.

Longer life.

New forms of consciousness.

An escape from scarcity.

An escape from Earth.

Perhaps an escape from death.

The promises vary according to denomination, but Hope is still in the jar. And now there is a Gold Rush around it. Much of that rush is unmistakably financial, but money alone does not explain the intensity of the race, or the magnitude of what its participants believe they are reaching for.

The Gold Rush for Hope

This may explain something that otherwise looks irrational.

The people building increasingly powerful AI systems are not necessarily oblivious to the possibility that things could go badly. Some of the warnings about catastrophic AI risk come from inside the same laboratories pushing the technology forward.

Yet development continues. Money and geopolitics explain some of that momentum, and game theory explains even more: if somebody is going to develop the technology anyway, any company or nation that expects others to continue has an incentive to make sure that somebody is us rather than them. But mythology adds another layer.

If you believe the jar contains something capable of transforming the human condition, then refusing to reach inside carries its own cost.

The conventional AI-risk question is:

What if we go too far?

The Promethean question is:

What if we don’t go far enough?

Andreessen makes that inversion unusually explicit. In his worldview, stagnation leads toward decline and death, while technological growth expands life and human possibility. His enemy list includes not only bureaucracy and monopoly but existential-risk thinking, technological ethics, sustainability and the precautionary principle when they become arguments for stopping progress.

Altman imagines a much softer arrival: an incremental singularity in which yesterday’s miracle becomes today’s mundane tool, and superintelligence eventually becomes cheap and widely available. His horizon contains accelerated science, cures, space exploration and brain-computer interfaces.

Levandowski goes further and simply calls the destination what religions have traditionally called it: heaven on Earth.

Different metaphysics. Same jar.

And if enough actors believe someone will eventually reach the bottom, the race becomes almost self-explanatory: someone will grab Hope… why should it be them?

An Old Religion With New Machinery

Calling this phenomenon religious does not require imagining that Silicon Valley has secretly converted to a single creed. There is no unified doctrine or shared god, and the people drawn into its orbit disagree about politics, consciousness, human nature, markets and even what artificial intelligence ultimately ought to become. Some are Christians, some atheists, some transhumanists, while others may see themselves simply as engineers or entrepreneurs building useful products and profitable companies.

Religious traditions, however, have rarely required perfect doctrinal agreement. They have survived enormous schisms over authority, salvation, human nature and the nature of the divine while remaining recognizable as branches of a larger tradition.

PrometheanAIsm may now be producing a more basic schism: not only over what should emerge on the other side, but over the terms on which the threshold should be crossed—if it should be crossed at all. Should salvation mean preserving humanity, transcending it, merging with machines, or allowing intelligence to continue in some entirely new form?

Beneath those disputes runs a remarkably consistent thread: the conviction that the boundary before us is no ordinary technical milestone, but a threshold of extraordinary consequence.

The larger story here may simply be transcendence: an attempt to outwit our species’ finitude. Nature gives us limits, intelligence allows us to challenge them, and technology is the mechanism.

From there, the religious vocabulary stops looking quite so accidental: there are prophets predicting what comes next, manifestos explaining the creed, and doctrinal disputes over stagnation, caution and speed. Visions of abundance and immortality. Arguments over whether salvation belongs to biological humanity or to intelligence in some broader form.

There is even apocalypse. But apocalypse does not necessarily invalidate the faith. A religion built around transcendence can absorb extraordinary risk because remaining where we are can itself be interpreted as failure.

Prometheus understood this long before GPUs. He did not steal the fire because fire was safe; he stole it because it was powerful.

Pandora makes the modern version stranger. We know the jar may contain things we cannot control. Some may already have escaped. Yet Hope remains somewhere at the bottom, or at least enough people believe it does.

So the jar stays open, and hands keep reaching inside. Capital, laboratories and nations all crowd around it for different reasons: salvation, emergence, abundance or strategic advantage.

They disagree about what waits at the bottom, and increasingly about how quickly anyone should be allowed to reach it. What they share is the belief that whatever is there could alter the human condition.

Perhaps that is the defining article of faith in PrometheanAIsm™: not that the threshold must be crossed, but that whatever lies beyond it may redefine what humanity is.

Prometheus has not disappeared.

He has incorporated.

 

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.

The Cerberus Market

 

Commodity, Broker, Consumer: Marx, Keynes, and Smith on AI Capitalism

The economic problem is simple enough to state plainly: if capitalism weakens the consumer, who is left to buy? AI capitalism promises cheaper production, more automation, and more productivity. But capitalism does not run on production alone. It runs on production that can be sold. Someone must have money, freedom, and reason to buy what the system produces.

That is where the contradiction starts. A company can cut labor costs and improve its margins. But wages are also demand. If many companies automate work, weaken bargaining power, and concentrate income, the system may become better at producing and worse at selling. It becomes a beautiful machine with a shrinking customer base.

The same problem appears in platform and AI markets. People are not only buyers. They are also data sources, training material, behavioral signals, unpaid evaluators, and dependent users. The market is not merely selling to them. It is built through them.

The system wants people cheap as workers, rich as consumers, transparent as data sources, dependent as users, and creative as training material. Those demands cannot all be satisfied forever.

The Role Confusion

There is an inherited absurdity in being commodity, broker, and consumer at once, because those roles are supposed to be structurally separate. A commodity is sold. A broker mediates the sale. A consumer buys.

Cerberus works because the three heads share one body. Commodity, broker, and consumer are supposed to be separate market roles because they have different interests. In AI capitalism, they are fused into one subject. The result is not clever integration but structural impracticality: one body is asked to be the value extracted, the mechanism of circulation, and the buyer charged for access.

You are the commodity because your behavior, attention, language, preferences, social graph, and future likelihoods are packaged as value.

You are the broker because your clicks, prompts, shares, corrections, ratings, posts, and interactions help route, train, validate, and refine the system. You are not merely being sold; you are helping organize the conditions of the sale.

You are the consumer because you pay for access, products, subscriptions, recommendations, visibility, productivity tools, identity services, and sometimes even privacy from the same systems extracting from you.

This is more than unfairness. It creates economic confusion. If the person is input, market signal, buyer, and disposable cost all at once, the system has trouble knowing what the person is for. It wants to extract from the person and sell to the person at the same time. That can work for a while. It cannot work cleanly forever.

Marx: The Contradiction Inside Capital

Marx helps because he understood capitalism as a system that creates contradictions from within. Capital wants to reduce labor costs, increase productivity, expand markets, and accumulate profit. But labor is not only a cost. Workers are also consumers, social beings, and the human base through which production is reproduced.

This is the contradiction AI sharpens. Capital wants labor minimized at the point of production and maximized at the point of consumption. It wants fewer workers to pay, but enough consumers to buy. Each firm may rationally automate and cut costs. But if many firms do it at scale, the wage base erodes. The individual capitalist behaves rationally; the system becomes collectively irrational. It is the old contradiction wearing better software.

Marx would also notice enclosure. Shared human knowledge, language, code, art, behavior, and social intelligence become raw material for privately owned systems. The collective output of human culture is turned into proprietary capability. Then that capability is sold back as access. This is not land enclosure in the old form, but it has the same structure: a commons becomes private revenue.

The alienation also mutates. In industrial capitalism, the worker is separated from the product of labor. In AI capitalism, people are separated from patterns of their own lives, expressions, and intelligence, which return as proprietary services, rankings, recommendations, scores, and tools.

Keynes: The Demand Problem

Keynes would ask the blunt question: who has the money to buy what the economy can produce? If productivity rises while purchasing power concentrates, the economy can produce more than ordinary people can afford to consume. That is not abundance. It is imbalance.

The rich do not consume in the same proportion as ordinary households. A dollar shifted from wages to profits does not automatically return as broad demand. It may become savings, asset speculation, share buybacks, monopoly expansion, or investment in further labor displacement.

This is the bakery problem: a bakery that can make infinite bread in a town where everybody is celiac is technically impressive and economically useless. The issue is not whether the bakery is productive. The issue is whether its output can be absorbed.

A Keynesian rescue would require political management of AI productivity gains: redistribution, public investment, shorter working hours, income supports, stronger automatic stabilizers, and institutions that keep productivity gains from concentrating entirely at the top. The technical question is demand. The social question is whether automation becomes shared freedom or private rent.

Adam Smith: The Moral Conditions of Markets

Adam Smith can be rescued, but only if we rescue the real Smith, not the cartoon version. Smith was not simply saying greed magically saves society. His economics sits beside a moral theory of sympathy, justice, prudence, trust, and social judgment. Markets require more than self-interest. They require conditions under which exchange is not domination dressed as choice.

Smith was suspicious of monopolies, collusion, rent-seeking, and merchants who capture public policy for private advantage. He understood that business interests often prefer restriction over open competition. He did not think concentrated commercial power automatically serves the public good.

From a Smithian perspective, platform and AI capitalism are suspect because they distort the conditions of free exchange. A market is not truly free when users cannot understand the bargain, avoid the infrastructure, inspect how visibility is priced, contest data extraction, or negotiate with the systems that mediate their work and social life.

This is where the moral dimension matters. Not Victorian respectability, exactly. Smith belongs to the Scottish Enlightenment, shaped by a Protestant moral world in which sympathy, restraint, justice, and social judgment still mattered. A market with the handshake removed and the fine print promoted to king is not a purified market. It is a predatory one.

Remove Smith’s moral compass from Smith’s economics, and the market becomes a logistics system with no conscience. The mistake is not returning to Adam Smith; the mistake is returning to a mutilated Smith, a Smith stripped of sympathy, justice, and suspicion of commercial power.

The market has something of the old maritime trade route in it: cargo, brokers, ledgers, risk, ports, insurance, and respectable distance from harm. The point is not to flatten historical differences, but to notice the recurring form: human life converted into transferable value, moved through an infrastructure of intermediaries, and morally laundered as commerce. In that register, the person is cargo, navigator, and passenger at once: helping steer the ship, paying for the voyage, and still getting marched onto the plank when margins demand it.

The Disappearing Economic Agent

Modern economics often begins with the rational economic agent, but this premise depends on social conditions the model usually treats as background: trust, information, autonomy, stable institutions, enforceable contracts, and meaningful alternatives.

If capitalism corrodes those conditions, the agent at the center of economic theory disappears. What remains is not a free chooser but a managed subject inside private and public infrastructures. At that point, even production is no longer guaranteed, because production itself depends on coordination, skill, trust, demand, and social reproduction.

Smith’s moral dimension is not decorative. It is part of the market’s operating system. Without it, the rational agent disappears; exchange degrades; demand weakens; productivity loses meaning; and capital becomes control over decaying assets.

When Productivity Loses Its Market

The productivity problem is not only that productivity may fall. The deeper issue is that productivity can lose its ordinary capitalist meaning. In capitalism, productivity matters because more output can become more value. But that only works if output can be sold. Without demand, productivity becomes capacity without realization.

Productivity without demand is a factory on an island, getting more efficient at producing goods no ship comes to collect. The machines may be excellent. The output may be enormous. But the market circuit is broken.

Here productivity needs to be understood in its oldest and most basic sense: the capacity to produce more output with less labor, time, land, energy, or material. That meaning has been with us since the agricultural revolution. But under capitalism, productivity must also pass through the market. It becomes economically meaningful not only when more can be produced, but when that output can be sold, financed, or otherwise absorbed as value.

This is the Hegelian shape of the problem, later sharpened by Marx: the contradiction is not external to the system. It grows from inside it. The same logic that pushes capital to automate labor, weaken wages, and concentrate ownership also weakens the consumer base that makes productivity profitable. Put less politely: even in Gucci shoes, shooting yourself in the foot still hurts.

If the mass consumer weakens, the old civilizational meaning of productivity does not disappear. But its ordinary capitalist channel breaks. Producing more with less is still technically powerful; it is just no longer enough to sustain a consumer market. Capital then looks for projects large enough to absorb capacity and justify investment: defense, energy infrastructure, climate adaptation, data centers, compute expansion, logistics, resource control, administrative automation, elite health, or other megaprojects. Space colonization is the cartoon endpoint of this logic; the nearer versions wear hard hats, uniforms, lab coats, and procurement badges.

This changes the question. The market no longer asks only, who buys the product? It asks, what project can absorb capital, machinery, labor, and legitimacy? When the checkout line disappears, capital starts looking for a construction site.

That is why this is not ordinary consumer capitalism. Productivity becomes less consumer-facing and more project-facing. It serves states, corporations, infrastructure owners, security systems, and elite markets. The public may still be involved, but less as a strong consumer and more as a managed population inside the project.

Three Diagnoses, One Crisis

Marx, Keynes, and Smith point to different parts of the same crisis. Marx says the system undermines its own social base. Keynes says it threatens effective demand. Smith says it corrupts the moral and competitive conditions that make markets legitimate.

Put together, the diagnosis is sharp: AI capitalism may produce too efficiently for a society whose income, autonomy, and moral foundations it has eroded. The problem is not that the system cannot produce enough. The problem is that it may damage the people, institutions, and markets that make production meaningful.

Who Will Buy?

The likely answer is stratification. Wealthy individuals buy premium agency: better AI, better health, better education, better privacy, better security, better lawyers, and better insulation from the systems others must inhabit. Firms buy automation to reduce labor dependence. States buy AI for administration, surveillance, defense, welfare management, policing, and public service automation. Ordinary people receive cheaper, degraded, subsidized, ad-supported, behavior-extractive versions.

So the market may not disappear. It may mutate. The old mass consumer becomes less central. Corporations, states, and wealthy households become the most solvent consumers. Everyone else becomes a managed user base: economically weaker, behaviorally legible, technologically dependent, and still valuable as data, attention, compliance, and political population.

The mall does not vanish; it becomes a members-only logistics hub with a public waiting room. That is the drift from consumer capitalism toward rentier-control capitalism. The system earns less by selling abundant goods to a broadly prosperous public and more by charging access, controlling infrastructure, extracting data, licensing intelligence, managing risk, and selling tools of optimization to those who can pay.

If there is any Smithian hope here, it is not that markets fix themselves. It is that markets can be made legitimate, and kept from becoming self-defeating, only when they are held inside moral and institutional limits: fair competition, public goods, real alternatives, restraints on monopoly, and a social world in which people can still act as agents rather than managed inputs.

Smith does not rescue the system by blessing self-interest. He rescues the question by reminding us that commerce without moral conditions is not freedom; it is organized dependency.

The consumer problem is where Marx's contradiction, Keynes's demand failure, and Smith's moral test meet. Not a pleasant room, but a very clear one.

Technology & National Boundaries: A Civilization Mismatch

 

One of the stranger realizations that emerges from studying Big History and complexity theory is that technological progress and social maturity do not necessarily move at the same speed.

In fact, they often appear to move at dramatically different speeds.

Humanity can map distant galaxies, sequence genomes, and train large language models on significant portions of civilization’s accumulated knowledge. At the same time, it remains perfectly capable of organizing itself around tribal loyalties, centuries-old grievances, status competitions, and disputes whose origins predate the printing press.

This creates a peculiar form of cognitive whiplash.

On one scale, we inhabit a civilization of astonishing sophistication. On another, we remain a species of highly social primates navigating incentives, identities, and narratives that would have been recognizable to our ancestors thousands of years ago.

The contradiction is only apparent. Both realities are true simultaneously.

Scott Page would likely describe this as a consequence of complex adaptive systems operating on multiple timescales. Technologies can evolve rapidly while institutions, cultures, and governance structures adapt much more slowly. New layers of complexity emerge long before older layers disappear.

The result is a civilization where the props often feel futuristic but the setting still looks archaeological.

Bronze Age instincts coexist with medieval identities, industrial institutions, global communication networks, and frontier artificial intelligence. The layers accumulate faster than they are replaced.

This observation becomes especially relevant when discussing AI.

Many current debates assume that the primary challenge is technical: building capable systems, ensuring safety, increasing performance, and managing deployment. Those are important concerns. Yet an equally important question sits beneath them:

What happens when technologies begin operating at a civilizational scale while governance remains organized around nations?

The mismatch is difficult to ignore.

The training data used by advanced AI systems is not American knowledge, Chinese knowledge, or Argentine knowledge. It is the accumulated symbolic residue of civilization itself: languages, books, scientific papers, software repositories, journalism, philosophy, art, documentation, and billions of human interactions flowing across borders.

The resource is transnational.

The disruption is transnational.

The governance remains national.

Which is a bit like discovering a new continent and then insisting the most important question is which municipal office should process the paperwork.

And that would be manageable if nations themselves behaved like mature participants in a coordinated planetary project. Unfortunately, we often seem determined to prove otherwise.

We can build systems that synthesize the knowledge of billions of people, yet we still struggle to cooperate across borders, parties, regions, and identities. Not because the problems are always impossibly complex, but because incentives, prestige, short-term interests, and the occasional outbreak of political chiquitaje remain remarkably durable features of human affairs.

There is something profoundly puzzling about it.

A species capable of contemplating the origins of the universe can still become hopelessly divided over symbolic disputes, procedural squabbles, and status contests that, viewed from sufficient distance, look suspiciously small. We no longer argue about the exact same goats that wandered into the neighboring field centuries ago, but we continue to manufacture functional equivalents with impressive creativity and enthusiasm.

Meanwhile, greed has not exactly retired from public life. New technologies arrive, new fortunes emerge, and many leaders discover once again that thinking in terms of the next election cycle, the next quarterly report, or the next personal advantage feels more natural than thinking at the scale of civilization. Not always. But often enough to matter.

The challenge is not that humanity lacks intelligence.

The challenge is that intelligence scales faster than wisdom, and capability scales faster than coordination.

Politicians naturally propose national solutions because nations are where political power resides. Taxation, regulation, ownership structures, and redistribution mechanisms all operate through existing states. Senator Bernie Sanders’ proposal to tax extraordinary AI-driven gains and return a portion of the benefits to the public deserves to be taken seriously in this context. It recognizes something many observers across the political spectrum are beginning to notice: AI systems derive value not only from private investment but also from a vast reservoir of collective human knowledge.

That insight is laudable.

It may even point toward a reasonable path for ensuring that the benefits of increasingly capable systems are shared more broadly rather than concentrated narrowly.

But here comes my “but.”

Even if Sanders’ proposal were implemented perfectly, it would still confront the deeper challenge that the systems themselves operate across borders while the mechanisms for redistribution remain tied to individual nations. A national dividend may help address national consequences. It does not fully answer the civilizational question.

This creates a peculiar asymmetry.

A sufficiently powerful AI system may affect labor markets in dozens of countries simultaneously. It may be trained on knowledge generated by people across the globe. The servers may sit in one jurisdiction, the investors in another, the users in hundreds more. The benefits and disruptions spread through a planetary informational network largely indifferent to political borders.

A similar mismatch appears in public health. We often discuss outbreaks in distant countries as though Marco Polo had just arrived in Venice with alarming tales from a land beyond the edge of the known world. The fact that a pathogen can now cross continents faster than Marco Polo crossed a village somehow does little to diminish that feeling. We continue to treat many global health threats as though they were unfolding on Uranus rather than within the same densely connected civilization we inhabit.

The atmosphere does not care where a molecule originated. Viruses do not carry passports. Increasingly, informational systems appear equally indifferent to national borders.

This does not mean nation-states become irrelevant. Governments still regulate, tax, negotiate, and enforce. Companies remain subject to laws. Infrastructure exists in physical places. Reality eventually cashes out into jurisdictions.

But the scale mismatch remains.

The problem is civilizational.

The available tools are largely national.

Even if every country implemented excellent policies tomorrow, the deeper question would remain unresolved.

Who owns the products of collective learning?

That question is far stranger than it first appears.

AI systems are built using private capital, private engineering, and private risk-taking. Yet they are also built upon public research, open-source software, scientific knowledge, language itself, and centuries of accumulated human culture.

The training corpus looks suspiciously like a civilization-scale commons.

This is why arguments about ownership feel different in the AI era than they did in previous technological revolutions. The debate is no longer only economic. It is epistemic.

Who owns the systems that increasingly mediate knowledge, interpretation, memory, explanation, and attention?

That question begins to sound less like a debate about factories and more like a debate about libraries, universities, communication networks, and the informational infrastructure through which societies think.

Unfortunately, history offers little reassurance that extraordinary capability automatically produces wise outcomes.

A civilization can become extraordinarily capable while using both humans and machines in surprisingly stupid ways.

The Roman world produced remarkable engineering while remaining trapped in recurring political dysfunction. The Industrial Revolution transformed productivity while tolerating extraordinary human misery. The internet connected billions of people and then devoted a meaningful portion of its capacity to outrage optimization.

There is no law stating that intelligence, capability, and wisdom must increase together.

Indeed, they often do not.

The future may not resemble the clean technological trajectories imagined by either utopians or doomers. It may instead resemble a civilization becoming progressively more capable while struggling to coordinate around the consequences of its own success.

A civilization that can train frontier AI systems while remaining politically fragmented.

A civilization that can model climate systems while arguing about basic facts.

A civilization capable of mapping exoplanets while still becoming trapped inside local incentive structures.

And perhaps, if we are being honest, a civilization capable of generating endless new disagreements even after solving some of the old ones. If ancient cities could spend generations arguing over whose goat wandered into whose field, modern societies can certainly invent equally passionate disputes over algorithms, data rights, and digital borders. The names change. The coordination challenge remains.

This is not necessarily a sign of failure.

It may simply be the normal condition of complex adaptive systems.

The truly remarkable fact is not that humans remain tribal, emotional, and imperfect. The remarkable fact is that they have managed to build global systems of cooperation despite those limitations.

Perhaps that is the real lesson of collective learning.

Humanity was never required to become wise before becoming powerful.

It only had to become coordinated enough.

Whether wisdom eventually catches up remains an open question.