The Receding Goal: AI, Development, and Class Divides

The artificial intelligence revolution does not simply divide people into optimists and pessimists. It divides those who have the conditions to use the future from those who have to survive it.

What makes AI so difficult to think about is not only its technical power, but the speed with which it arrives in deeply unequal societies. A tool can promise access, productivity, and augmented creativity; but that promise does not mean the same thing for someone with time, capital, education, and room for error as it does for someone who is indebted, precarious, or exposed to automatable work.

That is why both optimistic and pessimistic narratives about AI contain some truth. That is precisely the problem. Artificial intelligence is not simply salvation, and it is not simply catastrophe. It is a powerful technology entering a profoundly unequal world. For that reason, it is not distributed as a single experience. For some, AI appears as a tool of expansion. For others, as a new form of exposure.

The important question is not only whether someone is optimistic or pessimistic. The question is where they are looking from.

A person with capital, education, a professional network, free time, English fluency, economic stability, and room for error can experience artificial intelligence as a multiplier. They can experiment, learn, automate parts of their work, produce more, create businesses, access knowledge that was once unavailable, and turn technological speed into advantage. For that person, the future looks like a toolbox.

A person who is indebted, precarious, without job stability, short on time, without a safety net, and dependent on work vulnerable to automation may experience the same technology very differently. Not as a tool, but as a threat. Not as expansion, but as pressure. Not as an open future, but as yet another system arriving from above to reorganize their life without asking permission.

The optimistic narrative says artificial intelligence will democratize knowledge. And it might. There is something real in that promise: access to tools, translation, learning, augmented creativity, automation of tedious tasks, new forms of production. But for now, it also seems to be democratizing anxiety with admirable efficiency.

The problem is not only the technology. It is the speed of the technology inside a social system that distributes the capacity to adapt unequally.

Adaptation is not free. It requires time, money, education, rest, connection, equipment, language, stability, a professional network, mental health, and room to make mistakes. Exactly what not everyone has. That is why the phrase “just learn to use AI” sounds reasonable in the abstract and cruel in context. Learning a new tool is not the same when you have protected time and savings as when you are working two jobs, caring for children, paying rent, living paycheck to paycheck, and trying not to silently collapse, like someone updating internal software on 3% battery.

Here, a class divide emerges in the perception of the future. For the upper classes, AI is often a form of leverage: more scale, more efficiency, more investment, more automation, more capacity to turn previous resources into additional power. For professional sectors, AI is ambivalent: it can be assistant, accelerator, and threat all at once. For precarious workers, it often appears not as ChatGPT writing poems, but as scheduling algorithms, productivity surveillance, automated customer service, scoring, invisible dismissal, optimized delivery, remote management, and reduced bargaining power.

Artificial intelligence does not arrive only as “intelligence.” It arrives as infrastructure, property, platform, surveillance, capital, and control. The person who owns the infrastructure experiences it one way. The person measured by it experiences it another.

This difference in perception also occurs on a global scale. For decades, expressions like “developing countries” offered a temporal illusion: some countries were further ahead, others further behind, but everyone was supposedly moving toward the same destination. The phrase was paternalistic, but also reassuring. You have not arrived yet, but you are on your way.

Viewed from this new technological paradigm, that promise becomes more unsettling. The time to catch up with the center was never neutral. It was also the time during which the center kept accumulating capital, infrastructure, technology, intellectual property, data, platforms, and institutional power. The goal did not stand still. While some tried to industrialize, others captured the next phases: finance, software, cloud computing, chips, models, artificial intelligence, computational energy. The problem was not simply arriving late; it was discovering that the race was designed to produce lateness.

Before, we were told certain countries were “developing.” Now the promise sounds more like: you are in the process of updating the system, accepting cookies, learning Python, paying for the premium subscription, and not crying. We were sold the possibility of “catching up,” but no one clarified that the goal was not a fixed place: it was paying permanently to keep accessing the next version of the future.

This is one of the most difficult points to untangle: the digital revolution speaks the language of access, but it often reproduces the structure of dependency. A country can have AI users without having technological sovereignty. It can have platform consumers without owning data centers. It can have technical talent without controlling chips, energy, models, cloud infrastructure, capital, or intellectual property. It can “participate” in the future without capturing the main value of the future.

Every new technological wave arrives with the same promise: this time, everyone will have access. Then one reads the fine print and discovers that access requires chips, cheap energy, English, capital, cloud infrastructure, data, political stability, free time, and a spiritual calm no one included in the package. If Toffler spoke of waves, artificial intelligence is starting to look like a washing machine on spin cycle.

Alvin Toffler used the idea of a “third wave” to describe the transition toward a postindustrial and information-based society. The metaphor still works, but it falls short. What we are living through now does not look like one wave, but a surge of overlapping technological layers: the internet, platforms, smartphones, social networks, big data, cloud computing, automation, generative artificial intelligence, agents, robotics, perhaps AGI. Each new generation of models reopens the question of what counts as human skill, what counts as protected work, and what counts as a possible future.

The old industrial revolution transformed muscles, factories, transportation, and material production. The digital revolution transformed information, communication, and markets. The AI revolution is beginning to touch something even more intimate: language, knowledge, judgment, creativity, diagnosis, planning, translation, memory, administration, and decision-making. It does not automate only physical or repetitive tasks; it begins to automate fragments of what many people understood as their cognitive value.

That is why this wave produces so much confusion. It does not threaten only “manual” jobs, as a certain technocratic fantasy once promised. It also enters offices, universities, law firms, newsrooms, creative agencies, marketing departments, healthcare, education, programming, design, and consulting. Suddenly, the boundary between protected work and vulnerable work becomes less clear. The professional who once felt far from the factory discovers that they too can be broken down into tasks, measured, assisted, accelerated, partially replaced, or turned into the supervisor of systems that do in seconds what once justified years of credentials.

This does not mean that all human work will disappear. That prediction is usually too simple. What is more likely, at least in many areas, is not immediate total replacement, but restructuring: fewer people doing more, workers supervising tools, wages under pressure, tasks disaggregated, professions degraded, productivity captured by companies, and a growing demand to remain updated all the time. The future does not always arrive as a killer robot. Sometimes it arrives as a dashboard, mandatory training, and a “new opportunity for professional growth.” Terrifying, but with friendly branding.

This is where optimists and pessimists misunderstand each other. The optimist looks at the capabilities of the tool. The pessimist looks at the social conditions in which the tool will be deployed. One asks: “What can this technology do?” The other asks: “Who controls it, who pays the cost, and who captures the benefit?”

Both questions are necessary. Without the first, we fall into automatic rejection and lose sight of real possibilities. Without the second, we fall into naivete and confuse technical capability with human progress.

Artificial intelligence can help diagnose diseases, translate languages, personalize education, assist people with disabilities, accelerate scientific discoveries, reduce bureaucratic work, open creative possibilities, and give people access to powerful tools from which they were previously excluded. That is not minor. It should be said without embarrassment. Technological optimism is not always propaganda; sometimes it is the legitimate perception of a tool that really does expand capabilities.

But artificial intelligence can also concentrate wealth, displace workers, intensify surveillance, degrade wages, produce dependency, manipulate information, automate discrimination, extract data, erode privacy, and accelerate the obsolescence of skills before people have real time to adapt. Technological pessimism is not simply nostalgia either; often, it is historical memory. People remember that promises of efficiency rarely guarantee rest for those who work. More often, they guarantee more efficiency for whoever captures the surplus.

The question, then, is not whether AI will be good or bad. That question is too small. The question is: good for whom, under what conditions, with what protections, with what ownership, with what distribution of benefits, with what rights, with what time to adapt, and with what democratic capacity for decision-making?

Because technology does not arrive in a vacuum. It arrives in a world of unaffordable rent, unequal healthcare systems, debt-driven education, borders, monopolies, platforms, debt, precarious jobs, slow institutions, and ecological crisis. Saying “AI will increase productivity” without asking who captures that productivity is like announcing rain in a city where some people have roofs and others do not. Yes, water falls on everyone. No, it does not mean the same thing for everyone.

What produces unease is not only that the world is unjust. That, unfortunately, is not new. What overwhelms us is the speed. In the 1980s, the illusion that there was time could still survive: time to develop, to educate, to industrialize, to modernize, to catch up. Today, technological speed makes that promise feel fragile. The goal does not only move; it updates itself automatically.

Contemporary anxiety is born there: from the collision between technological acceleration and human lives that need time. Time to learn. Time to rest. Time to reorganize institutions. Time to protect workers. Time to legislate. Time to think. Time to understand what just happened before the next model makes the previous conversation feel old.

The future arrives faster, but not necessarily better distributed. It is like express delivery, except some people receive tools and others receive the invoice.

That is why the debate about AI needs less abstract fantasy and more material analysis. It is not enough to ask what the technology will be able to do. We have to ask what kind of society is receiving it. A powerful tool in an unequal system tends to amplify inequalities unless there are institutions capable of distributing its benefits and limiting its harms. Technology can open possibilities, but politics decides whether those possibilities become liberation, concentration, or discipline.

The challenge is not to choose between optimism and pessimism. The challenge is to understand what each position is seeing. Optimism sees capability. Pessimism sees power. Optimism sees a tool. Pessimism sees a structure. Optimism sees the future. Pessimism asks who has the material permission to live it.

A more honest reading would have to hold both things at once: AI may be one of the most extraordinary tools humanity has ever produced, and it may also deepen some of the oldest fractures of modern civilization. It can expand collective intelligence and also perfect systems of extraction. It can democratize access and concentrate control. It can help workers and also make them more replaceable. It can free time and also intensify the demand to produce more.

The contradiction is not only in the technology. It is in us, or more precisely, in the systems we have built to distribute power, time, risk, and benefit.

That is why the AI revolution does not simply divide humanity into optimists and pessimists. It divides those who have the conditions to use the future from those who have to survive it.

That is the plate of spaghetti we have to untangle. Public debate tends to mix everything together: fear of change, technical enthusiasm, corporate interests, labor anxiety, educational promises, geopolitics, science fiction, class resentment, marketing, investment, regulation, creativity, and existential panic. All of it together, with sauce and no fork.

But perhaps the main thread is this: artificial intelligence is not only a technological revolution. It is a test of distribution. It forces us to ask whether a society that already distributes housing, healthcare, time, education, and security badly will be able to distribute well a technology that multiplies cognitive capacities.

If the answer is no, pessimism is right.

If the answer can be built, optimism still has a task.

The real debate is not whether AI will change the world. It is already changing it. The debate is whether that change will be another round of concentration dressed up as progress, or a real opportunity to redistribute capacity, time, and dignity.

And that question cannot be answered by a model. It has to be answered by a society.

 

Meritocracy: A Slippery Eel in Olive Oil

On competence, inherited wealth, and the politics of deservedness

Meritocracy is one of the most cherished moral stories in highly individualistic societies. It offers a language of fairness, achievement, and earned reward. Like any enduring refrain, it contains enough truth to be persuasive. But like any sophism, it begins to unravel under rigorous examination.

The problem is not merit itself. The problem is that “merit” is a slippery eel in olive oil: every time one tries to pin it down, it reappears as competence, effort, credentials, market reward, virtue, or social approval.

Merit rhetoric operates across at least three distinct dimensions: as a competence standard, as a business measure, and as a theory of justice.

As a competence standard, the cleanest and most defensible meaning of merit is task-relevant competence: the ability to perform the task, solve the problem, or contribute meaningfully in a given domain. A society that abandons competence decays quickly. We should want doctors who can heal, engineers who can build, judges who can reason, teachers who can teach, and leaders who can actually lead. Standards matter. Skill matters. Performance matters. Nobody wants a pilot selected through vibes and institutional guilt.

As a business measure, it helps institutions decide who seems likely to perform well with the least training risk. That practical use is understandable: institutions make decisions under uncertainty, and they rely on signals. The obvious objection is that institutions cannot simply hand opportunities to the unproven. Must they hire, admit, or promote people with less trackable evidence in the name of fairness?

This is where opportunity enters the argument. Merit cannot be demonstrated, developed, or rewarded in a vacuum. A person cannot prove competence in a room they are never allowed to enter, or under standards they were never given a fair chance to understand. When people ask for broader access, they are not necessarily asking to be declared successful in advance. They are often asking for access to the arena where competence can be tested at all.

That is the asymmetry meritocratic rhetoric often hides. It also brings us to the third dimension: meritocracy as a theory of justice. If meritocracy is a skills-based or effort-based principle, then inherited wealth poses a serious problem. It grants opportunity, security, education, networks, and risk tolerance without requiring corresponding skill or effort from the recipient. One may defend inheritance on other grounds — family autonomy, property rights, emotional obligation, social continuity — but not on meritocratic grounds.

Once that is admitted, the discourse changes. The question is no longer whether society rewards merit in some vague sense. The question is why certain departures from merit are treated as natural while others are treated as scandalous.

Success derived from compound wealth, inherited networks, elite schooling, and family-backed risk tolerance is rarely subjected to the same suspicion as unproven potential from those without such scaffolding. The beneficiary of inherited advantage is treated as a promising investment; the outsider asking for a chance is treated as a deviation from fairness. One arrives with advantages already converted into credibility. The other is asked to produce credibility before being given the conditions in which credibility can be built.

This may sometimes be efficient from a business perspective. It is much harder to defend as a theory of justice. Once meritocracy presents itself not merely as a practical tool for predicting competence, but as a moral theory of deserved opportunity, it must explain why some forms of unearned advantage are treated as reasonable evidence while others are treated as contamination.

This is the point at which meritocracy, taken seriously as justice, indicts far more than its loudest defenders usually intend. If justice requires opportunity to track skill, effort, or earned contribution, then a system that allows opportunity to compound through ownership, inheritance, and capital is not merely imperfectly meritocratic. It is structurally non-meritocratic.

Inherited and compounding wealth do not simply give people more comfort. They create the conditions under which merit can be more easily developed, displayed, believed, and rewarded. Wealth becomes education. Wealth becomes time. Wealth becomes safety. Wealth becomes networks. Wealth becomes freedom to take risks. Wealth becomes the ability to fail without being destroyed. Later, the beneficiaries of these conditions appear in public as unusually talented, unusually confident, unusually prepared. The system then points to them and says: see, merit.

That is not proof of meritocracy. It is inherited advantage laundering itself as earned excellence.

A serious meritocracy would therefore require a very different social architecture from the one usually defended in its name. As a narrow competence principle, meritocracy can mean: choose the person who can do the work. As a business shortcut, it can mean: use imperfect signals to predict performance under uncertainty. But as a theory of justice, meritocracy must mean something much more demanding: build a society in which people have a fair chance to develop and demonstrate the capacities being rewarded.

That kind of meritocracy would not happen naturally in a deeply unequal society. It would have to be built. That does not mean abolishing standards; it means creating the conditions under which standards can measure ability rather than inherited advantage. Without those conditions, invoking merit becomes a tendentious rhetorical exercise, whether consciously or not: it rewards those already positioned to appear meritorious and asks everyone else to treat that appearance as proof.

This is also why debates over diversity and inclusion are really debates over opportunity. At their best, such efforts do not declare success in advance or replace competence with identity. They try to create access to the arena where competence can be developed, tested, and displayed. They intervene at the level of opportunity — the condition that allows merit to be developed, tested, and recognized.

That does not mean every diversity initiative is wise, fair, or effective. Some programs are shallow, performative, or badly designed. Some substitute optics for substance. Some allow institutions to look morally serious while avoiding the harder work of expanding opportunity at scale. The façade grows more elegant; the shacks behind it remain.

But the backlash against these efforts cannot be understood only as a defense of standards. It also reflects a zero-sum environment. In societies where stable jobs, affordable education, housing, healthcare, and mobility are scarce, every visible correction appears to come at someone else’s expense.

Without deeper structural repair, the system resembles a tailor cutting fabric from the pant legs to lengthen the sleeves: every small correction creates another exposure, while the people wearing better-fitted clothes insist the outfit proves their superior character.

For many working-class people, the reaction begins with a legitimate recognition: they have neither inherited wealth nor access to the visible corrective pathways designed for historically excluded groups. They are not protected by compound capital, family networks, legacy pipelines, or elite referral systems; but they also do not see themselves as beneficiaries of diversity-based institutional support. From that position, the question “Wait a minute — where do I fit in this theory of fairness?” is not irrational. It is a reasonable response to a system that asks them to compete under scarcity while presenting both inherited advantage and selective correction in the same moral language of merit.

The tragedy is that this legitimate complaint can be redirected toward the wrong target. Well-funded political and cultural narratives turn working-class frustration against other disadvantaged people competing for visible forms of access, rather than against inherited wealth, closed networks, legacy pipelines, referral brokers, and the quieter machinery through which opportunity is captured before most people ever arrive. The result is horizontal conflict among people fighting for entry, while the grievance itself often serves those who need the least help.

Those whose advantages arrived before the contest began keep their quiet protections; institutions advertise their moral corrections; everyone else is told to believe in merit and enter the Colosseum, where the lions are released, the arena is flooded, and the last person standing is praised as proof that the contest was fair.

This is why the conflict feels so poisonous. In lived experience, inherited advantage and diversity-based correction may appear to compound, but they do not operate symmetrically. Preexisting advantage shrinks the opportunity pool at scale. Institutional correction redistributes access within what remains. One is the architecture; the other is a disputed seating chart. Yet the seating chart is easier to rage against, because the architecture has been trained to look like common sense.

Meritocratic language is powerful because it speaks to different groups for different reasons. For those already protected by inherited advantage, it turns possession into evidence of deservingness. For those squeezed by scarcity, it preserves the hope that effort can still matter. But its political effect is often the same: it redirects attention from the architecture of opportunity to the moral character of individuals fighting inside it.

A culture that moralizes merit also produces a particular kind of stress. Competition is no longer only about securing work, education, or income; it becomes a referendum on personal worth. If recognition proves merit, then struggle begins to feel like evidence of deficiency. This pressure is especially acute for people whose career advancement is not their only urgent concern. They are asked to compete while also managing rent, debt, family obligations, unstable housing, health costs, transportation, and the daily logistics of survival. The result is a society where everyone is instructed to run their own race, while some are also handed a shovel and told the potholes are a personal growth opportunity.

 

The legitimate grievance behind meritocratic language is that institutions should not abandon standards. That concern deserves respect. Competence should matter. Effort should matter. Excellence should not be replaced by favoritism, symbolism, or ideological fashion. But the propaganda version of meritocracy says something else: that existing hierarchies are morally deserved, and that attempts to correct unequal opportunity are attacks on excellence.

This is the sleight of hand. It turns inequality from a political problem into a moral ranking.

A real meritocracy would not weaken standards. It would make standards more honest. It would ask whether we are measuring actual competence or merely rewarding the aesthetics of advantage. It would distinguish between lack of ability and lack of prior access. It would recognize that talent must be developed before it can be judged, and that many people never get the conditions required for their abilities to fully materialize.

The meritocratic ideal is worth saving, but only by refusing its most comforting lie: that those who rise highest necessarily had the most merit, and those who remain below simply had less. A serious meritocracy would not lower excellence. It would stop confusing inherited advantage with proof of it. Anything less is not meritocracy. It is hierarchy with an alibi.

The Rise of the Left-Libertarian

 

Why some liberals are becoming libertarian about power without becoming conservative about government.

Something is shifting inside liberal politics, but the old labels do a poor job of describing it.

My read does not come from a party platform or a neat ideological manifesto. It comes first from listening: watching the news, following social media, and paying attention to how people are arguing in real time. What stands out is that I keep hearing arguments that sound strangely libertarian coming from sectors where I would not normally expect them.

Not libertarian in the traditional economic sense. Not “abolish taxes,” “deregulate everything,” or “government is always the problem.” That is not the shift.

The shift is civil-libertarian: suspicion of centralized power, concern about surveillance, resistance to censorship, fear of digital control systems, distrust of public-private coordination, and a growing demand for privacy, autonomy, due process, and limits on authority.

What makes this interesting is where the language is coming from. Liberals worried about reproductive data. Progressives concerned about police surveillance. Younger Democrats skeptical of platform censorship and algorithmic control. People who still support public programs but do not want digital ID systems, age verification laws, or automated enforcement tools becoming access gates to ordinary life.

Social media is not proof by itself. We know it can make a handful of loud people look like a movement. But it is useful as a listening post. It shows what arguments are emerging before they harden into formal politics. And right now, the pattern is hard to miss: people who would never call themselves libertarian are making arguments about privacy, speech, surveillance, bodily autonomy, and institutional overreach that strongly resemble libertarian arguments.

The funny part is how strange some of the conversions look. I am seeing people with basically Trotskyist instincts drifting into prepper logic. The same people who once sounded like they wanted a central committee for every inconvenience are now talking about backup generators, cash on hand, water filters, canned peas, and enough instant ramen to survive six months of institutional failure. That is not a small vibe shift. That is an ideological witness protection program with a pantry.

Democrats are not becoming libertarians in the traditional sense. They are not suddenly abandoning healthcare access, labor protections, environmental rules, antitrust enforcement, or public investment. The old right-libertarian package still does not fit most of them. They are not becoming anti-government across the board.

But many are becoming more suspicious of control.

That suspicion is showing up across issues that used to be treated separately: digital privacy, reproductive data, platform censorship, police surveillance, AI scoring, banking access, age verification, biometric identity, emergency powers, and corporate control over speech and participation. Taken one by one, these look like isolated debates. Taken together, they suggest a new political temperament forming on the left.

Call it big-government, small-surveillance politics: a left-leaning instinct that still believes in public goods and regulation, but rejects the expanding machinery of tracking, censorship, digital identity, automated scoring, and permission-based access to ordinary life.

This type still supports public goods, but is less willing to trust institutions with systems that can monitor, restrict, rank, identify, or punish ordinary people. Government healthcare can make sense; medical surveillance cannot. Regulation can be necessary; bureaucratic black boxes are another matter. Safer online spaces may remain a goal; censorship tools create suspicion. Social programs still matter; digital identity systems that become access gates raise alarms. Corporate accountability remains central, but so does fear of banks, platforms, employers, and payment processors becoming private governments.

That is not traditional libertarianism. It is something more modern and messier.

The old libertarian model focused mainly on the state. The new anxiety is about networks of power: government agencies, tech platforms, banks, data brokers, universities, employers, NGOs, intelligence contractors, health systems, insurers, and AI vendors. No one has to declare tyranny for the system to become coercive. Control can arrive through compliance rules, risk scores, account suspensions, identity checks, automated flags, payment restrictions, and polite emails from departments with names like Trust and Safety.

This is why institutional trust matters so much. For years, many liberals accepted institutional power because they believed the institutions were mostly legitimate. That belief has weakened. People have watched public agencies reverse themselves, media institutions lose credibility, tech platforms moderate speech unevenly, universities moralize, employers police expression, and political leaders demand trust while offering little accountability.

When trust declines, people become less generous about granting new powers.

That is the hidden thread connecting many current debates. Digital ID is not just about identity. Age verification is not just about children. Platform moderation is not just about misinformation. Reproductive privacy is not just about healthcare. AI governance is not just about efficiency. Each issue raises the same underlying question:

Who gets to decide whether you can participate?

Once that question becomes visible, liberal politics changes.

Even Second Amendment language is beginning to cross strange boundaries. After the killing of Alex Pretti, a Minneapolis ICU nurse shot by federal agents, I noticed people who would normally reject gun-rights rhetoric making arguments that sounded almost Second Amendment-coded. Much of it began as a challenge to conservative consistency: if the right defends the Second Amendment, how can it justify a state response that treats lawful gun possession as an inherent threat?

But the discussion did not always stop at exposing hypocrisy. In some cases, it crossed into something more paradoxical: liberals known for opposing gun ownership began defending the logic of the right to bear arms, at least in that specific context. This was not old-school gun culture suddenly converting liberals into NRA lifers. It was distrust politics. In that frame, the right to bear arms stops looking like gun culture and starts looking more like the jailkeeper handing you the keys to the cage.

That is the deeper shift. People were watching a constitutional right become conditional in real time, and conditional rights have a way of making even institutionalists nervous. Once people start asking that question, they are already stepping outside the unwritten but mandatory liberal decalogue: the quiet list of positions one is expected to hold before being allowed to remain in good standing.

Reproductive politics may be the strongest accelerant. After the fall of Roe, privacy stopped being abstract for many women and liberals. Location data, period apps, pharmacy records, search history, payment trails, medical files, and travel records became part of the political battlefield. The lesson was simple: data collected for convenience can become evidence. A phone can become a witness. A platform can become a checkpoint.

That realization naturally pushes people toward stronger privacy instincts. Not as a niche tech issue, but as a civil-rights issue.

Speech is moving in a similar direction. Many liberals accepted aggressive content moderation when they saw it as a defense against extremism or dangerous misinformation. But censorship tools do not remain ideologically loyal. Once built, they can be expanded, privatized, redirected, or captured by a different administration. More liberals are likely to rediscover a very old civil-libertarian principle: powers created for emergencies rarely stay in their original box.

Corporate power also looks different now. The left has long criticized corporations for greed, inequality, and monopoly. But the newer concern is access. Banks, platforms, payment processors, app stores, employers, insurers, data brokers, and cloud providers can shape ordinary life without the constitutional restraints that apply to government. They can suspend accounts, throttle visibility, deny services, flag risk, sell data, and enforce norms at scale.

A private company can now do things that feel governmental, while still saying, technically, it is just business.

This is where a new liberal civil-libertarianism begins to make sense. It does not reject public power entirely. It rejects power without friction. Power without appeal. Power without transparency. Power without exit.

The likely platform of this emerging faction is not hard to imagine: strong privacy rights, limits on data brokers, encryption protections, medical data firewalls, police surveillance restrictions, due process before account or banking bans, transparency in AI decisions, protections for anonymous speech, and real alternatives to mandatory digital systems.

That platform would still sit on the left economically. It would support public investment, labor protections, healthcare access, antitrust enforcement, and consumer protections. But it would break sharply from establishment liberalism on trust. It would ask for proof, limits, audits, opt-outs, and enforceable rights before allowing institutions to build deeper systems of control.

This will create tension inside the Democratic coalition.

Institutional liberals will argue that modern problems require modern systems: digital infrastructure, expert administration, coordinated platforms, identity verification, content moderation, and automated enforcement. The left-libertarian answer will be that the more powerful the system, the stronger the restraints must be. They will not accept “trust us” as a governance model.

Both sides will claim to defend democracy. They will simply fear different failures. Institutional liberals will fear disorder. The left-libertarian faction will fear managed life.

That divide is likely to grow because the technology is not slowing down. Digital identity, AI scoring, biometric verification, financial surveillance, platform governance, and automated compliance are all expanding. Every expansion creates a new argument over convenience versus freedom, safety versus autonomy, inclusion versus control.

The old categories cannot contain this cleanly. Some liberals will still want more government in economic life and less government in personal life. Others will want regulation of corporations but strict limits on data collection. Some will favor public programs but reject centralized identity systems. Some will support safety rules but oppose speech enforcement.

The crossing is happening in the other direction too. Around AI, some conservatives and traditional libertarians are beginning to sound unexpectedly comfortable with state regulation. The same people who would normally flinch at government intervention now look at algorithmic control, synthetic media, job displacement, surveillance, and corporate AI power and ask whether the state has to step in. That is not a small contradiction. It shows that the old map is failing on both sides: liberals become more libertarian when public power turns into surveillance, while libertarians become more statist when private power starts looking like government.

To older political maps, that looks inconsistent.

It is not inconsistent.

It is a recognition that power has changed shape.

The next major ideological split may not be left versus right in the usual sense. It may be institutionalists versus big-government, small-surveillance politics. People who trust centralized systems versus people who believe those systems must be restrained before they become permanent.

A growing number of liberals are moving into that second camp. Quietly at first. Issue by issue. Privacy here. Speech there. Reproductive data. Police surveillance. AI scoring. Digital ID. Banking access. Platform control.

At some point, scattered instincts become a politics.

And when they do, the Democratic coalition may discover that its next internal rebellion does not come from the right. It comes from liberals who still believe in public goods, but no longer trust powerful institutions to define the terms of ordinary life.

Nativism Sells Like Hotcakes

 

The oldest fear in the human brain, served hot with fries.

Pete Hegseth went to Normandy, on the anniversary of D-Day, and spoke of European beaches being “stormed” again. Not by armies this time, but by “dangerous ideologies” arriving by sea. Boats. Men. Spain, Italy, Greece, Bulgaria. The setting was not accidental: military graves, Allied memory, Europe as a civilization once saved and now supposedly at risk again. The message came wearing a borrowed helmet from history.

Marco Rubio, in Munich, wrapped the same anxiety in a more diplomatic phrase: civilizational erasure. He did not say white erasure in those words, but the subtext was sitting in the front row, jingling its keys. Low birth rates, migration, loss of national identity, Europe ceasing to be Europe. The old fear in foreign-policy clothing.

You have to give this rhetoric one thing: it is efficient. It does not need to explain much. It does not need to prove everything it suggests. It only has to place three things close together — death, offspring, group — and let the brain do the rest.

The offer almost writes itself: a share of collective eternity. Amazon Prime Day for immortality. For ten cents, you get the promise that something of yours will continue: your blood, your people, your name, your civilization, your kind. Not just individual immortality. Collective immortality. The family-size combo.

The price is in the fine print, though not that fine: othering whoever ends up on the wrong side of the line. The other child. The other neighbor. The other citizen. The other body that ruins the family portrait of destiny.

It is not a hard sell. The emotional tinder is everywhere. Fear of death, fear of losing status, fear that your children will live in a world that feels foreign, fear that history has no owner. The firecrackers are ancient: blood, soil, women, children, borders, invasion, purity, honor, humiliation. The whole Paleolithic kit with a microphone and a foreign-policy panel. A half-damp match is enough.

The disturbing part is not that the trick is sophisticated. It is that it does not need to be.

When demography enters politics in an apocalyptic tone, it stops speaking only about births, migration, or integration. It starts speaking about disappearance. It tells people that if they do not control who enters, who is born, and who belongs, they will be erased. Not as individuals, but as a symbolic species. Your lineage. Your world. Your acceptable version of the future.

And then the conversation changes temperature. An immigration law stops being just a law. A birth-rate statistic stops being just a statistic. A neighbor stops being just a neighbor. Everything begins to carry an ugly electricity, as if every unfamiliar body had arrived to claim a piece of your grave.

That is when the product appears: a family-size combo against death. Fake, greasy, and wrapped in nostalgic packaging. It promises that something resembling you will keep occupying the world, and calls that continuity. It promises that the lineage can do what the body cannot: remain.

Ethnic purity, civilizational obsession, fertility turned into patriotic duty: all of it builds a blender with a halo. Reproduction, status, and anxiety packaged as destiny.

This is where identity enters the sale. Not as a private philosophical puzzle, but as the hinge that lets the product work. If the group is just a group, demographic change is political. If the group is you, demographic change becomes mortality.

Octavio Paz, in  The Labyrinth of Solitude, makes the first shock of self-consciousness briefly breathable. He writes of the adolescent startled by being, leaning over the river of his own consciousness and seeing a face distorted by the water, wondering whether it is his. That tremor is real. There is a moment when simply living is no longer enough: you also begin to watch yourself live. The reflection appears. Distance appears. The strangeness of being separated from yourself appears.

But nativism industrializes that tremor. It turns self-consciousness into bloodline panic. It takes the mirror and bolts it to a border fence.

The pitch does not work on everyone. For some, the question “who am I?” looks less like liberation than paperwork. The moment someone rehearses an answer in the mirror, the coherence audit begins.

But throughout history, most humans did not live that way. They lived through lineages, houses, tribes, religions, peoples, surnames, inheritances, borders, dead ancestors who still give orders, and futures to be administered. The idea of not seeking immortality through offspring, of not feeling the group as an extension of the body, of not needing “one’s own” to survive in order for one’s life to have mattered, is fairly rare. Modern, urban, individualist in the best sense. Also fragile.

That is why the product sells.

Because it does not present itself as a product. It presents itself as duty, memory, belonging, defense, love of children, respect for the dead. No one thinks they are buying fear. They think they are buying continuity.

And once someone is buying continuity, almost any price begins to look reasonable.

Even the other.

Especially the other.

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.

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.