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.