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.

Career Advice Is Not a Social Contract: AI & the Redesign of Society

 

Had Melquíades, the wandering merchant from Gabriel García Márquez's mythical town of Macondo, arrived today, he might have abandoned magnets and alchemy altogether. Instead, he would likely travel from village to village promoting flexibility, adaptability, lifelong learning, and prompt engineering. Hanging from the side of his cart would be the Swiss Army knife of educational advice, ready to be unfolded whenever uncertainty appeared.

The villagers would remain astonished, though perhaps no better informed about the future.

This is not because flexibility is useless. It is not. Adaptability is a real virtue. Lifelong learning matters. Education should absolutely help people develop judgment, range, curiosity, and the ability to keep moving when the ground changes under them.

But every era has its miracle cure. We have had snake oil, patent medicines, management fads, motivational posters, and now an unusually cheerful faith in adaptability. Whatever the problem, the answer arrives wearing sensible shoes: be more flexible, learn continuously, reinvent yourself, develop a growth mindset, and perhaps take a short course on prompting. Somewhere, one suspects, there is a conference panel titled “Thriving in Uncertainty” being delivered with absolute certainty.

The joke is not on adaptability. The joke is on our tendency to treat it as a universal solvent.

The Limits of Personal Adaptation

Much of our educational and professional advice assumes that the structure itself will remain fundamentally recognizable. A recession? Adapt. A new technology? Adapt. A volatile labor market? Adapt. The advice is not wrong. People do need tools for change.

The question is whether tools are enough.

Flexibility has become the Swiss Army knife of educational advice. Whatever the challenge, someone eventually unfolds the adaptability attachment. It is a useful tool, and perhaps an essential one. The concern is that we may be mistaking a toolbox for a blueprint.

A toolbox can help you repair a door, tighten a loose screw, or patch a leak. It does not tell you what kind of house you are building. It does not answer whether the foundation is stable. It does not decide who gets to live inside.

That distinction matters because the current moment may not be asking only for individual adaptation. It may be asking for institutional redesign.

Painting During an Earthquake

I sometimes wonder whether our conversation about the future resembles a group of people discussing painting techniques while the building is experiencing a mild but unmistakable earthquake.

The discussion is not wrong. Painting matters. Technique matters. Maintenance matters. But at some point, the urgency of the moment lies elsewhere. The question is no longer what color to paint the walls. It is whether the foundations require redesign.

Cosmetic upgrades are useful when the structure is sound. The possibility we seem reluctant to discuss is that the structure itself may require reconstruction.

This is where many conversations about education begin to feel simultaneously correct and insufficient. Schools and universities are told to prepare students for jobs that may not exist yet, industries that may transform beyond recognition, and tools that may change faster than curriculum committees can meet. Under the circumstances, teaching adaptability is not irrational. It is probably necessary.

But it is still a bet. It is not a strategy.

I do not blame educators for this. They are facing a problem for which nobody has a fully satisfying answer. The issue is not incompetence. The issue is genuine uncertainty. When uncertainty becomes too large to map, institutions naturally retreat toward durable virtues: critical thinking, communication, adaptability, collaboration, continuous learning. These are good things. They are also incomplete things.

They describe how a person should respond to change. They do not describe what kind of society we are trying to build through change.

The Wrong Question

Part of the confusion comes from the way we talk about artificial intelligence. Questions about intelligence, creativity, consciousness, understanding, and human likeness are not trivial; they are among the most fascinating questions the technology raises. But they are not the only questions in front of us. And they may not be the questions whose consequences arrive first.

The labor market is not asking whether AI has a soul. It is asking whether AI can do useful cognitive work.

We do not ask whether the tractor is “really” a horse. We ask whether it can pull the plow. We do not ask whether the calculator understands arithmetic. We ask whether it can perform arithmetic reliably enough to change the work around it. Economic consequences often arrive before philosophical consensus has finished putting on its coat.

That is what makes this moment different from ordinary technological disruption. We are not only automating muscle. We are beginning to automate parts of cognition: writing, coding, translation, analysis, design, research, synthesis, and coordination. Not all of it. Not perfectly. Not without human judgment. But enough to matter.

For centuries, many forms of human economic value were protected because cognitive labor was expensive to reproduce. Knowledge, language, analysis, and judgment took time to cultivate and were difficult to scale. Now we are watching some forms of cognitive work become cheaper, faster, and more reproducible. The shock does not come mainly from metaphysics. It comes from economics.

The Labor Problem Is Not the Knowledge Problem

This is why the conversation often becomes so polarized. On one side are people who look at AI and see an extraordinary tool for exploration, creativity, and collective learning. On the other side are people who see job loss, surveillance, power concentration, and institutional instability. Both reactions make sense because they are often responding to different problems.

If we perform a thought experiment and temporarily remove concerns about livelihoods, surveillance, and concentrated power, AI becomes much less frightening. In that world, many people would look at it and say: this is astonishing, useful, intellectually fascinating, and maybe even fun.

The trouble is that livelihoods are not a side issue. They are the mechanism through which most people secure autonomy, dignity, status, and participation in society. You cannot simply bracket them out and call the remaining picture progress.

This distinction matters. The labor problem is institutional, economic, and political. The knowledge problem is intellectual, scientific, and creative. They are connected, but they are not the same problem. Confusing them leads to bad arguments in both directions.

If the concern is income, then we should talk about income. If the concern is education, then we should talk about education. If the concern is surveillance, then we should talk about governance. If the concern is power concentration, then we should talk about institutions. But pretending all of this can be solved by telling individuals to reinvent themselves is a category error wearing a productivity badge.

Career Advice Is Not a Social Contract

The deeper problem is that modern societies have tied contribution, dignity, identity, purpose, and value very tightly to economic participation. Work is not merely how most people earn money. It is also how they are recognized, organized, measured, and often judged.

So if AI changes the role of human labor, the question is not only “What jobs will exist?” It is also: What happens to dignity? What happens to participation? What happens to status? What happens to education when information transfer becomes less central? What happens to identity when the old story of contribution no longer holds in the same way?

These are not problems that can be solved with a better personal brand. They are not solved by another webinar on resilience. They are not solved by telling everyone to become more adaptable, though adaptability may help people survive the transition.

At some point, we have to admit that career advice is not a social contract.

This is where the language of reconstruction becomes useful. The question may not be how to preserve every old category of human value against machines. The question may be what new forms of value, contribution, and collective learning we can build now that different capabilities are available.

From Defense to Construction

Much of the discussion around AI still has a defensive posture. It asks: What remains uniquely ours? Nuance? Consciousness? Creativity? Feelings? Meaning? These questions are understandable. They are also exhausting, because every new capability forces the boundary line to move again.

What if human value is not a fossil to be discovered but a project to be built?

That question changes the tone. It shifts us from defending a shrinking territory to designing a larger one. It suggests that the most important work ahead may not be proving that humans possess some untouchable essence, but building institutions that allow human beings to flourish in a world where intelligence, knowledge, and creativity are organized differently.

If knowledge becomes cheaper, education may need to move away from information transfer as its central identity. It may need to focus more deliberately on judgment, synthesis, framing, questioning, coordination, ethics, taste, and participation in larger systems of learning. Not because these things are magically immune to automation, but because they become more important when raw information is abundant.

This is the part that should be exciting. If the economic and political problems could be handled with seriousness instead of slogans, this might be one of the most intellectually alive moments in history. Never before have so many people had access to systems capable of helping them explore, combine, test, and express ideas at this scale. The possibility of collective learning is enormous.

But that possibility requires design. It requires institutions. It requires choices about power, incentives, access, labor, education, and dignity. In other words, it requires more than flexibility.

A Toolbox Is Not a Blueprint

Adaptability tells people how to respond to change. It does not tell society what future it is building toward.

That is the missing piece in so much of our current advice. We keep offering tools because nobody can confidently describe the building. So we hand people flexibility, lifelong learning, growth mindset, prompt engineering, and a small screwdriver attachment for emergencies. The tools are real. Some are valuable. But tools do not substitute for architecture.

The challenge may be larger than individual adaptation. If so, the honest response is not despair. It is construction.

Once we recognize that the problem is structural, we can stop blaming individuals for not running fast enough inside systems that are themselves being redesigned in real time. We can stop treating social questions as personal deficiencies. We can stop mistaking renovation for reconstruction.

The old map may be wrong. The old building may need work. The canvas may be blank in places we expected to find instructions.

That is unsettling. It is also strangely hopeful. A blank canvas is not the same as a dead end. It simply means we do not yet know what should be painted on it.

And perhaps that is the real task now: not merely to adapt to the future, but to participate in building one worth adapting to.