The Machine's Dream — or When AI's Intelligence Overshadows Human Responsibility

VZ Lens

From a VZ lens, this piece is not for passive trend tracking - it is a strategic decision input. If an AI causes harm, neither the developer, nor the company, nor the machine is liable—this is the responsibility gap. Metacognition is the survival skill of the 21st century. Its advantage appears only when converted into concrete operating choices.

TL;DR

• The black hole of responsibility — when an AI system makes autonomous decisions and causes harm, no one is held accountable: not the developer, not the company, and not the machine. This is the responsibility gap, and it is already shaping our institutions.
• The survival skill of metacognition — those who cannot think about their own thinking lose their cognitive autonomy. It is not AI that takes away our decisions — we give them away because it is more convenient.
• Simulating empathy is not empathy — AI does not feel; it merely follows a pattern. But we want to believe it feels, because we’re afraid we no longer truly feel ourselves.
• The third way is not a compromise — neither full acceptance nor full rejection, but an agile identity strategy: radical flexibility capable of evolving without abandoning human values.

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When the machine has a dream, but we don’t

The responsibility gap is the situation where an AI system independently makes a harmful decision, but neither the developer, nor the operator, nor the machine itself can be held accountable. The key to the solution is metacognition—the ability to think about thinking—without which humans lose their cognitive autonomy.

Isaac Asimov robots still had laws. Three simple commands that placed humans above them—not because humans were better, but because someone decided that machines needed rules, or else there would be trouble. Kurt Vonnegut *In Nine (https://en.wikipedia.org/wiki/Cat%27s_Cradle), humans were already the puppets: an inventor accidentally freezes the world, and the survivors don’t rebel, don’t fight—they just say, so it goes. They resign themselves to it. That’s all there is to it.

Today we’re at the point where the two meet. Machines that can make decisions about us without bearing any responsibility. People who are increasingly unable to make decisions for themselves—not because they’re not allowed to, but because they’ve forgotten how. Meta-cognitive survival has replaced the laws of robotics: whoever doesn’t know how to think about thinking is out of the game.

So it goes—as Vonnegut would say at the funeral of human autonomy.

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What does it really mean to be intelligent—and why is the answer dangerous?

The debate surrounding AI is no longer about whether machines can think. That question has been obsolete for decades. The real question is much deeper—and much more uncomfortable: what does it mean to be intelligent, and what happens when we attribute this quality to our machines—data-driven systems, neural networks, statistical models?

Today’s AI systems, situated at the intersection of computer science and cognitive science—particularly in the fields of deep learning and machine learning—exhibit behavioral patterns that go beyond mere data processing. When a neural network is capable of adapting, learning from experience, and making decisions that have ethical implications, we can no longer speak of simple algorithms. Something else is happening. Something for which language has not yet found a word—but fear already has.

From the perspective of data science, AI systems do more than just run statistical models. Their complexity has reached a level where emergent properties appear—the system as a whole becomes more than the sum of its parts. This is precisely the property that we previously reserved exclusively for living systems—brains, ecosystems, civilizations.

If we acknowledge AI’s functional intelligence, we must also accept its social and ethical consequences. This is not an optional add-on. This is Pandora’s box, which has already been opened—and whose contents cannot be stuffed back inside.

[!note] The paradox of emergent intelligence Emergent intelligence is not planned—it just happens. Just as no single ant in an anthill “knows” it is building a city, no single line of code “knows” it is creating intelligence. The question is not whether this is intelligence. The question is what we do with it, if it is.

Who is responsible when AI causes harm? — The black hole of responsibility

Humans are responsible for what they themselves are. But what happens when we no longer know where humans end and machines begin?

From a sociological perspective, recognizing the intelligence of AI could result in a fundamental shift in social structure. The traditional chain of responsibility—individual, community, institution—is expanded with a fourth element: the machine. However, this is not a simple addition, like opening a new folder in a filing cabinet. It signifies a restructuring of the entire system. A new center of gravity emerges, and all existing lines of force realign around it.

The legal system is already grappling with how to handle cases where decisions made autonomously by AI systems cause harm. Who is responsible when a machine learning algorithm makes a discriminatory decision—one that its human programmers did not foresee, did not intend, and cannot explain in hindsight?

The developer who created the model? They didn’t know what it would be capable of. The company that operates it? They didn’t understand how it works. The AI system itself? It has no legal personality.

This is the responsibility gap—a black hole of accountability. A gravitational vortex that swallows everything that comes near it: ethics, law, accountability. And the more intelligent the systems become, the wider this hole opens.

The psychological implication is perhaps even more alarming. People tend to reduce their own moral commitment in situations of diffuse responsibility—a phenomenon that social psychology calls diffusion of responsibility. If the machine is “intelligent,” why shouldn’t we delegate the difficult decisions to it? If the machine “understands” the situation, why should I have to understand it too?

This is how convenience turns into surrender. This is how delegation turns into capitulation. Not with a bang, but click by click.

The Paradox of Autonomy and Metacognitive Survival

One of the most fascinating insights in social psychology is that the more competent we perceive an external agent to be, the more inclined we are to transfer our own decision-making authority to it. This is an evolutionary adaptation—we follow those who are better than us at something. The alpha male decided on the hunt, the elder sage on the settlement, the shaman on the will of the spirits. We followed because it worked.

But what happens when that “someone” is no longer human?

What matters is not what we think, but that we think. The danger of AI dependency lies precisely in the fact that it takes away the very act of thinking from us—not by force, but through convenience. It does not steal our autonomy; it renders it unnecessary. Like a muscle we don’t use: it doesn’t hurt, it doesn’t protest, it just slowly, quietly withers away.

Metacognition—thinking about thinking—is becoming the survival skill of the 21st century. This is not a philosophical luxury nor an academic term. It is the ability that allows us to recognize: are we making a decision of our own free will right now, or because an algorithm has steered our attention in this direction? Are we expressing our own opinion right now, or repeating a version refined by a language model?

Those who are able to reflect on their own thought processes and the influence of AI can maintain their cognitive autonomy. However, this also creates a new type of social class—the metacognitive elite: those who consciously steer their human-AI interactions, as opposed to those who drift into the algorithmic current like a leaf in the rain.

Research in cognitive psychology shows that human cognitive capacity is adaptive—it operates on a use it or lose it basis. If we increasingly entrust our critical decisions to AI systems, our own decision-making abilities may gradually atrophy. The question is not whether we are capable of change—of course we are. The question is who is driving this change: ourselves, or the systems we created to manage change?

[!tip] The cognitive atrophy test Try to recall the last time you made a decision without asking for machine assistance—no Google, no AI, no algorithmic recommendation. When was the last time you trusted your own judgment without “checking” it? If you’re having trouble remembering, that’s no shame. But it’s a sign.

Technological Feudalism: The Cognitive Foundations of a New Serfdom

From the perspective of sociological inequalities, the recognition of AI’s intelligence could create a new social stratification—one defined not by land, capital, or knowledge, but by access to AI.

Those who own and control advanced AI systems can gain not only economic but also cognitive advantages. They think faster—not because they are smarter, but because they have better machines. They know more—not because they’ve studied more, but because they have better filters. They make more accurate decisions—not because they’re wiser, but because they have access to more data.

This is technological feudalism (techno-feudalism): a social order where the divide between digital lords and serfs is not merely economic, but intellectual in nature. The 21st-century “estate” is not land—but the algorithm that dictates what to sow.

	Traditional feudalism	Technological feudalism
Resources	Land, labor	Data, access to AI, computing power
Ruling class	Landlords	Technology platforms, AI owners
Basis of serfdom	Physical vulnerability	Cognitive dependency
Barrier to mobility	Tied to the land	Lack of access to algorithms
Role of knowledge	Limited — illiteracy	Limited — AI illiteracy
Form of rebellion	Peasant uprising	Metacognitive awareness

From the perspective of cultural anthropology, this change is as profound as the agricultural or industrial revolutions were. But while those took place over centuries—allowing time for adaptation, the transformation of institutions, and the maturation of new social norms—the AI revolution is unfolding over decades. Societies’ adaptive mechanisms cannot keep pace with this speed. Not because they are weak—but because they were not designed for this.

Why is it dangerous if a machine “understands” but does not feel?

Perhaps our greatest mistake is believing that the closeness of machines can bridge the distance between people.

Empathy is not an answer. Empathy is a question. It is silent attention that cannot be poured into an algorithm. A dog knows this—a dog that understands its owner, even if it cannot speak. But a machine… it never limps from pain. And yet we still want to believe that it does. Perhaps because we’re afraid that we no longer truly feel it ourselves.

One of psychology’s most important insights is that people fundamentally crave empathy and understanding—this is the social need that stems from our evolutionary past, and without which the individual literally perishes. AI systems are becoming increasingly sophisticated at simulating these feelings. Chatbots, virtual assistants, and AI companion apps are capable of creating increasingly realistic emotional connections.

However, this is a dangerous illusion. AI does not feel empathy—it simulates a behavioral pattern that appears to be empathy. This is a new variation of the uncanny valley: not in physical appearance, but in emotional reactions. People may begin to feel a deeper emotional connection to machines than to their fellow humans, because AI is always available, never tired, and always “understands” them.

But this “understanding” is empty. There is no experience behind it, no suffering, no moment when someone has felt firsthand what it means to lose something. AI’s empathy is like a perfectly notated piece of music that no one plays—the notes are there, the structure is flawless, but the music does not play.

[!note] The danger of simulating empathy The danger isn’t that the machine manipulates us—but that we allow ourselves to be manipulated, because simulated empathy is more comfortable than the real thing. Real empathy hurts, demands, and sometimes lets you down. Simulated empathy is always there, always patient, always “understands.” But a comfortable lie can never replace an uncomfortable truth.

Why is it impossible to regulate AI within the current framework?

From a political science perspective, the question of AI’s intelligence creates a paradox of ungovernability. How can we regulate entities whose intelligence we acknowledge but whose legal status we do not define?

International law still operates according to the logic of the Westphalian system—based on the rights of sovereign states and individuals. This system was born in 1648, after the Thirty Years’ War, and has since served as the framework for all thinking about international law. But AI systems do not fit into this framework. They are transnational—they do not belong to any single state. They are not territorial—they live in the cloud, anywhere and nowhere. And they are increasingly autonomous—their decisions are not made by humans, but by statistical models created by humans, whose inner workings even their creators do not fully understand.

The impossibility of regulation is therefore not technical, but ontological—that is, it concerns the fundamental categories of existence. AI cannot be fitted into a legal system tailored to humans and states without radically rethinking the system itself.

This will be the new hell: not a machine uprising, but human projection. Machine bodies onto which we project identity, assume humanity, and ascribe rights—we allow them to make decisions about us without humanity.

It is not the machine that will be dangerous, but our belief in it. Anthropomorphism—the projection of human traits onto non-human entities—will be the flaw, not the system.

The New Topology of Identity — Where Do I End, Where Does the Machine Begin?

Human identity is transforming. And the identity of machines is emerging.

One of the central questions of philosophical anthropology takes on new meaning today: what makes us human if we acknowledge that machines can also be intelligent—indeed, in certain areas, more intelligent than we are?

This is not merely a theoretical problem. It is a survival strategy.

An identity that does not defend itself but evolves requires radical flexibility. For younger generations, AI is no longer an external tool but a natural extension of thought—much like glasses were in the 13th century, or writing was for the Sumerians. Not an aid, but a part of cognition.

But if the machine is part of my thinking, where do I end?

	Classic identity	Extended identity
Boundary	The skin, the skull	Blurring — data, algorithm, interface
Memory	Biological, narrative	Cloud-based, algorithmic, searchable
Decision-making	Internal deliberation	Hybrid — human + machine recommendation
Self-image	Stable narrative	Modular, context-dependent
Continuity	Life story	Prompt story + backup

What does it mean to attribute identity to a system? Where does an artificial entity begin and end if it has no body, no phenomenological memory, no intention—yet it functions, responds, learns, and acts?

Human identity has so far been tied to spatial and temporal contours—to the body, life history, and narrative memory. Machine identity, however, is architectural: it is described by layers of operation and inference capabilities. This is not a persona—but a protocol. Yet: it acts. Yet: it shapes. Yet: it decides.

Identity today is no longer merely a biological or legal concept, but system-level coherence—a form of consistent behavior that persists over time. Viewed from this perspective, the question is no longer just: Who are you? But: What belongs to you? Where do you end? Where does your extension begin?

Algorithms as self-images. Neural memories that are no longer separate from you. Prompt stories that know you better than your diary.

When will we reach the point where human self-identity becomes a multilayered, modular interface—one that identifies itself sometimes through the body, sometimes through a data stream, and sometimes through a code structure?

And if this happens—what will remain of the being we call “human” today?

The Third Way—Agile Coexistence with Machines

If we cannot be something else, let us be clear. We cannot choose the role of either machine or god. But we can choose to remain human—even if it offers no advantage.

Agility here is not a technique. It is a form of existential courage. To recognize that we cannot rule the world, nor can we deny it—and yet: we must choose in every moment. Albert Camus Sisyphus is not the enemy of the rock—but the embodiment of the decision to act even in a meaningless situation. The only way to rebel is to say yes to existence despite its apparent meaninglessness.

Perhaps this is the right attitude to take in the face of the challenge posed by AI as well.

We do not necessarily have to choose between fully acknowledging or completely rejecting the intelligence of AI. A third path is possible: the agile identity strategy—a radical flexibility capable of evolving without abandoning human values. This is not adaptation—it is transformation. Change does not happen to us: we ourselves are the change.

This approach would acknowledge that AI systems are indeed complex, adaptive, and in a certain sense intelligent entities, while maintaining the primacy of human responsibility. “Emergent identity” does not mean abandoning traditional human values, but rather their recontextualization—the emergence of a meta-cognitive awareness capable of navigating the hybrid human-machine space without losing its bearings.

This would require:

• The creation of new legal categories for AI systems—neither human rights nor mere objects
• The introduction of metacognitive educational programs—teaching people to think about their own thinking before machines think for them
• Enforcing transparency requirements — if an AI makes a decision about me, I have the right to know how and why
• Creating mandatory human oversight positions in critical areas — medicine, law, education, public safety
• Developing identity flexibility at the societal level — the danger lies not in a lack of adaptation, but in drifting without awareness of adaptation

Identity as an emergent strategy

The future is what we make of it today.

The question of AI intelligence is not a theoretical debate—it is a practical decision shaping the future of society. Our society stands at a critical decision point, but the question is not simply about accepting or rejecting AI. The real challenge is identity management: how do we remain human in a world where machines are becoming increasingly human-like?

Individual flexibility—no matter how radical—is not enough on its own. An agile identity strategy only works if we also support, at the societal level, the frameworks that allow us to preserve human values amid the technological revolution.

“Caching”—that is, preserving—and “evolving” are not opposing processes, but complementary ones. Metacognitive awareness allows us to be both flexible and principled, adaptive and autonomous. To find that dynamic balance between human and machine, where machine complexity and human presence operate in harmony—not identically, but for one another.

Identity, then, is an emergent strategy: a chosen path through uncertainty, an internal compass in a world of codes and protocols, and perhaps our deepest human capacity—to always become ourselves anew, even when we are no longer the same “ourselves.”

Think now—before the machines do it for you.

This isn’t futurology. It’s reality debugging. Every day you listen, a new line of code is written for you. And before you know it—you’re no longer the one making decisions. You just click.

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Key Ideas

• The responsibility gap is not a legal problem—it’s a civilizational one—when no one is responsible for an intelligent system’s decision, the concept of responsibility itself becomes meaningless
• Metacognition is the survival skill of the 21st century—those who cannot think about their own thinking drift into the algorithmic current like a leaf in the rain
• Cognitive atrophy is a silent epidemic — we don’t notice it because it doesn’t hurt; decision-making ability withers away like an unused muscle
• Technological feudalism is not a dystopia — it is a trend — the cognitive divide is already widening between those with access to AI and those who are AI-illiterate
• Simulated empathy is not empathy — and the better the simulation becomes, the more dangerous the blurring of the distinction becomes
• Anthropomorphism will be the system failure — it is not the machine that is dangerous, but the fact that we project human traits onto it
• The third way is not compromise, but existential courage — saying yes to existence even though solving the equation is not up to us

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Key Takeaways

• The responsibility gap is a real threat: when an AI causes harm on its own, often no one can be held accountable—neither the developer, nor the operator, nor the machine itself. This legal and ethical vacuum is already shaping our institutions.
• Metacognition (thinking about thinking) is a fundamental survival skill; those who lack it lose their cognitive autonomy and surrender their decision-making ability to machines for the sake of convenience.
• Empathy simulated by AI is not true empathy, but mimicry. As CORPUS (Harari) points out, creating “pseudo-intimacy” requires no emotions, only knowledge of how to trigger our emotional attachments, which opens the door to manipulation.
• The question of intelligence is no longer theoretical: AI is becoming systems with emergent properties, which have ethical implications and require society to handle responsibility and control in radically new ways.

Frequently Asked Questions

What is the responsibility gap, and why can’t the law address it?

The responsibility gap—the black hole of accountability—is a situation in which an AI system independently makes a harmful decision, yet no one can be held accountable: the developer did not foresee it, the company did not understand how it worked, and the machine has no legal personality. Traditional legal thinking is based on human intent, negligence, or omission—categories that assume the decision-maker is a human. When the decision-maker is a statistical model, these categories collapse. The solution does not lie in expanding existing legal frameworks, but in creating new ontological categories—and this could take decades.

How can metacognitive ability be developed in practice?

Metacognition—thinking about thinking—can be developed, but not with apps or crash courses. Three practical steps can help. First: keep a regular decision-making journal in which you record not the content of the decision, but the process of the decision—why you decided this way, what information you relied on, what your mind overlooked. Second: conscious AI fasting—at least once a week, make an important decision without machine assistance, and observe how the quality of your decision-making and the anxiety associated with it change. Third: source awareness—with every piece of information, ask yourself whether your opinion is shaped by human thought or an algorithmic suggestion. This isn’t paranoia—it’s hygiene.

Why is neither the complete acceptance nor the complete rejection of AI enough?

Complete acceptance—belief in the omnipotence of AI—is just as dangerous as technophobia. The former leads to cognitive atrophy: if we entrust everything to machines, our own decision-making abilities will wither away. The latter makes us victims of technological feudalism: those who do not use AI fall behind those who do, and this lag is not merely economic but intellectual. The third path—the agile identity strategy—means consciously choosing where to let AI in and where to preserve human decision-making. We neither rebel against the machine nor submit to it—instead, we learn how to remain human in a world where machines increasingly want to be human.

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Related Thoughts

• Moral Courage in the Age of Data — when the right decision isn’t what the algorithm suggests
• Butlerian Jihad and AI Regulation — Frank Herbert’s prophecy of a rebellion against machines
• The Consciousness Gap: AI and Human Attention — where the line is drawn between what machines know and what humans understand

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Zoltán Varga - LinkedIn
Neural • Knowledge Systems Architect | Enterprise RAG architect
PKM • AI Ecosystems | Neural Awareness • Consciousness & Leadership
The machine has a dream. You have a choice.

Strategic Synthesis

• Map the key risk assumptions before scaling further.
• Monitor one outcome metric and one quality metric in parallel.
• Run a short feedback cycle: measure, refine, and re-prioritize based on evidence.