LONGER-The Intelligence Illusion
The Intelligence Illusion
Why Machines Cannot Become Human
Rahul Ramya
8 Febt 2025
At a crowded traffic signal in Bengaluru, a young man waits astride a food-delivery motorcycle. The red light stretches longer than usual. The digital box mounted behind him flashes a corporate promise: fast, efficient, intelligent. Beneath the helmet is a graduate trained in engineering—someone who once studied thermodynamics, feedback systems, and control theory. Today, his role is simpler and harsher: obey the instructions of an algorithm.
The application on his phone decides which road he takes, how long he may pause, how his “performance” is scored, and whether his day’s labour will be rewarded or penalised. The system prides itself on intelligence. Yet it does not know why he slows near a school, why he avoids a certain alley after sunset, or why stopping for a moment to help an injured stranger may cost him his incentive but save a life.
The machine measures speed.
The human navigates meaning.
This gap—between efficiency and understanding—is where the mirage of Artificial Intelligence begins.
Human intelligence remains superior to AI by a significant margin. Artificial Intelligence, for all its recent advancements, places excessive emphasis on efficiency, which is often equated with speed. But intelligence is far more than rapid execution and computational accuracy. Intelligence involves understanding context, navigating uncertainty, absorbing social conditioning, learning through bodily experience, and, most importantly, learning from failure. AI, by its very design, is highly deterministic. It processes inputs according to predefined logic and optimised objectives. It does not live through consequences.
This gives rise to what may be called a deterministic efficiency syndrome—a condition in which speed and optimisation are mistaken for intelligence itself. Under this syndrome, AI systems perform impressively within narrow, structured domains but remain incapable of internalising contextual, social, and practical knowledge. Such knowledge is not merely informational; it is experiential, embodied, and inseparable from living through time, society, and vulnerability.
Human beings know far more than they can ever fully express. Despite extraordinary advances in linguistics, neuroscience, and symbolic representation, language itself has a limited capacity to codify everything humans understand. Much of what people know is tacit—shaped by bodily memory, emotional history, cultural exposure, and socio-economic conditions. A farmer senses the coming of rain without calculating probabilities. A nurse recognises danger before vital signs collapse. A teacher understands silence better than speech.
This tacit knowledge arises from the interaction between biological makeup and lived social experience. It cannot be exhaustively translated into data. AI, by contrast, depends entirely on what humans have already formalised—datasets, labels, rules, and feedback mechanisms. Its “learning” is derivative. Human intelligence, however, emerges from an intricate interplay of comprehension, improvisation, intuition, and lived contradiction.
It is therefore unsurprising that while AI may outperform humans in speed-based efficiency, pattern recognition, and large-scale optimisation, it remains far behind in contextual, social, and empirical understanding. The danger lies not in recognising AI’s strengths but in misunderstanding their nature. The pursuit of human-like intelligence in machines is an illusion. The real task is to ensure that AI’s computational efficiency complements human intelligence rather than competes with or replaces it.
No amount of scientific research can fully capture the entire spectrum of contextual relationships, diverse biological interactions with the external environment, and the vast complexity of social variables that shape living beings. The empirical world humans inhabit stretches far beyond measurable inputs. It includes non-living phenomena, unconscious processes, unarticulated emotions, historical memories, imagined futures, and even unconceived realities. Human cognition is shaped not only by what is, but by what might have been and what ought to be.
To attempt to codify all of this into algorithms is not merely difficult—it is structurally impossible. Reality is dynamic, non-linear, and continuously re-interpreted through experience. Algorithms, however adaptive, remain bounded representations. They simplify in order to function. Life, by contrast, grows in complexity by resisting simplification.
Recognising this limitation, contemporary technological research has attempted to move beyond rigid computation. New approaches seek inspiration from biology, evolution, embodiment, and social interaction. Neuromorphic computing attempts to mimic neural architectures. Epigenetic algorithms borrow metaphors from genetic adaptation. Embodied AI explores learning through physical interaction with environments. Quantum machine learning experiments with probabilistic uncertainty. Affective computing attempts to model emotional responses. Social learning systems explore collective intelligence through multi-agent interaction.
These developments are intellectually impressive and technologically promising. They represent a shift away from purely deterministic computation toward more adaptive and context-sensitive systems. Yet they remain exploratory. They simulate aspects of biological intelligence without becoming biological. They imitate patterns of adaptation without possessing lived continuity. They borrow metaphors from life without inheriting life itself.
The distinction is crucial. Biological systems are non-linear, self-organising, and shaped by evolutionary history. They mutate unpredictably. They carry scars, memories, and inherited vulnerabilities. Artificial systems, however sophisticated, remain bounded by architecture, training data, and externally imposed objectives. They do not age. They do not suffer. They do not fear extinction or hope for dignity. They optimise—but they do not care.
This is not a minor technical gap; it is a fundamental epistemological divide.
Human intelligence is inseparable from consciousness, emotion, embodiment, and social embeddedness. Much of it operates below conscious awareness—through intuition, affect, and moral sensibility. Neuroscience itself has not fully explained consciousness, let alone replicated it. Intergenerational memory transmission, epigenetic adaptation, and subjective intuition remain only partially understood. To assume that machines can replicate what humans themselves do not yet comprehend is not scientific confidence—it is technological hubris.
Human cognition develops through lived experience. It is shaped by family, labour, culture, inequality, language, trauma, and hope. Cultural memory, embodied knowledge, moral judgment, and contextual wisdom are not discrete variables. They are lived processes. AI systems, in contrast, operate on predefined logic and statistical inference. They respond to patterns without understanding their meaning. They predict without comprehending consequence.
Once this distinction is accepted, the global discourse on AI must change direction. The central question should no longer be whether machines can replace human intelligence. They cannot. The urgent question is how AI should be deployed ethically, politically, and economically in a way that enhances human well-being rather than undermining it.
AI must be understood as an augmentative tool—not a substitute for human cognition or labour. In knowledge-based and labour-intensive societies, particularly in developing economies, the reckless pursuit of automation has already produced displacement, insecurity, and widening inequality. Productivity gains have been captured by capital while labour absorbs the risk.
A responsible AI framework must therefore insist on retraining and reskilling as a precondition for deployment. Productivity gains generated by AI must be shared through wage models that reward human contribution rather than rendering it disposable. Governments must regulate AI deployment to incentivise applications that assist workers instead of replacing them. Most importantly, AI ownership and access must be democratised, preventing monopolisation by a handful of technology corporations.
Efficiency without justice is not progress.
Speed without understanding is not intelligence.
Part II: Intelligence, Power, and the Ethics of Substitution
The insistence that machines can replicate or surpass human intelligence does not arise in an intellectual vacuum. It is inseparable from power—economic, political, and institutional. Throughout history, dominant systems have repeatedly attempted to redefine human worth in terms that suit prevailing technologies. In the industrial age, the human was reduced to labour-time. In the digital age, the human is increasingly reduced to data.
Artificial Intelligence fits neatly into this historical pattern. Its promise of efficiency is seductive not because it is philosophically sound, but because it aligns perfectly with managerial rationality and profit-driven governance. Speed, predictability, and optimisation are attractive to institutions that seek control, scalability, and cost reduction. Intelligence, in this narrow framing, becomes synonymous with performance metrics.
Yet human intelligence has never been primarily about optimisation. It has been about survival under uncertainty, moral choice under constraint, and meaning-making in the face of suffering. These dimensions are inconvenient for systems that prioritise efficiency over dignity. Consequently, they are either ignored or reframed as “noise” in data.
This is where the ethical danger of AI intensifies.
When intelligence is redefined as a computational function, humans themselves begin to be evaluated through machine logic. Workers are ranked, students are scored, patients are triaged, citizens are profiled—all through systems that cannot grasp the full context of a human life. Decisions appear neutral because they are automated, yet they often encode the biases, blind spots, and priorities of the institutions that deploy them.
The illusion of intelligence thus becomes a tool of depersonalisation.
Technocrats often argue that future AI systems will overcome these limitations by becoming more adaptive, more contextual, and more human-like. But this belief misunderstands the nature of both intelligence and technology. Adaptation in machines remains externally guided. Context in machines is pre-specified. Learning in machines is bounded by objectives defined by others. There is no internal standpoint from which a machine experiences the world.
Human intelligence, by contrast, is situated. It emerges from being somewhere, from belonging to a body, a family, a culture, a history. It is shaped by inequality as much as by opportunity. A child growing up amid scarcity develops forms of intelligence that cannot be replicated in laboratories. A woman navigating unsafe public spaces acquires situational awareness that no dataset can fully encode. A worker surviving precarity learns judgment that defies formal instruction.
These are not deficiencies awaiting technological correction. They are forms of intelligence forged through lived reality.
Scientific discourse itself recognises the limits of computation. Complexity theory shows that emergent phenomena cannot be fully predicted from initial conditions. Non-linear systems evolve in ways that resist precise modelling. Consciousness studies reveal that subjective experience cannot be reduced to neural activity alone. Evolutionary anthropology demonstrates that intelligence is not a static trait but a continuously adapting response to ecological and social pressures.
Even speculative theories that invoke quantum processes in cognition, whether ultimately validated or not, underline a crucial point: human intelligence operates across multiple layers of reality that we do not yet fully understand. To assume that these layers can be replicated mechanically is to mistake ignorance for mastery.
This epistemological humility is largely absent from contemporary AI discourse. Instead, we witness a confident extrapolation: because machines outperform humans in certain narrow tasks, they will eventually outperform humans in all cognitive domains. This is a category error. Speed does not scale into wisdom. Pattern recognition does not mature into judgment. Prediction does not evolve into responsibility.
The ethical consequences of this confusion are profound. When societies believe that machines are intelligent in the human sense, they begin to delegate moral and political decisions to systems that cannot bear moral responsibility. Accountability dissolves into code. Power hides behind algorithms.
This is particularly dangerous in governance. Public policy involves trade-offs between competing values: efficiency versus equity, growth versus sustainability, security versus freedom. These choices cannot be optimised without normative judgment. Yet algorithmic systems increasingly shape welfare distribution, policing priorities, surveillance regimes, and administrative decisions.
When such systems fail—and they inevitably do—the harm is experienced by real people, while responsibility becomes diffuse. The machine did not decide; it only executed. The designer did not intend harm; they only optimised. The administrator did not intervene; they trusted the system. In this chain, ethics evaporates.
Reframing AI as an augmentative tool rather than a substitute for human intelligence is therefore not a technical preference but a moral necessity. Augmentation preserves human judgment at the centre. It recognises that machines can assist with calculation, pattern detection, and scale, while humans retain authority over meaning, context, and consequence.
This reframing also has economic implications. The dominant model of AI deployment treats labour as a cost to be eliminated rather than a capability to be enhanced. Automation is celebrated even when it produces social dislocation. Productivity gains are privatised, while risks are socialised.
Such a model is neither inevitable nor just.
An alternative approach views AI as a means to reduce drudgery, improve safety, and expand human capacity. In healthcare, AI can assist diagnosis while doctors retain clinical judgment. In education, AI can identify learning gaps while teachers decide how to respond. In agriculture, AI can provide forecasts while farmers decide when to sow. In administration, AI can process records while officials remain accountable to citizens.
For this vision to materialise, policy intervention is essential. Workforce retraining cannot be an afterthought; it must be integral to AI adoption. Productivity gains must translate into shared economic benefits, not mass precarity. Regulatory frameworks must discourage reckless automation in critical sectors. Access to AI tools must be democratised, preventing concentration of power in a few corporate hands.
Ultimately, the question is not whether AI will shape the future. It already is. The question is who decides how, for whose benefit, and under what ethical constraints.
Human intelligence evolved not to dominate the world efficiently, but to survive within it meaningfully. If technology forgets this lesson, it risks becoming a force of alienation rather than liberation.
Part III: Choosing Intelligence Over Illusion
The future of Artificial Intelligence will not be decided in laboratories alone. It will be shaped in classrooms, hospitals, workplaces, courts, and streets—where technology encounters real human lives. The decisive question is not how intelligent machines can become, but how wisely societies choose to use them.
If intelligence is misunderstood as mere efficiency, then the most “intelligent” systems will be those that eliminate human presence as friction. In such a future, speed will be celebrated, productivity charts will rise, and yet human lives may become increasingly precarious, opaque, and disposable. This is not a technological inevitability; it is a political and ethical choice.
The insistence that AI cannot replicate human intelligence is therefore not a nostalgic defence of human superiority. It is a recognition of difference. Human intelligence is rooted in embodiment, vulnerability, and moral awareness. It emerges through failure, contradiction, and reflection. Machines do not grow through suffering, nor do they learn responsibility through consequence. They execute.
A society that forgets this distinction risks surrendering judgment to systems that cannot care about outcomes beyond their optimisation goals. It risks mistaking automation for wisdom and delegation for accountability. Most dangerously, it risks redefining human worth in terms that only machines can meet.
Ethical AI does not begin with code. It begins with restraint.
It requires acknowledging that some aspects of life should not be optimised away—deliberation, hesitation, disagreement, compassion. These are not inefficiencies; they are the conditions of democratic and moral life. A public-facing AI must therefore be designed not to replace these qualities, but to protect the space in which they operate.
This means placing humans firmly in the loop—not as token overseers, but as accountable decision-makers. It means ensuring that AI systems remain explainable, contestable, and reversible. It means recognising that errors in social systems harm people, not datasets. Above all, it means resisting the temptation to treat intelligence as a commodity rather than a lived capacity.
The promise of AI lies not in creating artificial minds, but in reducing unnecessary human suffering. When used ethically, AI can lower cognitive burden, extend access to services, improve safety, and free time for creative and caring labour. When used recklessly, it can deepen inequality, obscure power, and erode dignity.
The difference lies in governance.
A humane AI future demands democratic oversight, public accountability, and ethical literacy among technologists and policymakers alike. It demands that citizens understand not only what AI can do, but what it should not do. Without such collective vigilance, technological power will continue to concentrate while responsibility dissipates.
Hope, however, is not abstract. It already exists in quiet, uncelebrated spaces.
In a district hospital in Tamil Nadu, a government doctor begins her morning rounds with a tablet in hand. An AI-assisted diagnostic tool helps flag patients who may need urgent attention. The system processes scans faster than any human could. But the final decisions remain hers.
One patient, an elderly woman from a remote village, has symptoms that do not align neatly with the algorithm’s confidence score. The system suggests discharge. The doctor pauses. She notices something the data does not record—the way the woman avoids eye contact, the way her breathing changes when she speaks of pain. Trusting her experience, the doctor orders further tests.
The diagnosis reveals a condition the algorithm failed to prioritise. Early intervention saves a life.
The machine was efficient.
The human was intelligent.
The technology did not replace the doctor.
It reduced her burden.
She retained judgment.
The patient retained dignity.
This is not a story of resistance to technology. It is a story of alignment—where artificial systems serve human intelligence rather than mimic or marginalise it.
The future of AI does not belong to machines that think like humans. It belongs to societies that remember why humans think at all.
The real task before humanity is not to build intelligent machines, but to remain intelligent in the age of machines.
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Part IV: Probability Is Not Wisdom
At this stage of the argument, a technical clarification is necessary—not as a concession, but as a sharpening of the critique. Artificial Intelligence systems, particularly contemporary Large Language Models, are often described as “non-deterministic” because they employ probabilistic mechanisms to generate varied outputs. Temperature parameters, sampling strategies, and stochastic decoding introduce variability. In this narrow sense, AI is not deterministic in the way a calculator is.
But variability is not freedom, and probability is not understanding.
What appears as non-determinism at the surface is, in fact, bounded stochasticity operating within a fixed statistical landscape defined by training data, model architecture, and optimisation objectives. The system does not choose among possibilities; it samples from them. It does not revise its goals; it executes them. It does not learn from consequence in the moral or existential sense; it updates weights according to loss functions. Randomness, here, is a mathematical device—not a source of judgment.
This distinction matters because contemporary AI discourse often smuggles philosophical claims under technical language. When probabilistic output is mistaken for creativity, or stochastic variation is mistaken for agency, critique is dismissed as ignorance rather than addressed on its merits. Clarifying this does not weaken the argument; it immunises it.
This is what deterministic efficiency syndrome precisely refers to:
the systematic confusion of optimisation under constraints with intelligence grounded in understanding, judgment, and responsibility.
Efficiency is not corrupted intelligence.
Efficiency is intelligence without accountability.
This becomes most evident when we examine the so-called “black box” problem. Many AI systems today generate outputs that even their designers cannot fully explain. The internal reasoning pathways are opaque, distributed across millions or billions of parameters. Engineers may know how a system was trained and what it statistically tends to do, but not why a particular decision emerged in a particular case.
Contrast this with human judgment.
When the doctor hesitates before discharging a patient, she can explain her hesitation. Her explanation may not be reducible to equations, but it is narratable, contestable, and ethically accountable. It is grounded in experience, memory, and responsibility. If she errs, she can reflect, revise, and learn—not just statistically, but normatively.
The opacity of AI is not merely a technical inconvenience. It is an ethical fault line. Decisions that affect lives—credit approval, welfare access, parole assessment, medical triage—cannot be responsibly delegated to systems whose “reasons” cannot be interrogated. When justification disappears, power becomes unaccountable. When accountability disappears, harm becomes normalised.
This is why the debate cannot be reduced to whether machines might someday approximate human-like cognition. That remains an open empirical question. The ethical issue lies elsewhere: even if they do, who bears responsibility in the meantime?
The essay has deliberately resisted declaring a closed metaphysical verdict on the ultimate impossibility of non-biological intelligence. It does not deny that future architectures—neuromorphic, embodied, socially embedded, or substrate-independent—may narrow certain gaps. What it insists upon is something more immediate and more consequential: present-day AI systems are being granted social authority far beyond their epistemic and moral capacity.
Acknowledging this does not require anthropocentric arrogance. There are many forms of intelligence in nature that do not resemble human consciousness—swarm intelligence, collective optimisation, ecological coordination. AI already surpasses humans in narrow domains such as protein folding, theorem-proving, and large-scale forecasting. These achievements are real and valuable.
But they are partial.
They do not integrate meaning, consequence, and responsibility into a single cognitive act. They do not suffer the outcomes of their errors. They do not stand before those they affect and answer for what they have done.
Human intelligence is not superior because it is faster or more accurate. It is superior because it is answerable.
This is why the problem is not merely philosophical. It is political.
As AI systems expand into governance, logistics, finance, warfare, and welfare administration, narrow intelligence begins to exercise broad power. Errors compound. Hallucinations in high-stakes domains produce cascading harm. Autonomous systems fail in unexpected ways. Alignment problems—where system objectives diverge subtly but dangerously from human values—are no longer theoretical concerns. They are operational risks.
The more opaque and autonomous these systems become, the easier it becomes to displace responsibility. Decisions appear neutral because they are automated. Harm appears accidental because it is distributed. Power hides behind technical complexity.
This is the final danger the essay insists on naming.
The greatest threat posed by Artificial Intelligence is not that machines will become human.
It is that humans will stop insisting on being responsible.
A society that hands over judgment to systems it cannot question will soon find that it cannot question power either. A polity that mistakes prediction for wisdom will lose the capacity for deliberation. A civilisation that equates intelligence with efficiency will discover—too late—that it has optimised away dignity.
The ethical task before us is therefore stark.
Do not ask whether machines can think like humans.
Ask whether humans will continue to think as humans in the age of machines.
AI must remain a tool—powerful, augmentative, constrained. It must serve human judgment, not replace it. It must be governed publicly, explained transparently, and deployed ethically. Where it reduces suffering, it should be embraced. Where it erodes accountability, it must be resisted.
This is not a technical choice.
It is a civilisational one.
And history will not judge us by how intelligent our machines became,
but by whether we remained intelligent enough not to worship them.
Notes
Story 1: Delivery Worker Controlled by Algorithm (Bengaluru)
This story is grounded in extensively documented realities of platform work in India.
Fairwork India Report
https://fair.work/en/fw/publications/fairwork-india-ratings-2023/Centre for Internet and Society – Platform Labour
https://cis-india.org/internet-governance/platform-work-in-indiaIndian Express – Algorithmic Control of Gig Workers
https://indianexpress.com/article/explained/explained-economics/gig-workers-algorithmic-management-7603418/EPW – Gig Economy and Algorithmic Management
https://www.epw.in/engage/article/algorithmic-management-gig-work-indiaILO Report on Digital Labour Platforms
https://www.ilo.org/global/topics/non-standard-employment/publications/WCMS_645337/lang–en/index.htm
“The opening vignette is a composite narrative reflecting documented conditions of algorithmic management in India’s gig economy.”
Story 2: Government Doctor Using AI as Assistive Tool (Tamil Nadu)
This story reflects documented pilot deployments and real clinical practice patterns, though again not one named individual.
Supporting references:
NITI Aayog – AI in Healthcare (India)
https://www.niti.gov.in/ai-healthcareTamil Nadu Government – CM Comprehensive Health Insurance + Digital Health Initiatives
https://www.tn.gov.in/scheme/healthWHO – Ethics and Governance of AI for Health
https://www.who.int/publications/i/item/9789240029200Nature Medicine – AI as Clinical Decision Support
https://www.nature.com/articles/s41591-018-0300-7BMJ – AI Should Support, Not Replace, Clinicians
https://www.bmj.com/content/368/bmj.m689
“The concluding vignette is a composite illustration based on documented uses of AI as clinical decision support in public healthcare systems.”
The narrative vignettes used in this chapter are composite illustrations based on widely documented empirical realities in platform labour and public healthcare. They are intended to convey ethical and experiential truths rather than report individual cases.
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