Animal Farm under Surveillance

Rahul Ramya

3rd September 2025

Introduction

The dream of technology was once liberation. When scientists and engineers first imagined ubiquitous computing, they envisioned a world in which technology blended seamlessly into the background, freeing people from drudgery and enabling creativity. In this vision, computers would “disappear” into daily life: lightbulbs that adjust automatically, cars that drive themselves, or refrigerators that manage food supplies. It was a dream of empowerment, where machines handled routine tasks while humans enjoyed autonomy.

But the economic logic of surveillance capitalism, a term most comprehensively theorized by Harvard scholar Shoshana Zuboff, transformed this dream. Instead of being tools in our service, these ubiquitous devices became tools of surveillance, extraction, and manipulation. The Internet of Things (IoT)—the network of connected devices embedded into homes, streets, and bodies—has become the critical infrastructure of a new kind of capitalism. Importantly, the IoT is not inherently surveillance-based. It could exist in ways that respect privacy. Yet, surveillance capitalism cannot exist without IoT. Its entire business model depends on converting our ordinary lives into streams of behavioral data to feed prediction markets.

The convergence of ubiquitous computing with the prediction imperative—the economic drive to eliminate uncertainty about human behavior—has given birth to a new form of power. It is a power animated by certainty, built on continuous observation and subtle control. To explain this transformation, an unusual but striking analogy helps: just as scientists once tracked animals in the wild to study behavior, today’s corporations track humans in daily life, not for science but for profit. And here, George Orwell’s Animal Farm offers a chilling parallel: animals thought they were working for collective good, but were in fact being manipulated, exploited, and disciplined—much like humans today in the digital economy.

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The Internet of Things: Convenience or Cage?

The Internet of Things (IoT) refers to everyday objects embedded with sensors, processors, and network connectivity. Smart thermostats adjust to your patterns, fitness trackers measure your steps and heart rate, cars monitor driving habits, and voice assistants like Alexa or Siri listen constantly.

For ordinary people, the promise is convenience: lower electricity bills, healthier lifestyles, safer driving, personalized recommendations. But in reality, these devices have been designed as data-harvesting machines. Their primary function is not to serve you, but to serve corporations that trade in predictions of your behavior.

Consider a few examples:

United States & Europe (Global North): Fitness trackers like Fitbit and Apple Watch collect biometric data, ostensibly to encourage healthy living. But insurers and employers increasingly use this data to assess risk or productivity. What begins as “self-care” becomes corporate surveillance of the body.
China: IoT devices integrate into the Social Credit System, where driving behavior, online purchases, and even refrigerator sensors can contribute to a citizen’s “trustworthiness” score. Here, surveillance capitalism merges with authoritarian state control.
India (Global South): Cheap smartphones and digital payment apps like Paytm or UPI link purchasing behavior to advertising databases. Farmers using IoT-based soil sensors to improve yield often find their data resold to agri-business corporations who then manipulate pricing.
Brazil & Africa: In smart-city projects funded by Western tech companies, street cameras, connected transport systems, and public WiFi are pitched as modernization. But behind the scenes, massive troves of urban behavioral data are harvested, often with little transparency or citizen consent.

Across the globe, the IoT provides the raw material of surveillance capitalism: data extracted continuously, silently, and beyond the reach of individual control.

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The Prediction Imperative: Certainty as Profit

Why does surveillance capitalism need IoT? The answer lies in the prediction imperative.

Capitalism has always thrived on risk management—insurance against disaster, hedging against market shifts. But surveillance capitalism goes further: it aims not just to respond to uncertainty, but to eliminate it by predicting and shaping human behavior itself.

Every device, from smart TVs to connected cars, is a node in a vast system designed to:

Observe behavior in real time.
Infer patterns through machine learning.
Nudge or manipulate decisions to align with profitable outcomes.

Concrete examples:

Uber: Surge pricing doesn’t just reflect demand; it is designed to manipulate driver supply and passenger urgency. Studies show Uber predicts when a rider is most likely to accept a higher fare.
TikTok: Its algorithm not only predicts what you might watch, but nudges you into longer engagement by exploiting psychological weaknesses like boredom or loneliness.
Amazon Alexa: Beyond voice commands, it learns intimate household rhythms—when you cook, sleep, argue, or relax—feeding retail and advertising predictions.

This is not prediction in a neutral sense; it is behavioral modification disguised as service.

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The Animal Tracking Analogy: From Wild to Wired

To grasp the depth of this transformation, it helps to revisit the analogy presented in the original passage. Decades ago, scientists studying wild animals—elk, tortoises, dolphins—realized they couldn’t confine them in cages. Captivity distorted natural behavior. So they invented tracking devices to monitor animals unobtrusively in their own environments. The animals lived “freely,” but their every move was under observation.

Surveillance capitalism adopts this same logic—only now, we are the animals. Our “natural habitats” are homes, cars, workplaces, and social networks. Instead of collars or tags, we carry smartphones, wear fitness trackers, or live in houses embedded with sensors. We, too, live “freely,” but within a digitally monitored enclosure.

And just as animals were never asked for consent, humans are rarely given meaningful choice. The terms of service are labyrinthine, consent is coerced, and opting out means exclusion from modern life.

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Scale, Scope, and Actions of Extraction: From Data to the Reality Business

Zuboff explains that surveillance capitalism operates through three escalating mechanisms: scale, scope, and action.

Scale: At first, data extraction targeted online activity—search queries, clicks, likes. But scale expanded rapidly to cover offline life. With IoT, every physical movement, spoken word, and bodily rhythm is harvested.
Scope: Surveillance capitalism widens the range of data captured, from purchasing habits to emotional states, facial expressions, and even tone of voice. Nothing is too trivial; everything is a data point.
Action: This is the most critical phase, where prediction turns into intervention. Companies not only know what you might do but take steps to influence you—restructuring your choices, guiding your preferences, and shaping your reality.

Together, these three create what Zuboff calls the reality business. It is no longer about reflecting human life, but reconstructing reality itself so that behavior aligns with commercial outcomes. This is not surveillance for knowledge; it is surveillance for control.

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Orwell’s Animal Farm: The Politics of Normalization

George Orwell’s Animal Farm was an allegory of Soviet communism, but its insights resonate far beyond that context. In the novel, animals overthrow humans, believing they will create a free and equal society. But over time, pigs manipulate language (“All animals are equal, but some animals are more equal than others”), rewrite rules, and normalize exploitation. The animals think they are free, but they are trapped in a system of domination masked as liberation.

This logic mirrors surveillance capitalism. We are told IoT devices empower us—track health, save energy, connect loved ones. In reality, they normalize constant monitoring and subtle control. Like Orwell’s animals, we accept exploitation because it is presented as progress.

Language Manipulation: Orwell’s pigs twisted words to conceal domination. Similarly, tech firms use terms like “personalization,” “smart,” or “seamless experience” to conceal surveillance.
Consent Illusion: The animals thought they voted on farm rules; in practice, pigs decided. Today, we “accept cookies” and “agree to terms,” but real choice is absent.
Behavioral Conditioning: Animals adjusted their expectations with each betrayal. Likewise, humans adapt to ever-expanding surveillance: first location tracking, then voice data, then biometrics. What once felt invasive becomes normal.

In Orwell’s fable, domination succeeded not through force but through psychological capture. The same holds today: surveillance capitalism thrives because it convinces people that being tracked is natural, even desirable.

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Real-World Illustrations: Global North and South

United States: Facebook’s Cambridge Analytica scandal revealed how behavioral data could be weaponized to influence elections. Citizens were not merely tracked but nudged politically.
European Union: Despite GDPR regulations, companies find loopholes. Smart TVs in Germany were caught transmitting voice data to advertising servers without disclosure.
China: The fusion of surveillance capitalism and authoritarianism shows the extreme case: IoT data feeding a system of state discipline. A jaywalking detection camera can fine you automatically.
India: Aadhaar biometric identity, combined with mobile data and fintech apps, has improved welfare delivery but also created opportunities for surveillance and exclusion of marginalized groups.
Brazil: Smart agriculture projects promise higher yields, but farmer data often flows to multinational corporations, reducing autonomy and bargaining power.
Africa: In Nairobi’s smart-city Konza project, surveillance cameras and connected transport are promoted as modernization. Yet activists warn of “digital colonialism,” where African behavioral data enrich Western firms.

Together, these examples show that surveillance capitalism is not confined to Silicon Valley. It is a global system, shaping lives in both rich and poor societies.

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The Role of AI and AGI in Surveillance Capitalism

Artificial Intelligence (AI), and the anticipated rise of Artificial General Intelligence (AGI), represent the accelerant of surveillance capitalism. AI systems analyze vast datasets at unprecedented speed, detecting subtle correlations that humans cannot. They can predict moods from keystroke rhythms, infer political leanings from “likes,” or detect depression from voice tone.

AGI, if realized, would amplify this dynamic further. Unlike narrow AI, which excels in specific tasks, AGI would integrate across domains, processing behavioral, biological, and social data holistically. Imagine a system that not only knows your consumer preferences but understands your psychology, cultural context, and social networks deeply enough to forecast—and manipulate—life choices with chilling precision.

In the Global North: AI-driven recommendation systems already determine what billions watch, read, and believe. Deepfake technology powered by AI threatens to erode trust in evidence itself.
In the Global South: AI-based agricultural and health apps improve access but also harvest sensitive community-level data, often owned by foreign firms. This can entrench dependency rather than empowerment.

AI and AGI are not inherently oppressive, but under surveillance capitalism, they risk becoming tools for total behavioral capture, where human autonomy is not just constrained but computationally preempted.

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Why It Matters: The Loss of Human Autonomy

The implications are profound. At stake is nothing less than human autonomy and democracy.

Erosion of Privacy: Daily life becomes transparent to corporations. What was once intimate—your health, moods, conversations—becomes commodified.
Behavioral Manipulation: Prediction is not passive. Systems are designed to subtly steer choices: what to buy, who to vote for, how to feel.
Inequality and Exploitation: In the Global South, cheap IoT devices expand connectivity but also create dependency, with local data enriching foreign corporations.
Democratic Fragility: If citizens’ opinions are shaped algorithmically, political agency weakens. Elections become contests of behavioral targeting rather than free deliberation.

In short, surveillance capitalism turns free citizens into managed populations, echoing the fate of Orwell’s animals.

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Counterarguments: The Case for IoT and Data Collection

While the risks of surveillance capitalism are undeniable, proponents of IoT and large-scale data collection argue that these technologies bring tangible benefits that should not be dismissed. For instance, IoT devices have revolutionized public health by enabling real-time monitoring of disease outbreaks. Smart wearables can detect irregular heart rhythms, potentially saving lives by alerting users to seek medical care. In disaster response, IoT-enabled sensors in urban infrastructure can optimize evacuation routes or monitor environmental hazards, as seen in flood-prone regions like Bangladesh, where IoT water sensors have improved early warning systems. In agriculture, IoT tools have empowered farmers in sub-Saharan Africa to increase crop yields through precision farming, addressing food insecurity.

Moreover, data aggregation can drive societal advancements. Anonymized health data from fitness trackers has been used in epidemiological studies to understand population-level trends, such as the spread of influenza. In urban planning, IoT data from smart cities can reduce traffic congestion and lower carbon emissions, as seen in Singapore’s Smart Nation initiative. These benefits suggest that surveillance capitalism’s infrastructure could, in theory, serve the public good if managed responsibly.

However, these advantages come with significant caveats. The “anonymization” of data is often imperfect; studies have shown that individuals can be re-identified from supposedly anonymized datasets with relative ease. Public health or disaster response applications rarely require the granular, individualized data that surveillance capitalism thrives on. Instead, aggregated and truly anonymized data could suffice, yet corporations prioritize detailed behavioral profiles for profit. Furthermore, the benefits of IoT are unevenly distributed. In the Global South, for instance, farmers may gain short-term yield improvements but lose long-term autonomy when their data is controlled by multinational agribusinesses. Similarly, smart-city projects often prioritize corporate and state interests over citizen empowerment, as seen in Nairobi’s Konza project, where local communities have little say in how their data is used.

The trade-off, then, is not merely convenience versus privacy but a deeper question of power. Even when IoT delivers benefits, the underlying logic of surveillance capitalism ensures that control remains with corporations or states, not individuals. The promise of public good does not justify the erosion of autonomy, especially when alternative models—such as decentralized, user-owned IoT systems—could deliver similar benefits without the surveillance.

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Reclaiming Freedom: Can Humans Escape the Digital Farm?

History shows that new technologies need not always serve domination. Writing, the printing press, and the telephone disrupted power but were eventually democratized. The same could be true for IoT and ubiquitous computing—if society resists the surveillance logic.

Possible paths forward:

Regulation: Stronger global laws to limit data extraction, ban dark patterns, and enforce genuine consent.
Technological Alternatives: Development of privacy-respecting IoT, owned by users rather than corporations.
Democratic Action: Citizens demanding accountability, much as labor movements once fought industrial exploitation.
Philosophical Reframing: Rejecting the idea that convenience justifies constant monitoring.

To make these paths actionable, specific strategies are essential. For regulation, governments could adopt frameworks like the EU’s GDPR but with stricter enforcement and global coordination to prevent companies from exploiting jurisdictional loopholes. For example, mandating “opt-in” consent for data collection, with clear, non-technical explanations of what data is collected and how it is used, would empower users. Fines for violations should be substantial enough to deter corporate overreach, unlike the current system where penalties are often a fraction of tech giants’ profits.

Technological alternatives could include open-source IoT platforms where users retain ownership of their data. Projects like Home Assistant, an open-source home automation system, allow users to control smart devices locally without cloud reliance, ensuring data stays private. Scaling such solutions requires public investment and collaboration between tech communities and policymakers to create user-friendly, affordable options that rival corporate offerings.

Democratic action can draw inspiration from historical movements. Just as 19th-century labor unions organized against exploitative factory conditions, digital rights collectives could mobilize to demand transparency and data sovereignty. Grassroots campaigns, like those led by groups such as the Electronic Frontier Foundation, have already pushed for policies banning facial recognition in public spaces. Citizens could also support cooperative models, such as community-owned broadband networks, which prioritize local control over data infrastructure.

Philosophically, reframing technology’s role involves public education campaigns to shift cultural attitudes. Schools and media could teach “digital literacy” that emphasizes critical thinking about surveillance, much like financial literacy teaches skepticism of predatory lending. By normalizing the expectation of privacy as a right, not a privilege, societies can reject the notion that tracking is an inevitable trade-off for modern life.

Orwell’s warning remains urgent: freedom is not lost in one stroke but eroded gradually, normalized through language and habit. To resist becoming the “new animals,” humans must reclaim control over the digital infrastructures that shape their lives.

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Conclusion

The convergence of ubiquitous computing and surveillance capitalism has transformed technology from a servant into a master. The IoT, once imagined as a liberating force, now serves the prediction imperative, feeding markets that trade in human futures. The animal tracking analogy captures the essence of this system: like tagged animals in the wild, humans live “freely” but under constant, invisible observation.

George Orwell’s Animal Farm deepens the insight: domination succeeds not only through force, but through language, normalization, and the erosion of memory and choice. Today’s digital economy similarly persuades people that surveillance is empowerment, turning autonomy into managed compliance.

From the United States to China, from India to Africa, examples show that surveillance capitalism is not a local aberration but a global regime. It threatens not only privacy but the very foundations of democratic freedom.

To break free, societies must imagine alternatives: technologies that serve people, not markets; infrastructures designed for autonomy, not control. The choice is stark. Either humans remain the tracked animals of a digital farm, or they reclaim their role as free agents shaping their own destinies.

The future of freedom depends on recognizing the stakes today.

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To achieve the ends outlined in the essay “Animal Farm under Surveillance”—namely, escaping the “digital farm” of surveillance capitalism, reclaiming human autonomy, and fostering technologies that serve people rather than markets—a multifaceted transformation is necessary. This involves structural adoptions (institutional and systemic changes), technological trajectories (directed paths of innovation), philosophical impregnation (the deep infusion of ethical and conceptual frameworks into societal discourse), and individual attitudinal changes (shifts in personal mindsets and behaviors). Below, I elaborate on each category, building on the essay’s proposed paths forward while integrating practical considerations for implementation.

Structural Adoptions

Structural adoptions refer to the institutional, legal, and organizational reforms needed to dismantle the foundations of surveillance capitalism and create equitable systems. These must be systemic to counteract the global scale of IoT-driven data extraction.

Regulatory Frameworks and Enforcement Mechanisms: Governments and international bodies must adopt comprehensive laws that prioritize data sovereignty and limit corporate overreach. For instance, expanding models like the EU’s GDPR to include mandatory data minimization principles—where companies collect only essential data—and prohibiting the resale of behavioral data without explicit, revocable consent. This could involve creating independent oversight agencies, similar to financial regulators, with the power to audit IoT devices and impose escalating penalties, such as revoking operating licenses for repeat offenders. Global coordination through forums like the United Nations could harmonize standards, preventing “surveillance havens” in lax jurisdictions.
Economic and Corporate Restructuring: Shift economic incentives away from prediction markets by adopting policies that tax data extraction profits or subsidize privacy-focused businesses. Corporations should be required to transition to user-centric models, such as mandatory interoperability standards for IoT devices, allowing users to switch providers without data lock-in. Public-private partnerships could fund decentralized data infrastructures, ensuring that critical sectors like healthcare and agriculture benefit from IoT without exploitation, particularly in the Global South where digital colonialism exacerbates inequalities.
Institutional Accountability in Governance: Integrate digital rights into democratic institutions by establishing citizen assemblies or ombudsmen dedicated to technology policy. This would address the merger of surveillance capitalism with state control, as seen in China’s Social Credit System, by mandating transparency in public IoT deployments and prohibiting algorithmic decision-making in sensitive areas like welfare or law enforcement without human oversight.

These adoptions require political will and cross-sector collaboration to ensure that structures evolve from enabling domination to protecting freedom.

Technological Trajectories

Technological trajectories involve steering innovation toward privacy-enhancing and democratizing paths, rather than the current surveillance-oriented ones. The essay emphasizes that IoT is not inherently exploitative, so redirecting development is key.

Privacy-by-Design IoT Ecosystems: Prioritize trajectories that embed privacy as a core feature, such as edge computing where data processing occurs locally on devices rather than in corporate clouds. This could include widespread adoption of protocols like zero-knowledge proofs for verifying data without revealing it, or blockchain-based systems for user-controlled data sharing. For example, evolving smart home devices to operate offline by default, with optional encrypted sharing, would reduce the scope of extraction highlighted in the essay.
Open-Source and Decentralized Innovations: Accelerate the development of open-source alternatives, building on projects like Home Assistant or Mozilla’s WebThings, to create scalable, affordable IoT platforms. Trajectories should focus on interoperability standards (e.g., via the Matter protocol) and AI/AGI advancements that emphasize ethical constraints, such as federated learning where models train on decentralized data without central aggregation. In the Global South, this means investing in low-cost, modular IoT kits for agriculture and health, ensuring local ownership to counter dependency on Western firms.
Counter-Surveillance Tools and Standards: Develop trajectories for “defensive” technologies, like automated data obfuscation tools that inject noise into behavioral profiles or AI auditors that detect manipulative nudges in apps. Standardization bodies, such as the IEEE, could mandate “surveillance impact assessments” for new IoT products, similar to environmental impact statements, to preempt the prediction imperative’s expansion.

These trajectories demand investment in research and development, shifting from profit-driven silos to collaborative, open ecosystems that align with human autonomy.

Philosophical Impregnation

Philosophical impregnation entails deeply embedding ethical, existential, and humanistic ideas into cultural, educational, and intellectual frameworks to challenge the normalization of surveillance. Drawing from Orwell’s insights and Zuboff’s critique, this involves infusing society with concepts that prioritize freedom over convenience.

Reframing Human-Technology Relations: Impregnate discourse with the philosophy that technology should augment, not commodify, human experience. This could draw from existentialism (e.g., Sartre’s emphasis on authentic choice) to argue against algorithmic determinism, or from communitarianism to stress collective data rights. Educational curricula should integrate these ideas, teaching that “personalization” often masks control, much like the pigs’ language manipulation in Animal Farm.
Ethical Imperatives in Design and Policy: Infuse tech ethics with principles from phenomenology, viewing the body and mind as inviolable rather than data sources. Philosophical impregnation could manifest in manifestos or think tanks advocating for a “right to opacity”—the idea that not all aspects of life need be transparent or predictable. In AI/AGI development, embed Kantian ethics to ensure systems respect human ends, not treat individuals as means to commercial goals.
Cultural Narratives and Critique: Promote art, literature, and media that impregnate public consciousness with anti-surveillance themes, extending Orwell’s allegory to modern contexts. This includes philosophical dialogues on the “reality business,” questioning whether certainty through prediction erodes the uncertainty essential to free will and creativity.

By impregnating philosophy into everyday discourse, societies can resist psychological capture and view surveillance not as progress but as a threat to human dignity.

Individual Attitudinal Changes

Individual attitudinal changes focus on personal shifts in perception, habits, and agency, empowering people to reject passive acceptance and actively participate in resistance.

From Convenience to Vigilance: Individuals must adopt a skeptical attitude toward “free” services, recognizing them as surveillance traps. This involves habitual practices like regularly reviewing app permissions, using privacy tools (e.g., VPNs or ad-blockers), and opting out of data-sharing where possible. Attitudinally, shift from viewing tracking as innocuous to seeing it as an infringement, inspired by the essay’s animal analogy—refusing to be “tagged” without consent.
Embracing Agency and Solidarity: Cultivate attitudes of empowerment by engaging in digital minimalism, such as limiting IoT device use or supporting ethical brands. Foster a sense of collective responsibility, where individuals join or form advocacy groups, mirroring labor movements, to demand change. This includes attitudinal resilience against normalization, questioning phrases like “seamless experience” and advocating for peers in vulnerable communities, such as those in the Global South facing digital exclusion.
Lifelong Learning and Ethical Reflection: Develop attitudes of continuous digital literacy, reflecting on how personal data fuels inequality. Individuals should internalize philosophical shifts, viewing autonomy as a daily practice—e.g., discussing surveillance in family or social settings to build grassroots awareness. Over time, this attitudinal evolution can create bottom-up pressure for broader changes.

Achieving these ends requires interplay among the categories: structural adoptions provide the framework, technological trajectories the tools, philosophical impregnation the vision, and individual changes the momentum. While challenging, historical precedents—like the democratization of the printing press—suggest it’s possible if pursued with urgency and unity.

The lesson of Orwell’s Animal Farm is that freedom is not lost in a single moment of tyranny but through slow habits of acceptance, until animals forget they were ever free. Today, the “digital farm” works the same way: each click, each consent button, each smart device silently trains us to believe that surveillance is normal. Yet, history reminds us that people have always broken free from cages—whether built of iron, ideology, or information. The task now is not only to regulate corporations or design better technologies, but to remember that human dignity cannot be reduced to data. If we choose vigilance over convenience, solidarity over silence, and freedom over managed comfort, we can turn the tools of the digital age into instruments of empowerment rather than control. The farm is only a prison if we accept its fences—resistance can still turn us from animals to citizens again.

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