Oil shaped the economic and geopolitical order of the twentieth century. Data may be shaping the order of the twenty-first. Yet the two resources differ in a fundamental way; oil is consumed when used, whereas digital information can be copied, processed, recombined and monetised repeatedly. As human life moves online, an expanding record of our activities becomes available to governments, corporations and technology platforms.

India has played a significant role in building the digital infrastructure of the modern world through its information technology services industry. Indian professionals have developed, maintained and modernised enterprise systems, websites, databases and cloud applications for organisations across the globe. Today, artificial intelligence is being trained and deployed within this increasingly digitised world. The same technological transition that created opportunities for India’s IT industry may also challenge its traditional labour-intensive business model.

This essay proposes a hypothesis: humanity may be moving from an economy organised around the extraction of physical resources to one increasingly organised around the extraction of data, and then towards an AI economy in which both human dependence and economic value are concentrated in systems that people may not own or control.

The term digital slavery is used here as a metaphor for structural dependence, not as an equivalence with historical slavery. The question is whether people, institutions and even entire economies are surrendering autonomy as they become dependent on digital infrastructure. A further question follows; could India, having helped build much of the digital world, find itself economically dependent on an AI ecosystem that reduces the value of some of the very services it has traditionally supplied?

The hypothesis is not that a single organisation or country planned this outcome. It is that individually rational decisions, driven by convenience, efficiency, profit and competition, may be creating a collective outcome that deserves serious scrutiny.

Blue – Established facts: digitisation, data generation, aggregation, value capture and AI.

Gold – Hypothesised connections: data as a strategic resource, asymmetric control, digital dependence and AI dependence.

Red – Potential consequences: loss of autonomy, privacy erosion, economic displacement, exclusion and concentration of power.

Note: The diagram presents digital slavery and AI slavery as possible consequences of the hypothesis, not as established facts.

1. From oil to data: the changing nature of strategic power

Oil transformed industrial society. It powered transport, manufacturing and military capability, and became central to international trade and geopolitical influence. The modern petroleum industry developed during the nineteenth century, and the relationship between oil-producing states and major consuming powers became a defining feature of twentieth-century geopolitics.

The US–Saudi relationship, strengthened by the meeting between President Franklin D. Roosevelt and King Abdulaziz aboard the USS Quincy in February 1945, is often invoked in discussions of oil and dollar power. The history is more complicated than a single bargain in which Saudi Arabia supposedly agreed to sell all its oil exclusively in dollars in exchange for American protection. Dollar dominance emerged through a broader combination of financial, political and economic arrangements.

The underlying lesson, however, remains relevant; control over a strategically important resource can translate into economic and geopolitical influence.

Data now occupies a comparable position in the emerging digital economy. It informs financial decisions, commercial strategies, public administration, healthcare, advertising, scientific research and artificial intelligence. But data differs from oil in important respects. It can be copied without destroying the original, reused across multiple applications and combined with other datasets to generate new insights.

Data is not literally infinite or costless. Its usefulness depends on quality, context, lawful access, storage, computing capacity, energy and the ability to extract meaningful information from it. Nevertheless, the possibility of repeated use gives digital information a distinctive economic character.

The strategic question is therefore no longer simply who possesses natural resources. It is increasingly who possesses the infrastructure, computing power, models, platforms and legal rights needed to turn information into economic value.

2. India’s IT boom: building the infrastructure of the digital economy

India’s IT industry emerged through a combination of economic liberalisation, telecommunications development, global outsourcing, the Y2K transition and the growth of enterprise software services. Indian companies became important partners in building and maintaining the digital systems used by businesses around the world.

Indian professionals helped develop banking applications, pharmaceutical systems, insurance platforms, e-commerce websites, customer databases, enterprise resource planning systems and cloud-based applications. They supported the migration of paper-based processes into digital workflows and helped organisations manage increasingly large volumes of information.

This work created enormous value. It expanded India’s services exports, built technical expertise, generated employment and connected Indian professionals to global markets.

Yet it also contributed to a profound structural change: more of human activity became digitally recorded, searchable and processable.

A hospital moved patient records into an electronic system. A bank digitised transaction. A pharmaceutical company moved clinical and regulatory processes onto enterprise platforms. A retailer recorded customer behaviour. A government agency digitised identity and service delivery. A university placed applications, examinations and learning resources online.

Each decision had an immediate purpose. The cumulative effect was the construction of a world in which more human activity could be converted into data.

India’s IT industry did not create this transformation alone. Governments, technology companies, businesses, universities and consumers across the world contributed to it. Nor is there evidence that Indian IT professionals collectively set out to create a system of digital dependence.

The more interesting question is whether the cumulative result of these developments has produced consequences that few participants considered in full.

Did India help build the infrastructure of a data-driven economy without securing a proportionate share of ownership, control and long-term value from that economy?

That is a question of economic structure, not national guilt.

3. The internet as a growing record of human existence

Consider the journey of an individual from birth to adulthood.

A birth registration creates a record. School enrolment generates educational information. A bank account records transactions. A phone records communications and interactions with applications. A hospital stores medical history. A tax system records income and financial obligations. Travel bookings, employment portals, shopping platforms and digital payments add further information.

Not every record is held by the same organisation, and not every digital interaction is retained indefinitely. But across multiple systems, fragments of an individual’s life accumulate.

These fragments can reveal more than their original purpose. A series of purchases may indicate preferences. Financial transactions may reveal patterns of income and expenditure. Location information may suggest routines. Health records may help identify risks. Search and browsing behaviour may reveal interests or concerns.

When data from different sources can lawfully be linked, the resulting picture may be more informative than any individual record.

The individual experiences these systems as separate services. The organisations operating them may see something else; information that can improve operations, predict behaviour, personalise services, assess risk or support commercial decisions.

This creates a fundamental asymmetry. People generate much of the information, but they may have limited visibility into how it is combined, how long it is retained, who receives it or what economic value is created from it.

The issue is not that every digital record is inherently harmful. Many records support better healthcare, safer financial transactions, scientific discovery and more efficient public services. The concern arises when the collection and reuse of information extend beyond reasonable expectations, meaningful consent or effective accountability.

4. The smartphone: from convenience to necessity

The smartphone was introduced as a communication device. It has increasingly become a gateway to everyday life.

For many people, a phone is now central to banking, digital payments, travel reservations, education, healthcare appointments, employment applications, identity verification and access to government services.

In India, digital payments and online public services have expanded convenience and access for millions. They can reduce transaction costs, improve service delivery and help people who previously had limited access to formal systems.

Yet the same development creates a difficult question: what happens when a service that is technically optional becomes practically unavoidable?

A person without a suitable phone, reliable connectivity, sufficient digital literacy or the ability to navigate an application may find it harder to complete an ordinary task. Someone who cannot afford a device or maintain a data connection may face additional barriers. Older people, people with disabilities and individuals unfamiliar with digital interfaces may need assistance even when a service is formally available to everyone.

A digital system can therefore expand access for one group while creating new obstacles for another.

The deeper issue is the difference between access and autonomy. Access means being able to use a system. Autonomy means having a meaningful choice about whether and how to use it.

If a person must participate digitally to function in society, but has little control over the terms of that participation, convenience begins to resemble dependency.

A genuinely inclusive digital economy must therefore preserve accessible alternatives, provide effective assistance and avoid making essential services conditional on technological capabilities that people do not possess.

5. Digital identity, trust and the security of the system

Digital dependence becomes especially consequential when identity, finance and public services rely on interconnected systems.

India’s digital infrastructure includes identity mechanisms, tax administration, banking systems, digital payment networks and a growing range of online public services. These systems can improve efficiency, but they also make trust in the underlying infrastructure essential.

A person may reasonably ask: who can access my information? What happens if an identity credential is compromised? Can an incorrect record prevent me from receiving a service? Can I challenge a decision made through an automated system? Who is accountable when several organisations share responsibility?

Security incidents involving digital certificates, authentication mechanisms or other components of the technology infrastructure are reminders that trust depends on many interconnected layers.

For example, the October 2026 incident involving eMudhra exposed weaknesses in the cryptographic keys of eight intermediate certificate authorities within its global TLS business. The affected certificates were revoked, and eMudhra stated that they had not issued publicly trusted end-entity certificates and that there was no evidence of a compromise of its infrastructure or customer data. The incident was limited to the global TLS trust infrastructure. CERT-In clarified that eMudhra’s digital signature certificate and eSign services in India were not affected. The episode nevertheless illustrates why digital trust depends on sound technical processes, independent scrutiny and transparent incident reporting.

The broader lesson is not that digital systems are inherently unsafe. It is that their reliability depends on sound architecture, independent oversight, transparent incident reporting and effective remedies.

As society becomes more dependent on digital systems, a failure in one layer can have consequences far beyond the original technical problem.

The more essential a system becomes, the stronger its obligations must be to protect the people who depend on it.

6. Data as capital: who creates value, and who captures it?

Traditional economic analysis recognises land, labour and capital as important sources of production. In a data-driven economy, information has become another strategic input.

However, data does not automatically create value. Value emerges when information is collected, organised, analysed and applied through technology, expertise and commercial or public institutions.

A patient’s medical records may contribute to research. A company’s operational records may help improve a process. A large body of text may help train a language model. A collection of images may support the development of a medical imaging algorithm.

In each case, value is produced through a chain of contributions: people create information, organisations collect or generate it, professionals structure it, infrastructure stores and processes it, and models or applications turn it into something useful.

The distribution of the resulting value is not necessarily proportional to each participant’s contribution.

An individual may provide information without receiving direct compensation. A company may hold the data and determine how it is used. A cloud provider may charge for storage and computing. A model developer may sell access to a system trained on or informed by many sources of information.

The arrangement can be legitimate when the relevant rights, consent, contracts and safeguards are respected. But it raises a question of economic justice when people contribute information to systems that generate substantial value while having little influence over the terms of its use.

This is particularly important when data concerns human beings rather than merely machines or commercial inventory. Personal information can affect privacy, reputation, opportunity and freedom of choice.

The argument should not be that every individual must own or be paid for every data point. That would oversimplify the economics and legal questions involved. Rather, it is that people should have appropriate rights, meaningful safeguards and a fair basis for understanding how their information is used.

The central concern is the concentration of control over the systems that convert widely generated information into privately captured value.

7. Artificial intelligence: the next stage of the transformation

Artificial intelligence changes the significance of digitisation.

Digitised information can be searched and processed. AI systems can use large bodies of data to recognise patterns, generate content, support decisions and automate tasks that previously required substantial human effort.

The relevant inputs extend beyond personal data. AI systems may be trained or developed using licensed material, public information, synthetic data, proprietary datasets, human feedback and other sources. Different systems use different combinations of these inputs, and the legal status of particular uses varies.

Nevertheless, the connection between digitisation and AI is important. The world’s growing stock of digital information provides a foundation on which many AI capabilities can be developed.

Humanity has spent decades creating documents, images, software, scientific publications, medical records, business processes and other forms of digital information. AI systems can learn from some of these materials and use what they learn to perform tasks at scale.

The implications are profound.

A body of human knowledge can help build a system that subsequently performs certain tasks without requiring the same amount of direct human effort each time. A software library can support an AI coding assistant. A collection of scientific literature can inform a research tool. A large set of labelled medical images can contribute to the development of an imaging model.

But the analogy has limits. An AI model is not simply a container holding every piece of information it encountered, and it does not automatically acquire legal ownership of the source material. Training, inference, retrieval and commercial deployment are distinct processes.

The underlying economic question remains; who has the right to use the inputs, who owns or controls the resulting systems, and who captures the value generated by their deployment?

If individuals and institutions supply the information, expertise and feedback that help improve AI, but control over the most valuable systems remains concentrated, the imbalance between contribution and reward may become more pronounced.

The concern is not that AI necessarily enslaves humanity. It is that dependence on systems whose operation and governance remain beyond ordinary users’ influence may reduce their practical autonomy.

8. From digital slavery to AI slavery

The term digital slavery is deliberately provocative. It does not equate the modern digital economy with historical slavery, which involved direct ownership, coercion and the denial of fundamental human rights. It describes a different concern; a form of structural dependence in which participation in society increasingly requires engagement with systems that individuals cannot meaningfully refuse, inspect or control.

The metaphor becomes more useful when divided into two related dimensions.

Digital slavery concerns citizens and consumers. They generate data, depend on digital platforms and may have limited choice over how their information is used or how automated decisions affect them.

AI slavery concerns workers, businesses and institutions. They may become increasingly dependent on AI to remain productive and competitive, even when the systems they use are owned, priced and governed by others.

A business may need AI tools to match competitors’ productivity. A worker may be expected to use AI to meet targets that keep rising as automation makes tasks faster. A public institution may depend on proprietary systems whose costs and technical requirements it cannot easily escape.

In these situations, AI can increase individual capability while also shifting control towards the owners of models, platforms, computing infrastructure and distribution channels.

The paradox is that a tool that promises to free people from repetitive work may also create new forms of dependence.

The outcome is not inevitable. Open standards, competition, public-interest technology, effective regulation, worker participation and the ability to switch providers can all reduce dependency. AI can also improve access to expertise, support people with disabilities and make services more affordable.

The question is therefore not whether AI is inherently liberating or oppressive. It is whether its benefits and risks are distributed in ways that preserve human agency.

9. The irony for India: could AI disrupt the industry that helped build the digital world?

This is where the hypothesis becomes particularly important for India.

India’s IT-services industry grew by supplying skilled human labour to global businesses. Its traditional model often involved large teams of software developers, testers, analysts, consultants and support professionals delivering work at competitive costs.

The model created considerable economic value. It generated exports, employment, technical capabilities and a global presence for Indian companies.

But the model also depends, to varying degrees, on the relationship between the amount of human labour supplied and the revenue earned.

AI is beginning to change that relationship.

AI-assisted software development can generate or modify code. Testing tools can automate parts of test creation and execution. Systems can assist with documentation, data analysis, incident management and routine support. AI can also help consultants research problems, produce initial analyses and accelerate implementation.

These capabilities do not eliminate the need for human judgement, domain expertise, accountability or complex engineering. Their effects differ by task, organisation and industry. Yet they can reduce the time required for some activities.

When a client can obtain a comparable result with fewer person-hours, the client may demand lower prices, redirect spending or expect more output for the same budget. A supplier that historically earned revenue by deploying large teams may face pressure to redesign its commercial model.

The risk is not simply that AI will replace every software professional. It is that the economic value of supplying labour by the hour may decline even when the value of the final service remains high.

There are already signs of this pressure. In July 2026, CRISIL Ratings said that AI-driven disruption and weak discretionary spending were weighing on growth in India’s IT-services sector, and highlighted the need for companies to reinvent their business models. In October 2026, Infosys revised its FY2027 revenue-growth guidance to 1–2% in constant-currency terms. The company reported that AI was reshaping its business and client engagements, even as it continued to record large deal wins and growth in AI-related revenues. The revised guidance, therefore, should be seen in the context of broader demand conditions and a changing technology-services market, rather than as evidence of an AI-driven decline alone.

Sources: CRISIL Ratings, July 2026; Reuters, 23 October 2026.

Employment evidence also calls for care rather than alarmism. An ICRIER study based on a survey of 651 IT firms across ten Indian cities, conducted between November 2025 and January 2026, found a modest but broad-based moderation in hiring, concentrated primarily at entry level, rather than evidence of immediate large-scale job losses. It also found that occupations more exposed to AI experienced stronger growth in demand, while a majority of firms reported productivity gains. The study suggests that rising demand for AI-enabled goods and services could support net job creation in India’s IT sector. The longer-term impact will depend on how rapidly AI capabilities expand and how organisations choose to deploy them.

Source: ICRIER, AI and Jobs: This Time Is No Different.

India’s dilemma is therefore not that its IT industry is doomed. It is that the industry may need to move from selling human effort to selling outcomes, intellectual property, domain expertise, AI-enabled products and trusted solutions.

This transition will not be easy. If fewer people are needed to perform routine tasks, entry-level opportunities and traditional career pathways may change. If clients capture most of the productivity gains through lower prices, technology providers may find it difficult to convert higher productivity into proportionately higher revenue. If Indian companies rely on external AI platforms without developing their own capabilities, they may remain dependent on infrastructure and intellectual property controlled elsewhere.

The irony is striking. Indian professionals helped digitise the operations of global organisations. That digitisation helped create the environment in which AI could become increasingly useful. AI can now automate parts of the work that once required large teams of those same professionals.

This is not a moral failure by the people who built the systems, nor evidence that they intended to make themselves redundant. It is a structural consequence of technological progress interacting with a particular business model.

The decisive question is whether India can capture a meaningful share of the new value created by AI, or whether it will continue to provide the labour while others own the most valuable platforms, models and intellectual property.

10. Is this a coordinated plan or an emergent system?

A hypothesis of this scale must distinguish between evidence and speculation.

It would be easy to interpret the growth of digital identity, smartphone dependence, data collection and AI as evidence of a single, coordinated plan to control humanity. But the existence of a pattern does not prove the existence of a master plan.

Governments digitise services to improve delivery. Businesses collect information to understand customers and reduce costs. Technology companies build platforms because they can generate revenue. Consumers adopt digital tools because they are convenient. IT-services firms develop systems because clients pay for them. AI developers use data and computing resources to improve their products.

Each decision may be rational within its own context. Together, however, they can produce a system in which digital participation becomes unavoidable, information becomes an economic asset and control over essential infrastructure becomes concentrated.

This is an emergent-system hypothesis: the outcome may arise from the interaction of many incentives rather than from a single design.

That distinction strengthens the argument. It allows us to investigate observable structures—market concentration, terms of service, data rights, pricing power, digital exclusion, employment patterns and ownership of AI infrastructure—without making unsupported allegations about hidden intentions.

The question is not whether somebody secretly planned every stage. The question is whether society is adequately governing the consequences of the system it has built.

11. What would support or challenge this hypothesis?

A serious hypothesis must be open to testing.

Several lines of evidence would strengthen it:

• Growing dependence: essential services increasingly require digital access, with insufficient non-digital alternatives or effective assistance.
• Concentrated control: a small number of organisations control important platforms, cloud infrastructure, AI models or distribution channels.
• Unequal value capture: individuals and smaller organisations contribute data or expertise but have limited bargaining power over how the resulting value is distributed.
• Weak accountability: people cannot readily understand, challenge or correct consequential decisions made through digital or AI systems.
• Economic displacement: productivity improvements reduce demand for certain kinds of labour, while workers have inadequate opportunities to retrain or move into higher-value roles.
• Persistent technological dependence: countries that contribute skilled labour and digital services remain reliant on externally controlled infrastructure and intellectual property.

The hypothesis would be weakened if evidence showed that digital systems consistently expand people’s choices, that value and bargaining power are broadly distributed, that effective alternatives remain available, and that AI-driven productivity creates accessible employment and ownership opportunities at a scale sufficient to offset displacement.

It would also need refinement if people gained greater control through privacy-enhancing technologies, open infrastructure, stronger competition, collective bargaining or effective public regulation.

The purpose is not to assume the conclusion. It is to establish what evidence would justify it.

12. What must India do differently?

If the hypothesis identifies a genuine risk, the response cannot be to reject digitisation or AI. That would sacrifice enormous opportunities in healthcare, education, scientific research, public administration and economic development.

The response must instead address ownership, capability, inclusion and accountability.

First, India must move beyond labour arbitrage. The country needs to develop and own more high-value intellectual property, AI products, domain-specific models, specialised datasets and trusted enterprise solutions. Expertise in healthcare, life sciences, financial services, manufacturing and regulatory systems can be a source of differentiation.

Second, productivity gains must translate into wider opportunity. Companies should not treat workforce reduction as the only measure of AI success. Reskilling, redesigned career pathways and stronger entry-level training will matter if fewer routine tasks are available for new professionals to learn through experience.

Third, people need meaningful data rights. Transparency, purpose limitation, appropriate consent, security, accountability and effective remedies must accompany the collection and reuse of personal information. People should not be forced to surrender unreasonable amounts of privacy merely to participate in ordinary life.

Fourth, essential services must remain inclusive. Digital delivery should not become a barrier for people without smartphones, reliable connectivity, digital literacy or the ability to navigate complex applications. Accessible alternatives and human assistance are not signs of technological failure; they are conditions of a fair system.

Fifth, India must strengthen its control over critical capabilities. This does not mean building every technology domestically or pursuing technological isolation. It means developing enough domestic expertise, research, infrastructure, standards and negotiating power to avoid excessive dependence on a small number of external providers.

Finally, AI governance must consider not only whether a system is accurate or secure, but also who benefits, who bears the risk, who can challenge a decision and who remains accountable when something goes wrong.

The goal should be to make technology serve human development rather than making human beings adapt endlessly to the demands of technology.

Conclusion: the next resource is not merely data, but control

Oil helped shape the geopolitical order of the twentieth century. Data is helping shape the economic order of the twenty-first. Artificial intelligence may determine who can extract the greatest value from that data and from the knowledge accumulated through human activity.

India helped build much of the digital infrastructure that made this transition possible. Its IT industry created wealth and opportunity, but it also helped make business and social activity increasingly digitised. The AI transition now presents India with a new challenge: the industry that once supplied large volumes of skilled human labour must adapt to systems that can perform some of that work faster and at lower marginal cost.

This is not proof of a conspiracy, nor does it mean that every digital service is exploitative or that AI will inevitably destroy jobs. The more credible concern is structural. Dependence may grow faster than accountability. Data may become more valuable while the people who generate it retain limited influence over its use. Productivity may rise while the benefits accrue disproportionately to those who own the infrastructure and intellectual property.

The term digital slavery captures the danger of losing practical choice in a world where participation increasingly requires digital systems. AI slavery captures the related danger of becoming economically dependent on technologies whose owners determine the terms of access, while human workers face pressure to compete with the tools they are expected to use.

The question facing India is particularly urgent. Will it remain primarily a supplier of labour to the global technology economy, or will it become an owner and shaper of the systems that generate the next wave of value?

The answer will depend on choices made by companies, policymakers, institutions and citizens.

The country that helped digitise the world must now decide whether it will merely supply the labour to operate the AI economy or help own, shape and govern it.

The ultimate test of technological progress is not how much data we collect, how many processes we automate or how intelligent our machines become. It is whether these advances expand human freedom, distribute opportunity and preserve the ability of people to make meaningful choices about their own lives.

If technology makes society more efficient but leaves people with less control over their information, their work and their essential services, progress will have to be judged by a more difficult measure.

The question is no longer only who owns the oil or who owns the data.

It is who will own the future, and whether the people who create its value will have a meaningful say in it.