# Whose Displacement Counts: The Human Cost of AI in India > Source: > Published: 2026-06-29 01:00:30+00:00 ## Whose Displacement Counts: The Human Cost of AI in India In Bengaluru, a software career was never just a job. It was a contract written across generations. A family in a small town in Maharashtra or Telangana would sell farmland, mortgage a house, or empty a lifetime of savings to put a child through an engineering degree, on the understanding that the degree was a bridge. Cross it, and you reached the other side: a salaried position at a multinational, a flat with reliable electricity, a marriage proposal that mentioned the company's name like a credential, an ageing parent's medical bills quietly absorbed. The promise was specific and it was widely believed, because for two decades it largely held. An engineering degree in Bengaluru, Hyderabad, or Chennai was the most reliable path that India's emerging middle class had ever been offered. That contract is now being torn up by the same technology those cities helped build. In January 2026, the publication Rest of World reported on a wave of suicides, layoffs, and automation-driven despair moving through India's IT sector. The reporting documented something that the productivity dashboards of Silicon Valley do not capture: that when an algorithm absorbs the code-writing, testing, and maintenance work that defined a profession, it does not simply free up labour for higher-value tasks. It can dismantle an entire architecture of expectation, one that entire neighbourhoods, marriage markets, and family survival strategies were built around. The crisis is concentrated, communal, and intergenerational. And it is unfolding in some of the cities least represented in the rooms where the future of artificial intelligence is actually decided. This is a story about who counts. ## The Contract That Held for Twenty Years To understand the scale of what is being undone, you have to understand how much was built. India's technology industry is not a sector in the ordinary sense. It is closer to an economic organ. According to the industry body Nasscom, the technology sector reached roughly 282 billion dollars in revenue in the 2025 financial year and was on track to approach the 300 billion dollar mark in the year following. It employs in the region of 5.8 million people directly, and many millions more indirectly through the dense web of housing, transport, catering, retail, and services that grows up around any concentration of salaried workers. The United States accounts for the majority of India's IT outsourcing revenue, which means that for years the prosperity of a young engineer in Pune was tethered to procurement decisions made in boardrooms in New Jersey and California. The human meaning of those numbers is easy to lose. For the generation that came of age after India's economic liberalisation, the IT services boom offered something close to a guaranteed escape route. The work was often unglamorous: maintaining legacy systems, writing and testing routine code, staffing the support functions that kept Western enterprises running through the night. The phrase that recurs in the industry is the round-the-clock cycle, the 24/7 model in which Indian engineers handled the maintenance and overflow that clients in other time zones did not want to do themselves. Krishnakumar Natarajan, a former chairperson of Nasscom who co-founded the services firm Mindtree, has described how the economics of outsourcing depended on this always-on availability. It was demanding, but it was stable, and stability was the entire point. The trouble is that routine, rules-bound, well-documented work is precisely what large language models are best at absorbing. The very characteristics that made Indian IT services scalable and exportable, their standardisation and their predictability, are the characteristics that make them automatable. The sector built its success on doing enormous volumes of structured technical work reliably. AI systems are now extremely good at exactly that. ## The Year the Promise Broke The numbers from late 2025 onward read like a structural rupture rather than a cyclical dip. In the autumn of 2025, Tata Consultancy Services, widely regarded as India's largest private-sector employer, cut close to 20,000 jobs in what was described as its biggest layoff round ever, framed by the company as part of an AI-driven overhaul and a response to a skills mismatch in its workforce. The figure ending the September 2025 quarter showed the company's headcount falling below 600,000 for the first time in recent memory, with nearly 20,000 employees leaving in a single three-month period. The cuts did not go uncontested. The Nascent Information Technology Employees Senate, a union known as NITES, accused TCS of illegal termination and unlawful layoffs, alleging forced resignations, denial of statutory dues, and coercive practices. Its president, Harpreet Singh Saluja, claimed that the true number of departures was thousands higher than the company publicly admitted, suggesting deliberate underreporting. In November 2025, the Pune Labour Commissioner summoned TCS over the complaints, and the union wrote to the Maharashtra Chief Minister alleging the illegal layoff of around 2,500 mid-to-senior employees in Pune alone. Whatever the precise figures, the dispute revealed how little legal cushioning India's IT workforce actually has when the contract breaks. This was not an isolated Indian event. It was the local edge of a global contraction. Across the technology industry worldwide, the first quarter of 2026 alone saw well over 73,000 jobs cut, according to tallies drawn from layoff trackers, with some counts running closer to 80,000 by early in the second quarter. The defining feature of this wave, distinguishing it from previous rounds of tech belt-tightening, was attribution. Where in 2025 employers had cited AI as a factor in a small minority of layoff announcements, by early 2026 close to half of the quarter's cuts were being attributed to reduced need for human workers because of AI and workflow automation. The companies were no longer being coy about it. The machines were named in the redundancy notices. For India, the exposure is structural and disproportionate. Aditya Vashistha, who works on the Global AI Initiative at Cornell University, has argued that India's outsourcing sector is more vulnerable to AI disruption than many Western labour markets precisely because so much of its value rested on the kind of standardised technical work that automation handles most readily. The country that became the backbone of global software services did so by specialising in the tasks that are now first in line to be absorbed. The startup ecosystem offered no shelter either. Through 2025, thousands of additional jobs disappeared across Indian startups, including layoffs at AI-focused ventures, which carried its own bitter irony: people building the automation losing their livelihoods to the automation. ## The Bottom Rung Disappears First The cruelty of this transition is that it lands hardest on the youngest. The work that artificial intelligence absorbs most readily is the entry-level work, the routine code-writing, the unit testing, the bug-fixing and minor upgrades, that historically formed the first rung of the career ladder. That is the rung where a fresh graduate learns the craft, builds a track record, and earns the experience that protects more senior workers. When automation removes the bottom rung, it does not merely cut current jobs. It severs the pipeline by which the next generation was supposed to enter the profession at all. The data on this is stark, and it is not confined to India. Research from economists at Stanford University, drawing on payroll records covering tens of millions of workers, found a roughly 13 percent relative decline in employment among early-career workers in occupations most exposed to AI, even as employment for older and more experienced workers in the same fields held steady or grew. The pattern is consistent across software engineering and other knowledge work, and the explanation is intuitive: the book learning that a recent graduate brings is precisely the kind of generalised, codified knowledge that large language models reproduce most easily, whereas the tacit judgement of an experienced worker is harder to automate. In India, this dynamic collides with a demographic and educational reality that magnifies the harm. The country produces well over a million engineering graduates a year, and even before the current AI wave, the proportion who could secure a job in their field was a fraction of the total, with one widely cited figure suggesting only around a tenth of a recent graduating cohort was likely to find suitable employment. Entry-level salaries, by the assessments of career analysts, had barely moved in real terms across a decade and a half, even as the cost of the education that produced those graduates, and the cost of the housing and food they needed to take up the work, rose steeply. The bargain was already fraying. AI is now pulling the loose thread. ## A Life Plan, Collapsed Statistics describe the shape of a crisis. They do not convey its texture. For that, consider a single case that the publication American Bazaar documented in April 2026, describing it as a stark reminder that AI-driven job losses carry profound human costs that do not appear in productivity statistics. The account concerned Banu Chandra Reddy, a 32-year-old man from Telangana who had built a software career in the United States, and his wife, Bibi Shaziya Siraj, who had worked at IBM. Reddy lost his American job to AI-driven restructuring. He returned to Bengaluru and spent close to a year searching, without success, for work in a market that had absorbed thousands of returnees and freshers all competing for a shrinking pool of roles. Their interfaith marriage, the reporting noted, had already strained ties with family on both sides, depriving the couple of the financial and emotional buffer that, in Indian society, often makes the difference between a setback and a catastrophe. The story, as American Bazaar told it, ended in tragedy, with their deaths framed as the consequence of prolonged unemployment compounded by isolation. What that case makes visible is the load-bearing role a tech salary plays in an Indian family system. In much of the West, a job loss is, however painful, frequently an individual or household event, cushioned to varying degrees by unemployment insurance, savings, and a social safety net. In the communities that fed India's IT boom, a software job is a node in a web of obligation. It pays the school fees of a younger sibling. It services the loan that funded the degree. It underwrites a parent's diabetes medication. It is the reason a marriage was arranged. When that node fails, the failure propagates outward through the entire web, and it does so in a cultural context where professional failure carries a heavy stigma. There is no anonymity in it. The neighbours know. The extended family knows. The matchmaker knows. ## The Stigma That Makes a Setback a Catastrophe That stigma is not incidental to the crisis. It is one of its engines. The Bridge Chronicle, a publication based in Pune at the heart of Maharashtra's IT belt, reported in June 2025 on rising suicides in India's IT sector, tracing them to a combination of work stress, job insecurity, and a social weight that falls especially hard in communities where a technology career carries the expectations of an entire family across generations. When a young person from a modest background becomes the first software engineer in their family, they do not simply hold a job. They hold a thesis: that the sacrifices made to put them there were justified, that the family's bet on education paid off, that mobility is real. The collapse of the job can feel like the collapse of the thesis. The mental health picture that emerges from the reporting is alarming, and that word is not chosen lightly. Jayanta Mukhopadhyay, a senior professor of computer science and engineering at the Indian Institute of Technology Kharagpur, told Rest of World that tech workers face a huge uncertainty about their jobs because of AI, and described the situation as very alarming, noting that job insecurity had worsened markedly since the pandemic. Sanjeev Jain, a professor of psychiatry at the National Institute of Mental Health and Neurosciences in Bengaluru, observed that suicide is increasingly reaching into a professional class whose jobs have become precarious, a class that the old models of risk did not expect to be vulnerable. The conditions surrounding the work compound the danger. Rest of World's analysis of local news reporting identified 227 reported cases of suicide among Indian tech workers between 2017 and 2025. Surveys cited in the reporting suggested that a large majority of Indian tech workers experience burnout and that a substantial share routinely work well beyond 70 hours a week. This is an industry whose most prominent leaders have publicly championed extreme hours. The Infosys co-founder Narayana Murthy called for a 70-hour working week. SN Subrahmanyan, chairperson of the engineering conglomerate Larsen and Toubro, pushed the idea of a 90-hour week. Bhavish Aggarwal, the founder of Ola, advocated a 70-hour week and characterised the idea of work-life balance as a Western cultural import. The result is a working culture that prizes endurance, layered on top of a job market that has suddenly stopped guaranteeing the reward. Bino Paul, a professor of human resources at the Tata Institute of Social Sciences, has described the pandemic as a point of inflection that dissolved the boundary between work and home. When that boundary disappears and is then combined with the fear that the work itself may vanish, the psychological terrain becomes treacherous. Union organisers describe workers who feel trapped, unable to speak out for fear of being blacklisted across an industry that, as the Uni Global Union general secretary Christy Hoffman has noted, offers comparatively weak legal protections and where outspoken employees risk being shut out permanently. ## When the Pillar Was Also a Marriage Plan The collapse of the IT promise does not fall evenly across a household, and it is reshaping social arrangements that extend well beyond the workplace. In the communities that fed the boom, a software job was woven directly into the marriage market. A position at a recognised multinational was a desirable credential in arranged matches, a signal of stability and earning power that families weighed as carefully as caste, education, and horoscope. Reporting on the sector's restructuring has noted how the crisis is unsettling not only careers but the gender roles and matrimonial expectations that were built on top of them, as the assumed reliability of a tech salary, long treated as a near-certainty, becomes a question mark. For women in the sector, the squeeze can be sharper still. India's IT industry was, for many women, one of the more accessible routes into formal, well-paid professional work, a sphere where merit and qualification could, at least relatively, outweigh the constraints they faced elsewhere in the labour market. As that route narrows, the loss is not only of income but of one of the few escalators of independence available to a generation of women whose families had finally been persuaded that a daughter's engineering degree was worth the investment. The retreat of those jobs risks pushing hard-won gains backward, returning to households a question they had thought settled about whether such an education pays off. The case of the Bengaluru couple captures how these threads tangle together. Here was a marriage that had already cost both partners the support of their families, sustained instead by two incomes in a sector everyone had assumed was secure. When AI restructuring removed one of those incomes and a saturated market refused to replace it, the couple lost not only money but the very thing the careers were supposed to guarantee: a basis for independence from the family networks they had stepped away from. The job was never just a job. It was the foundation a whole life had been built to stand on, including the parts of that life that had nothing to do with writing code. ## The People Building the Machine There is a deeper layer of irony here, and it concerns who actually builds artificial intelligence. The popular image of AI is of brilliant systems that learn on their own. The reality is that they learn from oceans of human-labelled data, and a great deal of that labelling is done in exactly the kind of communities now being displaced. India is one of the central hubs of the global data annotation industry, the painstaking and largely invisible work of tagging images, transcribing audio, moderating content, and rating model outputs that makes machine learning possible. From a market worth a few hundred million dollars at the start of the decade, India's share of the global annotation business is projected to reach several billion dollars by 2030, with estimates suggesting that around a million data annotators may be active in the country by 2028. These ghost workers, as researchers have come to call them, are often paid little, work without protections, and remain absent from the story the industry tells about itself. Among the most marginalised are Adivasi women, drawn from tribal communities that have long been excluded from formal employment, who now label the data that trains systems they will never be consulted about. The structure is stark. The labour that teaches the machine is sourced from the global majority. The decisions about how the machine is governed are made elsewhere. And when the machine is ready to replace human work, it is frequently the labour of the global majority that it replaces first. Data workers across the global South, in India, Kenya, the Philippines, and elsewhere, have for years performed the human effort behind nominally automated systems, sometimes for only a couple of dollars an hour and frequently in psychologically gruelling conditions involving the review of disturbing content. They are the part of the supply chain that the marketing never mentions, and they sit at the precise intersection of the two harms this article describes: undervalued while building the technology, and exposed when it matures. This is the pattern that the Western conversation about AI has been slow to see. The communities most acutely harmed by AI displacement are, in significant part, the same communities that supplied the human effort to build it. ## Whose Displacement Counts Here is the question that the Indian crisis forces, and that the brief at the heart of this article asks directly: whose displacement counts in the global reckoning with AI? The dominant Western framing of AI and labour tends to imagine displacement in particular bodies and particular places. It pictures a warehouse worker in the American Midwest, a lorry driver anticipating autonomous vehicles, a mid-career professional in Europe whose white-collar role is being hollowed out. These are real and serious harms. But they share a geography, and that geography is also where the policy gets written. The displaced worker in Ohio and the policymaker drafting the AI strategy in Washington or Brussels inhabit the same political space. Their concerns can travel relatively short distances to reach the rooms where decisions are made. The displaced engineer in Bengaluru has no such proximity. The research on AI governance is blunt about the imbalance. Studies of the international AI governance landscape have found that the overwhelming majority of AI policy and governance activity is concentrated in Europe and North America, with one analysis putting the European and North American share at around 58 percent against roughly 1.4 percent for the entire African continent. Work presented at the academic conference on Fairness, Accountability, and Transparency has documented how grassroots organisations, indigenous communities, speakers of low-resource languages, workers in the AI data supply chain, and young people who will inherit these decisions are routinely marginalised in the global dialogues that set the agenda. The Brookings Institution and other bodies have catalogued how the coloniality of power persists in AI governance, with the priorities of richer countries shaping the ethics debate far more than those of the societies bearing much of its cost. The consequence is not merely symbolic. Representation determines what gets measured, and measurement determines what gets addressed. When the harms of AI are framed primarily as a problem of Western workers, the policy instruments designed to respond, the retraining programmes, the disclosure requirements, the social protections, are calibrated to Western labour markets. The Keep Call Centers in America Act, debated in the United States in 2025, sought to protect American customer service jobs from both offshoring and automation. There is no remotely comparable political momentum to protect the Indian worker whose job is being automated, in part because the rooms where such momentum would be generated do not contain many people who can speak to that worker's reality. This is what it means that the communities most acutely harmed are also among the least represented. It is not only that they suffer. It is that their suffering does not register in the instruments built to track and mitigate the technology's effects. A productivity statistic that records a 30 percent efficiency gain at a multinational does not record the engineer in Hyderabad who can no longer pay his sister's tuition. The human cost is real, but it falls outside the frame, and a cost outside the frame is, for policy purposes, a cost that did not happen. ## The Weight a Career Was Made to Carry It would be a mistake to treat the suicides as a separate tragedy, a mental health footnote to an economic story. They are the most extreme expression of the same structural fact: that in India, a tech career was loaded with more meaning, and more weight, than any single job should reasonably be asked to bear. You can see the same load operating at the top of the educational pyramid, in the institutions that feed the industry. A 2026 investigation by Al Jazeera documented around 160 student suicide deaths recorded across the Indian Institutes of Technology over two decades, with 69 of them in the most recent five years. These are the country's most elite engineering schools, the apex of the very aspiration that defines the sector, and the pressure inside them is lethal. In 2024, by the figures the investigation cited, 38 percent of IIT graduates went unplaced, a remarkable number given that admission to these institutions is treated across India as the ultimate validation of a young person's worth. More than a million school leavers sit the entrance examination each year for a few tens of thousands of seats. The funnel is brutal at every stage, and the reporting found that students from Scheduled Castes, Scheduled Tribes, and Other Backward Classes were significantly overrepresented among the suicides, the same communities for whom the engineering degree was supposed to be the great equaliser. What connects the IIT student and the laid-off TCS engineer and the returnee couple in Bengaluru is a single cultural fact. In these communities, a technology career was never sold as one option among many. It was sold as the option, the load-bearing pillar of an entire mobility strategy that families pursued at enormous personal cost across decades. When AI restructures that career, it does not remove a pillar from a building with many pillars. It removes the pillar that the whole structure was balanced upon. ## What a Fairer Reckoning Would Require None of this is an argument that the technology should not exist, or that automation can be wished away. The data annotation projections, the productivity gains, the sheer momentum of the systems make clear that the transformation is underway and will not reverse. The question is not whether AI reshapes work but whether the reshaping is governed by anyone who can see the full cost of it. A fairer reckoning would begin by widening the frame of measurement. The human costs that do not appear in productivity statistics, the family obligations broken, the medical bills unpaid, the mental health crises, are invisible largely because no one with power has chosen to count them. They are countable. India keeps records. Unions like NITES are documenting layoffs that companies prefer to underreport. Researchers at NIMHANS and at institutions like IIT Kharagpur and the Tata Institute of Social Sciences are studying the patterns. The information exists; what is missing is its incorporation into the instruments of global AI governance. A fairer reckoning would also require changing who is in the room. The structural underrepresentation of the global majority in AI policy is not a regrettable accident to be acknowledged and then ignored. It is the mechanism by which the harms of AI become invisible. As the governance researchers argue, inclusion is only the first step toward a redistribution of agenda-setting and decision-making power. Until the engineer in Bengaluru, the union organiser in Pune, the data annotator in a tribal district, and the psychiatrist in a Bengaluru hospital have a route into the conversations where AI's labour effects are weighed, those conversations will continue to mistake a partial view for the whole. And it would require a kind of honesty about reciprocity. The communities now bearing the sharpest edge of AI displacement are not bystanders to the technology. They wrote its code, staffed its support lines, and labelled its training data. They were the backbone of the global software economy and a substantial part of the human scaffolding on which modern AI was built. There is a particular injustice in a system that draws so heavily on a community's labour while excluding that community from any say in how the resulting technology is deployed against it. The contract that held in Bengaluru, Hyderabad, and Chennai for twenty years was, in the end, a promise made by a global economy to the people who agreed to do its most exportable work. That promise is being rewritten by machines those same people helped create, in rooms those same people are rarely allowed to enter. Whose displacement counts is not an abstract question of ethics. It is a question about whose pain shows up in the data, whose voice shapes the policy, and whose generational bet on the future is allowed to fail in silence. So far, the answer has been written by the people least likely to pay the price. The communities of India's tech cities are asking, with growing urgency, for that to change while there is still something left to save. ## References and Sources - Rest of World, “India's tech workers in crisis amid suicides, layoffs, and AI,” January 2026. 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Six Facts about the Recent Employment Effects of Artificial Intelligence,” Stanford Digital Economy Lab, 2025. [https://digitaleconomy.stanford.edu/](https://digitaleconomy.stanford.edu/) - WION, “Anxiety from AI driving Indian IT workers towards suicide: Report,” 2026. [https://www.wionews.com/trending/artificial-intelligence-job-threat-layoffs-india-it-workers-suicide-1769766292342](https://www.wionews.com/trending/artificial-intelligence-job-threat-layoffs-india-it-workers-suicide-1769766292342) **Tim Green** *UK-based Systems Theorist & Independent Technology Writer* Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at [smarterarticles.co.uk](https://smarterarticles.co.uk), challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship. His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities. **ORCID:** [0009-0002-0156-9795](https://orcid.org/0009-0002-0156-9795) **Email:** [tim@smarterarticles.co.uk](mailto:tim@smarterarticles.co.uk) Listen to the free weekly [SmarterArticles Podcast](https://www.smarterarticles.fm)