The Convenient Story of AI

When employees lose their jobs during a period of rapid technological change, the explanation often appears obvious: artificial intelligence has taken the job. It is a compelling story because it fits the anxieties of the present. Generative AI is expanding across industries, companies increasingly speak about automation and “leaner” organisations, and investors are rewarding businesses that promise to achieve more with fewer employees. In this environment, every major layoff can easily be presented as evidence that machines are replacing human beings.

India’s startup sector has recently become a prominent example of this narrative. In February 2026, reports based on data from executive search firm Longhouse Consulting indicated that Indian startups had laid off more than 4,500 employees since July 2025. The figure attracted particular attention because companies were simultaneously restructuring their operations around AI, profitability and efficiency. However, the timeline behind these layoffs reveals a more complicated reality. The job cuts did not emerge from a single technological event, nor can all of them reasonably be attributed to AI. They were shaped by a combination of funding constraints, investor pressure for profitability, regulatory shocks, strategic restructuring and, in some cases, genuine automation.

This distinction matters. To say that AI is changing the labour market is increasingly accurate. To say that AI is the principal cause of every recent layoff is not. In several cases, artificial intelligence appears less as the original cause of workforce reduction than as a language of justification—a powerful explanation through which older corporate pressures are reframed as technological inevitability. The more important question, therefore, is not simply whether AI is eliminating jobs. It is this: when companies announce “efficiency” layoffs, how much of that efficiency is genuinely created by AI, and how much reflects financial pressures and business decisions that existed before AI became the preferred explanation?

A company-by-company examination of India’s recent startup layoffs suggests that the answer varies considerably. The result is not a simple story of humans versus machines. It is a story about how technology, capital, regulation and corporate strategy increasingly interact in determining who remains employed and who does not.

The Timeline Problem: What Came Before the AI Narrative?

The strongest test of the claim that “AI caused the layoffs” is chronology. If AI-driven automation was the dominant cause, one would expect the major workforce reductions to follow directly from identifiable technological deployment. Yet the broader Indian startup layoff cycle began accelerating amid pressures that cannot be reduced to AI. The more than 4,500 layoffs reported in February 2026 covered the period beginning in July 2025. This is important because the figure is sometimes presented as if it were a continuously updated “February–August 2026” total. It was not. The reported figure referred to job losses since July 2025, meaning that any serious analysis must preserve the original timeframe rather than attach a misleadingly newer date to an older dataset.

By late 2025, Indian startups were already confronting a changed investment environment. The era in which rapid expansion and aggressive hiring could be justified primarily by growth expectations had been giving way to a stronger demand for discipline. Investors increasingly wanted companies to demonstrate a credible path to profitability. Executive search firm Longhouse's analysis, as reported by major business outlets, described startups as becoming more selective in hiring, prioritising essential and senior roles while designing organisations around smaller headcounts.

This shift cannot be understood as an exclusively AI-driven phenomenon. The pressure to control costs existed because the economics of startups had changed. Capital had become more selective, valuations could no longer justify unlimited expansion, and investors increasingly evaluated businesses through efficiency as well as growth. AI subsequently became part of this transformation because it offered a technological means of operating with fewer people. But the desire to reduce headcount was often already present.

This distinction between cause and instrument is central. A company may use AI to reduce its workforce, but that does not necessarily mean AI created the underlying economic pressure to reduce it. If profitability targets, funding constraints or investor expectations first created the incentive to shrink teams, AI may function as an enabler of restructuring rather than its original cause.

Zepto: Efficiency Without the AI Explanation

The case of Zepto demonstrates why corporate layoffs should not automatically be interpreted through the lens of automation. In October 2025, around 300 employees were reported to have been affected as the quick-commerce company pursued greater cost efficiency amid intense competition. Reports linked the exercise to efforts to reduce spending and tighten operations rather than to an announced programme of replacing workers with AI.

The distinction became even clearer in subsequent reporting. A Zepto spokesperson specifically told India Today that the employees affected in the earlier layoffs were not impacted because of AI optimisation. The company associated the reductions with cost-saving measures and operational efficiency.

This does not mean Zepto operates outside the broader technological transformation. Like virtually every major technology-enabled company, it has incentives to adopt automation and AI where they improve speed and productivity. But technological adoption and AI-caused layoffs are not identical propositions. A company can adopt AI while laying off employees for financial reasons, organisational duplication or changing competitive conditions.

The Zepto example exposes a common weakness in public discussion. Once AI becomes a dominant social narrative, almost every workforce reduction at a technology company can appear to confirm it. Yet correlation is not causation. A firm may simultaneously be investing in AI, reducing costs and restructuring its workforce. Without examining the company's stated reasons and the timing of its decisions, it is impossible to know which factor carried the greatest weight.

Zupee: When Regulation, Not AI, Destroys the Existing Business Model

If Zepto complicates the AI narrative, Zupee directly challenges it. The company’s layoffs occurred in the aftermath of a major regulatory shock: India’s 2025 legislation prohibiting online real-money gaming.

In August 2025, Parliament passed legislation banning online games involving monetary transactions. The change immediately disrupted companies whose businesses depended heavily on real-money gaming. Major platforms began shutting down or suspending such operations, and concerns quickly emerged about substantial job losses across the sector.

Zupee subsequently laid off approximately 170 employees in September 2025 and around another 200 in January 2026 as it attempted to realign its business following the regulatory ban. Reports connected the workforce reductions directly to the collapse of the company's existing real-money gaming model and its transition towards free-to-play games, esports and other content formats.

Calling these layoffs “AI job losses” would therefore be analytically misleading. The immediate shock was regulatory. The government changed the legal conditions under which an important part of the company’s business could operate. The company then had to redesign its organisation around a different business model.

AI may have entered Zupee's future strategy, particularly as the company explored newer forms of digital content. However, a technological strategy developed after a regulatory disruption should not erase the original sequence of events. First came the policy shock; then came the business-model adjustment; within that adjustment came restructuring and new technological priorities.

The wider gaming industry reinforces this point. Reporting in September 2025 indicated that nearly 2,000 employees across real-money gaming companies had lost their jobs following the regulatory ban, while companies scrambled to suspend operations, pivot or reduce costs.

This episode illustrates a broader principle about employment analysis: technology may be visible, but visibility does not establish causality. A regulatory decision can eliminate the economic foundation of a business far more immediately than an algorithm ever could.

Porter: The Pressure of Profitability and the Coming IPO

Another major example is the logistics startup, Porter. In November 2025, the company was reported to have cut approximately 300 to 350 jobs as it rationalised costs while considering plans for a potential public listing. The company had already turned profitable in FY25, yet profitability did not remove the pressure for greater efficiency.

This case is particularly revealing because it challenges another popular assumption: that layoffs occur only when companies are failing.

Modern corporate restructuring does not necessarily wait for financial collapse. A company preparing for an IPO may seek to improve margins, simplify its organisation and demonstrate disciplined expenditure to future investors. Under these circumstances, “efficiency” becomes a strategic objective even when the business is generating revenue or profits.

AI may eventually help companies such as Porter optimise logistics, forecasting and operations. But the reported rationale for the layoffs was cost rationalisation and preparation for the company's next financial stage. Again, the evidence points towards a broader corporate logic in which headcount is treated as a variable to be optimised.

The implication is uncomfortable but important: some contemporary layoffs are not signs that companies have been defeated by technological change. They may instead reflect companies becoming more financially demanding about the relationship between labour and output.

Livspace: Where AI Is More Than an Alibi

The argument that AI is sometimes used as an alibi should not be misunderstood as an argument that AI has no real employment consequences. Livspace provides the strongest counterexample.

In February 2026, the home-interiors startup laid off approximately 1,000 employees—about 12% of its workforce—while undertaking an organisational shift towards AI agents and automation. The affected functions reportedly included sales, design, operations and marketing. Company statements described the restructuring as part of an effort to integrate AI and automation more deeply into core functions and to build a more AI-native organisation.

Here, AI cannot simply be dismissed as a public-relations explanation. The company explicitly connected workforce restructuring with a transition in how work itself would be performed. Predictive systems, automation and AI-enabled processes were intended to reduce the need for some forms of manual oversight and repetitive activity. Yet even Livspace should not be interpreted through a single-cause model. AI adoption and cost reduction were occurring together. The company was reorganising because it believed technology could allow a different cost structure and operating model. Thus, AI was both a genuine technological force and part of a wider strategic decision about where human labour should remain.

This is perhaps the clearest example of what the future may increasingly look like. AI will not always “replace a worker” in the dramatic sense imagined by popular culture. More often, it may reorganise entire workflows. Once a process can be completed by a smaller team using automated tools, the company may decide that the previous workforce is economically unnecessary.

The consequence for workers is the same: jobs disappear. But analytically, the process is different from a machine independently causing unemployment. Human managers, investors and corporate boards still decide whether the productivity gains from technology will be used to expand output, reduce prices, shorten working hours or eliminate positions. AI does not make that decision by itself.

Krutrim and the Paradox of the AI Company

Perhaps the most revealing case is Krutrim, an artificial intelligence company itself. If AI companies are assumed to be the inevitable winners of the technological transition, one might expect them to be immune from layoffs. Krutrim demonstrates otherwise. The company underwent repeated restructuring. Reports documented layoffs in 2025, further changes as the company narrowed its strategic focus, and another significant workforce reduction in July 2026. The latest reported round affected roughly 20 to 25 employees, or nearly half of its then-current workforce, following an earlier restructuring that had affected sales, go-to-market and business operations. The company stated that it periodically reviewed team structures to align with evolving priorities.

Krutrim's story complicates the simplistic claim that “AI is taking jobs” because the company creating AI was itself restructuring. Its employment decisions reflected strategic uncertainty, changing priorities and the difficult economics of building a domestic AI business. Earlier reports also connected the company’s repositioning towards cloud infrastructure and enterprise services with a retreat from some of its previous ambitions.

This reveals an important truth about technological industries: being an AI company does not guarantee stability. New technology can create opportunities while simultaneously producing enormous strategic uncertainty. Companies must decide which products to pursue, which markets to enter and which skills remain valuable. Employees may lose their jobs not because AI replaced them, but because management changed its understanding of what the company should become. The future of work, therefore, is shaped not only by technology but by strategic volatility.

Why “Efficiency” Has Become the New Corporate Language

Across these cases, one word repeatedly appears in different forms: efficiency. It sounds neutral. Few companies would publicly describe a layoff as a decision to improve investor returns by reducing payroll. “Efficiency,” however, gives the decision a broader sense of necessity. It suggests that a company is adapting rationally to a changing world. AI strengthens this language because it provides a credible technological basis for leaner organisations. Investors increasingly reward companies that can demonstrate greater output with fewer resources, while founders are encouraged to build “lean by design.” Longhouse's analysis of the startup environment described investors as favouring businesses capable of achieving significant milestones with optimised headcounts and longer financial runways.

This is where AI can become an alibi—not necessarily a false one, but an incomplete one. A company facing funding pressure may already want to reduce headcount. AI then offers a persuasive explanation for why the reduction is not merely a cost-cutting exercise but part of a technologically advanced transformation. The language changes from “we need fewer employees” to “we are becoming AI-native.”

Sometimes that transformation is real, as Livspace demonstrates. Sometimes the primary driver lies elsewhere, as the cases of Zepto, Zupee and Porter indicate. Often, the truth is a combination.

The danger lies in allowing AI to become a universal explanation. If every layoff is described as technological inevitability, companies and investors escape scrutiny for decisions that are also financial and strategic. Workers are told that history itself has made their jobs obsolete, even when the immediate cause may be a funding shortage, a regulatory shock or a boardroom decision.

The Human Cost of a Simplified Narrative

The AI narrative also affects how society understands unemployed workers. If a job disappears because “AI took it,” the worker may be told to reskill and adapt. Reskilling is important, but it cannot solve every form of job loss.

A Zupee employee displaced after a regulatory ban cannot simply acquire a prompt-engineering certificate and recreate the lost business model. An employee affected by an investor-driven restructuring faces a problem of organisational strategy, not merely obsolete skills. A worker removed during an IPO-oriented cost rationalisation may be perfectly capable of doing the job that has disappeared; the company has simply decided it no longer wishes to pay for that position.

This does not reduce the importance of technological adaptation. India's labour market will undoubtedly require new skills as AI becomes embedded in software development, customer support, marketing, design, data analysis and operations. Recent analysis by Nomura, reported in August 2026, even suggested that AI-related hiring in India had so far exceeded the documented AI-related layoffs and attrition in its dataset, while simultaneously contributing to a more polarised labour market that favours workers with deeper experience and context.

That finding deserves attention because it offers a more nuanced picture than technological pessimism. AI may not simply destroy work; it may redistribute opportunity. Some routine and entry-level functions may face pressure while demand grows for people capable of supervising systems, applying domain knowledge and making complex decisions.

The real question is therefore not whether India will have jobs “with AI” or “without AI.” It is whether the gains from AI-driven productivity will create enough new, secure and meaningful opportunities for workers displaced during the transition.

The Machine Is Not the Only Decision-Maker

India's recent startup layoffs should be understood as a warning against simplistic explanations. Artificial intelligence is unquestionably changing how companies organise work. Livspace demonstrates that automation can directly reshape workforce requirements, while the wider AI economy is encouraging firms to imagine smaller and more technologically intensive organisations. But the evidence does not support treating AI as the universal cause of recent job losses.

Zepto's reported layoffs were explicitly separated from AI optimisation and linked to cost efficiency. Porter's cuts were associated with cost rationalisation and its strategic financial trajectory. Zupee's workforce reductions followed a dramatic regulatory disruption to the real-money gaming sector. Krutrim, despite being an AI company, repeatedly restructured because technological ambition alone did not eliminate the pressures of strategy, funding and business viability. The deeper lesson is that technology does not operate outside economics. AI can automate tasks, but it does not independently decide to dismiss employees. Those decisions remain embedded in corporate strategies, investor expectations, market competition and public policy.

Calling every layoff an “AI layoff” therefore risks giving technology too much agency and institutions too little responsibility.

The more honest description of the present moment is one of efficiency-driven restructuring in which AI is one important force among several. In some companies, it is genuinely transforming the organisation. In others, it is accompanying a pre-existing drive towards profitability and smaller teams. And in still others, it may be serving as a convenient vocabulary through which difficult business decisions are made to appear technologically inevitable. That distinction matters for India's future of work. If policymakers, workers and educational institutions misdiagnose the problem, they may prescribe the wrong solution. A country cannot reskill its way out of regulatory shocks, nor can an AI course solve employment losses created by a funding squeeze. At the same time, ignoring genuine technological displacement would leave workers unprepared for a labour market that is undeniably changing.

The challenge, then, is to resist both extremes: neither treating AI as a harmless tool nor blaming it for every corporate decision. The future of employment will depend on how India governs technology, supports innovation, protects workers during transitions and asks companies a question that “efficiency” often conceals: When technology makes it possible to do more with fewer people, who decides what happens to the people no longer considered necessary?

That is the question behind India's efficiency layoffs. And unlike an algorithm, it still demands a human answer.

References

1. Economic Times. (2025, September 16). Ban-hit real money gaming companies fire 2,000, setting off a scramble for jobs
2. Economic Times. (2025, November 5). Logistics startup Porter lays off 300–350 employees
3. Economic Times. (2026, January 30). Zupee lays off 200 in second round of job cuts after real money gaming ban
4. Economic Times. (2026, February 21). Livspace lays off 1,000 employees, cofounder exits amid AI-led overhaul
5. Economic Times. (2026, February 26). Indian startups laid off over 4,500 employees since July
6. India Today. (2026, February 27). Indian startups fired more than 4,500 employees in the last eight months: Is it because of AI?
7. India Today. (2026, July 28). Krutrim layoffs: AI startup restructures again as it narrows its focus
8. Moneycontrol. (2025, October 18). Around 300 employees impacted at Zepto amid cost-efficiency drive
9. Moneycontrol. (2026, January 30). Zupee cuts 200 jobs amid organisational restructuring
10. Moneycontrol. (2026, February 20). Livspace shakeup: 1,000 employees fired as company focuses on AI
11. Reuters. (2025, August 21). India passes bill banning money-based online games, app shutdowns loom
12. The Times of India. (2026, February 26). Over 4,500 startup employees lose jobs since July
13. The Times of India. (2026, August 8). AI creating more jobs than it cuts in India
14. YourStory. (2026, July 28). AI firm Krutrim lays off nearly half of its workforce

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