Education Reform, The Answer to the AI Jobs Threat
Indian IT stocks have fallen of late, on fears that large language model agents such as those developed by Anthropic can automate chunks of work currently outsourced to Indian IT services. This is not a distant threat; it is happening now. The question is not whether AI will disrupt India’s IT sector, but how quickly and how deeply.
India is already facing a problem with jobs and growth in real wage rates. Real wages for salaried jobs in India have experienced stagnation or decline in recent years, with data up to mid-2024 showing they were 1.7 per cent lower than pre-pandemic 2019 levels. While nominal wages have increased, high inflation has eroded purchasing power, causing negative real wage growth in 2020–21 and uneven recovery since.
The Scale of the Crisis
Indian IT companies are already laying off workers, with over 50,000 employees at risk of losing their jobs this year. The top six IT companies added just 3,847 employees in Q1 of FY26, a 72 per cent drop from Q4FY25. This is not a slowdown; it is a structural shift.
This is bad news, as around 80 per cent of India’s 50 million white-collar workers belong to the IT sector, which indirectly supports the livelihoods of another 250 million blue-collar workers such as drivers, security guards, and domestic help, with the implication of further worsening their economic conditions. When IT jobs disappear, the ripple effects are felt throughout the economy.
According to a report from the Federal Reserve Bank of New York, among college graduates aged 22–27, computer science and computer engineering majors are facing some of the highest unemployment rates, ranging between 6.1 per cent and 7.5 per cent, respectively. That is more than double the unemployment rate among recent biology and art history graduates, which is just around 3 per cent.
In an ironic twist, job applicants leverage AI tools such as Simplify to mass-customize their resumes and applications, only to have companies use similar AI technology to automatically screen them out. The arms race between applicants and employers is being fought with the same weapons.
The Human Dimension
The silver lining is AI cannot be a perfect substitute for human workers. Humans and AI make decisions differently, and these differences create powerful complementarities rather than pure competition.
Humans excel at contextual understanding and intuitive judgment. They can make sense of ambiguous situations, use experience-based knowledge, and adapt quickly when circumstances change. Much of this “implicit knowledge” comes from hands-on experience and is difficult to codify. AI, on the other hand, thrives in environments rich with data, processing vast amounts of information that humans might miss. It is highly consistent and does not suffer from fatigue or cognitive overload. And because it scales at low cost, AI can perform millions of decisions rapidly.
Yet AI also has weaknesses: it requires large, high quality datasets to function effectively, and it struggles in unfamiliar or novel situations where historical data is limited. This is where humans retain an edge—for now.
The Policy Gap
The government and the educational ecosystem in India need to gear up for this change before AI starts transforming itself from being “task” specific to the whole job itself. Unfortunately, that does not appear to be the case.
For example, there has been a cut in revenue expenditure of ₹6,701 crore in the education sector in the 2025–26 Budget. At a time when education reform is most urgent, resources are being reduced. This is a misalignment of priorities.
Indian IT companies are also not keeping up with the required investment needed for AI-led innovation. India spends only 0.6 per cent of GDP on research and development, against 3.75 per cent of GDP spent by China. This gap is not sustainable. Innovation requires investment, and investment requires commitment.
The Path Forward
There is a need for government intervention by combining reskilling, sector-specific support, and pro-employment policies, rather than trying to stop automation. Trying to hold back the tide of technological change is futile; the only sensible response is to prepare for it.
A clear direction should be given to educational institutions to design curricula suited to producing STEM graduates, and to incentivize NIRF rankings on the basis of how well universities adapt to that goal. The metrics by which institutions are judged should reflect the skills that the economy needs.
As in the case of the UK, the government could also provide direct subsidies to students enrolling in AI-related courses or pursuing a PhD. Making education in critical fields affordable and accessible is a direct investment in the country’s future.
The Risk of Inaction
India should upgrade its workforce with AI, not allow replacement by it. Otherwise, India will slowly be transforming into a gig economy where the labour market is increasingly characterised by the prevalence of short-term contracts, low skilled, and freelance work.
The gig economy is not inherently bad, but it is not a substitute for stable, well-paying employment. It offers flexibility but not security. It allows for quick adjustments but not long-term planning. It may suit some workers, but it cannot be the future for 50 million white-collar professionals.
Conclusion: A Call to Action
The AI challenge to India’s IT sector is real and urgent. It requires a response that is equally real and urgent. Education reform, reskilling, investment in R&D, and policy support are not optional; they are essential.
The government and educational institutions must join hands to meet this challenge. The alternative is a future where India’s once-proud IT sector becomes a cautionary tale, and where millions of workers are left behind by a technological revolution they could not adapt to.
That future is not inevitable. But avoiding it requires action now.
Q&A: Unpacking the AI Jobs Challenge
Q1: What is the current employment situation in India’s IT sector?
The top six IT companies added just 3,847 employees in Q1 FY26, a 72% drop from the previous quarter. Over 50,000 employees are at risk of losing jobs this year. Real wages for salaried jobs are 1.7% lower than pre-pandemic 2019 levels, with inflation eroding purchasing power. Computer science graduates face unemployment rates of 6.1-7.5%, double that of biology and art history graduates.
Q2: How does AI differ from human workers in ways that create complementarity?
Humans excel at contextual understanding, intuitive judgment, adapting to novel situations, and applying implicit knowledge from experience. AI thrives in data-rich environments, processes vast information consistently at low cost, and doesn’t suffer fatigue. AI struggles with unfamiliar situations lacking historical data; humans retain the edge in ambiguity. These differences suggest collaboration rather than replacement.
Q3: What is India’s R&D spending compared to China?
India spends only 0.6% of GDP on research and development, while China spends 3.75% of GDP. This gap is unsustainable for AI-led innovation. Additionally, the 2025-26 Budget cut education revenue expenditure by ₹6,701 crore—at a time when education reform is most urgent, resources are being reduced.
Q4: What policy interventions does the article recommend?
Three key recommendations: combine reskilling, sector-specific support, and pro-employment policies rather than trying to stop automation; direct educational institutions to design curricula suited to producing STEM graduates and incentivize NIRF rankings based on adaptation to that goal; provide direct subsidies to students enrolling in AI-related courses or pursuing PhDs, following the UK model.
Q5: What is the risk of inaction?
Without action, India risks transforming into a gig economy characterized by short-term contracts, low-skilled work, and freelance arrangements. This would not replace stable, well-paying employment for 50 million white-collar professionals and 250 million indirectly dependent workers. The once-proud IT sector could become a cautionary tale of technological disruption.
