What AI Means for Your Career
The recent fall in Indian IT stocks due to the belief that large language model agents such as those developed by Anthropic can automate chunks of work currently outsourced to Indian IT services has led to a downfall of the Nifty IT index by more than 6%. After Anthropic unveiled a suite of enterprise-focused AI tools, such as "Claude Cowork" and specialised plugins for legal, sales, finance, and data analysis workflows, global software and IT services stocks sold off. Indian IT majors like TCS, Infosys, HCLTech, Tech Mahindra, and Wipro fell by roughly 4–7% in a single session on February 6, and the market is yet to stabilise at the time of writing this article.
Investors see Anthropic’s tools as capable of automating repetitive coding, testing, contract review, dataprocessing and routine support tasks that form a core part of Indian IT’s offshoreservices playbook. Because these AI agents can run 24×7 at marginal cost, the market worries that clients may shrink low-end, labour-intensive projects, compressing volumes and margins for Indian vendors.
The AI-related shock is particularly relevant for India, which is often touted as having the advantage of a young population relative to the rest of the world, with a median age of 28 years. This demographic dividend could disappear in the face of the AI revolution and soon turn into a demographic burden.
This is particularly relevant as over the last few years, India is facing problems with respect to employment generation 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% lower than pre-pandemic 2019 levels. While nominal wages have increased, high inflation has eroded purchasing power, causing negative real wage growth in 2020-2021 and uneven recovery since.
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% drop from Q4FY25. This is bad news, as around 80% 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.
How true is the fear that AI is going to take over all the jobs? The answer is both Yes and No. Yes, because technology is changing too fast making it difficult for the supply of engineers and tech hires to keep up with the jobs getting created. AI, automation, and cloud computing are changing the face of tech hiring, something which came too quickly, especially when there is a growing pool of computer science graduates specializing in coding that has been growing over the years. Now co-pilots have taken over the jobs of these engineers working as coders.
According to a report from the Federal Reserve Bank of New York, among college graduates ages 22 to 27, computer science and computer engineering majors are facing some of the highest unemployment rates, ranging between 6.1% and 7.5%, respectively. That is more than double the unemployment rate among recent biology and art history graduates, which is just around 3%. In an ironic twist, job applicants leverage AI tools such as Simplify to mass-customise their resumes and applications, only to have companies use similar AI technology to automatically screen them out.
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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. By processing vast amounts of information, AI can detect subtle patterns 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.
The government and the educational ecosystem in India need to gear up for this change. Unfortunately, that does not appear to be the case. The Indian government still spends only 4.2% of its GDP on education, compared to upwards of 5.5% of GDP spent by developed nations such as the USA and South Korea – which translates to significantly higher figures in absolute dollar terms. Even money allocated for education sector is not spent entirely. For example, there has been a cut in revenue expenditure of Rs. 6,701 crore in the education sector in the 2025–2026 budget.
There is a need for government intervention by combining reskilling, sector-specific support, and pro-employment policies, rather than trying to stop automation. To protect livelihoods, the government must scale up reskilling through programmes such as Skill India, offering AI-aligned courses in data science and AI operations. A clear direction should be given to educational institutions to design curricula suited to producing STEM graduates, and to incentivise NIRF rankings on the basis of how well universities adapt to that goal.
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. Additionally, investment is also necessary to create AI infrastructure, data centers, semiconductors, and startups to absorb displaced talent into new roles. Rather than resisting AI, India should upgrade its workforce with it, 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 as opposed to high paid permanent jobs.
Nilanjan Banik and Pranjal Chandrakar are associated with the School of Management, Mahindra University.
This article went live on February nineteenth, two thousand twenty six, at forty-two minutes past five in the evening.The Wire is now on WhatsApp. Follow our channel for sharp analysis and opinions on the latest developments.
