The layoff economy

That explanation has steadily lost its force. The layoffs have continued despite healthier balance sheets, rising share prices, and investment commitments. What is unfolding is not simply a cyclical adjustment but a structural change in how technology businesses expect to grow. Several forces are at work. Investors have rewarded tighter cost controls and higher margins, while managements have sought to remove layers of bureaucracy and abandon projects offering uncertain returns.

Artificial intelligence (AI) has accelerated the rethink. Companies are spending heavily on chips, data centres, and computing capacity, even as they question the need for large teams performing routine coding, customer support, testing, and administrative functions. Yet it would be simplistic to blame every lost job on AI. Some cuts reflect restructuring, changing customer demand, or earlier management mistakes.

The distinction matters because technological progress should not become a convenient excuse for indiscriminate downsizing. There is a contradiction: businesses that once competed aggressively for talent now treat employment as a variable to be adjusted rapidly. Repeated layoffs damage morale, weaken institutional memory, and erode the trust on which innovation depends. The country’s information technology services industry has long linked revenue expansion with recruitment, offering millions of graduates a route into the formal workforce.

That model is under pressure as global clients demand productivity improvements and seek to retain more of the savings from automation. Global capability centres, startups, and technology operations elsewhere face similar pressures. The greatest risk may be to fresh graduates: entry-level assignments in programming, testing, and support have traditionally served as training grounds, but many of those tasks are susceptible to automation.

Companies cannot merely announce ambitious AI strategies while leaving workers to finance their own transition. Large employers should commit measurable budgets to continuous skilling, paid learning time, internal mobility, and credible certification. Industry associations can create shared programmes for smaller firms, while universities must update curricula with employers rather than teach yesterday’s tools.

India’s skilling architecture needs focus on emerging roles, from cybersecurity and data engineering to industrial automation and AI oversight. Public funding should reward verified placements and wage gains, not simply enrolment or certificates issued. Apprenticeships, tax incentives for employer-funded retraining, and portable learning accounts could help workers move between occupations. States should work with industry clusters to identify demand before commissioning courses, while stronger unemployment assistance and placement services would make transitions less punishing.

None of this absolves corporate boards of accountability. Workforce plans should be examined as seriously as capital expenditure, with transparent disclosures on job creation, displacement, and retraining outcomes. The industry cannot celebrate record investment in intelligent machines while treating human capabilities as disposable. Innovation will alter employment; whether it produces wider prosperity or deeper insecurity depends on choices companies and governments make now, especially for a young and rapidly expanding workforce.