Can Indian IT make the most of the AI age? |
For more than two decades, India’s information technology industry built something genuinely admirable: a world class services engine that became the back office of the global economy. TCS, Infosys, Wipro, HCL Tech, and Tech Mahindra collectively generated hundreds of billions of dollars in revenue, employed millions of engineers, and powered an entire ecosystem of technical education across the country. Then came the AI revolution – and the industry was caught almost entirely unprepared.
The launch of ChatGPT in November 2022 was not a sudden shock. It was the visible peak of a transformation building since deep learning went mainstream after 2015. But Indian IT firms, locked into low-risk service contracts and investing less than three per cent of revenues in R&D compared to ten to twenty per cent at global technology companies, had neither the incentive nor the institutional culture to see it coming. By the time the alarm was impossible to ignore, the frontier had already been claimed by OpenAI, Google DeepMind, Anthropic, Meta, and a set of well-funded Chinese laboratories.
What followed was a period of genuine reckoning. The traditional pyramid model, in which armies of junior engineers churned out standardised code with revenues scaling directly with headcount, began to crack. AI could now perform many of those base layer tasks: routine code generation, testing, data processing, and documentation. At the same time, the growth of over 2,000 Global Capability Centres meant multinationals that once outsourced wholesale were now building substantial in-house teams in India. The existential question became unavoidable: if Indian IT cannot build the AI, and its traditional clients are increasingly building their own teams, what exactly is the industry’s role? Four structural failures explain why Indian IT missed the frontier AI race entirely. The first is the services trap.
Indian IT thrived on labour arbitrage and client-centric execution. Revenue was built on contracts, not on products or intellectual property. There was little incentive and considerable risk in allocating capital to speculative, long-horizon research. Shareholders were rewarded with........