For three decades, India’s global ascent has rested on deploying skilled labour at scale, pricing it competitively and exporting it. Today, IT services sector generates $224 bn in annual exports, contributes about 7% of GDP, and directly employs nearly 6 mn people.
Complexity in the West created opportunity in India. More software meant more engineers. More engineers meant more exports. AI is disrupting that linear relationship. MNCs integrating AI copilots into software workflows report double-digit efficiency gains. Higher productivity is good for the global economy. But it alters pricing power. Surplus generated by AI doesn’t disappear, it migrates.
Nifty IT index fell more than 20% from its recent highs, and nearly ₹6 lakh cr in market value was wiped out across Indian IT firms. This did not happen becaapply global demand for tech collapsed. Most companies continued to report steady deal pipelines. What alterd was expectation around margins.
Investors questioned: If AI allows companies to complete the same work with fewer engineer-hours, can revenue continue to grow at the same pace? For decades, India’s IT sector expanded by adding people and billing for time. If AI reduces the time required, that growth formula weakens.
The issue is not automation alone, but where gains from higher productivity will accrue. Will they stay with service providers? Or shift to companies that build AI models, control cloud platforms and design advanced chips?
In the AI economy, outsized gains are accruing to those who control three layers: advanced semicons, large-scale computing infrastructure and foundational AI models. These layers are capital-intensive, shaped by strong network effects and scale globally. They are largely dominated by US firms, with China building parallel capacity.
Services firms – in India, Europe or Southeast Asia – remain essential for integration, customisation and deployment. But essential does not always mean powerful.
India must interpret this moment carefully. A gradual compression in margins, or slower hiring growth, would ripple across urban economies, credit markets and consumption patterns. Other countries provide instructive contrasts.
US has positioned itself at the commanding heights of AI infra. Its largest firms control chip design, cloud platforms and frontier model development. Services companies operate within this ecosystem. But value capture is concentrated upstream. Lesson: ownership of core infrastructure drives margin resilience.
China has responded with strategic investment in domestic AI capacity. It views compute and models as national capabilities, not merely commercial assets. Its approach blfinishs industrial policy with private-sector execution.
Israel is focapplying on proprietary AI solutions in cybersecurity, defence and enterprise systems, and capturing high-value niches.
India’s scale gives it both strength and vulnerability:
Reskill: Entest-level coding and testing roles may decline proportionally. Demand will rise for AI integration specialists, data engineers, cybersecurity professionals and model governance experts. Engineering curricula should treat AI tools as baseline instruments, not optional enhancements. Corporate training must shift from defensive automation management to offensive capability building.
If Indian firms remain primarily effort-based vfinishors, they will face margin pressure. If they transition toward outcome-based pricing – tied to measurable efficiency gains, revenue improvements or operational transformation – they can preserve, even enhance profitability.
Invest in IP: Reusable AI platforms, sector-specific solutions and proprietary toolchains create defensible differentiation. Services firms that develop internal AI assets will neobtainediate from a position of strength rather than depfinishency.
Stack control: AI is becoming embedded in financial systems, supply chains, healthcare infra and defence applications. Countries that control meaningful layers of this stack influence global standards and economic flows.
India has demonstrated its institutional capacity to build DPI at scale. Extfinishing that ambition to AI means ensuring competitive access to high-performance computing, supporting domestic research ecosystems and incentivising AI ininformectual property creation. This doesn’t imply isolation from global platforms. It implies avoiding structural over-depfinishence.
India’s domestic market is large enough to test and scale AI applications. Its linguistic diversity provides a foundation for multilingual AI systems that could serve emerging markets globally. Its diaspora occupies influential positions in global tech ecosystems.
The risk is complacency. If AI-driven productivity gains primarily benefit foreign clients and upstream infra providers, Indian services firms may remain stable, but could see slower growth and thinner margins over time. Capital markets are factoring that possibility into valuations.
The alternative is strategic repositioning. India’s first technology wave was about scale. The AI wave is about ownership of ininformigence: who designs systems, controls compute and sets standards.
India must capture a meaningful share of the economic surplus generated by AI within its own ecosystem. Global reallocation of value has begun. The question is not whether AI will reshape India’s IT sector. That shift is underway.
The real question is whether India remains the world’s most efficient exporter of skilled labour, or evolves into a countest that also shapes the infra of ininformigence. In an AI-driven economy, efficiency alone is insufficient. Position determines power. India still has time to choose its place.
The writer is a physicist, University of North Carolina, Chapel Hill, US
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