Last week, global chatter about Indian IT and AI policy ambitions hit a new gear. Anthropic blocked access to Fable 5 and Mythos 5 for foreign nationals, citing export controls on national security grounds. A popular X thread by Piramal reframed what Indian IT can mean in the OpenAI era. He argues that the sector is not failing in the race, just choosing a different playbook. He contends that blaming Indian IT for not building a ChatGPT competitor misses the larger point of what these companies are designed to do. The takeaway gained support from Infosys co-founder Kris Gopalakrishnan, who praised the perspective as timely. The message is less a defense of complacency and more a reminder that infrastructure, capital, and business models shape outcomes in AI policy. In short, Indian IT leaders are playing a long game that blends cash flow with careful risk management in AI policy.
Indian IT and AI policy: A pragmatic balance
Piramal’s four points outline a broader philosophy. Frontier AI is not just a software challenge but a capital and governance challenge. Building models at scale is an infrastructure race funded by deep pockets and long horizons. OpenAI and Anthropic operate with massive backing, while Indian IT firms navigate public markets and dividend expectations. If an Indian IT CEO announced a dramatic dividend cut to fund 50,000 Nvidia chips, the stock would react fast. This is not a failure of ambition; it’s a different risk appetite and regulatory environment shaping AI policy decisions in practice.
Moreover, Indian IT‘s economic footprint is larger than many realize. The sector brings in over $200B in foreign currency annually and helps stabilize the rupee. Those dollar inflows underpin RBI reserves and influence how India can manage imports, inflation, and global price swings. Yet policymakers also recognize that strong export axes require domestic innovation. The latest Economic Survey shows room to grow in high-tech manufacturing and private R&D expenditure, areas where Indian IT can contribute more with better incentives and investment in indigenous IP.
The employment argument is equally important. The sector directly employs over 5 million people and supports millions more in real estate, hospitality, transport, and retail. Piramal argues that the industry has helped lift the Indian middle class, especially in tier-2 and tier-3 towns. He suggests the focus should be on upskilling, not sprinting toward automation that could erase jobs. The debate is ongoing among AI experts and policy watchers. The broader view leans toward a managed transition rather than a sudden shift.
Finally, Piramal frames AI’s biggest opportunities as deployment and governance. Indian IT firms excel at integrating tech into business, tailoring AI solutions, and managing large enterprise deployments. They can fine-tune models with private data, maintain compliance, and deliver measurable ROI. The analogy is helpful: the builders of the actual skyscrapers are the IT service integrators, not the raw steel producers. Frontiers in AI modeling look more like a government and research ecosystem task; the profits lie in deployment, integration, and enterprise operations.
Indian IT’s role in AI policy and practical deployment
In 2026, the story isn’t about who makes the most powerful model; it is about who can put AI to work safely, ethically, and economically for millions. The dual focus on robust AI policy and strong currency management gives Indian IT a distinctive role in global strategy. The country benefits when policy keeps pace with technology and when industry players invest with a long horizon. The result is a resilient, adaptable tech sector that quietly fuels growth while shaping the rules for AI policy.
The future will reward those who connect policy, people, and powerful compute. Indian IT can act as both a policy advisor and an implementation partner. By aligning AI policy with real-world deployments, India can export not just software but trusted, scalable AI solutions. The conversation about OpenAI-level breakthroughs should coexist with a steady cadence of trustworthy deployments that protect jobs and data while delivering value.
Practical steps for Indian IT in AI policy and deployment
Here are concrete steps for Indian IT to participate in AI policy shaped outcomes while expanding employment and innovation.
- Invest in domestic R&D and indigenous IP, tying AI policy to startups and universities.
- Build data governance and privacy practices that meet global standards and regional norms.
- Scale upskilling programs to move workers into AI-enabled roles without abrupt job losses.
- Offer end-to-end deployment capabilities that shorten time-to-value for clients.
- Form partnerships with government and enterprises to export trusted AI solutions.
FAQ
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Why can’t Indian IT simply replicate a model like ChatGPT?
Replicating a frontier AI model requires massive compute, long timelines, and a different business model. Indian IT firms typically prioritize stable margins, cash flow, and risk management over venture-capital-scale bets. This does not mean stagnation; it reflects a strategic focus on deployment, integration, and client-ready solutions that align with AI policy and governance needs.
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What should Indian IT emphasize to stay competitive?
Emphasize deployment, integration, and private-data customization that deliver measurable ROI. Strengthen local R&D and IP, cultivate talent in AI-enabled services, and maintain regulatory compliance to reassure global clients under AI policy expectations.
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How does AI policy affect employment in the sector?
Managed transitions and upskilling can preserve jobs while expanding opportunities in higher-value AI roles. The emphasis on governance and responsible AI helps ensure that workers move into roles that complement automation rather than be displaced by it, aligning with ongoing discussions about AI policy.
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What’s the biggest opportunity for Indian IT right now?
The greatest value lies in deploying and managing AI at scale for enterprises—building robust systems, ensuring privacy, and delivering tangible outcomes. This deployment-centric approach aligns with AI policy goals and strengthens India’s role in global tech ecosystems.
External sources and policy context can help readers navigate this evolving landscape. The links below provide authoritative perspectives on AI governance, policy, and practical deployment in a broader economy.
External sources
- White House OSTP: AI policy and national direction
- NIST: Artificial intelligence standards and guidelines
- Brookings: AI and the labor market