Building India’s Second Moat in AI: Beyond Code, Beyond Cost, Towards Sovereign Value

Every technological revolution poses the same uncomfortable question: who will shape the future, and who will merely serve it. History has rarely favoured those content only to adopt tools built elsewhere, while surrendering the deeper layers of their societal existence, ownership and governance.

India’s $250‑billion IT–ITES sector now stands at such an inflection. For decades, it built a formidable moat around cost efficiency, disciplined delivery and the world’s largest pool of technically trained, English‑speaking talent. This quietly propelled millions into the middle class, anchored Indian equity markets and established the country as a trusted node in global technology supply chains.

Yet the moat is breached. The familiar pyramid of mass, entry‑level hiring is giving way to a diamond‑shaped structure built around mid‑level specialists and domain expertise. Announcements of layoffs by some firms and record retraining by others are not contradictions but responses to the same truth: cost arbitrage alone cannot anchor long‑term competitiveness when machines now write, test and maintain code once reserved for armies of young engineers. Welcome to the global new-arms race of AI.

A global technology strategist and friend recently offered an elegantly framed answer: rather than wait for global contracts, why not build portfolios of AI‑infused applications rooted in India’s complex realities, yet globally relevant – and persuade Big Tech to back them? He added, that after all, “Indian‑origin CEOs now lead Microsoft, Alphabet and others shaping the AI frontier. Our entrepreneurial ecosystem is vibrant, our domestic market vast and our data universe enormous. These two variables could intersect as opportunities for the Indian tech sector.”

At first glance, this vision seems compelling, even inevitable. Yet ambition must meet complexity. And complexity, in this case, runs deeper than code, capital or headlines.

Beyond Optimism

Building meaningful applications requires more than engineering skill. It demands a cultural pivot from solving client‑defined problems to defining the problems worth solving, investing beyond first versions and retaining the intellectual property that makes iteration and scaling possible. For an industry shaped by quarterly metrics and client service, this requires patience and product discipline that cannot be built overnight.

Data remains a structural challenge. India generates enormous volumes, but much remains fragmented, unstructured or inaccessible. AI thrives not on raw quantity but on curated, longitudinal, high‑integrity data sets. Without them, promising pilots risk becoming elegant showcases rather than scalable products.

On the demand side, sectors from mid‑market manufacturing to rural healthcare recognise AI’s promise but hesitate, wary of uncertain returns and regulatory exposure. Without strong domestic demand signals, even subsidised applications struggle to gain traction.

Overlaying all this is the geopolitical reality. Only days after these discussions, the US administration announced fresh tariffs on Indian goods while citing “obnoxious” non‑monetary barriers and IP concerns. The message is clear: the US welcomes Indian talent and market access but prefers that design control, IP ownership and product margins remain elsewhere.

Put plainly, what is often framed as partnership risks becoming a modern form of tech subservience: India supplies the labour and data, but the highest value remains beyond our grasp.

Yet India is no longer willing to play only that part. Across policy, industry and academia, a new consensus is forming: global engagement, yes - but on terms that protect domestic capacity, IP and data sovereignty.

Beyond Rhetoric
It is tempting for nations to meet each new technological frontier with speeches, policy documents and high‑level task forces. Yet history reminds us that competitiveness is built not in conference rooms alone, but in factories, laboratories and data centres, through the hard, often unglamorous work of capital formation.

India cannot afford to treat AI, semiconductors, quantum computing and other emerging technologies merely as rhetorical signposts of modernity. If we remain content to issue policy narratives and political statements, we risk waking up a decade from now to discover that the foundational layers - chips, data infrastructure, open research platforms and industrial capacity - have been built elsewhere.

True sovereignty in technology demands more than regulation and intent. It requires decisive, long‑horizon capital expenditure: high‑performance computing clusters, domestic semiconductor capacity, AI‑ready data centres and open‑access research labs where universities and start‑ups can experiment without prohibitive cost.

Other nations are moving with urgency and scale. The US CHIPS and Science Act, Europe’s semiconductor strategy and China’s multi‑billion‑dollar AI funds reflect a sober recognition that emerging technologies shape not only competitiveness but national security and societal resilience.

Just Symbolism
Setting aside modest sums under national AI missions or digital innovation grants may seed pilot projects, but they rarely build the deep, patient capital pools required to birth globally competitive products. Competing with economies investing tens of billions of dollars demands a different magnitude of national commitment — measured not in announcements but in factories built, patents filed, and research talent retained.

Equally, building more data centres in India without sovereign or open cloud architecture still leaves the crown jewels vulnerable. Data may rest physically on Indian soil, but the software layers — orchestration tools, analytics engines and proprietary algorithms — often remain controlled by Western Big Tech. Without domestic capability to run, govern and secure these platforms, India risks becoming a landlord in name but a tenant in practice: its data stored locally, yet governed elsewhere.

The second moat must therefore be full‑stack: manufacturing scale, robust data pipelines, sovereign cloud layers and domestic AI research ecosystems. Anything less keeps India in the role of assembler and consumer, rather than designer and owner.

This is neither an argument against foreign capital nor a rejection of global collaboration. It is the need for strategic balance: to welcome investment and ideas while retaining the capacity to set national priorities, build proprietary IP and protect critical data assets.

If India aspires not only to deploy but to design, own and export next‑generation technology, it must lead with capital, not caution. The alternative is clear: to remain consumers and assemblers of innovations defined elsewhere.

The first digital moat India built rested on cost, scale and disciplined delivery. The second must rest on intellectual property, product excellence, sovereign data and long‑term investment. AI offers India a chance not just to keep pace with global change but to help write its rules.