• Member
• January 24, 2026

From AI Stack to National Capability An Indian Path Forward

A discussion note by Srijan Sanchar

India’s ambition in Artificial Intelligence is no longer limited to adoption or catch-up. With the articulation of a national AI architecture spanning applications, models, chips, infrastructure, and energy, the country has clearly signalled its intent to shape the full value chain.

Yet the real question before us is not whether this stack can be built.
It is whether this stack can learn, adapt, and scale meaningfully across India’s social and institutional diversity.

India’s Advantage Is Not Scale Alone

Most countries build AI systems for homogenous contexts—few languages, limited institutional variation, predictable user behaviour. India is different. Its complexity is not a problem to be solved later; it is the primary design condition.

Agriculture varies every 50 kilometres.
Health outcomes differ by district.
Mobility, governance, education, and livelihoods are deeply local.

An AI strategy that succeeds in India is, by definition, one that can succeed anywhere.

Educational Institutions as Living Laboratories

India’s greatest underutilised AI asset is not compute or capital—it is its educational ecosystem.

From central universities and state universities to technical institutes, medical colleges, agricultural universities, polytechnics, and teacher education colleges, India already possesses a nationwide network capable of grounding AI in real problems.

When students, faculty, and local institutions work on:

• district health systems,

• regional transport flows,

• local language services,

• public service delivery,

AI stops being an abstract technology and becomes institutional capability.

This approach does not require new missions as much as a new mindset:
education not as a pipeline to industry alone, but as a co-creator of national intelligence.

Many Models, One Purpose

The future will not belong to one monolithic AI model attempting to represent everything. It will belong to families of models, shaped by domain knowledge, language nuance, and real-world constraints.

India is uniquely positioned to lead here—not by centralising intelligence, but by orchestrating plurality:

• models that understand public systems,

• models that grasp informal economies,

• models fluent in regional languages and administrative realities.

Such diversity is not inefficiency. It is robustness.

Infrastructure as a Commons, Not a Trophy

National compute infrastructure is most powerful when treated not as a reward for scale, but as a commons for learning and experimentation.

When access is tied to relevance and problem clarity rather than institutional branding, unexpected innovation emerges—from smaller universities, regional centres, and interdisciplinary teams.

This is how ecosystems mature: quietly, broadly, and sustainably.

Energy, Ethics, and Endurance

As AI infrastructure grows, energy choices become civilizational choices. Clean energy and responsible power planning are not side conversations; they are signals of intent.

An AI future aligned with long-term societal well-being demands that efficiency, sustainability, and responsibility be built in—not retrofitted.

A Subtle but Important Shift

The most durable AI strategies will not be those that move fastest, but those that integrate deepest—into education, governance, institutions, and everyday life.

India’s opportunity is to show the world that:

• sophistication does not require exclusion,

• scale does not require uniformity,

• and leadership does not require imitation.

At Srijan Sanchar, we believe the next phase of AI progress will be defined less by technological leaps and more by how thoughtfully societies learn to think with technology.