Beyond the Stack: Expanding the AI Application Sphere in India

Introduction: From Layers to Sphere

Much of today’s discourse on artificial intelligence is framed in terms of a technological “stack”—chips at the base, models in the middle, and applications at the top. This layered view, while useful, is increasingly inadequate.

Artificial intelligence is no longer confined to digital workflows. It is moving into factories, mines, refineries, energy grids, logistics corridors, and transport systems. In doing so, it is not merely advancing within a fixed stack—it is expanding outward into the real economy.

This shift calls for a new conceptual framework: an expanding AI application sphere, where technological layers continuously push one another deeper into real-world deployment, enlarging the domain of application. For India, recognising this shift is critical.

The Four Layers and Their Convergence

At the core of this transformation are four interlinked layers:

Chips: the raw compute substrate

Compute Infrastructure (Data Centres): scaled, orchestrated compute systems

Models: the intelligence layer

Applications: real-world deployment

Semiconductor companies such as NVIDIA and Intel are forward-integrating into full-stack ecosystems. Data centres are becoming the bridge between compute and intelligence — and the infrastructure that determines scale, cost, and accessibility of AI. AI model developers are advancing fast both upstream and downstream; while AI appliers are  moving to embed AI into real systems.

The emerging result is a mutual push downwards—towards deployment in the physical economy.

The Expanding Sphere: AI Is Entering the Physical Economy

The most consequential transformation is unfolding not within software, but within the physical economy.

Factories, mines, refineries, power grids, transportation systems, and logistics corridors are becoming increasingly:

data-rich

sensor-driven

connected

and partially autonomous

Edge AI systems are enabling real-time decision-making at the point of action—detecting defects, predicting equipment failures, optimising energy use, and improving operational safety. These are not incremental improvements to existing workflows; they represent a shift toward continuous, system-level intelligence.

In effect, physical industries are being transformed into computational systems.

This transition marks the leading edge of the expanding AI application sphere. Productivity gains in this domain arise not from better documentation or communication tools, but from improvements in yield, uptime, efficiency, and reliability—areas with far greater economic impact.

India’s Opportunity: Deployment at Scale

India is uniquely positioned to benefit from this transition—not necessarily through leadership in frontier models or chips, but through large-scale, real-world deployment.

Several structural advantages stand out:

- a vast and diverse industrial base

- a large MSME sector (6.7 crore)

- a large engineering workforce and academia 

- fast expanding green energy and transport infrastructure

These factors create an environment where AI can be deployed across varied contexts—from large industrial corporations to small manufacturing clusters.

India’s strategic opportunity lies in becoming:

the world’s leading environment for cost-effective, large-scale AI deployment in the physical economy

This path differs from that of countries focused on model supremacy, but it is no less consequential.

The Role of IT Companies: From Services to Edge Application 

India’s IT services sector stands at a critical juncture.

Traditional roles—application development, maintenance, and enterprise workflow management—are increasingly being compressed by advances in AI models. 

However, this does not imply decline. Instead, it necessitates a transition.

IT firms must move from:

centralised, software-centric operations

to:

distributed, domain-intensive, edge-focused deployments

This involves:

- working within industrial environments

integrating AI with machines, sensors, and control systems

- developing domain-specific expertise in sectors such as manufacturing, energy, and logistics

This transition would be technologically and organisationally demanding, but it represents a viable path to continued relevance.

Simultaneously, the expanding sphere creates space for regional IT startups, particularly in MSME clusters, to develop localized, sector-specific solutions.

Friction Points: Why Expansion is Not Automatic

The expansion of the AI application sphere would not frictionless. Several technical constraints would shape its trajectory:

Hardware heterogeneity: diverse devices and architectures across industrial environments

Integration complexity: AI must interface with legacy systems such as PLCs and industrial control networks

Model adaptation: performance varies across real-world conditions, requiring continuous tuning

Data challenges: fragmented, unstructured, or low-quality data in many industrial settings

Most critically, cybersecurity would be a foundational requirement. As AI systems are embedded into operational environments, security is becoming inseparable from safety. Breaches can disrupt production, damage equipment, or compromise critical infrastructure. This necessitates:

- secure device architectures

- trusted model deployment pipelines

- robust monitoring and anomaly detection

Without addressing these frictions, the expansion of the AI sphere will remain uneven and constrained.

Policy Architecture: Enabling the AI Application Sphere

Given the complexity of this transformation, market forces alone are unlikely to deliver optimal outcomes. A coordinated policy framework is required, with the central government acting as the architect.

1. Incentivising Adoption and Transition

- Incentivise edge AI deployment in large industrial corporations, with a phased expansion toward MSMEs

- Encourage IT companies to shift operations toward industrial clusters

- Support workforce upskilling in edge AI, systems integration, and domain-specific applications

2. Ecosystem Development

- Incentivise global chip and model companies to develop India-specific, integrated edge platforms/solutions

- Facilitate partnerships across chips, models, and application layers

3. Security as a Mandate

- Mandate the integration of cybersecurity tools and protocols in all edge AI deployments

- Promote standards for secure, reliable, and interoperable systems

4. Federal Coordination

- Nudge state governments to provide enabling infrastructure:

reliable energy

water systems

transport infrastructure 

industrial parks 

Such coordination would enable convergence of all three layers at the ground level.

Towards a Dynamic Equilibrium

The emerging AI ecosystem is unlikely to stabilise into a fixed equilibrium. Instead, it will evolve toward a dynamic equilibrium, characterised by:

- continuous interaction across layers

- expansion into new domains

- recalibration of roles rather than elimination

Each layer—chips, infra, models, and applications—will both shape and be shaped by the others.

Conclusion: From Topping to Transformation

The current phase of AI adoption, centred on digital workflows, represents only the initial stage of a broader transformation. The long-term trajectory lies in embedding intelligence into the core of the physical economy.

For India, the choice is clear. AI can remain a 'topping'—an overlay on existing systems delivering incremental gains. Or it can reshape the 'cake' itself, transforming how industries operate, produce, and grow.

Realising this transformation requires more than technological adoption. It demands a coordinated effort to expand the AI application sphere—across industries, across regions, and across layers of the technological ecosystem.

If pursued effectively, this framework would not only deliver economic gains, but would become a distinct and durable model for AI-led national development.