When AI Meets Federalism: India’s Emerging Polycentric Knowledge Economy

According to the latest Quess Corp Pulse report, released yesterday, formal employment in India is no longer a metro-only story. In H1-FY26 (April to September, 2025), Tier-3 cities accounted for the largest share (40%) of organised workforce deployment, overtaking Tier-1 cities (31%) and Tier-2 cities (29%). Entry-level hiring is increasingly leaning toward local colleges rather than exclusively targeting top-ranked institutions. Companies cite cost efficiency, easier retention, better cultural fit, local market understanding, and operational exposure as key reasons.

This is not an anecdotal adjustment. It signals a measurable redistribution in India’s labour geography.

At the same time, a parallel development is unfolding at the other end of the corporate spectrum. Global corporations continue to build/expand Global Capability Centres (GCCs) in India, deepening their presence in megacities such as Bengaluru, Hyderabad, Mumbai, Pune, NCR, etc (just yesterday, Google secured 2 million square feet of additional office-space in Bengaluru, which would accomodate upto 20,000 additional employees). These companies routinely cite India’s depth and breadth of technical talent, scalability, and deployability as core advantages. The paradox is striking: Developed countries are home to world-class universities, yet multinational companies are increasingly scaling operations and manpower in India.

Taken together, these two trends point to something more structural than cost arbitrage. They suggest that India has evolved a distinct talent architecture.

India’s Dual-Layer Talent Architecture

India’s higher education system is often judged against the metric of research intensity or global university rankings. On that scale, it remains uneven. Yet corporate behaviour reveals a different metric at work: deployable capability at scale.

India appears to operate a dual-layer talent architecture.

The first layer is elite, megapolitan, and globally integrated. Graduates from top institutions anchor frontier R&D, product architecture, advanced AI work, and strategic functions within global corporations. These hubs remain critical to India’s integration into the global knowledge economy.

The second layer is broader and more distributed. Tier-2 and Tier-3 engineering colleges produce large cohorts of industry-ready graduates—rapidly-trainable, process-compatible, and English-proficient. They may not generate high research output, but they generate applied engineering capacity at scale. This second layer is increasingly visible in the hiring data. It underpins the growth of manufacturing clusters, logistics parks and corridors, BFSI operations, telecom operations, retail chains, etc beyond megapolises. 

India, in other words, may not be converging toward a Western-style research-hubs led economy. Instead, it is consolidating a scalable applied engineering model, complemented by a smaller frontier segment.

AI as Institutional Augmentor 

The rise of AI introduces a catalytic variable into this structure. AI tools are beginning to compress capability differentials across institutions.

In Tier-2 and Tier-3 colleges, AI can augment faculty capacity through syllabus design, problem-set generation, simulation environments, and research synthesis. Students can leverage coding copilots, automated debugging, rapid prototyping tools, and structured conceptual explanations. The marginal cost of accessing high-quality technical assistance is falling.

In elite institutions, AI can raise research productivity by accelerating literature review, hypothesis generation, modelling, and cross-domain synthesis, etc. 

AI does not make all institutions equal. But it lowers the threshold at which capability becomes globally competitive. Thus, the feasibility of high-quality applied engineering hubs outside traditional Tier-1 ecosystems increases substantially.

This shift strengthens the case for distributed capability models.

A Strategic Opportunity for Indian Companies 

If global corporations can build large-scale capability centres in India because of its talent base, an obvious question follows: why should Indian companies replicate the megapolitan concentration as their default strategy?

Global multinationals can afford wage premiums and clustering in megapolises. Indian companies often operate under tighter capital discipline and face sharper sensitivity to attrition and real estate costs.

There is therefore a strategic opportunity for Indian companies to establish Tier-2 and Tier-3 AI-focused capability centres—complementing, not displacing, megapolitan hubs.

Such centres could focus on:

- Industrial AI deployment across manufacturing clusters

- Sector-specific customisation for retail, logistics, healthcare, and financial services

- Platform integration and data engineering

MSME technology enablement

- Regional client-facing technical operations

These functions align with India’s applied engineering strength. They are less capital-intensive than frontier research labs, but high in value-addition when integrated with domestic industry.

The advantages are broad-based, not merely cost-based:

- Retention tends to be stronger when employees work closer to home regions. 

- Wage inflation pressures are lower. 

- Companies can build deeper relationships with local universities and state skilling missions. 

- Urban congestion is mitigated. 

- Middle-class expansion becomes distributed.

This is not a call to dilute elite megapolitan ecosystems. Those remain vital for frontier work and global integration. It is a call to layer the system — to add a distributed capability tier that strengthens domestic value-capture.

Designing the Federal Architecture

A distributed GCC model will not emerge automatically. Its geography will be shaped by policy design. If left entirely to market forces, economically stronger states may corner a disproportionate share of new capability centres. Over-concentration would reproduce megapolitan-style clustering at the state level. At the same time, heavy central direction risks artificial placement without ecosystem depth.

Therefore, a calibrated approach is preferable: light-touch Union incentives combined with state-led execution.

The Union could provide limited, performance-linked incentives for first-mover GCCs in emerging states, tied to metrics such as industry-academia integration, local hiring ratios, and skill pipeline development. 

States, in turn, must drive the fundamentals: infrastructure reliability, urban planning, law and order, regulatory coherence, and skilling alignment.

In this framework, slow reformers—not structurally weak states—would lose out. 

To put it in other words, competitive federalism, with guardrails, would determine the geography of distributed capability.

Labour Geography and Urban Rebalancing

If this layered model takes root, it could yield substantial demographic and sociological dividends, but gradually rather than dramatically.

Gradual reverse migration from megapolises may occur, particularly among early- and mid-career professionals seeking quality-of-life improvements. Migration inflows to megapolises may moderate, as viable white-collar opportunities emerge in smaller cities. Over time, proactive states could experience parallel growth alongside traditional hubs.

The outcome would not be the decline of Bengaluru or Hyderabad. Rather, it would be a polycentric expansion of India’s knowledge economy — reducing extreme congestion pressures while broadening middle-class formation across regions.

Prestige hierarchies may also flatten incrementally, but elite institutions would likely continue to anchor the frontier segment. The system becomes more layered, not less differentiated.

Risks and Preconditions

The success of such a model depends on institutional alignment. Municipal governance deficits, infrastructure bottlenecks, and regulatory inconsistency can quickly erode distributed advantages. Without deliberate efforts to build IP ownership and domestic product capabilities, distributed centres risk remaining execution arms rather than value creators.

AI itself introduces volatility: automation may compress demand for certain engineering roles even as it creates new ones. Continuous upskilling will be essential.

Geography alone does not generate multiplier effects. Ecosystem integration does.

Conclusion: Horizontal Expansion, Not Vertical Imitation

The recent hiring diffusion beyond megapolises and the sustained expansion of global GCCs in India are not two isolated phenomenons. Together, they reveal a structural shift in how capability is getting organised and scaled.

India need not replicate Western research clusters, across every state. Nor must it dismantle its megapolitan ecosystems. Its next upgrade may lie in horizontal expansion — preserving frontier hubs while building distributed, AI-enabled, applied capability hubs across Tier-2 and Tier-3 cities.

If designed thoughtfully, this layered, polycentric model could rebalance labour geography, deepen domestic value capture, and widen economic and social opportunities—without sacrificing excellence. The recent small-town edge in hiring may thus be the early signal of a broader reconfiguration in India’s knowledge economy.