Mishik Sharma

Over the past few months, it has become increasingly clear that artificial intelligence is entering an infrastructure-heavy phase. What was less clear, until recently, was how quickly this shift would begin to express itself in physical investment patterns—particularly outside the United States. That clarity is now emerging. On 13 April, Bengaluru-based real-estate company RMZ announced plans to invest approximately $35 billion in India over the next five years in data-centre, office, housing, retail, and logistics infrastructure.  Even allowing for execution uncertainty and phased deployment, the scale of this commitment is difficult to ignore. It implies sustained, multi-billion-dollar annual capital deployment into long-duration physical assets. At first glance, this appears to be a large-scale real-estate expansion push. On closer examination, it signals something more consequential: the spatial expression of the industrialisation of AI. Infrastructure, Not Real Estate The natu...

India’s vehicles industry is booming. Retail sales of vehicles reached an all-time high of 21.71 million units (+13.30% YoY) in FY26. All categories — two-wheelers (+13.4%), cars (+13.0%), commercial vehicles (+11.7%), and tractors (+18.9%) — reported record-breaking sales.  Each new vehicle adds to human mobility and economic activity. It also adds to a growing stock of embedded metals—steel, aluminium, copper, and specialised alloys engineered for durability and performance. Over time, this stock transitions out of primary use. What India does with that transition will increasingly determine whether it remains an extraction-led industrial economy or evolves into a transformation-led one. Today, the transition is weak. Despite a formal vehicle scrappage policy, incentives for owners, and provisions for authorised scrappage centres, throughput remains limited. The constraint is not merely regulatory—it is economic. End-of-life vehicles (ELVs) do not yet command sufficiently high, p...

India’s industrial growth narrative is undergoing a visible shift. The language of Industry 4.0/5.0 now dominates corporate strategy, policy discussions, and public discourse. Factories are expected to be powered by green energy, optimised by Artificial Intelligence, and embedded within digitally coordinated supply chains. This vision is directionally sound—but structurally incomplete. The current conversation makes a conceptual leap: from renewable energy parks to intelligent factories. In doing so, it overlooks the critical infrastructure that connects the two. Between the generation of electricity and its use in industrial systems lies an entire ecosystem—one that remains under-emphasised despite being indispensable. That ecosystem is electrical infrastructure. The Missing Middle: More Than Just Power Electrical infrastructure is often reduced to a simplistic idea: wires carrying electricity from point A to point B. In reality, it is a complex, multi-layered system that determines w...

India’s AI ambitions are rising rapidly—but they are being built on a fragile foundation. The problem is not a shortage of algorithms, talent, or even capital. It is far more basic: India lacks a reliable, continuous, and trustworthy data generation system for its physical economy. Without fixing this, the promise of AI-driven governance will remain uneven, delayed, and often ineffective. Most current public systems rely on periodic, human-reported data—monthly updates, quarterly filings, delayed surveys. These are prone to error, manipulation, and lag. In a country where environmental risks—from floods to landslides to droughts—are intensifying, such latency is not just inefficient; it is dangerous. What India needs is not just more AI—it needs AI-ready infrastructure. This requires a shift from episodic reporting to continuous sensing, from fragmented datasets to shared, verifiable data systems, and from discretionary inputs to machine-generated ground truth. The Missing Layer: A Nat...

When rumours of LPG cylinder shortage spread across India, the result was seen in the most intimate of spaces—the kitchen. Households scrambled for alternatives, and induction cooktops flew off shelves at unprecedented rates. What might have appeared as panic-driven substitution, however, is now revealing itself as something far more consequential: the early formation of a new, electrified cooking economy. This transition has not been led by climate commitments, corporate ESG mandates, or even deliberate state policy. Instead, it has emerged from a convergence of crisis, market reflex, and technological readiness. In doing so, it offers a rare glimpse into how structural change in India often unfolds—not from the top down, but from the ground up. A Shock That Rewired Demand The LPG supply disruption acted as a trigger, compressing years of gradual adoption into a matter of days. Households, faced with uncertainty, did not simply substitute one appliance for another; they diversified. E...

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...