Mishik Sharma

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...

Design is often understood in narrow terms—form, engineering precision, or product features. In physical industries, it is typically treated as the stage where a product is shaped, optimised, and readied for production. Once built, the product is expected to find its place in the market, and the company’s responsibility largely ends at the point of sale. But in several fast-scaling sectors in India, this understanding is quietly becoming inadequate. The real shift is not in how products look or function. It is in how they are deployed, used, and sustained over time. Design, as a result, is moving beyond the product—towards the system in which the product operates. This is not a theoretical shift. It is already visible in India's green energy linked sectors. What is emerging is a new logic: design is no longer about creating products; it is about delivering real-world outcomes. Signals from the Market: The E-Bus Turn According to a recent report by Marqstats (released on 25 March), ...

India’s startup story is undergoing a quiet but consequential shift. For over a decade, the country’s startup energy has been synonymous with a handful of megapolitan hubs—Bengaluru, Hyderabad, Mumbai, NCR, etc. Today, that geography is changing. According to a recent report by Tracxn Technologies (released on 30 March), more than 68,000 startups are now headquartered outside India’s primary startup hubs, as of December 2025. Startups are increasingly emerging from tier-two cities (especially Jaipur, Indore, Kochi, Surat, Coimbatore, and Lucknow), driven by lower costs, expanding digital infrastructure, and locally rooted demand. Yet, beneath this diffusion lies a structural imbalance: the report says that tier-two cities' startups account for just 8.6% of total funding rounds and only 2.1% of total capital deployed, over the last ten years. This reveals that while entrepreneurship is decentralising, capital, scale, and institutional support remain concentrated. The result is a fra...

Like every year, the placement season of the Indian Institutes of Technology (IITs) and the Indian Institutes of Management (IIMs) is unfolding right now. And like every year, the same metrics are dominating media attention: placement percentages, salary packages, recruiting companies, and what all these mean for the economy.  These institutions have, without doubt, become some of India’s most successful post-Independence creations—efficient engines that identify, train, and distribute talent into the economy. But beneath this success lies a quieter question:  India has built powerful talent pipelines. But has it built enough institutions that develop and sustain strategic capabilities over long horizons? The Quiet Outlier One institution sits somewhat outside this narrative: the Indian Institute of Science (IISc).  Unlike the IITs or IIMs, IISc does not dominate headlines during placement seasons. Its visibility comes instead from research breakthroughs, long-term projec...

India’s e-commerce sector is sending out a subtle but powerful signal. A recent CIEL HR report (released on 26 March) notes that hiring in the sector has grown by roughly 35% over the past two years, with demand for engineering roles—software developers, DevOps professionals, AI/ML specialists—rising sharply. At first glance, this looks like a routine expansion story. It is not. What we are witnessing is a capability reorientation . India’s e-commerce companies are no longer scaling primarily through warehouses, delivery fleets, and geographic reach. They are quietly rebuilding themselves as technology-intensive systems, investing in the infrastructure needed to handle a far more complex operating model. The question is: why now? The Limits of the First Wave For much of the past decade, the dominant playbook was straightforward—expand into new cities, add sellers and dark stores, subsidise deliveries, and chase Gross Merchandise Value (GMV). This model delivered scale, visibility, and ...