Mishik Sharma

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 ...

I. Disruption Narrative vs Strategic Recalibration  The AI Impact Summit 2026 in New Delhi unfolded under the long shadow of disruption. Weeks earlier, Anthropic’s release of its agentic system, Claude Cowork, intensified global anxiety about autonomous AI executing complex workflows — coding, analytics, integration, compliance tasks — with minimal human input. Commentators projected that up to 40% of knowledge work in IT service companies could be automated over time. For India’s $280 billion IT services sector, the implication was blunt: if execution is automated, the intermediary collapses. This “Anthropic-triggered” narrative was amplified precisely because agentic AI differs from earlier automation waves. These systems do not merely assist; they decompose problems, call APIs, iterate against databases, and complete end-to-end workflows. The threat is not incremental efficiency — it is value-chain compression. Yet at the Summit, India’s political and industry leaders have artic...

For the past few years, the global AI discourse has been remarkably narrow. AI stakeholders and commentators have talked about models, startups, automation, India's IT industry, job loss, AI chips, and Nvidia’s valuation. The conversation has oscillated between techno-euphoria and techno-anxiety. Artificial Intelligence has been framed as software — as code — as something that lives in servers and manifests as chatbots, copilots, and generative tools. What has almost never been discussed is the electro-physical backbone that makes AI possible. Yesterday, at the ongoing AI Impact Summit, the Adani Group unveiled one of the world’s largest integrated energy-and-compute investments, committing 100 billion dollars to develop renewable-energy-powered, hyperscale AI-ready data centres by 2035. The investment is expected to catalyse an additional 150 billion dollars across server manufacturing, advanced electrical infrastructure, sovereign cloud platforms and allied industries creating a ...

Introduction India's AI momentum is unmistakable. The AI Impact Summit 2026, taking place from February 16 to 20, at Bharat Mandapam, Delhi, has drawn top leaders from global technology companies like Microsoft, Google, Nvidia, OpenAI, Anthropic, and more, with 250,000–300,000 expected participants, 600+ startups in the Expo, and discussions on governance, skilling, job creation, and equitable growth.  This is also a time when sovereign initiatives are advancing:  Sarvam AI recently inked partnerships with the Odisha government to establish a 50MW Sovereign AI Capacity Hub (~25,000 GPUs, ~₹20,000 crore for Odia population-scale apps) and the Tamil Nadu government to establish a Digital Sangam (20MW research park with IIT Madras). Reliance Industries Ltd recently announced gigawatt-scale data centers in Jamnagar, Gujarat and Visakhapatnam, Andhra Pradesh, with more than 20 billion dollars investment. RIL would license-in global models for affordable Jio-bundled access. These ef...

India's healthcare system is undergoing one of the most consequential technological transitions in modern history. Artificial intelligence—particularly edge AI (also termed embedded AI, embodied AI, on-device AI, or physical AI)—is moving beyond pilot projects into everyday clinical practice.  Portable diagnostic tools, AI-enhanced stethoscopes, retinal cameras, pocket ECGs, and real-time ultrasound guidance are already deployed in primary health centres (PHCs), district hospitals, and Ayushman Arogya Mandirs across the country. These devices do not merely assist; they capture high-fidelity clinical signals, process them locally with low latency, generate suggestions, auto-document into electronic health records via the Ayushman Bharat Digital Mission (ABDM), and continuously learn from real-world interactions—all while keeping the final clinical decision firmly in the hands of the nurse or doctor. This architecture is not accidental. It reflects a deliberate sociological and polic...

Over the last few days, the top five US technology companies have announced a cumulative capital expenditure of about $670 billion for 2026 alone(!). This figure exceeds the annual GDP of nations like Switzerland and Poland.  But, this is not just a large number. It's a structural break.  For more than two decades, technology was defined by its asset-light character. Code scaled faster than concrete. Software displaced steel. Platforms replaced plants.  This paradigm is now reversing. Artificial Intelligence at frontier scale is not asset-light. It is infrastructure-dependent. It demands hyperscale data-centres, gigawatts of power, liquid cooling systems, semiconductor supply chains, and physical redundancy engineered for near-zero downtime. The firms once celebrated for minimal capital intensity are now building industrial-scale infrastructure. We are entering the era of Industrialisation of AI — where tech giants are no longer just coding—they are building the physical ...