Mishik Sharma

Government and industry leaders increasingly speak of an AI stack — chips, cloud, models, and applications — as the foundation of technological sovereignty. The ambition is clear: domestic compute capacity, indigenous foundation models, and national applications together reduce strategic dependence. Yet a structural asymmetry persists. While the AI stack is being articulated layer by layer, an equivalent cybersecurity stack is rarely defined with the same architectural clarity. This omission is no longer theoretical. It is operational.  Yesterday, Amazon Web Services (AWS) confirmed that two of its facilities in UAE were directly struck by drones, causing structural damage, power disruptions, and additional water damage from fire suppression efforts. A third facility in Bahrain was also affected by a nearby strike. The strikes led to widespread service outages, including elevated error rates for services like Amazon S3, EC2, RDS, Lambda, and others, with AWS warning of prolonged r...

Artificial intelligence is no longer simply another technological wave. It is rapidly becoming a general-purpose cognitive infrastructure, collapsing the boundary between knowledge production and real-world deployment. Across sectors, AI is shortening the half-life of expertise: models evolve faster than textbooks, and applied knowledge shifts more quickly than curricula can traditionally accommodate.  In such a landscape, the question facing universities is not whether AI should be taught. The deeper question is how universities must reorganize themselves when intelligence itself becomes computationally augmented. Adding AI electives or partnering with online platforms may signal responsiveness, but these remain surface-level adjustments. If AI is infrastructural, the response must be structural. Universities must move beyond offering AI as a specialized track and instead embed it within the epistemic core of each discipline. The challenge is not “AI education”. It is disciplinary...

India's e-commerce sector stands at a pivotal inflection point. According to a new BCG report released yesterday, the the Indian e-commerce market is currently worth $120–140 billion. It is on track to nearly double to $280–300 billion by 2030, fuelled by 440 million online shoppers (up from 300 million today), rapid rural penetration, and the explosive rise of quick commerce and connected formats. Yet beneath the headline growth lies a stubborn reality: most pure-play operators remain deeply unprofitable despite years of aggressive scaling. The first wave of Indian e-commerce was defined by single-category dominance and frantic geographical expansion. The next wave, I argue, will be won by platforms that evolve into multi-experience conglomerates—blending high-frequency daily services with emotionally resonant, culturally attuned offerings. This is not speculation; it is already playing out in real time. The Limits of Pure Geographical Expansion For much of the past five years, th...

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 or code—that live 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 projected 250 bill...