Mishik Sharma

Every morning I open my phone expecting to catch up on the day's business and economic pulse—only to slam into paywalls. The Economic Times (ET) has become particularly brazen: 80–90% of their stories are locked behind the ET Prime paywall. Worse, they even wall off stories that are already public—PIB releases, government announcements, even widely shared updates on X. It is creating a growing black hole in accessible information. This isn't just an irritation for readers. It's a systemic problem for AI too. Most large language models (LLMs) were trained on broad web crawls from years ago, before paywalls hardened and publishers started aggressively blocking crawlers. Newer models increasingly miss high-quality, exclusive content, falling back on open snippets, social media noise, aggregated summaries, or lower-effort reposts. The result? AI knowledge on current events gets shallower over time—especially for nuanced stories from countries like India. I’ve been turning this ...

Early 2026 has brought two noteworthy developments in the AI sphere. First, the SpaceX-xAI merger, announced a few days back. Elon Musk outlined a vision for scaling AI compute to extraordinary levels through orbital data centers in space and, in the longer term, lunar manufacturing and further scaling. Second, Anthropic’s Claude Cowork, introduced in preview in January and now expanding. Cowork has been positioned as a virtual teammate capable of handling desktop tasks, file management, and workflow coordination for knowledge workers. Both initiatives carry genuine ambition. They reflect serious efforts to address real constraints — whether solving energy and infrastructure limits for large-scale data centres, or automating clerical work of enterprises.  At the same time, they invite a calm, contextual re-look at what is actually achievable in the near and medium term, and where familiar structural realities apply. The Two Visions in Brief Musk’s post-merger direction centers on ...

India’s technology discourse has recently shifted towards projecting India as an "AI-led services hub”.  Union IT Minister Ashwini Vaishnaw has framed the Union Budget's data-centre hosting initiative as a step toward positioning India as a global centre for AI-enabled services. Leading companies like Tata Consultancy Services (TCS) have similarly articulated ambitions to become AI-first transformation partners for enterprises worldwide. This alignment between government and industry is strategically sound. India already dominates global IT services. AI could deepen that advantage. But we must delve deeper and ask a structural question: Should India’s AI ambition be primarily export-facing — or domestically rooted? The “River” Advantage — and Its Limits India occupies a powerful middle position in the AI stack. If we imagine the AI ecosystem as a landscape: - The “mountain” consists of frontier LLM/LMM developers building foundation models. - The “river” consists of systems in...

Much of commentary on the Union Budget 2026–27 has focused on fiscal arithmetic, capital expenditure, and strategic manufacturing. They are important. But a closer reading reveals something deeper: a deliberate, sector-spanning architecture for job creation, skilling, and entrepreneurship.  What stands out, to me, is not just the emphasis on employment—but where that emphasis is placed.  The Central government deserves appreciation for foregrounding sectors that rarely feature in mainstream economic conversations, yet carry enormous employment and entrepreneurship potential.  This is not a metro-centric, unicorn-driven employment strategy. It is far broader—and far more distributed. Reviving and Upgrading Traditional Value Chains 1. Coconut, Cocoa, Sandalwood and High-Value Agriculture The Budget places renewed emphasis on high-value agricultural value chains—particularly coconut, cocoa, sandalwood, cashew and related tree crops. These are not minor crops. They represent...

Every few weeks, a familiar argument resurfaces in Indian policy discourse: that regulation — not capital, labour, or technology — is the binding constraint on India's economic growth. This argument was again articulated today as an opinion article — complete with English and Roman allegorical references  — by an "assistant consultant" at the EAC-PM, in the Economic Times.  The claim is seductive. It simplifies complexity, identifies a villain, and promises acceleration through subtraction.  But when applied indiscriminately, this framing is analytically narrow and institutionally risky. India’s regulatory ecosystem is too vast, too heterogeneous, and too sector-specific to be reduced to a single friction variable. Treating “regulation” as a monolithic growth suppressant is not reformist clarity — it is reductionism. The way forward, I argue, is not deregulation absolutism. It is regulatory maturity. Regulation Is Not a Single Object “Regulation” in India spans profoundly...

In Indian policy discourse, universities are typically framed in narrow functional terms: centres of research, skill formation, placement pipelines, faculty employment, or sites of student politics. Budget debates revolve around grants, autonomy, accreditation, and rankings. What is almost entirely missing is a structural economic lens. Residential universities are not merely educational institutions. They are bounded, recurring, governable economic ecosystems. And once we recognise that, an entirely new development architecture becomes visible. The Blind Spot: Universities as Controlled Consumption Clusters A large residential university possesses: A stable, predictable population Annual demand cycles Centralised governance Defined geographic boundaries Captive consumption markets In economic geography terms, this is a dense micro-urban node. Yet higher education policy remains siloed within academic and regulatory frameworks, while economic ministries rarely model universities as gro...