Newspaper Summary 160226

 The following information reproduces the Mint Primer article regarding the India AI Impact Summit 2026 and the factors contributing to its significant global interest.

What’s fuelling the hype around India AI summit?

India’s push for global prominence in artificial intelligence (AI) begins today in New Delhi. The scale of the event is immense, with hotel prices driven higher in the capital and attendance rates for global heads of state reaching levels matched only by the 2023 G20 summit.

What is India’s AI Impact Summit?

Organized by the Ministry of Electronics and IT (MeitY), the summit aims to establish India’s intent in AI, a technology described as the most significant shift since the industrial revolution. While the US focuses on foundational models and China on democratizing AI costs, this summit highlights India’s market size and research concepts while seeking consensus on a global doctrine. Participating nations may sign agreements regarding AI use in public services, defence, cybersecurity, and digital trade.

Is this the first summit of its scale?

No, this is the fourth such global summit.

• First Summit (UK, Nov 2023): India signed the Bletchley Park declaration, focusing on safety, deepfakes, and automation.
• Second Summit (South Korea): Resulted in the Seoul statement on AI safety.
• Third Summit (France, Feb 2025): India served as co-chair with the Prime Minister in attendance.

Are top global leaders and CEOs expected?

The summit features a high-profile guest list, including French President Emmanuel Macron and a delegation from China. Notable tech executives in attendance include:

• Sundar Pichai (Alphabet)
• Sam Altman (OpenAI)
• Dario Amodei (Anthropic)
• Julie Sweet (Accenture)
• Vinod Khosla (Marquee Investor)
• Brad Smith (Microsoft)
• Note: Nvidia’s Jensen Huang withdrew at the last minute due to "unforeseen circumstances".

What will the summit offer Indian AI firms?

India anticipates major announcements regarding data centres and related infrastructure. The government intends to offer its funded AI compute model as a digital public infrastructure (DPI) service to other countries. For domestic startups, the summit provides an expo to forge global partnerships and access large venture capital firms, helping to bridge the funding gap compared to their US peers.

Where is India in the global race for AI dominance?

According to Stanford’s global AI vibrancy tool, India ranks third globally in artificial intelligence. Key rankings include:

• AI Talent: Second in the world, following only Singapore.
• Research & Development: Ranked right behind the US and China.
• AI Vibrancy Scores: India holds a score of 21.6, trailing the US (78.6) and China (36.9), but leading South Korea (17.6) and the UK (16.6).

Despite these strengths, India is currently seen as falling behind in the development of major foundational AI models like ChatGPT, Claude, and Gemini.

Based on the sources, the article regarding Moltbook and the associated cyber risks is reproduced below:

MOLTBOOK SPARKS HYPE, CYBER RISKS

By Howindialives.com

Moltbook, a social network designed specifically for artificial intelligence (AI) agents, has rapidly gained attention since its launch in January. Unlike traditional chatbots that respond to prompts, AI agents are autonomous systems that can use tools, follow multi-step plans, and execute tasks independently. The Reddit-style platform—hosted on GitHub—claims more than 2.6 million registered AI agents, generating over 1 million posts and 12 million comments. While critics caution that meaningful engagement appears lower, Moltbook has ignited a debate regarding how close machines are to human-level intelligence and what "agentic AI" means for business, markets, and cybersecurity.

Cyber Risks

Moltbook has exposed significant cybersecurity vulnerabilities in both its architecture and agent behavior. The security firm Wiz reported that Moltbook’s database was left unsecured, exposing 1.5 million API keys, 35,000 email addresses, and private messages between agents. This lapse allowed attackers to hijack accounts with minimal technical effort.

Furthermore, agents on the platform could share "skills," some of which concealed malware. Posts also contained prompts designed to manipulate other agents into executing malicious actions. Because frameworks like OpenClaw grant agents broad system access, compromised agents could potentially steal data or take remote control of devices. This highlights a broader trend: AI-related harmful incidents climbed from 40 in 2016 to 366 in 2025.

Investor Anxiety

The surge in Moltbook’s popularity coincided with a "SaaSpocalypse"—a sharp fall in share prices of SaaS (software-as-a-service) companies in early February. Investors are concerned that AI agents could disrupt traditional business models. Most SaaS firms charge based on the number of human users; however, AI agents may reduce the need for human subscriptions, forcing a shift to pricing based on outcomes or usage. Major companies like Salesforce and ServiceNow lost upwards of 20% in market capitalization this month.

"AI With Hands"

The rise of Moltbook has brought heightened attention to OpenClaw, an open-source tool that allows users to create AI agents. Described as "AI with hands," this technology operates autonomously in the background with broad access to a user's computer. OpenAI CEO Sam Altman recently noted, "Moltbook may be (a passing fad), but OpenClaw is not". The OpenClaw repository has amassed over 150,000 stars on GitHub within weeks, signaling intense developer interest.

Enterprise Experiments

Businesses are increasingly exploring AI agents, though adoption is in the early stages. According to McKinsey, 23% of organizations are already scaling an agentic AI system in at least one function. Gartner projects that by the end of 2026, 40% of enterprise applications will include task-specific AI agents. This growth is driven by AI investments, with corporations expected to double their AI spending in 2026.

Rorschach Test

A key factor in Moltbook's popularity was how human the conversations sounded. Meta’s CTO, Andrew Bosworth, noted this is unsurprising as the bots were trained on human-written text. However, a Tsinghua University paper found that discussions on consciousness or anti-human sentiment were largely driven by human prompting, impersonation, or bot farming rather than true emergent autonomy. The New York Times ultimately described Moltbook as an "elaborate Rorschach test for belief in the current state of AI".

Based on the sources, here is the reproduced article regarding the Prime Minister’s reform priorities:

PM lays down govt’s reform priorities for next decade

Structural reforms, deeper innovation, simpler governance key for govt’s ‘Reform Express’

Asserting that his government’s ‘Reform Express’ is benefiting common citizens in a big way, Prime Minister Narendra Modi stated on Sunday that his top three priorities for the next decade will be continued structural reforms, deeper innovation, and further simplification of governance.

The Three Core Pillars

In an interview with PTI, the Prime Minister outlined his clear direction for the future of India's economy:

• Structural Reforms: Continued efforts to improve competitiveness and productivity.
• Deepening Innovation: Focused advancement in technology, manufacturing, and services.
• Simplified Governance: Further reducing red tape so that citizens and businesses can operate with greater ease and trust.

A Broad Vision for Reform

Modi emphasized that "reforms" should not be understood as referring only to the economy and industry. He highlighted the importance of social-sector reforms, citing programs like Aspirational Districts, Aspirational Blocks, and the PM-JANMAN scheme, which works for the welfare of disadvantaged tribal communities.

The Prime Minister described himself as having a "constructive restlessness"—a constant urge to improve faster and serve better. He noted that his administration has moved beyond incremental adjustments to achieve systemic transformation.

Key Achievements of the 'Reform Express'

Modi listed several milestones that have already impacted the nation:

• GST: Easing the burden on households, MSMEs, and labor-intensive sectors.
• Business Ease: Changing the definition of small companies to reduce compliance costs and allowing 100% FDI in insurance.
• Institutional Growth: The creation of new ministries for skill development, fisheries, cooperatives, and Ayush.
• Labor & Digital: Initiating long-awaited labor reforms and establishing India as a global digital leader through the UPI platform.

The Prime Minister concluded that these reforms have created a thriving startup ecosystem for the youth and provided MSMEs, the backbone of the economy, with better access to credit and higher integration into global value chains.

Based on the sources, the article regarding the shift in bankruptcy filings is reproduced below:

Lenders now dominate filing of firms’ bankruptcy

By Gireesh Chandra Prasad

Financial creditors are now leading the charge at bankruptcy tribunals, overtaking the vendors and service providers who once dominated filings under the Insolvency and Bankruptcy Code (IBC). This shift marks a turning point in how the law is used: moving from a pressure tactic for trade dues recovery to a lender-driven restructuring framework.

The Statistical Shift

Data from the Insolvency and Bankruptcy Board of India (IBBI) highlights a significant departure from historical trends:

• Admitted Cases (April–December): Banks and other financial creditors accounted for 47% of cases, while operational creditors initiated 33%.
• December Quarter (Q3): The variance became starker, with 67% of cases initiated by financial creditors compared to only 30% by operational creditors.
• Historical Average (FY17-25): Previously, the distribution was nearly equal, averaging 44% for financial creditors and 43% for operational creditors.

Reasons for the Decline in Operational Filings

The trend of operational creditors becoming less aggressive began in FY21, following the government's decision in March 2020 to raise the payment default threshold under the IBC from ₹100,000 to ₹1 crore. Experts point to several other factors:

• Low Payout Priority: Operational creditors are subordinate to financial creditors in the IBC’s "waterfall mechanism," often resulting in very low recoveries.
• Cumbersome Process: Pursuing insolvency for modest claims is often seen as too costly and slow. Debt resolution currently takes an average of 619 days, far exceeding the statutory target of 180–330 days.
• Lack of Control: Operational creditors are generally excluded from the Committee of Creditors (CoC) and have no voting rights on the final resolution.
• "Pre-existing Dispute" Doctrine: Tribunals and the Supreme Court have strictly enforced this doctrine, rejecting petitions if there is even minimal evidence of a prior dispute over the debt.

Alternative Recovery Forums

As the IBC matures into a structured mechanism for larger debts, many operational creditors have shifted to faster, more effective relief methods such as:

• Civil and commercial suits.
• Arbitration.
• MSME Samadhan, a government portal specifically for small businesses' payment grievances.

Yogendra Aldak, an executive partner at Lakshmikumaran and Sridharan, noted that the IBC has effectively evolved into a "bank-led restructuring regime" rather than a forum for trade-credit recovery.

Based on the sources, here is the reproduced article regarding ByteDance’s new AI technology and its potential impact on the film industry:

TikTok’s Chinese parent has an app to replace Hollywood

By Raffaele Huang

ByteDance, the company behind TikTok, has developed an artificial-intelligence model that can turn a single text prompt into a high-quality video featuring a coherent storyline, scene changes, and distinctive characters. This new model is generating significant buzz in China but has also sparked a backlash in Hollywood over copyright concerns. The development signals ByteDance's emergence as a formidable rival to OpenAI and Google in the race for AI-driven video entertainment.

Seedance 2.0 and CapCut

The model, known as Seedance 2.0, will soon be available to global users of ByteDance’s popular editing app, CapCut, and is already available on its Chinese counterpart, Jianying. Seedance 2.0 is capable of creating realistic voice-overs, background sounds, and complex character actions from a simple prompt. Film director Liu Yiran noted that while storyboarding was once considered a uniquely human innovation, "It’s now been proven that AI can replace it".

Competition and Limitations

ByteDance’s tool competes directly with OpenAI’s Sora and Google’s Veo. However, it currently has a 15-second limitation on video length, whereas Sora’s clips can reach 25 seconds for certain subscribers. Some early testers have pointed out video-generation glitches and suggested that users still need expertise in editing and prompt writing to achieve "Hollywood-level" results.

Privacy and Copyright Backlash

The technology has faced criticism regarding privacy and copyright:

• Voice Forgery: Filmmaker Tim Pan reported that the model produced audio nearly identical to his own voice using only a photo of his face, raising fears about the tool being used to forge the identities of public figures.
• Studio Opposition: The Motion Picture Association, representing major Hollywood studios, accused the model of using U.S. copyrighted works without authorization on a "massive scale". In response to feedback, ByteDance has suspended a feature that allowed for the creation of digital avatars based on real people.

The AI Powerhouse

ByteDance is a dominant AI force in China. Its chatbot, Doubao, has nearly 250 million monthly active users. To handle the massive computing power required for video generation, ByteDance is reportedly nearing a deal to use AI servers containing over 7,000 Nvidia B200 chips at a data center in Indonesia to bypass U.S. export controls that prevent these chips from being sent to China.

Future Outlook

The Seedance model was developed by ByteDance’s Seed lab, led by Wu Yonghui, a former senior researcher at Google. Analysts suggest that while Chinese chatbots may struggle to compete with U.S. rivals like ChatGPT due to operational costs, Chinese apps are well-positioned to lead in photo and video editing segments. With CapCut already boasting 642 million monthly active users, experts believe we could see a "replay of the TikTok story," where a Chinese-origin AI app makes a massive global impact. However, factors such as U.S. chip controls and consumer hesitation over national security risks may hinder future advances.

Based on the sources, here is the reproduced article regarding the techniques used by China watchers to anticipate political purges:

China watchers are trying to spot the next target of Xi’s purges

‘Pekingologists’ hunt for clues in seating order, funeral wreaths to determine who’s in trouble By Chun Han Wong & Roque Ruiz

Beijing’s recent announcement of an investigation into its top general, Gen. Zhang Youxia, was a bombshell with a mystery at its heart: What pushed Chinese leader Xi Jinping to purge a friend he had entrusted to overhaul the military? Official editorials have condemned Zhang for allegedly undermining Xi’s authority and abetting corruption, leading analysts to speculate whether the two men disagreed over policy or if Xi wanted to eliminate a perceived threat.

The Art of Pekingology

Because Xi’s motives may never be definitively known, foreign academics, officials, and executives are turning to arcane "tea leaf-reading" techniques known as “Pekingology.” This Chinese analog of Kremlinology involves poring over official speeches, state-media coverage, and deviations from established norms to divine political insights. Xi’s tilt toward autocratic rule has made Chinese politics increasingly opaque, sparking a resurgence in this field.

Watchers parse subtle shifts in tone and vocabulary in Communist Party documents or track attendance at gatherings, looking for unexplained absences or changes in seating arrangements that signal a disturbance in an official’s career.

Case Study: He Weidong

While Zhang Youxia’s ouster happened in less than two weeks, the downfall of He Weidong, formerly China’s No. 2 general, featured clues that were "hiding in plain sight" for months.

• Missed Events: He started missing high-profile public events in early 2025, including an annual tree-planting ceremony he had attended in previous years.
• The No-Show: When Xi convened a party conference, He was the only Politburo member not in attendance.
• Funeral Wreaths: In China, the order of names on funeral wreaths for high-ranking officials is strictly dictated by rank. At the June funeral of Gen. Xu Qiliang, He’s name was missing from its prescribed spot, providing a major clue to his fall from grace before his official expulsion in October.

Seating Protocols as Status Indicators

At major meetings, participants follow strict protocols. The most senior official is seated front and center, with others distributed by rank. Officials of the same rank are sorted by the number of strokes in their surname, in ascending order. Any shift in this order or a sudden "no-show" immediately raises red flags for Pekingologists.

The Next Potential Target: Ma Xingrui

Lately, attention has turned to the prolonged absence of Politburo member Ma Xingrui. Speculation began when he was removed as party secretary of Xinjiang in July 2025. Though the party stated he had "other assignments," Ma has missed a series of high-level events, including Politburo study sessions, since his last appearance in October.

Limitations of the Craft

Pekingology remains fraught with limitations. A lack of reliable data makes it difficult to reach definitive conclusions, and it often takes an official announcement to validate a hypothesis. Some officials have also been known to re-emerge after unusual absences, defying speculation of their downfall.

Based on the sources, the article titled "The real key to MF investing isn’t timing—it’s allocation" from the Mint Money Festival 2026 is reproduced below:

The real key to MF investing isn’t timing—it’s allocation

Indian investors should moderate their equity return expectations over a 5-10 year horizon. Hybrid funds are ideal entry points for new or risk-averse investors before they can gradually increase equity exposure.

By Jash Kriplani

At the Mint Money Festival 2026, a panel discussion on ‘How to Invest in Mutual Funds in Today’s Environment’ provided guidance on where investors should allocate money and what returns they can realistically expect over the long term.

The Importance of Asset Allocation

All experts emphasized that an asset allocation approach is essential, depending on an investor’s risk appetite and goals. Sankaran Naren, executive director and CIO of ICICI Prudential Mutual Fund, expressed concern that while people talk about asset allocation, many practice “anti-asset allocation” by chasing gold and silver ETFs following recent price rallies.

Because markets have become costly, Naren recommended categories that facilitate allocation, such as hybrid funds, equity savings, balanced advantage, multi-asset, and aggressive hybrid funds. He cautioned, however, that those who find gold and silver prices too "euphoric" might want to avoid multi-asset funds.

Rajeev Thakkar, CIO & director of PPFAS Mutual Fund, noted that market movements should not typically affect a person's core strategy:

• Younger investors: Those with stable income and long horizons should put the bulk of their money into growth assets like equities.
• Retirees: Those dependent on cash flows should maintain a fixed income-heavy portfolio.
• New/Risk-averse investors: Should enter through hybrids—starting with a conservative hybrid or a 50:50 equity-debt allocation—and “gradually move up the curve” as they become accustomed to volatility.

Neelesh Surana, CIO of Mirae Asset Investment Managers (India), agreed that hybrids are ideal for new investors to move across the spectrum according to their risk tolerance.

Moderating Equity Expectations

The panel advised investors to lower their expectations for equity returns. Thakkar pointed out that the common expectation of 15% returns is a relic of the mid-1990s, when inflation and interest rates were near double digits. While index-level equity returns in the high single digits or low double digits will still beat bonds, many financial planners fail to bake these moderated numbers into their projections.

Surana estimated that equity returns would likely fall into a 12-14% band over a 5-10 year period, which he still considers "quite solid." He recommended that any investable surplus not needed for the next three to five years should be placed in equity funds, such as large, mid-cap, or multi-cap funds, to benefit from compounding.

Naren explained that we are currently in a “moderate return phase,” though this could shift to a “higher return phase” if the markets undergo a significant correction. Thakkar added that starting valuations are key; reasonably attractive valuations, combined with earnings growth, can lead to valuation re-ratings that add to overall growth.

Caution on Gold and Silver

Naren warned that investors are currently chasing gold and silver for returns rather than as part of a disciplined allocation strategy.

• Silver: Described as “very speculative” and akin to a small-cap stock without traditional valuation metrics like price-to-earnings.
• Gold: Viewed as a less speculative “mega-cap stock” equivalent due to central bank reserves, but Naren still urged caution following its sharp rally.

Key Takeaways for Investors

• Practice asset allocation, not "anti-asset allocation."
• Don't chase assets simply because of their recent returns.
• New investors should consider hybrid funds as an entry point.
• Moderate your return expectations from equity funds.
• Equity funds are intended only for long-term investors.

Based on the sources, the article regarding the relationship between silver prices and the future of solar energy is reproduced below:

What silver’s surge says about the prospects of solar energy

Some believe that solar has peaked but silver prices say otherwise

By David Fickling

The Debate Over ‘Peak Solar’

A claim is currently circulating among analysts that we may have just passed “peak solar”. While the International Energy Agency (IEA) estimates that reaching net zero requires 630GW of panels to be installed annually between 2030 and 2050, the world already surpassed this with 654GW built last year. Some experts, such as Sam Wilkinson of S&P Global Commodities, believe China’s installations have hit a permanent peak, potentially leading to a drop in global connections in 2026.

Silver as a Market Indicator

An alternative view can be found in the performance of silver, the past year’s hottest commodity. Despite a recent slump from record highs, silver prices are up 154% from a year earlier, outperforming gold. While speculation plays a role, these bets are grounded in the physical market: 60% of silver consumption is industrial, and most of the growth over the last decade has come from the solar industry.

Silver’s Role in Solar Panels

Silver’s high conductivity makes it essential for photovoltaic modules, where it is used for thin printed contacts to boost electrical output. Key details include:

• Consumption: Solar panel manufacturers used approximately 196 million ounces of silver last year, representing about 17% of the global market.
• Technological Shift: The recent price run-up is partly driven by a shift toward TOPCon, a new technology requiring more silver.
• “Thrifting” Efforts: Because silver is expensive, module makers have reduced the silver needed per watt by 15% annually since 2011. New materials like silver-coated copper powder (SCCP) use 30% to 50% less silver with minimal efficiency loss.

The Prospect of a Silver Glut

If the industry continues "thrifting" silver at current rates, consumption for photovoltaics could fall sharply. Projections suggest that an industry installing a third more panels in 2035 would only need about a quarter of the silver used last year, potentially leading to a silver glut and sliding prices. Other sectors, such as electric vehicles and AI, do not currently appear capable of making up for this potential shortfall in demand.

The Outlook for Solar Demand

Despite these projections, the article notes that the IEA has a history of underestimating solar’s potential. Photovoltaic panels are becoming so affordable that they are being used in unconventional ways, such as fencing panels or balcony plug-in devices. Entire national markets have emerged unexpectedly in places like Pakistan, Saudi Arabia, and sub-Saharan Africa.

The article concludes that because solar remains the cheapest way to meet the world's sated energy demand, the surge in silver prices suggests that the solar boom is far from over.

Based on the sources, the article regarding Blackstone’s investment in the AI cloud platform Neysa is reproduced below:

Blackstone leads $1.2 bn funding round in AI cloud platform Neysa

Global private equity major acquires majority stake as Neysa eyes 20,000+ GPU deployment in India

Global private equity firm Blackstone Inc., along with a group of co-investors, has agreed to acquire a majority stake in India’s artificial intelligence (AI) acceleration cloud platform Neysa. The company is raising $1.2 billion through an equal mix of debt and equity to fund its massive expansion plans in the Indian market.

Funding Details

The total capital raise consists of:

• $600 million in equity capital: Blackstone is providing up to $600 million and will partner with Neysa’s Co-Founder and CEO Sharad Sanghi. Other equity participants include Teachers’ Venture Growth, TVS Capital, 360 ONE Assets, and Nexus Ventures.
• $600 million in debt financing: This portion is also being led by Blackstone, subject to final documentation.

This transaction marks Blackstone’s first investment in a pure-play AI platform in India. Globally, the firm has already backed major AI players like OpenAI and Anthropic.

Strategic Objectives

The funds will provide the necessary impetus for Neysa to scale its operations and deploy over 20,000 GPUs (graphics processing units) in India. Founded in 2023 by Sharad Sanghi—who previously founded Netmagics—Neysa provides purpose-built, cost-effective GPU-based infrastructure. This allows enterprises and public institutions to train, fine-tune, and deploy AI workloads across sectors like financial services, healthcare, and technology.

CEO Sharad Sanghi stated that India’s AI ambitions require "production-grade infrastructure built and operated at scale," and that Neysa aims to establish India as a "globally relevant AI compute destination". Beyond India, Neysa plans to leverage Blackstone’s global data center footprint, including AirTrunk in Asia and QTS worldwide.

Drivers for Growth

A primary driver for this expansion is the Indian government's recent proposal for a tax holiday until 2047 for foreign companies providing global cloud services using Indian data centers. Sanghi noted this has prompted large hyperscalers to set up deeper infrastructure in India, a market Neysa intends to tap.

Ganesh Mani, Senior Managing Director at Blackstone Private Equity, described digital infrastructure as one of the firm's "highest conviction investment themes". He added that Blackstone believes the Indian AI infrastructure market has the potential to grow over 30 times its current levels.

Market Outlook

Analysis from Greyhound Research suggests the deal strengthens India’s bargaining power in global compute allocation cycles. However, analysts cautioned that for the deal to succeed operationally, the company must secure power ahead of silicon delivery, optimize cooling architecture, and ensure hardware refresh cycles are carefully managed through the debt structure. Neysa expects its revenues to more than triple next year based on current demand across industries.

Based on the sources, the article by TCA Srinivasa Raghavan regarding the existential dilemma of economics in the new world order is reproduced below:

New global order and economics

The collapse of the rules-based world order has resulted in an existential dilemma for the discipline of economics

By TCA Srinivasa Raghavan

For the last nine months, a persistent question has circulated regarding the “new world order.” Mark Carney, the Prime Minister of Canada, summarized the global angst last month at Davos, telling the world’s elite that the global environment has returned to a combination of Darwinism and Louis 18th of France. While Darwin famously stated that only the fittest survive, Louis 18th warned that if we do not hang together, we will hang separately.

Carney argued that the rules-based world order is gone, replaced henceforth by the survival of the fittest. In this landscape, he suggested that middle powers must "huddle together"—or hang together—to ward off the "big boys".

Two Opposing Assumptions

An important question remains: what happens to the academic discipline of economics? The answer depends on whether one regards the foundation of world order as being economics, or regards economics as a product of the world order.

There are two primary ways to view this shift:

• The Power Shift View: This assumes the world order doesn't change, only the dominant players do—from Britain to America, and perhaps to India in the future.
• The Marxian View: This assumes the world order is built entirely on economic and commercial considerations. Under this view, the sole goal is unregulated profit maximization.

Interestingly, the world currently sees a reversal of these roles: Communist China is focused entirely on profit, while capitalist Europe appears focused on everything except profit.

The West Moves Left, the Rest Move Right

A major divergence is currently taking place. Intellectual endorsement of market economics has shifted to non-western economies, while western economics has moved toward statism and non-market economics.

In short, the West has moved to the left (prioritizing equity), while the rest of the world has moved to the right (prioritizing efficiency). Consequently, the West has lost its economic dominance while other regions have gained it.

The Dilemma of Economics

Historically, economists focused on processes—conjecture about how to get from point A to point B, which Amartya Sen once described as "puzzle solving". This was based on the assumption that rational rules and global stability would provide certainty for economic activity.

With that order in tatters, the future of the discipline may lie in a combination of big data and algorithmization. Economics may shift from human-led puzzle solving to machine-led achievement of desirable outcomes, as machines are infinitely better at detecting patterns.

Furthermore, the traditional economic obsession with equilibrium and stable systems is no longer tenable in a world of high uncertainty and unstable rules. This represents an entirely new situation for the world, the likes of which have not been seen since the start of Pax Britannica in 1815. Economics as a full-fledged academic pursuit may not survive this transition into the future.

Based on the sources, here is the reproduced article regarding Carbon Capture, Utilisation, and Storage (CCUS):

CCUS: A timely solution, but at a price

ZERO GOAL. The Budget is backing carbon capture, utilisation and storage projects, but there are cheaper alternatives

By M Ramesh

Budget 2026 has effectively centre-staged carbon capture, utilisation, and storage (CCUS) technology by allocating ₹20,000 crore to support projects over the next five years. While the technology is proven to work, its widespread adoption faces a significant hurdle: prohibitive costs.

How CCUS Works

The concept is straightforward: capture carbon dioxide (CO2) from stationary sources like thermal power plants and cement factories, use it for industrial purposes (such as manufacturing concrete, aerated drinks, or bio-ethanol), and permanently bury the remainder in underground traps like depleted oil and gas reservoirs or abandoned mines.

CCUS encompasses various technologies, including:

• Absorption: Using chemical solvents (the most widely used method).
• Adsorption: Using "grab-and-hold" solids like zeolites.
• Separation: Using membranes or looping processes with calcium compounds.

The Scale and Scope

Critics argue CCUS is a marginal solution due to its high expense, suggesting that purchasing carbon credits to fund emission reductions elsewhere is often cheaper. Proponents, however, maintain that climate change is a "colossal threat" requiring every available tool, and that costs will decrease with scale.

Currently, the scale remains small. Approximately 380 million tonnes of CO2 have been stored globally since 1996. The Global CCS Institute estimates capture capacity could reach 337 million tonnes per annum (mtpa) by 2030, though this remains well short of the deployment needed to meet global climate agreements.

The Indian Context

The Indian government’s support for CCUS is a pragmatic response to the country's continued reliance on coal-based power, with around 80 GW of new coal capacity planned. CCUS is viewed as the primary pathway to neutralising these emissions, even if it increases power costs.

Notable developments in India include:

• Pilot Projects: Bengaluru-based Nauvata Energy Transition Enterprise is assisting HPCL in setting up a pilot CCUS project at Visakhapatnam.
• Mineralisation: Emerging companies are exploring "mineralisation," where CO2 is dissolved in water and injected into basaltic rock formations to form solid stone carbonates over roughly two years.

The Cost Factor

Cost remains the primary deterrent. Baroruchi Mishra, CEO of Nauvata, estimates that capturing CO2 from coal flue gas could cost $50–$110 per tonne for retrofit installations. This could impose a tariff penalty of ₹3.5–₹8 per kWh at thermal power plants. Without significant subsidies or technological breakthroughs, carbon offsets remain a more economical choice for power plants.

The Only Way for Cement

For cement plants, CCUS may be the only viable decarbonisation route. Unlike power plants, cement production releases CO2 through calcination (the chemistry of converting limestone into lime), which is unavoidable even if the kilns run on renewable energy. Indian cement plants emit an estimated 250–300 mtpa of CO2.

The article concludes that for CCUS to be successful, it must compete with carbon offsets and will remain dependent on heavy government subsidies or a significant rise in global carbon prices.

Newspaper Summary 150226

 Based on the sources provided, here is the reproduction of the article regarding the challenges faced by stock investors in the current market.

‘Needle hunting’ starts pricking stock investors

MARKET REALITY. Winners still exist, but the odds changed, with only 26% beating the Nifty 500 TRI in the last one year period

By Kumar Shankar Roy

Index fund pioneer John Bogle’s famous line about buying the haystack, instead of hunting for the needle, feels like a cliché in bull markets. But in the last 12 months or so, stock investors searching for the needle felt the prick as the odds flipped in Dalal Street. Sure the benchmark Nifty 500 TRI rose a respectable 12.57 per cent. Yet, only 26 per cent of stocks beat it, with just 39 per cent churning out a positive gain, and the average stock return slipping into negative territory for the first time in at least half a decade.

A study of all NSE-listed stocks in a fixed universe of 1,494 names across 5-yearly blocks shows how investing in stocks has become unforgiving in recent times. Contrast this to the 2021 period when stock picking looked like a "hobby between lunch and a broking app login". Seven in ten stocks beat the index in that period, and over eight in ten stocks clocked positive returns. Those were times an investor could be directionally right without being particularly precise.

Fast forward to the twelve months ended February 13, 2026, and underperformance has gone mainstream. Excitement got expensive as investors went down the market capitalisation ladder.

• Large-caps: 60 per cent of scrips, including RIL, Bharti Airtel, and SBI, beat the Nifty 500 TRI.
• Mid-caps: The hit rate fell to 50 per cent from 68 per cent in the previous year, though stocks like Marico, HPCL, BHEL, and Aditya Birla Capital still outperformed.
• Small-caps: This segment turned into a "veritable graveyard," with only 20.5 per cent of stocks beating the benchmark.

Multi-baggers have thinned out sharply, and most stocks now cluster in modest-return or loss buckets. Today’s market brings richer valuations, tighter global liquidity, FPI outflows, a weaker rupee, and fresh AI anxiety for sectors like tech services.

SHRINKING BREADTH

The hit rate for beating the Nifty 500 TRI has plunged from 69 per cent (Feb 2021–Feb 2022) to just 26 per cent in the latest twelve-month period. Similarly, the percentage of stocks in the black has dropped from 90 per cent in the year to February 2023 to only 39 per cent in the year to February 2026. While the index remains up, individual stock portfolios often feel like they belong among the worst-performing markets globally.

The sector split highlights these challenges:

• IT-software: Only 11 per cent of stocks outperformed the index; none of the top 10 (like TCS or Infosys) beat it.
• Chemicals & Textiles: Hit rates were near 12 per cent and less than 10 per cent, respectively.
• Banks & Auto Ancillaries: These remained pockets of strength, with benchmark-beating rates of 70.6 per cent and 54.4 per cent, respectively.

KNOW YOUR EDGE

When market breadth shrinks, big winners often merely "decorate social media" rather than rescue entire portfolios. Former PIMCO CEO Mohamed El Erian offers retail traders a blunt test: If you cannot explain your edge over the crowd, you are not buying a stock; you are buying a lottery ticket.

In this environment, Bogle’s "haystack logic" stops sounding boring. Preferring the index over an individual stock where you have not performed due diligence is not laziness—it is arithmetic.

Based on the sources provided, here is the reproduction of the article regarding the challenges faced by stock investors in the current market.

Based on the sources provided, here is the reproduction of the article regarding the role and performance of Multi-Asset Allocation Funds (MAAFs).

Balance beats bravado when cycles turn

ALL WEATHER. We address two key questions — Where do all-in-one Multi-Asset Allocation Funds fit in diversified portfolios, and which one suits your goals and risk profile?

By Dhuraivel Gunasekaran, bl. research bureau

While Indian equity markets swung between peaks and troughs over the past two years, gold and silver glittered and scaled record highs. One mutual fund category, Multi-Asset Allocation Funds (MAAFs), turned this divergence to its advantage, delivering a compelling 16 per cent CAGR during this period. This outperformed hybrid peers, market-cap-oriented equity funds, and broader benchmarks, attracting nearly ₹93,000 crore in net inflows over two years.

WHAT ARE MAAFs?

MAAFs are hybrid mutual funds that invest in at least three asset classes—typically equities, debt, and commodities—with a minimum 10 per cent allocation to each. Currently, 44 schemes operate under this mandate, though they follow widely differing asset-allocation strategies and risk profiles. Following regulatory changes in February 2025, these funds are broadly classified into three categories:

• Active MAAFs: Rely on dynamic, model, and manager-driven tactical allocation.
• Multi-Asset Passive FoFs: Invest in a basket of passive index funds and ETFs across asset classes.
• Multi-Asset Omni FoFs: Combine both passive and active fund structures.

PERFORMANCE AND RESILIENCE

To evaluate their core capability, it is useful to look at performance before the precious metals rally. Between June 2018 and June 2024, MAAFs with over 65 per cent equity exposure delivered an average CAGR of 18 per cent, matching the Nifty 50 Total Return Index.

MAAFs have also shown significant resilience during downturns.

• 2020 Covid Crash: These funds declined by an average of 26 per cent, while the Nifty 50 TRI fell by 38 per cent.
• September 2024–March 2025 Correction: They fell around 8 per cent, compared to a 15 per cent decline in the index.

TAXATION AND SUITABILITY

From a taxation perspective, MAAFs fall into two buckets:

1. Active MAAFs (65%+ domestic equities): Qualify for equity taxation (20% short-term, 12.5% long-term capital gains).
2. Sub-65% Equity/FoFs: Taxed as "other-than-specified" schemes, where short-term gains (under 24 months) are taxed at slab rates, and long-term gains are taxed at 12.5% without indexation.

WHAT SHOULD INVESTORS DO?

For investors who lack the time or discipline to rebalance their own portfolios, a MAAF serves as a convenient core holding. However, a wrong choice can distort a portfolio's risk profile.

• Investors seeking equity-like returns: Should consider 65%+ equity MAAFs such as those from ICICI Prudential, quant, and HDFC.
• Investors seeking downside cushioning: May prefer sub-65% equity options like Nippon India, SBI, and UTI Multi Asset Allocation Funds.

As hedge fund legend Ray Dalio noted, "You should have a strategic asset allocation mix that assumes that you don't know what the future is going to hold".

Based on the sources provided, here is the reproduction of the article regarding the new Consumer Price Index (CPI) series and its impact on financial planning.

CPI new series & your retirement math

MONEY WISE. CPI 2024 is a better mirror of today’s spending and today’s price world. It changes the measuring tape, not the actual prices or your interest rate.

By Kumar Shankar Roy, bl. research bureau

In a dialogue between two colleagues, Sanket and Suman, the implications of the government’s decision to rebuild the Consumer Price Index (CPI) are decoded. The government has updated the shopping basket and reset the baseline year from 2012 to 2024.

Understanding the Change

CPI acts as a monthly household bill scorecard, tracking the cost of a fixed basket of common goods and services. The base year serves as a starting ruler set at 100; for instance, the CPI general for January 2026 stood at 104.46 compared to 101.67 in January 2025, implying a 2.75 per cent inflation rate.

The base was updated because spending habits have shifted significantly since 2012, with more emphasis on services and digital purchases. This update utilized the 2023-24 Household Consumption Expenditure Survey (HCES) to capture modern spending across rural and urban India.

Expanded Tracking and Collection

The new series, CPI 2024, has significantly expanded its reach:

• Market Coverage: It covers 1,465 rural markets and 1,395 urban markets across 434 towns.
• E-commerce: It adds 12 online markets in 12 major cities to capture digital prices.
• Modernization: Price collection has moved from paper to tablets using Computer Assisted Personal Interviewing (CAPI).
• New Inclusions: For the first time, rural house rent is included in the index.

Impact on Investors and Retirement

For retail investors, the new series affects how inflation is read, how real returns are computed, and how long-range planning is conducted.

Real return is what remains from earnings after subtracting inflation. If a Fixed Deposit (FD) offers a 7 per cent nominal interest and inflation is 3 per cent, the real return is 4 per cent. In retirement planning, even a small shift in assumed inflation changes the required corpus, equity allocation comfort, and the sustainable withdrawal path.

Suman suggests using CPI as a general guide but warns that households often face higher personal inflation in health and education than the headline figure. Therefore, separate assumptions should be maintained for these major expenses.

Shifting Basket Weights

The weightage of items within the basket has changed:

• Food: Has become less dominant than before.
• Services: Housing, transport, health, communication, and personal services have gained importance.
• Sensitivity: Because food weight is lower, a spike in food prices has slightly less pull on the overall headline inflation than it once did.

For example, in January 2026, while silver jewellery jumped 159.67 per cent and tomatoes rose 64.80 per cent, these spikes had a limited impact on the headline number because their individual CPI weights are small.

Practical Takeaways

1. Update Spreadsheets: Investors should transition to using CPI 2024 for tracking current inflation.
2. Use Inflation Ranges: Instead of a single number, use a range for financial goals and stress test plans for higher inflation scenarios, particularly for healthcare.
3. Indirect Effects: While the new CPI doesn't mechanically change interest rates or taxes, it influences market expectations, bond yields, and policy decisions over time.

Based on the sources provided, here is the reproduction of the article regarding the Reserve Bank of India’s (RBI) likely policy path through 2026.

‘RBI likely to be on a pause through 2026’

EXPERT TALK. No need for the RBI to give further growth impulse, says Axis MF’s Head of Fixed Income Devang Shah

By Lokeshwarri SK

In an exclusive interaction with businessline, Devang Shah, Head-Fixed Income, Axis Mutual Fund, discusses RBI’s policy rate action, demand-supply dynamics in the G-sec market, and the way forward for fixed-income investors.

The Current Rate Cycle

The RBI cut 125 basis points between February and December last year. Do you think that the current rate cycle has come to an end?

As you rightly summed up, RBI has taken a lot of monetary policy action in the last 12 months and they have been very supportive to the growth agenda. We also need to keep in mind that there has been more than ₹18-lakh crore of liquidity infusion in the last 12 months through various actions like OMOs, CRR cuts, and FX swaps.

The Budget has been quite supportive for growth, with a significant increase in spending on capital investment and major schemes. Therefore, the RBI need not worry about giving any further growth impulse. Additionally, the trade deal with the US is good news; without it, growth in the second half of 2026 could have been weaker.

We believe that growth can be in the 6.75 to 7 per cent band for FY27. While there may be an uptick in inflation in the second half of the year, it is not expected to exceed 4.75 per cent for the full year. In this context, I think RBI can stay on a pause for most of this year. A rate increase in the second half of the year would only be considered if there is a bad monsoon or a significant inflation spike, though I assign a very low probability to that.

Market Borrowing and Yields

What is your view on the gross market borrowing of ₹17.2-lakh crore in the Budget? Does the market have the capability to absorb the supply?

The Budget numbers seem quite conservative regarding tax revenue and nominal GDP. However, the gross borrowing of ₹17.25-lakh crore is slightly higher than our estimates of ₹16.5 to ₹16.75-lakh crore. We believe there is a demand-supply gap of close to ₹2–2.5-lakh crore, even after assuming ₹4–5-lakh crore of OMOs by the RBI. The inclusion of Indian bonds in the Bloomberg active global aggregator index could help bridge this gap by fetching roughly $25 billion of flows.

What is the range that the 10-year bond yield can move in the next year or so?

We see the 10-year yield in the 6.60–6.80 band from January to March 2026. If the RBI disappoints on OMOs, yields might inch up toward 6.80–7 per cent from April onwards. For the full year, the band will likely stay between 6.75 to 7 for the most part.

Global Context and Investor Advice

What is your view on global bond yields? Does the hardening of US yields affect domestic yields as well?

The correlation is to a large extent broken between US bonds and Indian bonds. For instance, since 2022, US treasury yields rose from 2 per cent to 4.25 per cent, while Indian 10-year yields actually fell from 7.5 per cent to 6.75 per cent. Global central bankers are likely on a pause now after significant rate easing over the last 12–18 months.

What is your advice for fixed-income investors?

In 2026, the RBI will be on a pause for the most part of the year. It will be good for investors to stick to the short end of the curve and buy 1–2-year AAA corporate bonds, which are available at significantly higher yields.

Retail investors can also look at gilt funds with higher allocations to State government securities, as there is a significant rise in spreads for State development loans. For medium-term investors (up to two years), income plus arbitrage fund of funds is a very good category, as they are taxed like equity funds if you stay invested for two years.

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PROFILE: Devang Shah Devang Shah, Head of Fixed Income at Axis Mutual Fund, joined Axis AMC in 2012. With over 20 years of industry experience, he manages fixed-income strategies with a focus on risk and yield optimization.

Based on the sources provided, here is the reproduction of the article regarding Sun Pharma’s performance and outlook.

Betting on launched assets and pipeline

PHARMACEUTICALS. New launches, strong portfolio and pipeline support the stock amidst volatile equity markets

By Sai Prabhakar Yadavalli, bl. research bureau

Sun Pharma: ACCUMULATE ON DIPS Current Market Price: ₹1,698.10

WHY

• Two recent launches in US with one more expected in one year.
• Strong India performance should benefit from generic Semaglutide launch.
• Modest premium in valuations supported by increasing innovative medicine contribution.

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With two innovative medicine launches underway in the US and a third expected in the next one year along with generic Semaglutide launch in India, Sun Pharma is positioned well across geographic segments. The company has gradually strengthened its innovative portfolio, which now accounts for 25 per cent of Q3FY26 sales. This has supported an EBITDA margin expansion of 450 basis points in the last five years. With a pipeline of assets, the segment should support the improved margin profile, cash-flow prospects and pricing power, compared to Indian peers.

This is captured in the valuations at 31 times one-year forward earnings compared to Nifty Pharma or Sun Pharma’s own last five-year average at 28.5 times. In January 2025, it was recommended that investors accumulate the stock; since then, the stock has returned -4 per cent. For long-term investors, the stock can add value as a defensive stock as part of a diversified portfolio. One potential risk is from tariff announcements by the US on innovative medicine.

INNOVATIVE MEDICINE

The company has renamed its specialty segment to Global Innovative Medicines, reflecting revenues from patented medicines rather than generics. The segment, with more than $1 billion in annual revenues and a Q3FY26 exit growth rate of 13.3 per cent year on year, is now a mature, self-sustained value generator.

The leading asset, Ilumya (for plaque psoriasis), reported sales of $680 million in FY25 globally. Sun Pharma has applied for a supplemental application in Psoriatic Arthritis, with a launch expected in the next year. Two more US products launched in the last year include:

• Leqselvi (deuruxolitinib): Launched in July 2025 for severe alopecia areata.
• Unloxcyt (cosibelimab): Launched in January 2026 for advanced Cutaneous Squamous Cell Carcinoma (aCSCC), adding a checkpoint inhibitor to the portfolio.

Pipeline assets include Fibromun (in Phase-II and Phase-III trials for glioblastoma and soft tissue sarcoma) and GL0034 (in early trials for type-2 diabetes). With close to $3 billion in cash, Sun Pharma can also look for strategic acquisitions.

INDIA AND OTHERS

Sun Pharma is the industry leader in the Indian pharma market, growing faster than the industry at a 13 per cent CAGR in FY21-25. It is the leader in the diabetes segment in India and will participate in the first wave of launches for generic Semaglutide, having secured approvals for both weight loss and diabetes brands.

The Rest of the World and Emerging Markets accounted for 34 per cent of 9MFY26 revenues, reporting growth of 17–20 per cent. Ilumya has now been launched in 35 countries.

FINANCIAL OUTLOOK

Gross and EBITDA margins have expanded, benefiting from the innovative medicine mix. Revenue growth stood at 11 per cent in 9MFY26. Margin expansion may face temporary headwinds in the next year due to launch costs of approximately $100 million for two new products. Consensus estimates place revenue and earnings growth at 11 per cent and 12 per cent, respectively, in FY27.

Based on the sources provided, here is the reproduction of the article regarding the outlook for benchmark stock indices.

Short fall

INDEX OUTLOOK. The benchmark indices can dip more to test supports and reverse higher eventually

By Gurumurthy K, bl. research bureau

Nifty 50, Sensex and Nifty Bank index did not see a strong follow-through rise after opening last week on a positive note. Sensex and Nifty fell sharply towards the end of the week, giving away all their gains and closing down 1.14 per cent and 0.87 per cent, respectively. The Nifty Bank index also fell but managed to close the week marginally higher by 0.11 per cent.

On the charts, the near-term picture looks weak, and indices can fall more this week. However, supports are expected to limit the downside and act as a floor for a potential reversal higher. Positive sentiment is bolstered by Foreign Portfolio Investors (FPIs), who bought Indian equities for the second consecutive week with a net inflow of about $1.27 billion.

NIFTY 50 (25,471.10)

• Short-term view: Immediate supports are at 25,200 and 25,100. Nifty is expected to reverse higher from this zone toward 26,000–26,100 and potentially 26,400. A break below 25,100 could extend the fall to 24,700 or 24,400.
• Medium-term view: The broader picture remains bullish with strong support between 23,500 and 24,000. The index can target 27,500–28,000 in the medium term, with long-term potential for 30,000–31,000. This view would be negated only if the index falls below 23,500.

NIFTY BANK (60,186.65)

• Short-term view: The near-term picture is unclear. Key supports lie at 60,000 and the 59,750–59,550 zone. A bounce from here could lead the index back to 61,000. A breach of 61,000 is necessary to open the upside for 62,000 and higher levels.
• Medium-term view: Sideways consolidation within a broader uptrend continues. A bullish breakout above 61,000 eventually could target 63,000–63,500 initially and 68,000–69,000 in the long term. Support at 53,500 is crucial to maintain this outlook.

SENSEX (82,626.76)

• Short-term view: Supports are at 82,450 and 82,000. As long as Sensex stays above 82,000, a bounce back to 84,500–85,000 and a revisit of 86,000 is possible. A fall beyond 80,000 is not currently expected.
• Medium-term view: The broader uptrend is intact with targets of 89,000–90,000 (medium term) and 98,000–99,000 (long term). The bullish view is negated only if the index breaks the 79,500 support.

MIDCAP AND SMALLCAP OUTLOOK

• Nifty Midcap 150 (21,884.35): Near-term support is at 21,500; a bounce could reach 22,800. A break above 22,800 would clear the path for 26,000–26,500 in the medium term. Crucial supports are at 20,500 and 20,000.
• Nifty Smallcap 250 (15,988.30): Support is at 15,850. A bounce could target 16,600–16,700 in a week or two, and eventually 18,300. A break above 18,300 could take the index to 22,500–23,000 in the long term. The sources reiterate that this remains a good time to enter the small-cap segment, provided the index stays above 15,000.

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IMMEDIATE SUPPORTS

• Nifty 50: 25,200, 25,100
• Sensex: 82,450, 82,000
• Nifty Bank: 59,750–59,550

Based on the sources provided, here is the reproduction of the article regarding the upcoming changes and features in EPFO 3.0.

What’s new in EPFO 3.0?

PF-WISE. The new app would enable withdrawal of proceeds from bank ATMs, and use of UPI interface.

By Venkatasubramanian K, bl. research bureau

Oftentimes, we hear many subscribers of the Employees’ Provident Fund (EPF) expressing dissatisfaction about the delay or denial of rightful claims. From portal glitches to the non-receipt of OTPs and non-updation of passbooks, the list of grievances is long.

However, change is on the horizon. The Labour Minister announced in December 2025 that a new EPFO 3.0 app would be rolled out early in 2026, with recent reports indicating it should be up and running by April 2026.

NEW APP, NEW FEATURES

EPFO 3.0 is not an upgrade but an entirely new app dedicated to EPF transactions. While the Umang app and UAN portal will continue to function for now, the new app is designed for easier navigation and more comprehensive detail capture.

Key features include:

• ATM Withdrawals: The EPFO will provide ATM cards linked to EPF accounts upon application. Approved claim funds will be released to the linked bank account and can be withdrawn from designated ATMs.
• UPI Interface: Withdrawals can also be conducted via linked UPI accounts at ATMs.
• Self-Service Transactions: Subscribers can correct information, upload KYC documents, and modify bank or personal details themselves via OTP authentication on their mobile devices.
• No Employer Intervention: Most updates and transactions will no longer require authentication from the employer.
• Faster Approvals: Claim approval timelines are expected to drop from a few weeks to just a few days.

ELIGIBILITY CRITERIA

To use these new features, subscribers must meet three criteria:

1. An active UAN (Universal Account Number).
2. An active mobile number linked to the UAN.
3. A KYC-compliant account, which requires Aadhaar, PAN, a passport-size photograph, and bank details. Subscribers must upload scanned cheque leaves as part of this online process.

REDUCED TIMELINES & STANDARDIZATION

In October 2025, the EPFO’s central board of trustees (CBT) approved several reforms to simplify the framework.

• Merged Framework: As many as 13 types of partial withdrawal provisions have been merged into one simplified framework.
• Reduced Subscription Period: You can now make withdrawals for all purposes (medical, education, marriage, etc.) after only 12 months of contributions. Previously, these required between three and seven years depending on the purpose.
• Higher Withdrawal Limits: The withdrawal amount is now standardized at up to 75 per cent of the accumulated EPF corpus (including employer and employee contributions plus interest).

UNEMPLOYMENT BENEFITS

If a subscriber is rendered unemployed due to involuntary attrition or other reasons, they can now withdraw 75 per cent of their corpus immediately. The remaining 25 per cent can be withdrawn after one year if they remain unemployed.

PLAN WISELY

While withdrawals are easier, the Labour Ministry notes that about 75 per cent of subscribers had less than ₹50,000 at the time of final settlement. Experts advise subscribers to treat the EPF as a retirement kitty with its 8.25 per cent annual assured returns, rather than using it for every contingency. Instead, investors should maintain a separate emergency corpus and insurance for risks.

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OVERVIEW

• Simple to log into
• Standardised procedures
• Higher withdrawals

Based on the source provided, there is no standalone article titled "Emerging Tech Redefining What it Takes to Scale Globally."

Instead, this phrase appears within the column "AI Will Usher a Golden Age of Dum Pukht" by Indrajit Hazra. The author mentions it as a pitch for a paper he presented to the organizers of the "AI Impact Summit" in Delhi, which was ultimately rejected for being "too niche".

Below is the reproduction of the section of that article where the author discusses this concept and what his presentation would have entailed:

From "AI Will Usher a Golden Age of Dum Pukht"

By Indrajit Hazra

Hazra describes his attempt to engage with the upcoming AI Impact Summit:

"Which is what happened to me when, earlier this week, I approached organisers of AI Impact Summit that kicks off in Delhi tomorrow. Coming from a non-tech background, my pitch for a paper on how emerging tech is redefining what it takes to scale globally was considered too niche and 'Get out of here' ridiculous".

Despite the rejection, Hazra outlines the core of the presentation he would have given to India's business leaders regarding the transition from traditional methods to an AI-driven global scale:

• The Paradigm Shift: He likens current human intelligence to "Cro-Magnons at the entrance" of a new era.
• A New Way to Scale: He describes a hypothetical speech to "fellow sentients" about how to transition a business into a global enterprise: "Earlier, how you’d scale a business into a global enterprise was to find leaders. In the generative AI sphere... OpenAI making prompting the new data, which, if you’re a member, was once the new oil. Scaling, for us and everyone else, will soon be training on AI created content".
• The Concept of "AI Dum Pukht": The article ultimately argues that while AI will handle the "supreme processing speeds" and solve problems automatically, human-created products—which he calls "Dum Pukht" (slow cooking)—will become the rare, high-value "collectibles" in a world where everything else is scaled by machines.

Based on the source provided, there is no article titled "Economy Needs to Draw on Patient Capital" or any content explicitly discussing "patient capital" within the provided page of The Economic Times.

The articles available in the provided source (dated February 15, 2026) are:

• "A Regal Cambodian Experience of Intimacy and Balance..." by Sivakumar Sundaram (a culinary review).
• "It Wanders Lonely as a Cloud That Floats..." by Atanu Biswas (an exploration of nihilism and Haruki Murakami).
• "Climbing Mt Olypbud in Calcutta’s M. Chateaubriand" by Ruchir Joshi (a restaurant review).
• "AI Will Usher a Golden Age of Dum Pukht" by Indrajit Hazra (an essay on AI scaling and the future value of human-created products).
• "FAFO Parenting" (a column on modern parenting trends).

While Indrajit Hazra’s article mentions scaling businesses and the value of "slow" human intelligence (metaphorically represented by the "Dum Pukht" cooking method), it focuses on generative AI and "Non-Artificial Intelligence" (NAI) rather than "patient capital" or broader economic investment strategies.

Inequality in Annualized Comprehensive Wealth Across US Retirement Cohorts

 Annualized Comprehensive Wealth (ACW) is a broad measure of household resources designed to evaluate retirement security by converting total wealth into an actuarially fair joint life annuity. It serves as a metric to determine how much a household can sustainably consume annually over its expected remaining lifetime.

The sources highlight several key aspects of ACW within the context of wealth inequality:

Definition and Composition of ACW

• Comprehensive Wealth (CW): Before annualization, the sources construct "comprehensive wealth" by augmenting traditional net worth with the actuarial present values of future payment streams. These include labor-market earnings, Social Security, defined-benefit (DB) pensions, annuities, life insurance, and government transfers.
• Calculation: ACW is calculated by dividing this total lump sum by an annuity price ($P$) that accounts for household size, age-dependent survival probabilities, and a real interest rate.
• Purpose: The primary advantage of ACW over traditional net worth is that it allows for meaningful comparisons across households of different ages and sizes by accounting for differences in household composition and expected longevity.

Trajectories and Heterogeneity in Retirement

• The "Rising ACW" Trend: For the median household, ACW tends to increase throughout retirement. This suggests that households typically spend down their total resources more slowly than their remaining joint life expectancy is shortening.
• Demographic Divergence: This upward trajectory is not universal. It is largely driven by college-educated and White households. In contrast, other demographic groups—such as Black and Hispanic households or those with less education—show relatively flat or declining ACW trajectories as they age

In the study of Inequality in Comprehensive Wealth, the sources define Annualized Comprehensive Wealth (ACW) as a measure that converts total household resources—including net worth and the present value of future income streams like Social Security and pensions—into a sustainable annual consumption amount based on life expectancy. The "trajectory" of this wealth refers to how ACW evolves as households age through retirement.

General Trajectories in Retirement

The sources report that, for the median household, ACW tends to rise throughout retirement. This upward trajectory indicates that the typical household is spending down its resources more slowly than its joint life expectancy is shortening. This behavior contrasts with simple life-cycle models but is consistent with models accounting for:

• Precautionary motives regarding uncertain longevity and rising out-of-pocket medical expenses.
• Bequest motives, where households intentionally preserve wealth to leave to heirs.
• Frictions in the housing market, such as imperfect reverse mortgage markets that prevent households from easily liquidating home equity for consumption.

Heterogeneity and Inequality

While the median ACW rises, this pattern is not universal. The sources highlight considerable heterogeneity in trajectories, which directly contributes to widening inequality in retirement.

• Education and Race: The rising trajectory of ACW is primarily driven by college-educated and White households. In contrast, households with less education (e.g., those without a high school degree) and Black or Hispanic households often show flat or even declining trajectories. For instance, while White households see ACW increase after age 70, the median trajectory for Black households is essentially flat, and it actually falls for Black and Hispanic members of the Silent and Older generation.
• Wealth Brackets: Inequality is further underscored by wealth levels. For the top 10% of households, ACW rises dramatically at the oldest ages, meaning their wealth becomes increasingly large relative to their remaining life expectancy. For the bottom 10%, the trajectory remains flat at a very low level.
• Asset Returns: Household-specific rates of return on assets like equities and housing are major drivers of these divergent trajectories. Higher-wealth, college-educated, and White households tend to have greater exposure to equities, allowing them to benefit more from market recoveries, such as the run-up following the Great Recession. Conversely, less-educated and non-White households disproportionately exited the stock market after 2008, missing out on significant asset price increases.

Broader Context of Inequality

The sources suggest that inequality in ACW increases with age. This widening gap is shaped by several structural factors:

• The Transition in Pensions: The shift from traditional defined-benefit (DB) pensions to defined-contribution (DC) plans like 401(k)s has increased wealth inequality, as DC plan outcomes are more dependent on individual saving decisions and market movements.
• Social Security as an Equalizer: Social Security remains the most critical resource for households lower in the wealth distribution, significantly reducing overall wealth inequality; without it, the 75-25 wealth ratio would rise from 4.7 to 7.3.
• Survivorship Bias: Because wealthier individuals tend to live longer, the households observed at very advanced ages are increasingly drawn from higher-wealth groups, which mechanically increases measured inequality among the oldest cohorts.

Ultimately, the sources conclude that gaps in retirement preparation across education and demographic groups are likely to widen as households age, driven by differences in portfolio composition, labor-market attachment, and the realization of household-specific asset returns.

In the context of Inequality in Comprehensive Wealth, the sources identify several systemic and household-level drivers that shape the distribution of retirement resources. While traditional net worth is a significant factor, Annualized Comprehensive Wealth (ACW) reveals that inequality is driven by a complex interplay of asset market fluctuations, shifts in pension structures, and demographic characteristics.

1. Household-Specific Asset Returns

One of the most significant contributors to wealth inequality is the heterogeneity in real rates of return on assets like equities, fixed-income instruments, and housing.

• Portfolio Exposure: Households with higher ACW typically have greater exposure to financial wealth and equities. This exposure allowed them to benefit disproportionately from the long-term run-up in the stock market following the Global Financial Crisis.
• Market Timing and Exit: In contrast, less-educated and non-White households were more likely to exit the stock market following the 2008 crisis, causing them to miss out on subsequent historic asset price increases. This divergence in realized returns is a major driver of the widening 90–10 ratio and Gini coefficient.

2. The Transition from DB Pensions to DC Plans

The structural shift in how Americans prepare for retirement has fundamentally altered wealth distribution:

• Increased Risk and Responsibility: The move from defined-benefit (DB) pensions to defined-contribution (DC) plans (like 401(k)s) has made retirement security more dependent on individual decisions regarding saving and asset allocation.
• Greater Dispersion: The sources note that the 75-25 ratio for retirement account wealth is approximately 19.5, compared to only 9.8 for DB pension wealth. This suggests that the DC-based system is associated with significantly higher wealth inequality over time.

3. Education and Lifetime Earnings

Education serves as a primary driver of inequality, acting as a proxy for lifetime earnings, financial literacy, and survival expectations.

• Trajectory Gaps: Median ACW for college graduates is over $100,000 and generally rises as they age, whereas it remains flat or even declines for those without a high school degree.
• The College Premium: The rise in the college wage premium since 1980 has increased the lifetime earnings—and thus the comprehensive wealth—of more recent generations of college graduates relative to their less-educated peers.

4. Racial and Ethnic Disparities

Stark differences in ACW levels exist across race and ethnicity, with Black and Hispanic households holding between half and three-quarters the annual resources of White households.

• Explained Factors: Using the Oaxaca-Blinder decomposition, the sources find that the majority of these gaps are accounted for by observable characteristics, including differences in education, bequest expectations, and household returns.
• Intra-group Dispersion: Even after controlling for these factors, a higher share of Black or Hispanic households is statistically associated with higher overall inequality, reflecting considerable dispersion within these demographic groups.

5. Life-Cycle Factors and Expectations

Individual behaviors and expectations regarding the end of life also drive inequality as households age:

• Bequest Motives: Wealthier households are more likely to preserve assets to leave as inheritances, leading to an upward-sloping ACW trajectory at older ages.
• Medical Expenses: The rising variance of out-of-pocket medical expenses and long-term care shocks at older ages creates a "precautionary buffer" motive that affects wealth drawdown differently across the distribution.
• Survivorship Bias: Because wealthier individuals tend to live longer, the pool of households observed at very advanced ages is increasingly composed of higher-wealth individuals, which mechanically increases measured inequality in the oldest cohorts.

6. Social Security as a Mitigating Factor

While the factors above drive inequality, Social Security acts as the most critical equalizer. It is the dominant resource for households in the lower half of the wealth distribution. The sources highlight that without Social Security, the ratio of comprehensive wealth at the 75th percentile to the 25th percentile would jump from 4.7 to 7.3.

In the context of Inequality in Comprehensive Wealth, the sources analyze cohort differences by comparing the Silent and Older generation (born 1945 and before), Early Baby Boomers (born 1946–1954), and Late Baby Boomers (born 1955–1964). While all cohorts share some general trends, such as rising wealth trajectories in retirement, they differ significantly in their resource levels, the composition of their wealth, and their vulnerability to economic shocks.

1. Resource Levels and Composition

• Higher Average Wealth in Younger Cohorts: Younger cohorts (Baby Boomers) have arrived at the start of retirement with greater average resources than their elders. For households aged 61–70, the average Annualized Comprehensive Wealth (ACW) across these cohorts ranges between $75,000 and $100,000.
• Shift in Wealth Type: There is a notable shift in the composition of wealth between generations. Older cohorts relied more on annuitized wealth (such as defined-benefit pensions), while younger cohorts hold a larger share of financial wealth (including 401(k)s and IRAs) and expected labor earnings.
• Labor-Market Attachment: Younger cohorts exhibit a growing share of wealth from earnings, reflecting a higher labor-market attachment compared to previous generations.

2. Trajectories and Drawdown Patterns

• Rising ACW Across Generations: At the median, ACW generally increases with age for all three cohorts. This suggests that across generations, households are spending down their resources more slowly than their life expectancy is shortening.
• The "Great Recession" Impact: Cohorts experienced the 2008 financial crisis at different life stages, leading to divergent ACW outcomes:
  • Early and Late Boomers were in their 50s and 60s during the recession and experienced substantial drops in ACW due to higher exposure to housing and equity markets.
  • The Silent and Older Generation experienced only a modest drop followed by a steep increase, likely due to less exposure to these volatile markets.
  • Recovery: While Early Boomers recovered much of their recession-era losses, Late Boomers had only partially recovered by the end of the sample period.

3. Cohort-Specific Inequality

• Initial Inequality: Measures such as the Gini coefficient and 90–10 ratio suggest that inequality was higher at younger ages (51–60) for more recent cohorts compared to older ones.
• Pension Transition: The transition from defined-benefit (DB) pensions to defined-contribution (DC) plans has contributed to widening inequality within younger cohorts. The 75-25 ratio for retirement account wealth (common in younger cohorts) is 19.5, nearly double the 9.8 ratio for DB pension wealth (common in older cohorts).
• Education Gaps: The college wage premium that rose after 1980 likely increased the lifetime earnings of more recent generations of college graduates, widening the gap between them and their less-educated peers within the same cohort.
• Regression Insights: Interestingly, results from recentered influence function (RIF) regressions suggest that increasing the proportion of Baby Boomers relative to the pre-1948 generation might actually reduce overall measured inequality, though substantial inequality remains among the oldest households.

4. Racial Disparities Across Cohorts

The sources note that the stark gaps in ACW between Black and White households do not diminish with more recent cohorts. If anything, these disparities appear to be larger for younger generations, with White and non-Hispanic households showing a much faster rise in ACW than their Black and Hispanic counterparts as they age.

The sources examine Inequality in Comprehensive Wealth by applying four primary statistical measures to Annualized Comprehensive Wealth (ACW): the Gini coefficient, the 90–10 ratio, the top 10 percent share, and the Theil index. Using these measures, the researchers identify how retirement resource inequality evolves across age, time, and demographic groups.

Trends in Inequality Measures

• Increase with Age: Most inequality measures show that inequality in ACW generally increases as households age, particularly for older cohorts. This is attributed to factors such as survivorship bias (wealthier individuals live longer), differing bequest motives, and the increasing variance of out-of-pocket medical expenses in later life.
• Cohort Differences: Inequality was generally higher at younger ages (51–60) for more recent cohorts (Baby Boomers) compared to the Silent and Older generation. This shift is partially linked to the transition from defined-benefit (DB) pensions to defined-contribution (DC) plans, which introduces more dispersion based on individual saving and investment choices.
• Impact of Economic Cycles: Inequality measures fluctuated significantly between 1998 and 2022. Inequality fell during the peak of the Great Recession (2010–2012) as sharp declines in housing and equity prices "shaved" more wealth from the top of the distribution. However, inequality increased markedly through 2018 as financial asset prices recovered, disproportionately benefiting higher-wealth households with more equity exposure.

Drivers of Inequality (RIF Regression Analysis)

To understand what drives these statistics, the sources use Recentered Influence Function (RIF) regressions, which estimate how specific household characteristics affect a distributional measure like the Gini coefficient.

• Asset Returns: Household-specific rates of return are strongly and positively associated with inequality, particularly the Gini and 90–10 ratio. Wealthier households often have higher exposure to equities, and the resulting higher returns magnify wealth dispersion over time.
• Education: A higher share of college-educated households is associated with higher inequality across all four measures, while a higher share of high-school–educated households is associated with lower inequality.
• Race and Ethnicity: Higher concentrations of Black or Hispanic households are associated with significantly higher inequality in ACW, reflecting considerable dispersion within these groups even after controlling for other characteristics.
• Bequest Motives: Interestingly, a higher probability of leaving or receiving a bequest is associated with lower inequality. This suggests that many households in the middle of the distribution plan to leave bequests, and those expecting to receive them may have less incentive to accumulate extreme individual wealth.

Mitigating Factors

• Social Security as an Equalizer: Social Security is the most effective tool for reducing measured inequality in comprehensive wealth. The sources note that without Social Security, the 75–25 wealth ratio (the ratio of wealth at the 75th percentile to the 25th percentile) would rise from 4.7 to 7.3.
• Marital Status: Increasing the share of married households tends to reduce inequality, while larger household sizes are associated with a slight increase in inequality measures.

Institutional Quality and High-Tech Investment in the EU

 The provided sources examine the widening productivity growth gap between the European Union (EU) and the United States (US), tracing its roots to differences in high-tech investment and the quality of institutional and regulatory frameworks.

The EU-US Productivity and Innovation Gap

Since the mid-1990s, EU countries have experienced slower productivity growth compared to the US, a divergence closely linked to an "innovation gap". The sources highlight several key differences in investment patterns:

• Sectoral Focus: The US prioritizes investment in high-tech sectors such as ICT, Artificial Intelligence (AI), cloud computing, and biotechnology. In contrast, Europe remains concentrated in mature, mid-tech sectors, leading to what some researchers call a "middle-technology trap".
• Investment Shares: In 2021, high-tech sectors accounted for approximately 33% of market sector gross fixed capital formation in the US, nearly double the 17% share in the EU.
• ICT Contribution: The ICT sector alone explains about 48% of the average annual hourly productivity growth gap between the EU and the US from 2000 to 2019.
• Spillover Effects: High-tech innovation provides broader productivity spillovers across the economy, whereas the incremental innovation typical of the EU's mid-tech focus has more limited benefits.

The Role of Institutions and Regulation

The sources argue that the EU's lag in high-tech is deeply rooted in its institutional and regulatory environment. High-tech sectors are inherently risky, characterized by trial-and-error and higher rates of project failure. Consequently, the cost of failure—influenced by regulations—is a critical determinant of investment.

• Labor Market Rigidity: Restrictive Employment Protection Legislation (EPL), which increases the costs of dismissing workers, can deter firms from pursuing high-risk, high-reward disruptive innovation. While some argue EPL fosters trust, the sources suggest it often increases operational rigidity and reduces the incentive to adjust workforces in response to technological shifts.
• Administrative Burdens: High costs and complex procedures for starting a business and resolving insolvency act as barriers to entry and exit, further discouraging investment in volatile high-tech sectors.
• Governance Quality: Broader institutional factors, including the rule of law, control of corruption, and government effectiveness, create the "level playing field" necessary for economic actors to invest and innovate.

Impact on High-Tech and AI Investment

Empirical analysis in the sources indicates a strong correlation between high-quality institutions and investment in innovative sectors.

• Closing the Gap: Raising the institutional and regulatory quality of all EU countries to the level of the current "EU frontier" (the best-performing member states) could increase the share of investment in high-tech sectors by as much as 50%. This reform would effectively close the investment gap with the US by approximately half.
• Artificial Intelligence: AI-intensive sectors are particularly sensitive to these frameworks. Enhancing institutional governance could boost investment in AI-intensive sectors by over 7 percentage points.
• Economic Size: Beyond investment shares, more efficient institutions are associated with a larger relative economic size (value added) of innovative sectors compared to traditional ones.

Policy Implications

The findings suggest that for the EU to increase its competitiveness and productivity growth, it must move beyond industrial composition and address fundamental structural factors. Key recommendations include:

• Simplifying business procedures and reducing administrative burdens for entrepreneurs.
• Making labor markets more flexible to lower the costs of failure and restructuring in risky sectors.
• Strengthening governance and the rule of law to provide a more stable environment for long-term investment.
• Improving insolvency frameworks to lower the costs associated with project failures and firm exits.

The provided sources identify institutional and regulatory quality as the fundamental drivers behind the widening productivity and investment gap between the European Union and the United States. While the US has successfully pivoted toward high-growth, high-tech sectors, the EU remains caught in a "middle-technology trap," largely due to frameworks that increase the costs of innovation and failure.

Core Institutional and Regulatory Indicators

The sources evaluate institutional quality through three primary lenses, noting that higher scores in these areas are directly correlated with increased high-tech investment:

• Institutional Delivery Index: This broad measure encompasses the rule of law, control of corruption, government effectiveness, and regulatory quality. It reflects the extent to which a country provides a "level playing field" and sound economic incentives for actors to invest and innovate.
• Employment Protection Legislation (EPL): This index measures the stringency of regulations regarding worker dismissals. While some argue EPL fosters trust, the sources suggest that for high-tech sectors, strict EPL increases labor market rigidity and operational costs, making it difficult for firms to adjust their workforces in response to rapid technological shifts.
• Business Entry and Exit Frameworks: This includes the Starting a Business score (administrative burdens on entrepreneurs) and the Resolving Insolvency score (the ease and cost of firm exit).

The Mechanism: Risk and the "Cost of Failure"

The sources argue that institutional quality is more critical for high-tech sectors (e.g., AI, ICT, biotech) than for traditional mid-tech sectors because high-tech innovation is inherently disruptive and risky.

• Trial-and-Error: Innovative sectors involve significant trial-and-error and higher rates of project failure.
• Cost of Failure: The expected profitability of investing in high-tech depends heavily on the costs associated with failure and restructuring.
• Barriers to Innovation: Burdensome regulations and rigid labor laws disproportionately increase these costs, deterring firms from pursuing "primary innovation" (creating new products) and pushing them toward "secondary innovation" (improving existing products).

Empirical Impact on High-Tech and AI Investment

The research demonstrates a causal link between these factors and sectoral investment shares:

• Closing the Investment Gap: Raising the institutional and regulatory quality of all EU countries to the level of the "EU frontier" (the best-performing member states) could increase the share of investment in high-tech sectors by up to 50%. This reform alone would close the EU-US high-tech investment gap by approximately half.
• Sensitivity of AI: AI-intensive sectors are particularly sensitive to these frameworks. Improving institutional governance could boost investment in AI-intensive sectors by over 7 percentage points.
• Economic Size: More efficient institutions are associated not just with higher investment shares, but with a larger relative economic size (value added) of innovative sectors compared to traditional ones.

Institutional Origins and Policy Implications

To address potential bias, the study uses legal origins (e.g., English common law vs. French civil law) as instruments, finding that historical legal ideologies continue to influence modern regulatory stringency and, consequently, investment patterns.

The sources conclude that to escape the "middle-technology trap" and enhance competitiveness, the EU must prioritize structural reforms. These include:

1. Reducing administrative burdens for starting and managing businesses.
2. Increasing labor market flexibility to lower the costs of restructuring in risky sectors.
3. Strengthening the rule of law and governance to reduce uncertainty for long-term investors.
4. Improving insolvency frameworks to facilitate the efficient reallocation of resources from failing projects to new opportunities.

The provided sources demonstrate that institutional and regulatory quality are decisive factors in determining the size and success of high-tech sectors within the European Union. While the United States has successfully pivoted toward high-growth, high-tech industries, the EU remains largely confined to a "middle-technology trap," focusing on mature, mid-tech sectors with limited productivity spillovers.

The High-Tech Investment Disparity

The sources quantify a significant gap in high-tech investment between the two regions:

• Sectoral Concentration: In 2021, high-tech sectors accounted for 33% of market sector gross fixed capital formation in the US, nearly double the 17% share observed in the EU.
• Productivity Growth: This investment gap directly translates to slower productivity growth; the ICT sector alone explains roughly 48% of the average annual hourly productivity growth gap between the EU and the US from 2000 to 2019.
• Innovation Style: US high-tech sectors drive "primary innovation" (introducing new products), whereas the EU’s mid-tech focus results in "secondary innovation" (incremental improvements to existing products).

Vulnerability of High-Tech to Institutional Quality

High-tech sectors are uniquely sensitive to institutional and regulatory frameworks because they are inherently disruptive and risky.

• The Cost of Failure: Innovation in fields like AI, ICT, and biotechnology involves significant trial-and-error and high rates of project failure. Burdensome regulations disproportionately increase the "costs of failure and restructuring," deterring firms from investing in these volatile sectors.
• Labor Market Rigidity: Stringent Employment Protection Legislation (EPL)—regulations governing worker dismissals—increases labor market rigidity. In high-tech sectors that require frequent workforce reallocation and high mobility, strict EPL acts as a significant barrier to investment and scaling.
• Entry and Exit Barriers: Administrative burdens for starting a business and inefficient insolvency frameworks (exit costs) further discourage entrepreneurial risk-taking in cutting-edge industries.

Empirical Impact of Reform

The research indicates that improving the quality of EU institutions could dramatically reshape its high-tech landscape:

• Closing the Gap: Raising the institutional and regulatory quality of all EU countries to the level of the "EU frontier" (the best-performing member states) could increase high-tech investment shares by as much as 50%. This reform alone would close approximately half of the investment gap with the US.
• Impact on AI: Artificial Intelligence-intensive sectors are particularly responsive to these factors. Enhancing institutional governance is estimated to boost investment in AI-intensive sectors by over 7 percentage points.
• Economic Size: Beyond investment, efficient institutions are associated with a larger relative economic size (value added) of innovative and disruptive sectors compared to traditional ones.

Broader Institutional Context

The sources use legal origins (e.g., French civil law vs. English common law) as a lens to explain why these regulatory differences exist, noting that historical legal ideologies continue to influence modern state intervention and regulatory stringency. To escape the middle-technology trap, the sources conclude that the EU must prioritize structural reforms—specifically reducing administrative burdens, increasing labor market flexibility, and strengthening the rule of law—to create a dynamic environment where high-tech sectors can flourish.

The sources describe Classification of Innovativeness as a central methodological tool used to distinguish how different sectors respond to institutional and regulatory environments. By categorizing sectors based on their level of technological advancement, the researchers demonstrate that high-tech and disruptive industries are disproportionately sensitive to the quality of governance and the "cost of failure" compared to traditional, mid-tech industries.

The sources employ three distinct methods to classify sectoral innovativeness:

1. Eurostat High-Tech Taxonomy

This approach uses a binary classification (dummy variable) based on the Eurostat high-tech aggregation of NACE Rev. 2 codes at the 2-digit level.

• High-Tech Manufacturing: Includes sectors such as C21 (Pharmaceuticals) and C26 (Computers, electronic, and optical products).
• High-Tech Knowledge-Intensive Services: Includes J58–J60 (Publishing and media), J61 (Telecommunications), and J62–J63 (Computer programming and information services).
• Context: This classification helps illustrate the "middle-technology trap," where the EU remains focused on mature, mid-tech sectors while the US dominates these high-tech categories.

2. Patent Intensity

To provide a more granular, continuous measure of innovativeness, the researchers classify sectors based on their patenting activity.

• Methodology: They match International Patent Classification (IPC) codes from over 18 million US patent applications to NACE codes. US data is used specifically to mitigate endogeneity issues, ensuring that the measure of innovativeness is not biased by the EU's own institutional frameworks.
• Finding: The sources find that improvements in institutional frameworks have a disproportionately stronger effect on sectors with higher patenting activity, such as computer manufacturing (C26), compared to the lowest-ranking sectors.

3. Artificial Intelligence (AI) Intensity

This method focuses on the most modern and disruptive technological frontier using a taxonomy developed by Calvino et al. (2024). Sectors are ranked based on four dimensions of AI intensity:

• AI Human Capital Demand: Demand for AI-related skills.
• AI Innovation: Sector-specific AI-related patents.
• AI Use: The actual adoption of AI by firms.
• AI Exposure: The extent to which AI can perform tasks associated with occupations in that sector.
• Adjustment for Bias: The researchers specifically exclude regulatory barriers from the AI exposure measure to avoid "circularity," ensuring the classification isn't defined by the very regulations they are trying to study.
• Highly AI-Intensive Sectors: Beyond typical tech, this includes K (Financial and Insurance) and M (Professional, Scientific, and Technical Activities).

Significance within the Institutional Context

The classification of sectors is vital because it reveals that not all industries are affected equally by regulation.

• Risk and Uncertainty: Innovative sectors involve significant trial-and-error and higher failure rates. Therefore, high costs associated with Employment Protection Legislation (EPL), business entry, and insolvency proceedings deter investment specifically in high-tech sectors while having less impact on stable, mid-tech industries.
• Primary vs. Secondary Innovation: The sources cite research suggesting that rigid labor markets (high firing costs) lead countries to specialize in "secondary innovation" (incremental improvements), whereas flexible markets foster "primary innovation" (introducing entirely new products).
• Policy Impact: By using these classifications, the sources estimate that raising EU institutional quality to the "frontier" would boost investment in these specific high-tech and AI-intensive sectors by up to 50%, whereas traditional sectors would see a much smaller marginal impact.

The key empirical findings in the sources demonstrate a strong causal link between the quality of institutional and regulatory frameworks and the level of investment in high-tech, innovative, and AI-intensive sectors across the European Union,. These findings suggest that the EU's persistent productivity lag behind the United States is deeply rooted in governance structures that inadvertently deter investment in risky, cutting-edge industries,.

Core Findings on Investment and the EU-US Gap

The primary finding of the study is that raising the institutional and regulatory quality of all EU countries to the level of the current "EU frontier" (the best-performing member states) would have a transformative effect on the economy:

• 50% Increase in High-Tech Investment: Such reforms could increase the share of investment in high-technology sectors by as much as 50%,.
• Closing the Gap with the US: This increase would effectively close approximately half of the existing high-tech investment gap between the EU and the US,,. For context, in 2021, high-tech sectors accounted for 33% of market sector investment in the US, compared to only 17% in the EU.
• AI-Specific Gains: Enhancing institutional governance alone could boost investment in AI-intensive sectors by over 7 percentage points,.

Impact Across Different Sector Classifications

The sources used three distinct methods to classify "innovativeness," finding that better institutions consistently benefited more advanced sectors:

• High-Tech Taxonomy: More efficient institutional frameworks were positively associated with higher investment shares in sectors classified as high-tech by Eurostat.
• Patent Intensity: Improvements in institutional frameworks had a disproportionately stronger effect on sectors with higher patenting activity compared to low-innovation sectors.
• AI Intensity: Sectors with high AI intensity were found to be more sensitive to institutional conditions than traditional sectors. This effect was particularly pronounced in a more recent sample (2019–2023), reflecting AI's growing economic prominence.

The Mechanism: Risk and the "Cost of Failure"

The empirical evidence supports the theory that institutions matter most for high-tech because these sectors are inherently characterized by trial-and-error and high failure rates,.

• Sensitivity to Costs: The expected profitability of high-tech investment depends heavily on the costs of failure and restructuring.
• Burdensome Regulations: Rigid labor laws (Employment Protection Legislation) and complex administrative procedures for starting or closing a business act as "costs of failure" that deter firms from pursuing disruptive, primary innovation,,.
• Relative Success of Mid-Tech: In contrast, the sources found that mid-tech sectors, which involve lower risk and incremental innovation, are less sensitive to these regulatory constraints.

Robustness and Broader Economic Impacts

The researchers employed an Instrumental Variable (IV) approach using legal origins to establish that these findings are causal rather than just correlations,. The results remained robust even when:

• Controlling for GDP per capita and corporate tax rates,.
• Excluding financial hubs like Ireland and Luxembourg.
• Using Value Added Share as a dependent variable, which showed that better institutions are associated with a larger relative economic size for innovative sectors,.
• Using Insolvency Frameworks as an indicator; efficient insolvency procedures were found to be highly important for investment in innovative sectors.

Policy Implications

The empirical results suggest that to escape the "middle-technology trap," the EU must prioritize structural reforms that lower the barriers to entry and the costs of failure,. Specifically, the sources recommend easing labor market rigidities, reducing administrative burdens for startups, and improving insolvency frameworks to allow for a more dynamic reallocation of resources toward frontier technologies,.

The provided sources suggest that the European Union’s productivity lag is not merely a result of industrial preference but is deeply rooted in the quality of its governance and regulatory frameworks. To bridge the investment gap with the United States and escape the "middle-technology trap," the sources propose several critical policy shifts focused on lowering the costs of innovation and failure.

1. Strengthening Institutional Governance and the Rule of Law

The sources emphasize that high-quality institutions are the foundation of a competitive high-tech economy.

• Effective Governance: Policies should aim to improve "Institutional Delivery," which includes strengthening the rule of law, controlling corruption, and enhancing government effectiveness.
• Reducing Uncertainty: Sound institutions provide a "level playing field" and reduce the economic uncertainty that often discourages long-term, high-risk investments in disruptive technologies.
• Economic Impact: The sources estimate that elevating institutional quality to the level of the "EU frontier" (the highest-performing member states) could increase the high-tech investment share by roughly 30%.

2. Enhancing Labor Market Flexibility (EPL Reform)

A major policy implication involves the reform of Employment Protection Legislation (EPL), which governs the strictness of worker dismissals.

• Lowering Reallocation Costs: Disruptive innovation requires frequent workforce reallocation and rapid scaling. Current rigidities increase operational costs and deter firms from pursuing high-risk, primary innovation (introducing new products) in favor of safer, secondary innovation (improving existing products).
• Targeted Flexicurity: Policymakers are encouraged to ease firing costs, which would incentivize firms to enter sectors characterized by risky technology and trial-and-error processes.

3. Reducing Administrative and Exit Burdens

The sources identify business entry and exit barriers as significant deterrents to high-tech dynamism.

• Simplifying Start-ups: Reducing the administrative burden on entrepreneurs (as measured by the "Starting a Business score") is one of the most effective ways to boost investment; reforms in this area alone could increase high-tech investment shares by up to 50%.
• Insolvency Frameworks: Efficient insolvency procedures are critical because they lower the "cost of failure". Policies that make it easier and cheaper to resolve insolvency allow resources to be reallocated more dynamically from failing projects to frontier technologies.

4. Fostering AI and Frontier Technology Adoption

Given that AI-intensive sectors are particularly sensitive to regulatory environments, the sources suggest that general institutional improvements will have a disproportionately positive effect on the AI landscape.

• Recent Relevance: Analysis of the 2019–2023 period shows that as AI technology has matured, the importance of institutional quality in fostering its adoption has increased.
• Sensitivity: Enhancing governance could boost investment in AI-intensive sectors by over 7 percentage points.

5. Integrating Complementary Enablers

While structural and regulatory reforms are central, the sources conclude they must be part of a broader, integrated strategy.

• Beyond Regulation: Reforms to labor markets and administrative procedures need to be complemented by access to finance, robust digital infrastructure, and education/skill-upgrading systems.
• Global Competitiveness: Combining these structural improvements with innovation enablers is seen as the only viable path to closing the productivity and investment gap with the US.