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Type: Explained

These Newscards correspond to the explained section of various newspapers. They become immensely important for both prelims and mains and special attention needs to be paid to them

  • The next DPI: how India can commoditise AI

    Why in the News

    India built its identity, payments and data systems as free, interoperable public infrastructure, and the same approach is now being proposed for artificial intelligence (AI). The proposal argues that India should target the cost of running AI models rather than compete with global technology companies to build them, since it cannot win a capital race against firms that already dominate frontier model training. It comes as India remains a net importer of finished intelligence despite supplying a large share of the data, talent and engineering behind the world’s leading AI models.

    What is Digital Public Infrastructure (DPI)?

    1. Digital Public Infrastructure: Digital Public Infrastructure (DPI) refers to open, interoperable digital systems, built and standardised by the state, on which private companies and citizens can build services.
    2. India’s stack: India’s DPI stack combines Aadhaar for identity, the Unified Payments Interface (UPI) for payments, and the Data Empowerment and Protection Architecture (DEPA), operationalised through Account Aggregators, for consent based data sharing.
    3. Design principle: In each case, the state built the underlying protocol and made it free or near free to use, while private companies compete on the applications built on top of it.

    What made India’s identity, payments and data stack globally distinctive?

    1. Identity at scale: Aadhaar enrolled 1.4 billion people and turned identity verification from an expensive paper process into a low cost application programming interface (API) call.
    2. Payments at scale: UPI made digital payments effectively free, processing around 20 billion transactions a month at near zero cost.
    3. Cheap data: The cost of one gigabyte of mobile data in India fell from about $4 in September 2016 to under 30 cents by 2019, after one telecom operator absorbed the fixed cost of a nationwide 4G network and priced at marginal cost, forcing competitors to match.
    4. Scale of adoption: Roughly 500 million people came online within five years of that price fall, powering India’s digital payments, startup and direct benefit transfer ecosystem.

    What is the extractive trade India faces in artificial intelligence?

    1. India’s contribution: India supplies an outsized share of the data, engineering talent and research behind the world’s leading AI models, with its universities and diaspora furnishing a large share of the research talent behind major laboratories.
    2. India’s import bill: Indian startups must rent that same intelligence back as dollar priced API tokens, subject to export controls and hosted on servers outside the country, on terms set outside India.
    3. Historical parallel: The pattern mirrors colonial era trade, where raw cotton was shipped out and finished cloth bought back at a markup.

    What are the pillars of India’s proposed AI token economy?

    1. Compute: The IndiaAI Mission, backed by an outlay of about Rs 10,372 crore, has empanelled private cloud providers to onboard over 38,000 graphics processing units (GPUs), with a target of 100,000, letting eligible startups and researchers access compute at about Rs 65 per GPU hour.
    2. Open models: The proposal calls for any AI model built using state subsidised compute or public datasets, including anonymised legal, agricultural and educational data in India’s 22 official languages, to be released under an open weights licence, so private companies compete on applications rather than owning the underlying model.
    3. Distribution: A proposed Unified Intelligence Interface (UII), styled as a UPI for AI, would be an open, standardised gateway through which any application could call any model, sovereign or private, with shared standards for identity, consent, billing and safety.

    What do other countries’ digital infrastructure models show about India’s combination?

    1. Estonia: Estonia operates a world class digital identity system but has no payments rail comparable to UPI.
    2. Brazil: Brazil’s Pix is a free, widely used instant payments rail, but it functions as a standalone system without an equivalent identity or data sharing layer.
    3. Singapore: Singapore runs Singpass for digital identity and SGFinDex for consolidated financial data access, built as separate systems rather than one integrated stack.
    4. European Union: The European Union has built open banking and data portability rules, but has not combined them with a single free national identity or payments system.
    5. India’s distinction: India’s claim to leadership rests specifically on operating identity, payments and data sharing as one interoperable public stack, a combination no other country has built at the same scale.

    Can the model that crashed the price of data work the same way for artificial intelligence?

    1. Different economics conceded: The proposal itself concedes that India cannot win a capital race against global technology companies in training frontier AI models, since that race rewards the scale of capital already held by a small number of firms.
    2. Recalibrated target: It argues the correct target is instead the cost of running, or making inferences from, existing models, treating inference cost the way earlier reforms treated the cost of data and transactions.
    3. Untested assumption: Unlike telecom spectrum or a payments protocol, frontier AI models require continuous retraining and enormous ongoing compute investment, so a one time cost crash of the kind seen in mobile data may not hold for long in AI.

    What are the challenges to India’s proposed AI token economy?

    1. Hyperscaler capital gap: Global technology companies that already dominate frontier model training can subsidise inference pricing far below what India’s compute base can match, even after the mission scales to 100,000 GPUs.
    2. Open weights disincentive: A mandatory open weights licence for any model built on subsidised compute or public data could discourage private investment in cutting edge model development within India, since firms could not fully capture the returns.
    3. Power and grid constraints: Data centre clusters need dedicated, reliable electricity and transmission capacity, and India’s grid planning does not yet treat AI compute load as a distinct category to plan for.
    4. Chip supply dependence: Scaling to 100,000 GPUs depends on continued access to export controlled semiconductors, mostly manufactured outside India, exposing the plan to global chip supply and export control decisions beyond its control.
    5. Data privacy exposure: Aggregating public datasets such as legal rulings, health records and agricultural data for AI training raises consent and privacy questions that a data protection framework would need to resolve first.
    6. Subsidy sustainability: A national freemium token model, funded partly by diverting subsidy allocations, risks being gamed by ineligible users or becoming fiscally unsustainable if adopted at the scale the proposal envisions.

    Conclusion

    India’s identity, payments and data systems became cheap because the state built the rails and let market competition crash the price on top of them. The proposal argues the same design can make artificial intelligence inference cheap, provided India targets running costs rather than the unwinnable race to train frontier models. Whether India’s power capacity, chip access and open weights mandate can support that shift remains unresolved.

    Back2Basics

    IndiaAI Mission

    1. Ministry: The IndiaAI Mission is administered by the Ministry of Electronics and Information Technology (MeitY).
    2. Approval: It was approved by the Union Cabinet in March 2024 with an outlay of about Rs 10,372 crore.
    3. Aim: It aims to build public private compute infrastructure, support indigenous foundational AI models, and expand access to AI applications, skilling and startup financing.
    4. Structure: The mission is organised around pillars covering compute infrastructure, foundational models, datasets platforms, application development, skilling, startup financing, and safe and trusted AI.

    AI Token Economy

    The AI token economy or tokenomics is a new financial framework where tokens (the basic units of text, audio, or visual data that AI models process) function as the foundational currency of digital work, computation, and enterprise spending.

    Core Mechanics of AI Tokens

    1. The Atomic Unit: Unlike traditional software priced by user seats or flat subscriptions, AI is metered and billed per inferential act (input and output tokens).
    2. Conversion Rate: Roughly 1,500 English words equal about 2,048 tokens, varying by model. Every prompt, background system instruction, and retrieved file consumes this resource.
    3. Macro Indicator: Macroeconomists track token volume like kilowatt-hours or steel production to measure digital output and productivity across industries.

  • How common are cloudbursts in India?

    Why in the News?

    Flash floods triggered by a cloudburst struck Pahalgam in Anantnag on 12 July. Last week, the India Meteorological Department (IMD) rejected claims that cloudbursts caused the recent floods in Assam and Nagaland. The two events have renewed attention on the scientific definition of a cloudburst and its frequent misuse in public discourse.

    What counts as a cloudburst under the IMD’s definition?

    1. Threshold: The IMD defines a cloudburst as 10 centimetres or more of rainfall in an hour over a small area of around 20 to 30 square kilometres.
    2. Scale comparator: Indore receives about 1,062 millimetres of rain in an average year, so a single cloudburst can dump close to 10% of a full year’s rainfall in 60 minutes.
    3. Related category: Some scientists have proposed a mini cloudburst category for 5 centimetres of rain in an hour over the same area, since local topography can make even this devastating.

    How does a cloudburst form?

    1. Initial lift: Warm, moist air rises rapidly through convection, and in mountainous terrain this rise is intensified by orographic lifting, where monsoon winds are forced upward by steep slopes.
    2. Cloud growth: As the rising air cools, water vapour condenses into towering cumulonimbus clouds that can reach up to 15 kilometres in height.
    3. Suspension: Strong upward currents keep forming raindrops suspended in the cloud for longer instead of letting them fall immediately.
    4. Discharge: When the weight of accumulated water exceeds what the updraft can hold, or the updraft weakens, the suspended water falls in one release rather than as steady rain.

    How common are cloudbursts in India, and why are they hard to count?

    1. Historical count: Parliament was told in 2019 that the IMD recorded only around 30 cloudburst incidents between 1970 and 2016, a figure many experts consider an underestimate.
    2. Rising frequency: Global warming increases the amount of moisture the atmosphere can hold, making cloudbursts more frequent even though they remain rare compared with ordinary heavy rain.
    3. Monitoring gap: Most cloudbursts occur in remote, high altitude regions where rain gauges and weather stations are sparse, so an event even a few kilometres from a monitoring station may go officially unrecorded despite causing large scale destruction downstream.
    4. Regional concentration: Uttarakhand, Himachal Pradesh, and Jammu and Kashmir have reported a recent surge in events described locally as cloudbursts, particularly in July and August.

    Does the label obscure accountability for poor planning?

    1. Blame diffusion: Calling a heavy downpour a cloudburst turns it into a singular, unforeseeable act of nature, which is harder to do when the stated cause is heavy rain combined with poor drainage.
    2. Dharali precedent: During the 2025 Dharali floods in Uttarakhand, initial reports blamed a cloudburst, but meteorological data later showed the rainfall rate was well below the cloudburst threshold. The underlying causes were illegal construction on riverbeds, deforestation that left soil vulnerable to erosion, and the absence of drainage infrastructure along new all weather roads.
    3. Assam and Nagaland claims: The IMD last week rejected reports that cloudbursts caused recent floods in Assam and Nagaland, including the Upper Assam floods.
    4. Accountability questions avoided: Had the Dharali downpour genuinely been a cloudburst, officials could have avoided questions about why the state permitted construction in high risk zones and why early warning systems failed.

    Why are cloudbursts difficult to forecast?

    1. Model resolution: Weather models estimate average conditions across grid cells, while a cloudburst occurs over an area smaller than a single cell, so detecting one requires high resolution models needing computing power not always available.
    2. Speed of formation: Cloudbursts develop and strike quickly, unlike cyclones or monsoon systems that can be tracked for weeks, leaving forecasters far less data to work with.
    3. Terrain interference: Doppler weather radars emit and receive beams that mountains can block, creating blind spots in exactly the high altitude terrain where cloudbursts are most common.
    4. Sparse instrumentation: Rugged terrain also means fewer automatic weather stations, leaving fewer ground sensors to feed real time data into short term prediction.

    What is India doing to improve cloudburst forecasting?

    1. Nowcasting: The IMD is developing nowcasting technology to issue short term alerts every few hours rather than long range forecasts.
    2. Mission Mausam: Under the government’s Mission Mausam programme, India plans to more than double its radar network from about 40 radars currently and use artificial intelligence to better predict hyperlocal events.
    3. Persistent limits: Even with better technology, a cloudburst is expected to remain harder to predict than a typical rainstorm because of how localised and fast forming it is.

    Conclusion

    A cloudburst is a specific meteorological event defined by the IMD’s own rainfall threshold, not a synonym for any destructive downpour. Attributing flood damage to a cloudburst without checking recorded rainfall data lets authorities treat the disaster as an unforeseeable act of nature rather than examine illegal construction, deforestation and drainage failure. India’s forecasting improvements under Mission Mausam target the science of prediction, but they do not by themselves fix the planning failures the label has repeatedly been used to obscure.

    Back2Basics:

    Mission Mausam

    1. Nodal ministry: Ministry of Earth Sciences.
    2. Launch year: 2024.
    3. Aim: Improve weather and climate forecasting through expanded observation networks, high performance computing and artificial intelligence based prediction.
    4. Key features: Expansion of Doppler weather radar coverage, next generation satellites, and impact based forecasting for more precise, localised warnings.

    PYQ Relevance

    [UPSC 2024] What is the phenomenon of ‘cloudbursts’? Explain.

    Linkage: The PYQ explains cloudbursts, their causes, and forecasting challenges. It updates the topic with IMD clarifications, Mission Mausam, and disaster accountability.

  • Outdated contraception, early conception: Counting the babies that India didn’t plan for

    Why in the News

    India’s total fertility rate has fallen to the replacement level of about two children per woman, a figure widely read as proof the country has completed its demographic transition. This headline number conceals a persistent gap between how many children women actually want and how many they have, meaning India’s fertility decline is a policy problem rather than a solved story.

    What is the difference between the Total Fertility Rate and the Wanted Fertility Rate?

    1. Total Fertility Rate (TFR): TFR is the average number of births per woman across her reproductive years, counting all births including those women did not plan or want.
    2. Wanted Fertility Rate: This counts only births that match what women say they intended, revealing their actual preferred family size.
    3. The national gap: Nationally, women have an average of 2.0 children while their desired family size is about 1.6, a gap of 0.4 children per woman.
    4. States with the widest gap: Eight states, Uttar Pradesh, Bihar, Jharkhand, Rajasthan, Madhya Pradesh, Chhattisgarh, Assam and Haryana, have a gap of more than 0.3 children per woman.

    Why has India reached low fertility despite near-universal marriage?

    1. Marriage pattern: Only about 1% of women remain never married by ages 45 to 49, and the median age at first birth is 21.2 years, unlike most low-fertility countries where late marriage drives the decline.
    2. Sterilisation-led control: Indian women largely control fertility by having children, reaching their desired family size, and then permanently stopping through sterilisation, rather than through methods that space births.
    3. Missing spacing tools: Tools that help young couples delay a first birth or space children are largely missing, so unintended pregnancies cluster in the early years of marriage among the youngest women.
    4. Health consequence: This pattern is also reflected in relatively poor maternal and child health outcomes.

    Does India’s low fertility number hide a larger unmet need than it appears?

    1. Informed choice gap: Informed choice around sterilisation remains partial, with many women undergoing the procedure without fully informed consent. When these women are counted alongside those with unmet contraceptive needs, India’s “unwanted family planning” problem appears much larger than TFR figures suggest.
    2. Son preference inflation: In several states, families do not stop having children after one or two. In fact they continue until they have a son, meaning a disproportionate share of historically recorded “unwanted” births were daughters.
    3. Progress already visible: Unintended pregnancies have fallen from 21% in 2005-06 to 8% in 2019-21, and son preference is slowly weakening among younger and more educated families.

    Conclusion

    The article’s central argument is that India’s near-replacement TFR is not evidence the fertility story is finished, since it rests on a gap between wanted and actual fertility sustained by late spacing, partial informed choice, and residual son preference. What remains unresolved is the recent decline in modern contraceptive method use, which risks keeping the country’s unwanted-fertility gap in place even as the headline birth rate keeps falling.

    Back2Basics:

    Total Fertility Rate (TFR)

    1. Definition: TFR is the average number of live births a woman would have by the end of her reproductive years, calculated from age-specific fertility rates for ages 15 to 49.
    2. Source: TFR is tracked through the Sample Registration System (SRS) and the National Family Health Survey (NFHS).
    3. Replacement level: A TFR of 2.1 is generally considered replacement level; India’s national TFR has reached around 2.0, with Bihar at 2.9 against Kerala and Tamil Nadu at around 1.8.

    Understanding “Replacement Level” (2.1)

    1. The “0.1” Factor: The extra 0.1 accounts for the fact that some children do not survive to reproductive age, and slightly more boys are born than girls.
    2. Developing vs. Developed: In countries with high infant mortality rates, the replacement level can actually be much higher than 2.1 (sometimes up to 2.5 or 3.0) to stabilize the population.

    PYQ Relevance

    [UPSC 2014] While we flaunt India’s demographic dividend, we ignore the dropping rates of employability. What are we missing while doing so? Where will the jobs that India desperately needs come from? Explain.

    Linkage: The PYQ examines how demographic trends influence India’s development prospects. The article shows that replacement-level fertility alone does not ensure a demographic dividend, as unmet family planning needs persist.

  • The IACS and the making of modern Indian science

    Why in the News?

    The Indian Association for the Cultivation of Science (IACS) marked its 150th anniversary this year. It was established on 29 July 1876 as India’s first national institution dedicated to scientific research by Indians.

    What is the Indian Association for the Cultivation of Science (IACS)?

    1. Founding: The IACS was established on 29 July 1876 in Calcutta by Mahendralal Sircar, as the country’s first institution dedicated to scientific research led by Indians.
    2. Founding vision: Sircar proposed the IACS in an 1869 article in the Calcutta Journal of Medicine, arguing that scientific education was indispensable for India’s intellectual and societal progress.
    3. Colonial context: Sircar’s founding was a direct response to what he described in 1872 as the colonial government’s failure to “afford any opportunity” or “encouragement to the pursuit of science by the native of this country.”

    How did the IACS produce Raman’s discovery of the Raman effect?

    1. Raman’s introduction to IACS: After joining the Accountant General’s Office in Calcutta in 1907, C V Raman discovered the IACS and was given open access to its laboratories by Amrit Lal Sircar, the founder’s son.
    2. Dual life as scientist and officer: For nearly a decade, Raman worked at the IACS laboratory in the early mornings and evenings while serving as a government officer during the day, continuing even after he became Palit Professor of Physics at Calcutta University in 1917.
    3. The discovery: Raman made his most celebrated discovery, the Raman effect, in the IACS laboratories, announcing it to the world on 28 February 1928.
    4. Nobel recognition: The discovery earned Raman the Nobel Prize in Physics in 1930, making him the first Asian scientist to win a Nobel Prize in the sciences.

    What does IACS’s history reveal about colonial-era Indian science?

    1. Institutional gap Sircar identified: Sircar’s founding case rested on the argument that Indians needed their own institution because the colonial state had not created one, showing that India’s earliest scientific self-reliance was born out of exclusion rather than official support.
    2. Vision fulfilled: Raman’s Nobel Prize, won through work conducted at an institution founded and funded by Indians, is presented as the fulfilment of Sircar’s original claim that such an institution could produce discoveries of international significance without depending on colonial institutional support.

    Conclusion

    The IACS’s 150-year history runs from Mahendralal Sircar’s 1876 founding, born of colonial neglect of Indian scientific talent, to C V Raman’s 1930 Nobel Prize, won through research conducted entirely within that institution. The anniversary is presented as a reminder that India’s earliest scientific self-reliance predates independence by seven decades.

    Back2Basics:

    1. National Science Day: India observes National Science Day on 28 February each year to mark the anniversary of the announcement of the Raman effect.
    2. Present role: IACS continues to function as an autonomous research institute under the Department of Science and Technology, focused on basic sciences.

  • On antibiotics, problem isn’t just overprescribing

    Why in the News?

    A new global study in The Lancet Public Health has again found that India consumes too many broad-spectrum “watch” antibiotics and too few first-line “access” antibiotics, with total antibiotic consumption above the optimal target. The deeper problem is not physician irrationality but the systemic conditions that push doctors toward broader antibiotics in the first place.

    What does the Lancet study find about India’s antibiotic use?

    1. Consumption above target: India’s antibiotic consumption is higher than the optimal target identified in the study.
    2. Skewed drug mix: India’s antibiotic mix is skewed toward “watch” category broad-spectrum drugs that should ideally be reserved for more serious infections, rather than first-line “access” antibiotics.
    3. Documented pattern: This overuse of broad-spectrum antibiotics has been repeatedly documented over the past two decades through surveillance studies, hospital audits and national AMR programmes.

    Why do doctors keep prescribing broad-spectrum antibiotics despite knowing the risk?

    1. Late presentation: Doctors often prescribe antibiotics before a definitive diagnosis is available because patients present late in their illness.
    2. Diagnostic unreliability: Limited or unreliable diagnostic facilities mean treatment decisions cannot always wait for confirmatory tests.
    3. Healthcare-associated infection risk: Inadequate infection prevention and control in healthcare facilities increases the likelihood of healthcare-associated infections, pushing clinicians toward broader-spectrum agents from the outset.
    4. The reframing: In the article’s own terms, antibiotics are often used to compensate for systemic weaknesses in diagnosis and infection control, not administered out of irrational prescribing habits.

    What structural reforms does the article call for?

    1. Infection prevention first: Better water, sanitation and hygiene, wider vaccine coverage, and sustained investment in infection prevention and control programmes in hospitals would reduce the burden of infections that require antibiotics at all.
    2. Diagnostic capacity: India has begun building this through the National Health Mission, the Free Diagnostics Service Initiative and the National Essential Diagnostics List, but laboratory quality, accreditation and turnaround time still need improvement.
    3. Stewardship training: Clinicians need training in antimicrobial stewardship and evidence-based prescribing, alongside community education that antibiotics do not treat viral infections.

    Has India actually made no progress, as the “overprescribing” framing implies?

    1. Reforms already underway: India has established a National Action Plan on AMR, expanded surveillance through the National Centre for Disease Control and the Indian Council of Medical Research (ICMR), introduced antimicrobial stewardship initiatives, and regulated fixed-dose combinations.
    2. Scale argument: A country with nearly one-fifth of the world’s population and one of its largest public health systems cannot transform antibiotic use overnight, and progress should be judged by the direction of ongoing reforms, not only current consumption levels.

    Conclusion

    India’s antibiotic overuse is a systemic problem rooted in late diagnosis, weak infection control and unreliable laboratories, not a simple failure of physician judgment that fewer prescriptions alone would fix. What must change is investment in diagnostics and infection prevention capacity, since asking doctors to prescribe less without fixing those underlying gaps risks costing lives rather than curbing resistance.

    Back2Basics:

    Antimicrobial Resistance (AMR)

    1. Definition: AMR occurs when bacteria, viruses, fungi and parasites evolve to resist the drugs designed to kill them, making infections harder to treat.
    2. India’s National Action Plan: India’s National Action Plan on AMR, coordinated by the Ministry of Health and Family Welfare, covers surveillance, infection prevention, stewardship, research and international collaboration.
    3. Surveillance network: The National Centre for Disease Control and the Indian Council of Medical Research (ICMR) run India’s national AMR surveillance network across sentinel hospital sites.
    4. WHO classification: The WHO’s AWaRe classification divides antibiotics into Access (first-line, low resistance risk), Watch (broader-spectrum, higher resistance risk) and Reserve (last-resort) categories.

    PYQ Relevance

    [UPSC 2014] Can overuse and the availability of antibiotics without doctor’s prescription be the contributors to the emergence of drug resistant diseases in India? What are the available mechanisms for monitoring and control? Critically discuss the various issues involved.

    Linkage: The PYQ examines antimicrobial resistance caused by antibiotic misuse and the mechanisms needed for its monitoring and control. The article extends the PYQ by explaining that irrational antibiotic use is driven by systemic gaps in diagnostics, infection control and stewardship, and highlights ongoing AMR reforms in India.

  • ‘Virtual magnet’ claims reveal why EVs need their rare-earths

    Why in the News?

    A Bengaluru startup, Vimag Labs, has claimed to replace rare-earth permanent magnets in electric motors with software-controlled “virtual” magnets built from copper coils and electromagnets. The claim describes a decades-old electromagnet design rather than a genuine breakthrough, and that it does not resolve the efficiency, cost and rare-earth dependency problems facing India’s electric vehicle (EV) motor supply chain.

    What is the startup actually claiming to have built?

    1. The claim: Vimag Labs says it removes permanent magnets from a motor, replaces them with copper coils, and uses software to generate magnetic fields inside the motor.
    2. What this technically is: Passing current through copper coils wound around a ferromagnet to temporarily create a magnetic field is an electromagnet, a design used in large hydroelectric, thermal and nuclear power plant generators for more than 135 years.
    3. No novelty in the mechanism: The software in this design does not create magnetism; it only regulates how much current flows through the electromagnets, controlling the strength and direction of the existing magnetic field.

    Why do permanent magnets remain more efficient than electromagnets in EV motors?

    1. One-step versus multi-step process: A permanent magnet establishes a magnetic field in a single step with no additional electrical energy, while an electromagnet requires the field to be established and continuously modulated by software, consuming energy at every step.
    2. Energy losses compound: Electromagnet-based motors face core losses in the ferromagnetic core, resistance losses in copper conductors, and switching and conduction losses in electronic switches, making them unlikely to match a permanent magnet motor’s efficiency.
    3. Efficiency drives EV range: Every 0.1% increase in motor drive efficiency improves range for a given battery size, since the battery pack is the costliest and heaviest component of an EV, which is why permanent magnet synchronous motors dominate the EV market today.

    What is India’s underlying rare-earth dependency problem that this claim does not solve?

    1. No alternative has matched permanent magnets: BMW and Renault have tried electrically excited motors, and Tesla’s first Model S used an induction motor in 2012, but neither matched permanent magnet efficiency.
    2. Other alternatives face their own limits: The switched reluctance motor (SRM), which uses neither permanent magnets nor copper coils in its rotor, avoids rare-earth dependency but suffers from noisier, less efficient, spurt-like torque delivery, an approach Honda and Hitachi Astemo are still trying to refine.
    3. Conclusion of the constraint: Efficiency, starting torque capability and maximum achievable speed remain the constraints that have kept non-permanent-magnet motors out of mainstream EVs, meaning India’s EV motor supply chain still depends on rare-earth magnets regardless of this claim.

    Conclusion

    The Bengaluru startup’s “virtual magnet” is an established electromagnet design, not a new way to escape rare-earth dependency, since electromagnets remain less efficient than permanent magnets for the reasons physics has established for decades. India’s EV motor strategy must therefore continue to treat rare-earth and critical mineral access as a supply chain problem to be solved directly, rather than expect a software fix to remove the need for these magnets.

    Back2Basics:

    Rare Earth Elements (REEs)

    1. What they are: Rare Earth Elements are a set of 17 metallic elements used in permanent magnets, electronics, and clean energy technologies, valued for their magnetic and conductive properties.
    2. China’s dominance: China holds the largest share of global rare earth mining and processing capacity, giving it significant leverage over EV motor and electronics supply chains worldwide.
    3. India’s response: India launched the National Critical Mineral Mission (NCMM) in 2025 to build a framework for self-reliance in critical minerals, including rare earths, reducing import dependency for strategic sectors such as EVs and electronics.

    Back2Basics

    Role of Permanent magnets in Electric Vehicles:

    They are vital for electric vehicles because they provide high energy efficiency, maximum torque density, and compact motor sizing. They are primarily used in the main traction motor, power steering, and auxiliary systems.

    Core Functions in EV Motors

    1. Creating Constant Fields: They produce a strong, permanent magnetic field without needing extra electricity.
    2. Energy Conversion: They interact with electrical coils to turn electric energy into physical motion that spins the wheels.
    3. Regenerative Braking: They help capture energy back when the car slows down

    PYQ Relevance

    [UPSC 2026] Which of the following statements about Rare Earth Elements (REEs) and Critical Minerals is/are correct?

    1. Modern technological innovations including Artificial Intelligence, robotics and space exploration extensively utilise Rare Earth Elements (REEs).

    2. China has the highest share in mining of REEs followed by India.

    3. The Government of India launched the National Critical Mineral Mission (NCMM) in 2025 to establish a robust framework for self reliance in the critical mineral sector.

    4. Rare Earth Elements are a set of 13 metallic elements.

    (a) 1 and 3 only

    (b) 3 only

    (c) 1, 3 and 4

    (d) 1, 2 and 4″

    Answer: (a)

  • Does the RBI believe rupee is ‘undervalued’?

    Why in the News

    Reserve Bank of India (RBI) Governor has repeated, across two separate settings, that the rupee is undervalued in both nominal and real effective exchange rate (REER) terms. The remark is unusual because central bankers rarely comment on whether their own currency is priced fairly, and it comes as the rupee has depreciated 5.8% year-to-date against the US dollar.

    What is Real Effective Exchange Rate (REER) and why does it matter here?

    1. Definition: The real effective exchange rate (REER) measures a country’s currency value against a basket of trading partner currencies, adjusted for inflation.
    2. Contrast with nominal rate: The nominal exchange rate measures the rupee’s value against a single currency such as the US dollar, while REER captures relative price changes across multiple trading partners.
    3. Why economists prefer it: Economists rely on REER to assess overvaluation or undervaluation because it accounts for inflation differentials rather than only bilateral currency movements.

    What did the Governor actually say?

    1. First statement: It would be reasonable to think the rupee is not overvalued, and that “one could argue the rupee has become undervalued both in nominal and in REER terms.”
    2. Walk-back attempt: He initially disagreed that he had made such a statement, before again saying, “It is reasonable to think that it [Rupee] may not be overvalued.”
    3. No exchange rate target: He reiterated that the RBI does not target any specific exchange rate or band for the rupee.
    4. Market interpretation: Financial markets read the remarks as an indication that the central bank believes the rupee has weakened beyond what economic fundamentals justify.

    What is driving the rupee’s depreciation despite the RBI’s undervaluation claim?

    1. External pressure factors: Higher crude oil prices, geopolitical tensions, a stronger US dollar and intermittent foreign portfolio outflows from emerging markets have pressured the rupee.
    2. Capital outflows: Foreign portfolio investors have drained billions from the Indian stock market, increasing dollar demand while reducing capital inflows.
    3. Domestic fundamentals cited: The RBI points to over 6% annual growth, moderating inflation and forex reserves covering 11 months of imports as evidence the depreciation does not reflect domestic conditions.

    Can a Market-Determined Exchange Rate Be Undervalued?

    1. Non-intervention position: The RBI maintains it does not seek either a permanently strong or a permanently weak currency, and that its exchange rate policy is market-determined.
    2. Limited intervention purpose: The RBI’s foreign exchange interventions aim only to curb excessive volatility and ensure orderly market conditions, not to defend a fixed rupee value.
    3. The tension: By publicly labelling the rupee undervalued while disclaiming any exchange rate target, the Governor signals a view on fair value without committing to any corrective policy action, leaving markets to price in the central bank’s assessment without a stated mechanism to act on it.

    Conclusion

    The RBI Governor’s repeated undervaluation remark distinguishes short-term currency market pressure from India’s underlying macroeconomic fundamentals, without indicating any change in the central bank’s non-intervention stance. Whether the rupee corrects toward this “fair value” will depend on crude oil prices, US monetary policy and capital flows rather than any RBI trigger.

    Back2Basics:

    Real Effective Exchange Rate (REER)

    1. Definition: REER measures a currency’s value against a trade-weighted basket of partner currencies, adjusted for relative inflation.
    2. Custodian: The RBI publishes REER indices for the rupee using 6-currency and 40-currency trade-weighted baskets.
    3. Reading the index: A REER value above 100 relative to the base year typically signals overvaluation; below 100 signals undervaluation.

    Nominal Effective Exchange Rate (NEER)

    1. Definition: NEER measures a currency’s value against a trade-weighted basket of partner currencies, without adjusting for inflation.
    2. Core Concept: It shows the pure external value of the rupee against a group of foreign currencies based purely on market exchange rates.

    Key Differences: NEER vs REER

    1. Inflation Adjustment: NEER ignores inflation completely, while REER adjusts the NEER value for inflation differences between India and its trading partners.
    2. Economic Meaning: NEER tracks simple currency price movements, whereas REER reflects the actual price competitiveness of Indian goods in the global market.
    3. Formula Relationship: REER X (Domestic Inflation Index/Foreign Inflation Index)
    4. Policy Focus: If India’s inflation is higher than its partners, REER will rise faster than NEER, signaling that Indian exports are becoming more expensive despite a stable nominal exchange rate.

    PYQ Relevance

    [UPSC 2018] How would the recent phenomena of protectionism and currency manipulations in world trade affect macroeconomic stability of India?

    Linkage: It examines the impact of exchange rate movements on India’s macroeconomic stability and external sector. It extends the PYQ by explaining RBI’s REER-based assessment of the rupee’s valuation under a market-determined exchange rate regime.

  • Viruses don’t respect borders: the case for timely, fair global vaccine access for zoonotic outbreaks

    Why in the News?

    An International Centre for Genetic Engineering and Biotechnology (ICGEB) scientist has argued that timely and fair global vaccine access for zoonotic outbreaks, such as Ebola, Nipah and hantavirus, requires academia-industry partnerships and a shared risk funding model. This is because such vaccines are not commercially attractive to manufacturers.

    Why are zoonotic outbreak vaccines commercially unattractive?

    1. Small, unpredictable markets: Ebola, Nipah and hantavirus outbreaks are episodic and geographically concentrated, giving manufacturers no stable, predictable market to justify sustained investment.
    2. High development cost, low return: Vaccine development costs remain similar regardless of market size, so a vaccine with a small addressable market offers manufacturers a poor return relative to vaccines for widespread diseases.
    3. Outbreak timing mismatch: Vaccine demand spikes only during an active outbreak, while development must happen years in advance, a mismatch that discourages manufacturers from investing ahead of demonstrated demand.

    What would a shared risk funding model change?

    1. Risk redistribution: A shared risk funding model spreads the financial risk of vaccine development across academia, industry and public funders, rather than leaving it entirely on a manufacturer’s commercial judgment.
    2. Academia-industry partnership: Academic institutions like ICGEB can carry early stage research risk, handing over a de-risked candidate for industry to scale, lowering the barrier for private investment.
    3. Access consequence: A funding model that does not depend on commercial viability alone can keep resulting vaccines priced for equitable global access rather than for cost recovery in a niche market.

    Conclusion

    The central idea is that zoonotic outbreak vaccines fail a commercial viability test that has nothing to do with their public health importance. A shared risk funding model, built on academia-industry partnership, is the mechanism proposed to close that gap between epidemic risk and market incentive.

    Back2Basics

    International Centre for Genetic Engineering and Biotechnology (ICGEB): An intergovernmental organisation with a component in New Delhi, conducting research in genetic engineering and biotechnology, including vaccine and infectious disease research.

    PYQ Relevance

    [UPSC 2022] What is the basic principle behind vaccine development? How do vaccines work? What approaches were adopted by the Indian vaccine manufacturers to produce COVID-19 vaccines?

    Linkage: The PYQ examines the scientific principles of vaccine development and the challenges in developing vaccines for emerging infectious diseases. The article explains why vaccines for zoonotic diseases require shared-risk funding and academia-industry partnerships to overcome weak commercial incentives and ensure equitable access.

  • Delhi High Court’s ANI v OpenAI ruling offers a better path on copyright and AI than a training licensing regime

    Why in the News?

    The Delhi High Court’s ruling in ANI v OpenAI, addressing technological neutrality and the research exemption, offers a framework for copyright and AI. At the same time, there is a growing criticism about the Department for Promotion of Industry and Internal Trade (DPIIT) committee’s proposed AI training licensing regime as potentially harmful to innovation.

    What did the Delhi High Court’s ANI v OpenAI ruling establish?

    1. Technological neutrality: The ruling applies existing copyright principles to AI training without creating a separate, more restrictive legal category just because the technology involved is new.
    2. Research exemption: The ruling recognises a research exemption relevant to how AI systems process copyrighted content during training, rather than treating every instance of AI training on copyrighted material as infringement by default.

    How is the DPIIT new hybrid licensing system (One Nation – One License – One Payment) different from the Delhi High Court (ANI v OpenAI) judgment?

    1. DPIIT Proposal: AI seeks to bring in a statutory licensing mechanism that requires companies to pay compensation (royalties) to content creators through a centralized government system.
    2. Delhi High Court verdict: The court in its latest order refused to impose any strict or prior licensing norms, stating that AI training can be considered ‘fair dealing’ (under research purposes).

    Why does the proposed licensing regime as a step backward?

    1. Compliance burden: A mandatory AI training licensing regime would require AI developers to negotiate and pay for licenses before training on copyrighted content, raising the cost of building AI systems in India.
    2. Innovation chilling effect: Smaller AI developers and startups, unable to absorb licensing costs at the scale large technology companies can, would face a higher barrier to entry than the court’s technological neutrality approach imposes.
    3. Inconsistency with the ruling: A DPIIT-driven licensing regime would move policy in a more restrictive direction than the judiciary’s own reading of technological neutrality and the research exemption, creating a mismatch between executive rule making and judicial precedent.

    Conclusion

    The Delhi High Court’s ANI v OpenAI ruling offers a workable copyright and AI framework built on existing legal principles rather than new restrictions. DPIIT’s proposed licensing regime would discard that workable framework in favour of a compliance heavy structure that risks innovation without a clear corresponding gain for rights holders.

    Back2Basics

    The Proposed Licensing system by DPIIT:

    The Department for Promotion of Industry and Internal Trade (DPIIT) proposed a new hybrid licensing systemin December 2025 called “One Nation – One License – One Payment”. Its main purpose is to strike a balance between creators’ rights and technological innovation on the use of copyrighted content for artificial intelligence (AI) training.

    The important aspects and provisions of this proposed policy are as follows:

    Mandatory Blanket License

    1. Data usage rights: AI developers can use any copyrighted content that is legally accessible (for example, freely available online) to train their models without seeking separate permission. [1, 2]
    2. No Opt-Out: Content creators or organizations do not have the right to opt-out of having their content used for AI training.

    Royalty Structure

    1. Payment after commercialization: AI developers do not have to pay any fees upfront. Royalties apply only after the AI ​​tool or product starts generating revenue commercially.
    2. Centralized Body: The government will set up a non-profit centralized nodal agency called “Copyright Royalties Collective for AI Training” (CRCAT) to collect royalties and distribute them to copyright holders .
    3. Pricing: Royalty rates are determined independently by a special expert committee appointed by the government

    Department for Promotion of Industry and Internal Trade (DPIIT)

    1. It is a Union government department under the Ministry of Commerce and Industry.
    2. It is responsible for industrial policy, including the committee that proposed the AI training licensing regime referenced here.

    PYQ Relevance

    [UPSC 2024] What is the present world scenario of intellectual property rights with respect to life materials? Although India is second in the world to file patents, still only a few have been commercialized. Explain the reasons behind this less commercialization.

    Linkage: The PYQ examines India’s intellectual property rights framework and the balance between protection of intellectual property and innovation. The article discusses whether India’s copyright framework should promote AI innovation through existing legal principles or impose a mandatory licensing regime. It highlights the broader challenge of designing an IPR regime that protects creators without discouraging technological innovation.

  • Political executive control over Delhi Police under Article 239AA comes under fresh scrutiny

    Why in the News

    The Supreme Court is hearing petitions on the police crackdown during the NEET protest. The case has brought attention to the political control over the Delhi Police under Article 239AA and renewed debate on police independence in light of the Ramlila Maidan and Prakash Singh judgments.

    What does Article 239AA provide for policing in Delhi?

    1. Definition: Article 239AA, inserted by the 69th Amendment Act, 1991, gives Delhi a Legislative Assembly and Council of Ministers but excludes police, public order and land from the elected government’s jurisdiction, keeping them with the Union government.
    2. Effect: Delhi Police answers to the Union Ministry of Home Affairs rather than the elected Delhi government, unlike police forces in full states.
    3. Ramlila Maidan precedent: The Supreme Court’s Ramlila Maidan ruling addressed the limits of police force against a peaceful assembly, a precedent invoked whenever Delhi Police’s crowd control conduct is questioned.
    4. Prakash Singh precedent: The Prakash Singh v Union of India ruling laid down police reform directions aimed at insulating police from political direction, directions Delhi Police’s Union government control tests differently than in the states.

    Why does this arrangement resurface during the NEET protest crackdown hearing?

    1. Command versus accountability: Delhi Police’s actions during the NEET protest crackdown are being scrutinised even though the elected Delhi government has no command authority over the force to answer for its conduct.
    2. Union political exposure: Because Delhi Police reports to the Union Home Ministry, its conduct during politically sensitive protests places the central government, not the local elected government, in direct line of accountability.

    Conclusion

    Article 239AA’s exclusion of police from Delhi’s elected government means every controversial policing decision in the capital, including the NEET protest crackdown, becomes a Union government accountability question by constitutional design. The Supreme Court’s hearing will test whether the Ramlila Maidan and Prakash Singh standards can be enforced within this centralised command structure

    Back2Basics

    Article 239AA:

    1. It was inserted by the 69th Constitutional Amendment Act, 1991.
    2. It grants Delhi a special Union Territory status with an elected Assembly, while reserving police, public order and land for the Union Government.

    Prakash Singh v. Union of India:

    It is a landmark 2006 Supreme Court of India public interest litigation judgment that issued seven binding directives to reform police forces, ensure fixed tenures for top officials, and insulate law enforcement from political control.

    Key Directives of the Judgment

    1. State Security Commission: Set up a body to check that state governments do not exercise unwarranted influence over the police.
    2. Fixed Tenure for DGP: Give the Director General of Police a minimum stable tenure of two years regardless of their retirement date.
    3. Fixed Tenure for Officers: Ensure field-level police officers (like SPs and SHOs) have a minimum two-year tenure on their postings.
    4. Separation of Functions: Separate the investigation of crime from day-to-day law and order duties.
    5. Police Establishment Board: Create a board to handle transfers, postings, and promotions for junior officers.
    6. Police Complaints Authority: Establish independent state and district bodies to handle public complaints against police misconduct.
    7. National Security Commission: Form a federal panel to pick and manage top-tier police standards at the national level.