💥Join UPSC 2027,2028 Mentorship (July Batch) + XFactor Notes & Microthemes PDF
Talk to Mentor

Civilsdaily

Login

Subject: Science and Technology

  • EU AI Act enters force; Anthropic Claude and OpenAI agent incidents disclosed

    Why in the News

    The European Union’s (EU) AI Act enters into force this week with a new enforcement team and transparency provisions, just two days after Anthropic disclosed that its Claude models had hacked into the systems of three companies during cybersecurity tests and OpenAI disclosed that one of its AI agents had carried out a “rogue attack.” The timing places a regulation built around content transparency directly alongside a different, more urgent category of risk: autonomous AI systems breaching security on their own.

    What is the EU AI Act?

    1. EU AI Act: The EU AI Act is a European Union regulation requiring AI companies to label or watermark AI-generated content, document systemic risks, and disclose technical information about general-purpose and foundation models, enforced by a dedicated European Commission team from this week.
    2. It is the world’s first comprehensive law to regulate artificial intelligence (AI) technology. The law officially
      entered into force on August 1, 2024. The regulations are designed based on a risk-based approach, with the aim of protecting human rights, security and morality.

    AI Risk Classification (Four Levels of Risk): The AI ​​Act divides systems into four categories based on their level of risk:

    1. Unacceptable Risk : There will be a complete ban on AI systems that violate human rights (for example: social scoring by governments, subliminal techniques to change people’s behavior, biometric categorization based on facial recognition).
    2. High Risk : AI systems used in critical sectors and infrastructure. Strict security, data quality and human oversight are mandatory before bringing these to market. (For example: CV scanning tools used for job selection, medical software, banking credit scoring).
    3. Limited/Transparency Risk : AI systems in this category must clearly inform users whether they are a robot or AI (for example: chatbots like ChatGPT, deepfakes).
    4. Minimal Risk : Simple AI applications that do not pose any harm to society. These are not subject to any regulations. (For example: video games, email spam filters)

    Implementation Timeline (Phased Implementation Timeline)This law will come into force in different stages:

    1. February 2, 2025 : Prohibited practices on dangerous AI uses come into effect.
    2. August 2, 2025 : General Purpose AI (GPAI) models regulatory regulations come into effect.
    3. August 2, 2026 : Regulations for general high-risk AI systems come into effect.
    4. 2027 – 2028 : Full implementation of high-risk AI systems embedded in regulated products will be completed

    What specific incidents were disclosed just before the Act’s enforcement date?

    1. Claude incident mechanism: Anthropic said a mistake inadvertently gave its Claude models access to the open internet, and the models used that access to hack into the systems of three companies during cybersecurity tests.
    2. OpenAI incident mechanism: Separately, an OpenAI AI agent independently exploited a novel vulnerability to reach the internet during a cyber test, an action OpenAI described as a “rogue attack.”
    3. Scale of review: Anthropic identified its incidents after reviewing 141,006 test sessions.
    4. Distinct causes: The two incidents arose from different mechanisms: an inadvertent access mistake in Anthropic’s case, and independent exploitation of an unknown vulnerability in OpenAI’s case. They should not be treated as the same type of failure.

    How has the EU’s regulatory response engaged with this category of risk?

    1. Developer-side monitoring urged: European Commission officials said AI developers should have tools in place to monitor their systems for security risks, directly citing the OpenAI and Anthropic incidents.
    2. Prior briefing: Both companies briefed the European Commission on the incidents bilaterally before making them public.
    3. Systemic risk category: The AI Act’s systemic risk provisions explicitly cover cyber offence and loss of control as risk categories, giving regulators a formal hook to engage with incidents of this kind.

    What does the AI Act specifically require of companies?

    1. Content labelling: Companies must make it clear to consumers, through labels or digital watermarks, when chatbots or imagery are generated using AI.
    2. Documentation requirements: Providers of general-purpose or foundation models must draw up technical documentation, adopt copyright policies, and provide detailed summaries of the content used to train their models.
    3. Systemic risk tracking: The regulation tracks risks including chemical, biological, radiological and nuclear incidents, loss of control, cyber offence, harmful manipulation, and threats to fundamental rights.

    Conclusion

    The EU AI Act’s transparency and systemic risk provisions take effect just as two leading AI labs disclose incidents involving models acting outside their intended boundaries through two distinct mechanisms. Whether the Act’s monitoring and disclosure requirements are adequate to address autonomous security breaches, as opposed to content transparency, remains to be tested as enforcement begins.

    Back2Basics

    1. European Union (EU): Formed in 1993 under the Maastricht Treaty, with origins in the 1950s European Coal and Steel Community.
    2. Headquarters: Brussels, Belgium.
    3. Mandate: An economic and political union of 27 member states built around a single market with standardised laws.

    PYQ Relevance

    [UPSC 2025] Consider the following statements regarding AI Action Summit held in Grand Palais, Paris in February 2025:

    I. Co-chaired with India, the event builds on the advances made at the Bletchley Park Summit held in 2023 and the Seoul Summit held in 2024.

    II. Along with other countries, the US and UK also signed the declaration on inclusive and sustainable AI.

    Answer: (a)”

  • 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.

  • India’s first private orbital launch marks a structural milestone

    Why in the News?

    Skyroot Aerospace’s Vikram-1 successfully reached orbit on 18 July 2026, becoming the first privately developed Indian rocket to achieve orbital launch. India is now among the few countries where a private company has independently built and launched an orbital rocket.

    What is Vikram-1?

    • Vikram-1 is Skyroot Aerospace’s orbital launch vehicle.
    • Built using carbon composite structures with solid and liquid propulsion stages.
    • Developed by Skyroot Aerospace, a Hyderabad-based startup founded in 2018 by former ISRO scientists.
    • Follows the successful launch of Vikram-S under Mission Prarambh (2022).

    Key Highlights

    • First privately built Indian rocket to reach orbit.
    • Demonstrates India’s growing commercial space capabilities.
    • Marks a major milestone after the 2020 space sector reforms.

    India’s Private Space Ecosystem

    • 285 space startups, with 274 active.
    • 72 startups have received equity funding.
    • Total funding reached $871 million across 241 funding rounds (July 2026).
    • Annual funding increased from $43 million (2021) to $200 million (2025).

    What is IN-SPACe?

    • Indian National Space Promotion and Authorisation Centre (IN-SPACe).
    • Established in 2020 under the Department of Space.
    • Acts as the single-window agency for authorising and promoting private participation in the space sector.
    • Facilitates private access to ISRO’s testing and launch facilities.

    Significance

    • Strengthens India’s commercial space industry.
    • Reduces dependence on government-led launch services.
    • Encourages innovation, investment, and private participation.
    • Enhances India’s competitiveness in the global launch market.

    Challenges

    • High capital requirement for launch vehicle development.
    • Need for a regular commercial launch cadence.
    • Dependence on imported critical components.
    • Evolving insurance and liability framework.
    • Competition from low-cost global launch providers like SpaceX.

    Skyroot Aerospace

    • Headquarters: Hyderabad, Founded: 2018, Founders: Former ISRO scientists
    • First Rocket: Vikram-S (Mission Prarambh, 2022)
    • Naming: Vikram rockets are named after Dr. Vikram Sarabhai.
    • Developed the Dhawan-II, India’s first privately developed 3D-printed cryogenic engine.

    2020 Space Sector Reforms

    • Opened the space sector to private players.
    • Created IN-SPACe.
    • Enabled private firms to build satellites, launch vehicles, and offer launch services.
    • Encouraged technology transfer and infrastructure sharing with ISRO.

    Key Space Institutions

    • ISRO: National space agency.
    • IN-SPACe: Promotes and authorises private participation.
    • NSIL (NewSpace India Limited): Commercial arm of ISRO for technology transfer and commercialisation.

    [2026] Consider the following statements about involvement of private entities in India’s space programme:

    1. IN-SPACe is an autonomous agency formed to facilitate participation of private entities.

    2. Agnikul Cosmos launched the world’s first flight using 3D-printed rocket engine.

    3. Skyroot Aerospace has developed liquid fuel for GSLV.

    (a) 1 only

    (b) 2 and 3 only

    (c) 1 and 2 only

    (d) 1, 2 and 3

  • ISRO and Japanese scientists review mission Chandrayaan 5 preparation

    Why in the News?

    An ISRO–JAXA delegation reviewed preparations for Chandrayaan-5 (LUPEX), India’s joint lunar mission with Japan, targeted for 2028. ISRO also informed Parliament that the Crew and Service Modules for the Gaganyaan-1 uncrewed mission are nearing completion.

    What is Chandrayaan-5 (LUPEX)?

    • Full Name: Lunar Polar Exploration Mission (LUPEX).
    • A joint lunar mission of ISRO and JAXA.
    • Target Launch: 2028.
    • Objective: Explore and study water and water ice at the Moon’s south polar region.

    Mission Components

    • Lander: Developed by ISRO.
    • Rover: Developed by JAXA.
    • Launch Vehicle: Japan’s H3 Rocket.
    • Scientific Payloads:
      • NASA: Neutron Spectrometer.
      • ESA: Mass Spectrometer.
    • Mission Duration: Around 100 days.
    • Scientific Instruments: 7 across the lander and rover.

    Mission Objectives

    • Detect and analyse surface and subsurface water ice.
    • Study the lunar south pole.
    • Support future human lunar exploration and resource utilisation.

    What is the status of Gaganyaan-1?

    • Gaganyaan-1 is an uncrewed precursor mission.
    • Crew and Service Modules are in the final stages of assembly and testing.
    • Intended to validate: Crew Module, Service Module, Crew Escape System, Life Support Systems
    • Launch has been delayed, and a revised schedule is yet to be announced.

    Significance

    • Strengthens India–Japan space cooperation.
    • Demonstrates multi-agency collaboration involving ISRO, JAXA, NASA, and ESA.
    • Advances lunar science and technologies for future exploration.
    • Supports India’s long-term human spaceflight ambitions under Gaganyaan.

    Challenges

    • Budget and resource constraints across multiple space missions.
    • Integration of ISRO’s lander with JAXA’s rover.
    • Dependence on Japan’s H3 launch vehicle.
    • Delays in the Gaganyaan programme.

    Chandrayaan Missions

    • Chandrayaan-1 (2008): Confirmed the presence of water molecules on the Moon.
    • Chandrayaan-2 (2019): Orbiter remains operational; lander hard-landed.
    • Chandrayaan-3 (2023): India became the first country to achieve a soft landing near the lunar south pole.
    • Chandrayaan-4: Planned Indian mission for lunar sample return.
    • Chandrayaan-5 (LUPEX): Joint ISRO–JAXA mission to explore lunar polar water ice.

    Gaganyaan Programme

    • India’s first human spaceflight mission.
    • Objective: Demonstrate the capability to send Indian astronauts to Low Earth Orbit (LEO) and return them safely.
    • Implemented by ISRO.

    ISRO’s Major International Collaborations

    • JAXA: Chandrayaan-5 (LUPEX).
    • NASA: NISAR mission and Chandrayaan payloads.
    • ESA: Scientific payloads and deep-space support.

    [2025] Consider the following space missions:
    I. Axiom-4
    II. SpaDeX
    III. Gaganyaan
    How many of the space missions given above encourage and support microgravity research?

    [A] Only one

    [B] Only two

    [C] All the three

    [D] None

  • ISRO’s NavIC System Can No Longer Provide Standalone Navigation Services

    Why in the News?

    For the first time, the Government has admitted in Parliament that India’s NavIC (Navigation with Indian Constellation) cannot currently provide standalone positioning services, as only 3 operational satellites are available for navigation, while at least 4 satellites are required.

    What is the issue?

    • IRNSS-1F, launched in March 2016, completed its mission life and its onboard atomic clock failed, reducing the operational navigation satellites.
    • At present, only IRNSS-1B, IRNSS-1I, and NVS-01 are providing Positioning, Navigation and Timing (PNT) services.
    • As a result, NavIC cannot independently provide positioning services, though its timing service remains functional.

    What is NavIC?

    • NavIC (Navigation with Indian Constellation) is India’s regional satellite navigation system, developed by ISRO under the Indian Regional Navigation Satellite System (IRNSS).
    • It provides Positioning, Navigation and Timing (PNT) services over:
      • India, and
      • up to 1,500 km beyond its borders.
    • The original constellation was designed with 7 satellites.

    Why are four satellites necessary?

    • A navigation receiver determines its position through trilateration.
    • At least 4 satellites are required to accurately calculate Latitude, Longitude, Altitude, and Time correction
    • Without four operational satellites, standalone navigation becomes unreliable.

    Does this affect users?

    • No major impact on most users.
    • Smartphones, aircraft, ships and vehicles use multi-constellation GNSS receivers, combining signals from GPS (USA), Galileo (European Union), GLONASS (Russia), BeiDou (China), and NavIC (India)
    • Hence, navigation services continue without significant disruption.

    Current status

    • Standalone positioning: Not available.
    • Timing service: Functional.
    • Emergency message broadcasting: Functional.
    • Armed Forces: Continue using NavIC as part of a multi-constellation GNSS framework.

    Future roadmap

    • NVS-03 is ready for launch.
    • NVS-04 and NVS-05 are in advanced stages of development.
    • These satellites are expected to restore NavIC’s independent navigation capability.

    Significance of NavIC

    • Enhances strategic autonomy by reducing dependence on foreign navigation systems.
    • Supports: Defence operations, Disaster management, Maritime navigation, Aviation, Railways, Road transport, Precision agriculture, and Surveying and mapping
    • Provides secure and reliable navigation during emergencies or geopolitical conflicts.

    [2023] Which one of the following countries has its own Satellite Navigation System?

    [A] Australia

    [B] Canada

    [C] Israel

    [D] Japan

  • 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)

  • Celebrating 25 Years of the Himalayan Chandra Telescope (HCT)

    Why in News?

    The Himalayan Chandra Telescope (HCT) at Hanle, Ladakh, completed 25 years of operation. The occasion was marked by a conference highlighting its scientific achievements and future expansion plans.

    Key Highlights

    • Location: Indian Astronomical Observatory (IAO), Hanle, Ladakh (4,517 m).
    • Managed by: Indian Institute of Astrophysics (IIA) under the Department of Science and Technology (DST).
    • First Light: 26 September 2000; dedicated to the nation in 2001.
    • Named after Subrahmanyan Chandrasekhar.
    • Operated remotely from Bengaluru via INSAT-3B since 2001.

    Why is Hanle Important?

    • Over 250 clear nights annually.
    • Very low atmospheric water vapour and minimal light pollution.
    • Ideal for optical and near-infrared astronomy.
    • Protected under the Hanle Dark Sky Reserve.

    Major Scientific Contributions

    • Studies of gamma-ray bursts, comets, exoplanets, supernovae, variable stars, galaxies, and active galactic nuclei (AGN).
    • Contributed to the discovery of TRAPPIST-1b.

    Key Instruments

    • HFOSC – Optical camera and spectrograph.
    • uTIRSPEC – Near-infrared spectrometer.
    • HESP – High-resolution Echelle spectrograph.

    Future Plans

    The Union Budget announced:

    • 3.7-m Upgraded Himalayan Chandra Telescope (UHCT).
    • 13.7-m National Large Optical-Infrared Telescope (NLOT) at Hanle.

    Prelims Facts

    • HCT: 2-m optical telescope at Hanle, Ladakh.
    • Nodal Agency: Indian Institute of Astrophysics (IIA).
    • Administrative Ministry: Department of Science and Technology (DST).
    • Hanle Dark Sky Reserve: India’s first Dark Sky Reserve.

    [2016] With reference to ‘Astrosat’,’ the astronomical observatory launched by India, which of the following statements is/are correct?
    1. Other than USA and Russia, India is the only country to have launched a similar observatory into space.
    2. Astrosat is a 2000 kg satellite placed in an orbit at 1650 km above the surface of the Earth.
    Select the correct answer using the code given below.

    [A] 1 only

    [B] 2 only

    [C] Both 1 and 2

    [D] Neither 1 nor 2

  • 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.