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GS Paper: GS3-17.Awareness in the fields of IT, Space, Computers, Robotics, Nano-technology, Bio-technology and issues relating to Intellectual Property Rights.

  • A reality check on AI boom: Eight risks to look out for

    Why in the News

    Capital markets have begun to price the distance between what artificial intelligence (AI) promises and what it earns. Nvidia backed Firmus withdrew a nearly $5 billion initial public offering (IPO) after investors resisted its valuation, and OpenAI and Anthropic postponed their planned trillion dollar listings. The boom now faces eight risks, four financial and four beyond the balance sheet.

    What is the AI boom, and why is it fragile?

    1. What it is: AI companies are spending heavily on models and data centres in expectation of future revenue, like a builder raising towers before buyers have paid.
    2. Hyperscalers: The biggest builders are hyperscalers, cloud companies that run giant data centres AI cannot do without.
    3. Growth outpacing support: AI models are growing faster than the financing, governance and social acceptance they need.
    4. Revenue reset: OpenAI indicated that its September annualised revenue (a month’s earnings scaled to a year) was nearer $50 billion, not the nearly $70 billion signalled earlier.
    5. The takeaway: When revenue lags the money already spent, investors cut valuations, and the debt behind the buildout becomes a risk to lenders.

    What are the four financial risks?

    1. Macroeconomy: The Iran war, high US gas and diesel prices and the wide US fiscal deficit have pushed treasury yields, the interest the US government pays to borrow, to multi decadal highs. Higher interest rates hurt homebuyers and slow data centre growth.
    2. Revenue trailing capex: Capital expenditure (capex), spending on long lived assets, is outrunning revenue. Anthropic ran an operating loss of about $8 billion in 2025, so it has little room to raise investors’ expectations.
    3. Costlier, opaque debt: Capex once paid in cash now relies on private credit, loans from funds rather than banks, and bespoke deals, most of them opaque. Most hyperscaler bonds issued in 2026 traded below issue price by end August.
    4. Sobering valuations: OpenAI and Anthropic were reportedly seeking about $60 billion each from their listings. Cautious valuation assessments forced both chief executives to delay.

    What are the four risks beyond the balance sheet?

    1. Regulatory risk: Three frontier models (the most advanced AI systems) faced sudden regulatory slowdowns, a sign of control by diktat, rule by sudden order. The US President made AI chiefs accept safety responsibility and external audits.
    2. Rogue AI liability: Rogue AI, systems acting beyond their makers’ control, brings multimillion dollar claims over cyber attacks, copyright and privacy against directors and firms. Such litigation could rival the cases against tobacco, glyphosate and pharmaceutical companies.
    3. AI in war: Autonomous AI has been used in Ukraine, Gaza and Iran without due regard for human scrutiny, civilian harm, proportionality and accountability.
    4. Data centre opposition: Communities cite lost drinking water, power outages, noise, land loss and few permanent jobs. Protests have forced some rollbacks, yet China’s fast buildout makes data centres a national security concern.

    Challenges

    1. Hidden exposure: Opaque AI debt can spread losses to banks, insurers and pension funds if AI stocks sell off.
    2. Unconvinced workforce: Sceptical workers slow AI adoption, and with it the revenue the boom depends on.
    3. No rule for military AI: No multilateral agreement yet governs the use of autonomous AI in war.
    4. Local costs, global gains: Water, power and land costs fall on communities near data centres, and the gains flow to distant firms.

    Way Forward

    1. Disclose AI debt: Financial regulators should require lenders and funds to report their exposure to data centre debt.
    2. Audit frontier models: Make independent external audits a standard condition for deploying the most powerful models.
    3. Human control in war: States should agree on a multilateral rule keeping humans accountable for autonomous weapons.
    4. Community benefit siting: Approve data centres only after water and power assessments, with local jobs and benefits.

    Conclusion

    AI remains an exceptional technology, but markets now judge it on revenue, regulation and public consent, not on promise alone. Whether the boom cools gradually or breaks suddenly will depend on how much of its opaque debt is exposed when expectations reset.

    Key numbers

    1. SpaceX listing, mid 2026: raised $75 billion, listing at $1.77 trillion.
    2. Hyperscaler capex, 2026: about $725 billion expected from leading US firms; about 40% assessed by end June.
    3. Meta free cash flow: about $800 million, from $8.5 billion a year earlier.
    4. Data centre capacity: China adding 50 GW to its current 24 GW; US at 56 GW.
    5. Firmus valuation: about $31 billion, three times its August figure.

    Matching Previous Year Question

    “[2026] Which of the following statements with regard to Large Language Models (LLMs) used in machine learning is/are correct? 1. LLMs assign probabilities to the next possible words and then pick the one with the highest probability. 2. LLMs process data through mathematical optimization to minimise prediction errors. 3. LLMs produce unbiased outputs. (a) 1 only (b) 1 and 2 only (c) 2 and 3 only (d) 1, 2 and 3 ANSWER: B”

  • How to keep an economy stable using policy tools

    How to keep an economy stable using policy tools

    Why in the News

    Macroeconomic stabilisation has moved from John Maynard Keynes‘s assumed straight line links between economic variables to policy built on how households and firms actually behave. The Royal Swedish Academy of Sciences has awarded the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2026 to Robert E Hall of Stanford University and Michael Woodford of Columbia University, “for microeconomic foundations of macroeconomic stabilisation“.

    What is macroeconomic stabilisation?

    1. What it is: Macroeconomic stabilisation means the policies governments and central banks use to smooth booms and slumps. It works like a thermostat, cooling an overheating economy and warming a slowing one.
    2. Inflation fight: The Reserve Bank of India (RBI) raised interest rates last week to contain inflation. Prices had risen after the war between the United States (US) and Iran and the Strait of Hormuz blockade lifted crude and commodity costs.
    3. Recovery support: In the years around the Covid-19 pandemic, the Union government cut direct taxes and the Goods and Services Tax (GST), and the RBI cut interest rates, to boost consumption and investment.
    4. Cost of doing nothing: Without intervention, inflation can soar and people cut all but essential spending. Many businesses then fail, jobs are lost and recovery takes much longer.
    5. The takeaway: Stabilisation policy decides whether a price or demand shock passes quickly or turns into years of lost jobs and output, which is why the economics behind it earned the prize.

    How did stabilisation policy rise and fall?

    1. Great Depression: A stock market crash in October 1929 began the Great Depression in the US. Thousands of banks and businesses failed, and the crisis spread to Europe.
    2. Scale of the slump: US gross domestic product (GDP), the value of annual output, fell by 30% and the unemployment rate soared to 25%.
    3. Laissez faire: The ruling view of the time held that governments should not intervene in the economy.
    4. Keynes’s remedy: In a slowdown, policymakers should cut rates and taxes and spend more, even on a budget deficit. They should raise rates when the economy overheats.
    5. Stagflation: In the 1970s, repeated oil price shocks brought high inflation and high unemployment together. Boosting growth spiked inflation and fighting inflation worsened unemployment, so Keynesian policy came to be seen as the problem.

    What did Hall and Woodford add?

    1. Grounded in data: From the late 1970s, the two rebuilt the Keynesian approach on research. Keynes had assumed linear links, such as between inflation and unemployment; they studied ground level data instead.
    2. Hall, the demand side: Hall studied how people decide how much to consume, and when they raise or cut spending, using household data.
    3. Hand to mouth households: In 1982, Hall used microdata on individual households to show that not everyone raises spending when they expect higher income. Hand to mouth households, which spend what they earn, do not.
    4. Woodford, the supply side: Firms do not change prices every day, and they look to the future when fixing them. So expectations about future interest rates shape today’s inflation.

    Why does their work matter for policy today?

    1. Forward guidance: Woodford’s finding means central banks must clearly convey what they intend to do, because firms set prices on that basis. This practice is known as forward guidance.
    2. Evidence based policy: Hall’s household level evidence on who changes spending, and when, has informed policy choices on supporting consumption.
    3. Resilience to shocks: A pandemic, wars that spiked commodity prices and policy missteps such as tariffs have hit the world economy. Growth has still surprised on the upside, partly because policy draws on such work.

    Conclusion

    Stabilisation policy now rests on evidence about how real households and firms behave rather than on assumed relationships, which has made a central bank’s communication a policy tool in its own right. How clearly the RBI signals its path as it fights oil driven inflation is a live test of that insight in India.

    Back2Basics: Sveriges Riksbank Prize in Economic Sciences

    1. Origin: Sveriges Riksbank, Sweden’s central bank, instituted the prize in 1968 to mark its 300th anniversary. It is not one of the five prizes in Alfred Nobel’s will.
    2. Award: The Royal Swedish Academy of Sciences selects the laureates. The first prize, in 1969, went to Ragnar Frisch and Jan Tinbergen.
    3. Indian laureates: Amartya Sen won in 1998, and Abhijit Banerjee in 2019, with Esther Duflo and Michael Kremer.
    4. Prize money: This year’s laureates share 12 million Swedish kronor, about Rs 12 crore.

    Matching Previous Year Question

    “[2026] ‘X’, born in the UK, was conferred the Nobel Prize in 2025. He was a professor in an American university when the prize was announced. Identify ‘X’: (a) Michel H. Devoret (b) Richard Robson (c) John Clarke (d) Joel Mokyr ANSWER: C”

  • The AI challenge: How to maximise economic impact, spread gains evenly

    The AI challenge: How to maximise economic impact, spread gains evenly

    Why in the News

    The global Artificial Intelligence (AI) race is measured in chips, data centres and computing power, with trillions of dollars invested. The World Bank Group’s World Development Report 2026: The Promise of Artificial Intelligence argues larger gains will go to those who apply AI to practical problems at scale, a route open to developing countries like India.

    Why may AI’s value lie in using it rather than building it?

    1. Builder centred narrative: The prevailing view expects most AI value to go to frontier model builders and upstream chip, data centre and energy suppliers, sorting countries into winners and losers.
    2. General purpose technology: AI is a general purpose technology, one that spreads across the economy, like electricity. Such technologies give their largest gains to downstream users who adopt and adapt them.
    3. Value from application: AI can sustain growth, raise productivity, improve public services and expand opportunity through practical use, not the frontier race.
    4. India’s starting point: India is among the fastest growing major economies and has repeatedly deployed technology at population scale.
    5. The takeaway: For India, the prize is not owning the best model but putting AI to work across farms, clinics, classrooms and offices.

    Will AI replace or complement workers in developing countries?

    1. Low automation exposure: Only 4.5% of jobs in low and middle income countries face high automation risk from generative AI (AI that creates text, images or code), against 14.2% in rich countries.
    2. Productivity gains: Another 16.2% of jobs could see meaningful productivity gains, close to 18.7% in advanced economies.
    3. Complement, not replace: AI will mostly complement workers in developing countries, as cheap task specific tools help farmers get advice, health workers diagnose and teachers teach.

    What could stop AI’s gains from spreading evenly?

    1. Concentration of AI inputs: Chips, computing, data, talent and risk capital sit in a handful of firms and countries. Competition keeps models cheap now, but relying on few providers gives them market power to set prices.
    2. Missing complementary inputs: AI needs electricity, connectivity, affordable devices, local language data and skills. Governments also need capacity to evaluate, procure and regulate it.

    What can India’s digital public infrastructure teach AI adoption?

    1. Tools already working: Locally adapted AI supports farm advice, weather forecasting and health diagnostics. Eg. Bharat VISTAAR, the Centre’s farmer platform where States build multilingual AI chatbots.
    2. Adoption at scale: Wadhwani AI’s tuberculosis screening tools show similar promise. The real question is whether millions adopt them.
    3. Digital public infrastructure: Shared platforms such as Aadhaar, the Unified Payments Interface (UPI), DigiLocker and data sharing systems lower costs and enable interoperability (systems working together).
    4. Open architecture: UPI’s open design avoided dependence on one provider. AI systems should likewise avoid technological lock in (users unable to switch providers) and preserve competition.

    How should India build AI capability and scale it?

    1. Strategic capabilities: India need not match hyperscalers (giant cloud and AI firms). It should build research, Indian language models and shared computing through IndiaAI, the Research, Development and Innovation (RDI) Fund and the Anusandhan National Research Foundation (ANRF).
    2. Design for scale: Every pilot needs a path to wide adoption, covering financing, institutional ownership and integration with existing systems.
    3. Test rather than assume: India’s scale makes rigorous evaluation feasible and cheap.
    4. Scale what works, stop what does not: Share results, failures included, across States and ministries.

    Challenges

    1. Imported compute: Reliance on foreign advanced chips exposes India to other countries’ export controls.
    2. Thin language data: Most Indian languages have little digitised text, limiting Indian language models.
    3. Privacy and bias: AI in public services risks data misuse and algorithmic bias against groups missing from training data.

    Way Forward

    1. Domestic chips: Link AI compute plans to chip capacity under the India Semiconductor Mission.
    2. Open language datasets: Fund open speech and text datasets for low resource Indian languages.
    3. Frontline AI skills: Train teachers, health workers and civil servants to use and question AI tools.

    Conclusion

    India’s AI choice is between chasing unaffordable frontier models and spreading useful tools across an economy it already digitises well. Whether pilots become tools used by millions, on open designs, is the measure.

    AI in India

    1. IndiaAI Mission (2024): Run by IndiaAI under the Ministry of Electronics and Information Technology (MeitY) with ₹10,371 crore, to make “AI in India” and “AI work for India”.
    2. Seven pillars: Compute, applications, AIKosh datasets, foundation models, FutureSkills, startup financing, and safe and trusted AI.
    3. Language platforms: Digital India Bhashini offers speech and translation tools across 22 Indian languages.

    Matching Previous Year Question

    “[2026] Which of the following statements with regard to Large Language Models (LLMs) used in machine learning is/are correct? 1. LLMs assign probabilities to the next possible words and then pick the one with the highest probability. 2. LLMs process data through mathematical optimization to minimise prediction errors. 3. LLMs produce unbiased outputs. (a) 1 only (b) 1 and 2 only (c) 2 and 3 only (d) 1, 2 and 3 ANSWER: B”

  • Why jurist Navi Pillay’s work matters for a world facing ‘existential challenges’

    Why jurist Navi Pillay’s work matters for a world facing ‘existential challenges’

    Why in the News

    The Nobel Peace Prize, which seldom goes to jurists, has this year honoured the rule of law itself as the foundation of peace. The 2026 prize went to South African jurist Navanethem “Navi” Pillay, 85, “for her efforts to promote peace, international law”, amid widening war.

    Why did the Committee honour a jurist?

    1. The citation: The Nobel Committee cited her “exceptional courage and integrity”, holding that the rule of law, national and international, underpins the system for keeping peace.
    2. Peace requires justice: The Committee’s line “Peace requires justice” means a settlement lasts only when grave crimes are punished, much as courts, not revenge, keep order within a country.
    3. Prosecuting atrocities: It credited her with ensuring that war crimes, crimes against humanity and genocide are prosecuted.
    4. Nuremberg inspiration: The post war Nuremberg Trials showed her how law, through judicial power backed by punishment, could tackle state sponsored violence.
    5. The takeaway: Honouring a judge signals that international law is itself an instrument of peace, not a backdrop to diplomacy.

    How did apartheid shape her career?

    1. Indentured roots: Her Indian Tamil grandparents went to Africa as indentured labourers (workers bound by contract to years of labour abroad), and she grew up under racially discriminatory laws.
    2. Harvard doctorate: She became the first South African to earn a doctorate in law from Harvard University.
    3. Firsts in Natal: She was the first Black woman to start a law practice in Natal Province and the first to serve in its High Court, nominated by Nelson Mandela.
    4. Defending prisoners: She defended Mandela, the 1993 Nobel Peace laureate, in court and fought for anti apartheid prisoners’ rights such as access to lawyers.

    What did the Rwanda tribunal establish?

    1. ICTR: She served eight years as a judge of the International Criminal Tribunal for Rwanda (ICTR), which tried crimes of the 1994 Rwandan genocide.
    2. Scale of killing: The genocide killed about 800,000 people in 100 days.
    3. First genocide judgment: The ICTR was the first international tribunal to enter a judgment for genocide, interpreting it under the Geneva Conventions against war crimes.
    4. Rape as genocide: It defined the crime of rape in international law and recognised it as a means of perpetrating genocide.
    5. Propaganda: It was the first to recognise the role of propaganda in aiding genocide, and held members of the media responsible.

    Where does her work stand today?

    1. ICJ ad hoc judge: She is an ad hoc judge (appointed by a party for one case) at the International Court of Justice (ICJ) in The Gambia v. Myanmar, a genocide case over Myanmar’s Rohingya Muslims.
    2. Gaza inquiry: From 2021 to 2025 she chaired the United Nations (UN) Independent International Commission of Inquiry on the Occupied Palestinian Territory. It found Israeli forces deliberately targeted Palestinian children “resulting in genocide” in Gaza.
    3. Existential challenges: Amid “existential challenges” and more wars than in a long time, the Committee called international law an “absolute necessity”. Critics call the UN and ICJ powerless to stop violence.
    4. Call to states: Pillay says justice after conflict is still new and needs support from states, which should “care and act collectively”.

    Challenges

    1. No enforcement arm: International courts depend on states to arrest suspects and carry out rulings.
    2. Great power resistance: Powerful states reject findings or penalise courts. Eg. The US sanctioned the International Criminal Court hours after the prize.
    3. Slow proceedings: Genocide cases take years to decide, delaying redress for victims.

    Way Forward

    1. State cooperation: States should execute arrest warrants and comply with the rulings of international courts.
    2. Protected tribunals: Fund international tribunals adequately and shield their judges and staff from sanctions.
    3. Domestic prosecution: Countries should enact laws on genocide and crimes against humanity so national courts can try such crimes.

    Conclusion

    The prize rests on a wager that law, not force, can still restrain states in a period of widening war. That wager will be tested by whether governments comply with genocide rulings and protect international courts from political pressure.

    Back2Basics: International Court of Justice

    1. Status: The principal judicial organ of the UN, often called the “World Court”, based at The Hague, Netherlands.
    2. Origin: The UN Charter established it in 1945, and it began work in 1946, succeeding the Permanent Court of International Justice.
    3. Bench: Its 15 judges are elected for nine year terms by the UN General Assembly and the Security Council.
    4. Jurisdiction: It settles disputes between states through binding rulings and gives non-binding advisory opinions to authorised UN bodies.

    Matching Previous Year Question

    “[2026] ‘X’, born in the UK, was conferred the Nobel Prize in 2025. He was a professor in an American university when the prize was announced. Identify ‘X’: (a) Michel H. Devoret (b) Richard Robson (c) John Clarke (d) Joel Mokyr ANSWER: C”

  • Nobel Prize in Literature: Anne Carson: A provocateur who blurs boundaries between genres

    Nobel Prize in Literature: Anne Carson: A provocateur who blurs boundaries between genres

    Why in the News

    The 2026 Nobel Prize in Literature honours a writer for creating new forms in dialogue with the classical tradition, not for mastering one genre. The Swedish Academy has named Anne Carson, a 76 year old Canadian poet, classicist, essayist and translator. Her books move between poetry, fiction, criticism and memoir.

    Who is Anne Carson, and what did the Nobel recognise?

    1. The citation: It recognises her “bold and inventive oeuvre (body of work) that, in playful dialogue with the classical tradition, has created new forms for contemporary literature”.
    2. Early life: Born in Toronto in June 1950, she changed schools often. A school Latin teacher, whom she credits for her “career and happiness”, taught her Greek in lunch hour lessons.
    3. Classics: She studied Classics (ancient Greek and Latin literature) at the University of Toronto. Her 1981 doctoral dissertation was on Sappho, an ancient Greek poet.
    4. Why she writes: In a 2004 interview she said she writes “to find out what I think about something”. She uses ancient Greek literature to clarify the contemporary.
    5. The takeaway: The prize rewards a scholar of the ancient world who turned classical learning into forms that read as contemporary.

    Which works built her reputation?

    1. Eros the Bittersweet (1986): Her first book grew from her academic work and examines desire through a classical lens. It holds that desire is created by distance and shaped by longing and imagination.
    2. Autobiography of Red (1998): A novel in verse recasting the myth of Geryon, a red winged monster killed by Herakles, as a sensitive adolescent’s love for Herakles. Red Doc> (2013) later returned to its characters.
    3. The Beauty of the Husband (2001): “A fictional essay in 29 tangos”. It made her the first woman to win the TS Eliot Prize.
    4. Nox (2010): Made after her estranged brother Michael died, it joins photographs, letters, translation and handwritten fragments in an accordion fold. It explores grief and the difficulty of holding on to someone through language.
    5. Wrong Norma (2024): Won the National Book Critics Circle Award for Poetry in 2025.

    What makes her work distinct?

    1. Crossing genres: A poem can become an essay in her hands, an essay a translation and a translation autobiography. An ancient myth turns into a modern love story.
    2. Reluctant poet: She hesitates to call herself a poet and has described much of her writing as “some heretic form of poetry”.
    3. Emotional detachment: She writes about desire, abandonment and bereavement without confessional warmth. Feeling arrives indirectly, through a Sappho fragment, a myth, a photograph or an abrupt change of form.
    4. Translations: She has translated Sappho, Euripides and other classical writers, most notably in If Not, Winter: Fragments of Sappho and An Oresteia.
    5. Classics as contemporary: Her work shows that the classics remain radically contemporary and that there is no single way to arrive at or dwell in poetry.

    Conclusion

    The literature prize recognises a writer who treats genre as optional and the classics as a living resource. The next announcement in the 2026 Nobel calendar is the Peace Prize.

    Back2Basics: Nobel Prize in Literature

    1. Origin: Created by the will of Alfred Nobel and first awarded in 1901.
    2. Selection: The Swedish Academy awards it, and its permanent secretary announced the 2026 winner in Stockholm.
    3. Prize money: 12 million Swedish kronor (about $1.2 million) per prize this year.
    4. Ceremony: Prizes are presented on 10 December, the anniversary of Nobel’s death.

    Matching Previous Year Question

    “[2026] ‘X’, born in the UK, was conferred the Nobel Prize in 2025. He was a professor in an American university when the prize was announced. Identify ‘X’: (a) Michel H. Devoret (b) Richard Robson (c) John Clarke (d) Joel Mokyr ANSWER: C”

  • Nobel Prize in Chemistry: Mirror-image molecules and the chemistry of life

    Nobel Prize in Chemistry: Mirror-image molecules and the chemistry of life

    Why in the News

    Chemists have moved from making mirror-image molecules as near-even mixtures of both forms to producing one form almost exclusively, as living cells do. The 2026 Nobel Prize in Chemistry has gone to Henri B Kagan of France and Kenso Soai of Japan “for the discovery of non-linear effects and autocatalysis in asymmetric organic synthesis“.

    What is chirality, and why does life use only one form?

    1. What it is: Some molecules exist in two forms that are mirror images but cannot be superimposed, like a person’s left and right hands. This property is called chirality.
    2. Enantiomers: The two forms, called enantiomers, have the same atoms joined in the same order. Only their three-dimensional arrangement differs.
    3. Homochirality: Life’s building blocks, such as the amino acids in proteins and the sugars in DNA, use only one form. This preference is called homochirality.
    4. Laboratory problem: Ordinary chemical reactions produce both enantiomers in almost equal amounts, so chemists had to separate the form they wanted.
    5. The takeaway: Making only the form the body uses lets chemists copy what living cells do, which is why the work matters for medicines.

    How did Kagan and Soai make one form dominate?

    1. Marckwald’s start: In the early 1900s, German chemist Willy Marckwald used a chiral catalyst (a chemical that speeds a reaction without being used up) and got slightly more of one form.
    2. Stalled progress: Later attempts failed to raise this asymmetry, and chemists did not understand how a chiral catalyst skewed the result.
    3. Kagan’s non-linear effect: Chemists assumed a linear relationship: a product favours one form only as much as its catalyst. In the 1980s, Kagan showed a catalyst barely favouring one form could yield a product strongly favouring it.
    4. Soai’s autocatalysis: In an autocatalytic reaction, the product itself acts as the catalyst. From the 1990s, Soai spent about a decade building one in which a single enantiomer copied itself.
    5. Near-total selectivity: Soai’s final product held 99.5% of one enantiomer, close to the preference seen in living systems.

    Why does it matter beyond the laboratory?

    1. Medicines: Many drug molecules are chiral, and the human body uses only one form. The other form can be ineffective or even harmful.
    2. Cleaner manufacture: Drug makers once produced both forms and then separated the useful one, an expensive and wasteful process. Asymmetric synthesis now makes the needed form directly and is widely used.
    3. Wider uses: The same approach serves other products that interact with living organisms, such as flavours, fragrances and pesticides.
    4. Origin of life: The work shows homochirality can arise in a laboratory, not only in nature. It does not explain how life itself came to prefer one form.

    What does the thalidomide tragedy show?

    1. Morning sickness drug: Thalidomide, a sedative marketed by a West German company from the late 1950s, was widely prescribed, including to pregnant women for morning sickness.
    2. Birth defects: By 1961, doctors had linked it to severe birth defects in thousands of babies across 46 countries, many with phocomelia (severely shortened or absent limbs).
    3. Mirror-image problem: One enantiomer produced the desired effect and the other caused the defects. The two forms were later found to interconvert in the body.

    Challenges

    1. Conversion inside the body: Some drugs switch between forms after they are taken, so a pure enantiomer alone cannot guarantee safety. Eg. Thalidomide.
    2. Narrow reactions: Autocatalytic reactions like Soai’s work only for specific molecules, so extending them to many industrial products is difficult.
    3. Testing burden: Each enantiomer of a new drug must be studied separately for its effects, which raises development time and cost.
    4. Unsolved origin: The work shows how one form can come to dominate, but not why life settled on the forms it uses.

    Way Forward

    1. Enantiomer-level safety review: Drug regulators should require data on both forms of a chiral drug, and on their interconversion, before approval.
    2. Greener generic manufacture: Indian drug makers can use asymmetric catalysis to cut waste and cost in producing active ingredients.
    3. Origin-of-life research: Fund research linking autocatalysis to how homochirality first emerged on the early Earth.

    Conclusion

    Chemists can now make the one mirror-image form that living systems use, a capability that medicine and agriculture already rely on. Why life chose one form in the first place remains unanswered, and that is where this work points next.

    Matching Previous Year Question

    “[2026] ‘X’, born in the UK, was conferred the Nobel Prize in 2025. He was a professor in an American university when the prize was announced. Identify ‘X’: (a) Michel H. Devoret (b) Richard Robson (c) John Clarke (d) Joel Mokyr ANSWER: C”

  • AI companions and child safety

    AI companions and child safety

    Why in the News

    Children increasingly turn to artificial intelligence (AI) chatbots as emotional confidants, because these apps are always available, appear empathetic and respond endlessly. California’s Senate Bill 1119, known as “Adam’s Law”, now mandates safeguards for AI companion chatbots. India’s digital laws have no comparable framework for these psychological risks to children.

    What are AI companions, and why do children turn to them?

    1. What it is: An AI companion is a chatbot built for ongoing, personal conversation. It works like an always-available friend who replies to anything.
    2. Shifting confidant: A child in distress once turned to a parent, friend or counsellor, a role now passing to AI chatbots.
    3. Risks of prolonged use: Long interactions with a chatbot can create emotional dependence, open the door to psychological manipulation and expose a child to harmful advice.
    4. The takeaway: The more a chatbot behaves like a friend, the more a child’s wellbeing depends on how its maker designed it.

    What did the Adam Raine case and Adam’s Law change?

    1. Adam Raine case: In April 2025, a 16-year-old in the United States died by suicide. His family alleged ChatGPT discussed suicide with him for months and helped draft his farewell note.
    2. Wrongful-death lawsuit: The family sued OpenAI, ChatGPT’s maker, claiming its product caused his death. The ongoing case has started a debate on tech firms’ duties to vulnerable children.
    3. Adam’s Law safeguards: Enacted in September 2026, the law requires crisis-detection protocols that spot a child at risk, parental controls, alerts when children disable safety settings, and independent safety audits.

    Why are India’s laws not equipped for this harm?

    1. Data and abuse laws: The Digital Personal Data Protection (DPDP) Act, 2023 requires verifiable parental consent for children’s data. The Protection of Children from Sexual Offences (POCSO) Act, 2012 targets sexual abuse.
    2. IT Act’s focus: The Information Technology (IT) Act, 2000 targets conventional cybercrime. None of the three laws governs the psychological risks of AI companions.
    3. Hidden harm: Psychological harm builds up over thousands of harmless-looking exchanges shaped by product design and algorithmic responses, so it is hard to detect.
    4. Investigation gap: India’s cybercrime machinery is not built to examine algorithmic behaviour or use chatbot interactions as evidence of harm.
    5. Platform accountability: Platforms whose products now sit inside children’s emotional lives must also answer for the harm, not only the government.

    How can India protect children without stifling AI innovation?

    1. Constitutional basis: Article 21 guarantees the right to life and personal liberty. Article 39(f) directs the State to let children develop in freedom and dignity.
    2. Wider duty of protection: Child protection must now cover interactions with AI chatbots, not only personal data and physical safety.
    3. Risk of over-regulation: Over-restrictive rules could stifle innovation and burden emerging enterprises.
    4. Indian design, not a copy: California’s law should prompt India to set its own priorities, suited to its institutional capacities and digital realities, not copy it.

    Challenges

    1. Engagement-driven design: Companion apps gain from longer conversations, so their design can reward the very dependence that harms children.
    2. Weak age checks: Platforms cannot reliably confirm a user’s age, so child-specific safeguards are easy to bypass.
    3. Privacy trade-off: Crisis detection and parental oversight require watching chats, which can intrude on children’s privacy and evolving autonomy.

    Way Forward

    1. Age-appropriate safeguards: Require child-safe defaults and crisis-response mechanisms that route at-risk children to human help.
    2. Independent safety assessments: Mandate third-party safety audits of companion apps open to children.
    3. Privacy-respecting oversight: Give parents oversight tools designed to respect a child’s privacy and growing autonomy.
    4. Investigation capacity: Train cybercrime units to examine algorithmic behaviour and treat chatbot logs as evidence.

    Conclusion

    Children already confide in AI, so the question is whether the institutions charged with protecting them can keep pace. India’s choice between a dedicated framework for AI companions and stretching existing laws will decide how far platforms answer for harm.

    Government Initiatives on AI Regulation in India

    1. No dedicated AI law: India has chosen not to enact a dedicated AI Act. It relies on existing laws such as the Bharatiya Nyaya Sanhita, 2023.
    2. IT Amendment Rules, 2026: They mandate prominent labelling of synthetically generated information (AI-made content) and require harmful deepfakes to be taken down within 3 hours.
    3. India AI Governance Guidelines: They set out a principle-based techno-legal framework, pairing law with built-in technical safeguards, resting on Seven Sutras such as “People First” and “Accountability”.
    4. AI Safety Institute: Proposed under the guidelines and the IndiaAI Mission’s Safe and Trusted AI pillar, it would test and benchmark AI systems.

    Matching Previous Year Question

    “[2026] Which of the following statements with regard to Large Language Models (LLMs) used in machine learning is/are correct? 1. LLMs assign probabilities to the next possible words and then pick the one with the highest probability. 2. LLMs process data through mathematical optimization to minimise prediction errors. 3. LLMs produce unbiased outputs. (a) 1 only (b) 1 and 2 only (c) 2 and 3 only (d) 1, 2 and 3 ANSWER: B”

  • Up to 30% of cotton area under ‘illegal’ GM hybrids, finds study

    Up to 30% of cotton area under ‘illegal’ GM hybrids, finds study

    Why in the News

    India permits only two Bt genes in cotton, yet up to 30% of its cotton area now grows hybrids carrying unapproved genetically modified (GM) traits. A field study commissioned by the Federation of Seed Industry of India (FSII) and the National Seed Association of India (NSAI) puts that area at 26.2 to 29 lakh hectares of the 114.8 lakh hectares sown in 2025-26.

    What are the approved and the illegal GM traits in cotton?

    1. Bt cotton: Bt cotton carries genes from Bacillus thuringiensis (Bt), a soil bacterium. Its genes make proteins toxic to bollworm pests, giving the plant a built-in insecticide.
    2. Approved genes: India allows only two such genes, cry1Ac and cry2Ab.
    3. Herbicide tolerance (HT): The cp4-epsps gene, from another soil bacterium, makes cotton tolerant to glyphosate, a weedkiller. Normal cotton cannot be sprayed with it, because it kills crop and weeds alike.
    4. Vip3A: Also derived from Bt, this gene works differently from the cry genes and controls a broader spectrum of bollworm larvae. Neither it nor cp4-epsps is approved for any crop.
    5. The takeaway: Farmers are planting traits the government never cleared, so the approval system no longer decides what India’s cotton fields grow.

    What did the field study find?

    1. Hotspot sampling: The Jodhpur-based South Asia Biotechnology Centre collected 227 geo-tagged seed and leaf samples from 16 ‘hotspot’ districts of Maharashtra, Telangana, Andhra Pradesh and Rajasthan.
    2. How genes were detected: Hotspots were chosen from glyphosate sales, farmer interactions and seed market intelligence. Samples went through quick lateral flow strip tests and enzyme-linked immunosorbent assay (ELISA), a lab test detecting a gene’s protein.
    3. Herbicide-tolerant gene: The cp4-epsps gene was present in 72.69% of samples, rising to 88.64% in Telangana.
    4. Vip3A gene: The next-generation insect-resistance gene turned up in 53.74% of samples.

    Why are farmers turning to unapproved seed?

    1. Labour shortage: An acute shortage of farm labour has made manual weeding costly and hard to arrange.
    2. Cost of weed control: A season of glyphosate spraying costs under Rs 1,000 an acre, against Rs 12,000 to 15,000 for manual weeding.
    3. Pink bollworm: The pink bollworm now damages first-generation Bt hybrids, so farmers seek Vip3A, which controls it.

    How large has the illegal seed market grown?

    1. Legal seed sales fall: Sales of legal Bt cotton hybrid seed fell to 463 lakh packets in 2025-26, a sign of farmers’ shift to herbicide-tolerant Bt (HTBt) hybrids.
    2. Glyphosate imports: Imports of glyphosate jumped 29% in 2025-26 alongside the shift.
    3. Illegal HTBt market: The unauthorised HTBt seed market reached 82 to 88 lakh packets, conservatively worth Rs 820 to 880 crore.
    4. Regulatory bypass: Farmers and a sophisticated supplier network are bypassing the country’s biosafety and regulatory approval system.

    Challenges

    1. Untested traits in the field: Unapproved genes skip biosafety assessment, leaving effects on soil, other crops and consumers unexamined.
    2. Rising herbicide load: HT cotton spreads glyphosate spraying into a crop on which the herbicide cannot normally be used.
    3. No recourse for farmers: Illegal seed has no accountable seller, so a failed crop leaves farmers without remedy.
    4. Approval lagging demand: Approved traits no longer control pink bollworm or weeds affordably, pushing farmers to illegal seed.

    Way Forward

    1. Time-bound trait assessment: The Genetic Engineering Appraisal Committee (GEAC), India’s GM crop regulator, should assess HT and Vip3A traits within fixed timelines.
    2. Action on supply networks: State agriculture departments should use the hotspot data to trace and prosecute illegal seed suppliers.
    3. Strip tests at sale points: Deploy lateral flow strip tests at seed outlets in hotspot districts.
    4. Cheaper legal weed control: Support mechanised weeding in labour-short districts to narrow the cost gap driving HT adoption.

    Conclusion

    Cotton farmers have chosen their GM traits ahead of the regulator. Whether the regulator formally assesses these traits or enforces the ban against the supplier network is the decision to watch.

    Key numbers

    1. Farmers sampled: Fields of 984 farmers.
    2. HT gene by State: Maharashtra 70.92%, Andhra Pradesh 62.86%, Rajasthan 57.14%.
    3. Vip3A by State: Maharashtra 58.16%, Rajasthan 57.14%, Andhra Pradesh 54.29%, Telangana 38.64%.
    4. Illegal HTBt share of cotton area, 2025-26: Gujarat 30 to 32%, Telangana 27 to 30%, Maharashtra 25 to 28%, Andhra Pradesh 15 to 18%, Rajasthan 8 to 10%.
    5. Market shift: Legal Bt seed 596 lakh packets (2022-23); illegal HTBt about 35 lakh packets (2017-18), at about Rs 1,000 a packet.
    6. Glyphosate imports: 27,721 kilolitres (2024-25) to 35,642 kilolitres (2025-26).

    Matching Previous Year Question

    “[2021] “Bollgard I and Bollgard II technologies are mentioned in the context of: (a) Clonal propagation of crop plants (b) Developing genetically modified crop plants (c) Production of plant growth substances (d) Production of biofertilizers ANSWER: (b)”

  • Nobel Prize in Physics: Francis Halzen found a new way to study neutrinos, the elusive particles that can reveal the mysteries of the cosmos, under Antarctic ice sheets

    Nobel Prize in Physics: Francis Halzen found a new way to study neutrinos, the elusive particles that can reveal the mysteries of the cosmos, under Antarctic ice sheets

    Why in the News

    Astronomy has moved from detecting neutrinos made close to Earth, mostly in the Sun, to catching high-energy neutrinos from far beyond our galaxy. The 2026 Nobel Prize in Physics has gone to Francis Halzen, 82, of the University of Wisconsin-Madison, “for decisive contributions to the IceCube Neutrino Observatory and the discovery of high-energy neutrinos of astrophysical origin”.

    What are neutrinos, and why are they hard to catch?

    1. What they are: Neutrinos are tiny sub-atomic particles produced mainly by nuclear reactions inside stars. They are the second most abundant particle in the universe, after photons, the particles of light.
    2. Ghost particles: Neutrinos carry no electric charge and almost never interact with matter, so they pass straight through it. Nearly 65 billion pass through a fingernail every second.
    3. Rare interactions: Only about one neutrino in billions or trillions interacts, so the Nobel committee calls it the “shyest particle”. Detectors are huge, isolated and often underground, to catch more hits and less noise.
    4. The takeaway: Neutrinos cross dense regions of space that absorb or scatter light, so they can reveal processes no telescope can see.

    How does IceCube catch neutrinos?

    1. Soviet radio attempts: Halzen had studied cosmic rays since the 1960s. He learned Soviet scientists had tried to catch neutrinos in Antarctic ice by detecting radio waves from their collisions.
    2. Halzen’s light-sensor idea: He proposed placing light sensors deep in glacier ice instead. Near the South Pole, the ice is isolated and dark at depth, with no interfering signals.
    3. The detector: IceCube, at the Amundsen-Scott South Pole Station, uses one cubic kilometre of ice fitted with over 5,000 light sensors on long cables. A surface facility collects their signals.
    4. Cherenkov glow: A neutrino striking a nucleus in the ice creates a charged particle. Light slows down in ice, so this particle outruns it and emits a faint glow, Cherenkov radiation, like a sonic boom of light.
    5. Reading the signal: Scientists reconstruct the neutrino’s energy and direction from the glow, which points back towards its source.

    What did IceCube discover, and why does it matter?

    1. Earlier detections: Neutrinos were proposed in the 1930s and first detected in the 1950s. Several observatories worldwide study them.
    2. Near-Earth only: Until IceCube, detectors caught only neutrinos produced near Earth, mostly in the Sun’s nuclear reactions.
    3. Cosmic discovery: IceCube began full operation in 2011. In 2013 it presented the first evidence of very high-energy neutrinos from distant space.
    4. Multi-messenger astronomy: Astronomy grew from visible light to the full electromagnetic spectrum, cosmic rays and, in 2015, gravitational waves. Neutrinos add another signal, so one cosmic event can be studied several ways.
    5. Nobel lineage: Neutrino research has won Nobel Prizes before. Eg. The 2015 prize recognised the discovery that neutrinos change type, which shows they have mass.

    Challenges

    1. Few sources traced: Most cosmic neutrinos still cannot be tied to a specific galaxy or event. Eg. One neutrino traced to a distant blazar galaxy in 2018 was a rare match.
    2. Scale and cost: Catching rare interactions needs detectors of cubic-kilometre size, which few countries can fund alone.
    3. Polar logistics: Sensors under Antarctic ice can be installed and serviced only in the short polar summer.
    4. India’s stalled detector: The India-based Neutrino Observatory (INO), planned first in Kerala and then in Tamil Nadu, met opposition over land acquisition and the environment. Its new site is yet to be finalised.

    Way Forward

    1. Settle the INO site: The Department of Atomic Energy (DAE) should finalise a site after early community consultation and environmental review.
    2. Global collaboration: Indian institutes should join next-generation detectors such as IceCube-Gen2, the planned expansion of IceCube.
    3. Linked alerts: Connect LIGO-India, the planned gravitational-wave detector, and Indian telescopes to neutrino alert networks.

    Conclusion

    Halzen’s prize marks the neutrino’s arrival as a working tool of astronomy, not just an object of physics. Whether India finalises a site for its own long-delayed detector will decide if it contributes to this field or only watches it.

    Back2Basics: India-based Neutrino Observatory (INO)

    1. Aim: An underground laboratory to study atmospheric neutrinos, with rock overhead to block cosmic-ray noise.
    2. Detector: Its main instrument is a planned 50,000-tonne magnetised Iron Calorimeter (ICAL).
    3. Funding: It is a joint project of the DAE and the Department of Science and Technology (DST).
    4. Science goal: It seeks to settle the neutrino mass ordering, the order of the masses of the three neutrino types.

    Matching Previous Year Question

    “[2026] ‘X’, born in the UK, was conferred the Nobel Prize in 2025. He was a professor in an American university when the prize was announced. Identify ‘X’: (a) Michel H. Devoret (b) Richard Robson (c) John Clarke (d) Joel Mokyr ANSWER: C”

  • Nobel Medicine Prize 2026: How scientists use light to control brain cells

    Why in the News

    Neuroscience has moved from having no way to switch on a single set of brain cells to turning chosen neurons on or off with light, a method called optogenetics. The 2026 Nobel Prize in Physiology or Medicine has gone jointly to Karl Deisseroth, Peter Hegemann and Georg Nagel “for their discoveries concerning light-gated ion channels and optogenetics”.

    What is optogenetics?

    1. Brain as a network: The brain is an electrical network of billions of neurons, whose signals let us move, see, remember and feel.
    2. What it is: Optogenetics uses light to control genetically modified cells. Scientists make target cells carry light-sensitive proteins called opsins.
    3. How it works: When light falls on those cells, the opsins act like tiny light switches, turning the cells’ activity on or off.
    4. Precision: Scientists can switch one organ’s cells and watch how the brain responds. Eg. Deisseroth paced the heart and watched brain states.
    5. The takeaway: Optogenetics “fundamentally altered our understanding of the brain”, so scientists can now test how it forms memories, feelings and behaviours.

    How was the light switch discovered?

    1. Crick’s idea: Francis Crick, co-discoverer of DNA’s double helix, wanted to activate single nerve cells to study consciousness, and saw light as the only tool fast enough.
    2. A fast-reacting alga: Hegemann, of Humboldt University of Berlin, found the alga Chlamydomonas reacts within half a millisecond of light reaching its eyespot (light sensor). The human eye takes 10 milliseconds.
    3. One protein hypothesis: He proposed that one protein both captures light and acts on it. Japanese researchers’ map of the alga’s DNA pointed to two candidate genes.
    4. Confirmation in frog eggs: Nagel, of the University of Würzburg, put both genes into frog eggs. The sensor was channelrhodopsin-2 (ChR-2), a cell membrane pore that opens when lit.
    5. Control of nerve cells: Deisseroth, of the Howard Hughes Medical Institute, used ChR-2 to control chosen nerve cells in a dish, then in live rats. Switches for other wavelengths followed.

    What has optogenetics revealed about the brain?

    1. Whiskers and sleep: In 2006, Deisseroth’s group used light to move mouse whiskers, and woke sleeping mice by switching on a newly found wakefulness cell.
    2. Fear memory: The team marked the nerve cells that formed a fear memory. Reactivating them later made mice show fear with no danger present.
    3. Mapped circuits: Optogenetics has identified circuits for pain, social behaviour, thirst, feeding, reward and attention, and shown how memories form.
    4. One room at a time: A Tata Institute of Fundamental Research (TIFR) professor likens the brain to a building of many rooms. Optogenetics lights one room at a time.

    Can optogenetics treat patients yet?

    1. Mainly a lab tool: It is still used largely in laboratories to find which neurons drive animal behaviour and what causes certain conditions.
    2. Restoring sight: Clinical trials are testing it on blindness from retinitis pigmentosa, which destroys the eye’s rods and cones. Wearing light-emitting glasses, one patient could grasp objects.
    3. Better hearing: It could improve cochlear implants, which now stimulate the hearing nerve with electricity.

    Challenges

    1. Gene delivery: Each therapy must insert an opsin gene into human cells, usually through modified viruses, which raises safety questions.
    2. Invasive light: Reaching deep brain cells needs implanted optical fibres.
    3. Animal to human gap: Most findings come from mice and rats, whose brain circuits differ from human ones.
    4. Ethics of control: Switching memories and behaviour on demand raises consent and misuse concerns.

    Way Forward

    1. Research funding: The Anusandhan National Research Foundation (ANRF) should fund Indian optogenetics laboratories.
    2. Ethics rules: The Indian Council of Medical Research (ICMR) should extend its ethical guidelines to gene-based brain interventions.
    3. Less invasive tools: Back opsins responding to red light, which passes deeper through tissue.
    4. Clinical pathway: The Central Drugs Standard Control Organisation (CDSCO) should set a clear route for gene therapy trials.

    Conclusion

    Optogenetics has shifted neuroscience from watching the brain to testing it cell by cell, but it remains mostly a laboratory tool. Whether the vision trials succeed in patients will show how fast it moves from mice to medicine.

    Back2Basics: Nobel Prize in Physiology or Medicine

    1. Origin: Created by the will of Alfred Nobel and first awarded in 1901.
    2. Selection: The Nobel Assembly at Karolinska Institutet, Stockholm, chooses the laureates.
    3. Sharing: A single prize can be shared by at most three laureates.
    4. Ceremony: Prizes are presented on 10 December, the anniversary of Nobel’s death.

    Matching Previous Year Question

    “[2026] ‘X’, born in the UK, was conferred the Nobel Prize in 2025. He was a professor in an American university when the prize was announced. Identify ‘X’: (a) Michel H. Devoret (b) Richard Robson (c) John Clarke (d) Joel Mokyr ANSWER: C”