QUESTION 1
The success of UPI has demonstrated that zero-cost access can accelerate the adoption of digital financial services, but may also create questions of long-term financial sustainability. Discuss.
QUESTION 2
Artificial Intelligence is increasingly transforming drug discovery from a largely trial-and-error process into a data-driven process. Discuss its potential and limitations in accelerating the development of new medicines.
QUESTION 1: The success of UPI has demonstrated that zero-cost access can accelerate the adoption of digital financial services, but may also create questions of long-term financial sustainability. Discuss.
Relevance: The question links with GS Paper 3 — Indian Economy, financial inclusion, digital economy and fintech. The topic is relevant for understanding the sustainability of Digital Public Infrastructure (DPI) and the balance between inclusion, innovation and commercial viability.
Note: This is not a model UPSC answer. It only provides you with a thought process which you may incorporate into the answers.
Introduction:
— The Unified Payments Interface (UPI) has shown how eliminating transaction costs can speed up the adoption of digital financial services.
— UPI, which was launched in 2016, now accounts for 86% of all digital transactions in India, with over 55 crore users. In 2025-26, it completed over 24,000 crore transactions totalling ₹314 lakh crore.
Body:
You may incorporate some of the following points in your answer:
Why zero-cost access accelerated adoption?
— Zero MDR makes digital payments appealing to small businesses and individuals, especially for low-value transactions. The government also funded eligible low-value UPI transactions for small retailers.
— As more customers and merchants used UPI, its utility grew, resulting in a self-perpetuating cycle of acceptance.
— QR-based payments simplified bank transfers, allowing small enterprises to engage in the formal digital economy.
— Rapid acceptance prompted private investment in fintech and payment services, fostering innovation and the entry of new firms.
— UPI’s interoperability, rapid settlement, and ease of use enabled it to expand beyond metropolitan consumers to a much larger populace.
Conclusion:
— UPI’s experience demonstrates that affordability can be a tremendous motivator for digital financial inclusion; but, a public digital infrastructure of this size cannot rely on subsidies indefinitely.
— India must therefore maintain the “free at the point of use” status for ordinary users while adopting a properly calibrated, market-based revenue model for high-value transactions and commercial participants. This would enable UPI to stay inclusive, competitive, innovative, and financially viable as it enters its next stage of expansion.
(Sources: How UPI became India’s biggest digital payments system — and its next challenge, Why charge? Because smooth UPI payments cost Rs 20,000 crore a year, UPI free for citizens, merchants may face ‘nominal’ fee: Government)
Points to Ponder
Should India adopt a calibrated MDR framework while protecting small-value transactions and financial inclusion?
What role should government subsidies play in sustaining digital public infrastructure?
Related Previous Year Question
What is the status of digitalization in the Indian economy? Examine the problems faced in this regard and suggest improvements. (2023)
QUESTION 2: Artificial Intelligence is increasingly transforming drug discovery from a largely trial-and-error process into a data-driven process. Discuss its potential and limitations in accelerating the development of new medicines.
Relevance: The question is relevant to GS Paper 3 — Science and Technology, biotechnology, AI and healthcare innovation. It enables examination of the convergence of AI and biotechnology and its potential to transform pharmaceutical research.
Note: This is not a model UPSC answer. It only provides you with a thought process which you may incorporate into the answers.
Introduction:
— Artificial intelligence (AI) is transforming drug discovery by allowing researchers to evaluate vast biological datasets, anticipate molecular activity, and uncover viable drug candidates before undergoing thorough laboratory tests.
— The recent discovery of a possible inhibitor for the GIPC1 protein, which was previously thought to be difficult to target, demonstrates how artificial intelligence might open up new pathways for drug development.
Body:
You may incorporate some of the following points in your answer:
Potential of AI in accelerating drug discovery
— In the GIPC1 study, about 40,000 molecules were reduced down to five possibilities, with two eventually synthesised and evaluated. This shows how computational screening can significantly reduce the initial search space.
— GIPC1’s PDZ domain has previously been difficult to target due of its large, shallow interaction surfaces. AI-aided molecular modelling assisted in the identification of a chemical capable of binding to this domain, possibly increasing the variety of therapeutic targets.
— Generative AI can help with the design of new molecules, whereas predictive AI can analyse properties like toxicity and absorption, allowing researchers to prioritise potential possibilities.
— Molecular modelling and quantum chemistry can help machine learning by predicting how chemicals will interact with specific proteins. This results in a more integrated approach to medication design.
— AI-powered analysis can help identify disease-specific molecular targets and lead to more personalised medicines, particularly in cancer and genetic illnesses.
What are the limitations and challenges?
— The investigation into GIPC1 is still in its early stages. Although the candidate has moved to animal tests, its safety and efficacy in people have yet to be determined. While artificial intelligence can find intriguing chemicals, it cannot substitute biological and clinical confirmation.
— A medication may interact with multiple biological pathways. Even when artificial intelligence predicts that a molecule will bind to a specific target, the human body’s response can be far more complex.
— AI systems rely on the accuracy and representativeness of the data used to train them. In healthcare, skewed or incomplete datasets can have an impact on the accuracy and applicability of AI-generated results.
— The wider use of AI in healthcare creates the “black box” issue, in which researchers and regulators may not always be able to precisely explain how an AI system arrived at a specific prediction. This has issues for trust, safety, and regulatory compliance.
Conclusion:
— AI is unlikely to replace traditional drug discovery; rather, it can improve the process’s targeting, predictability, and efficiency. The GIPC1 instance indicates the potential for identifying therapeutic opportunities against hitherto tough targets, but its ultimate usefulness will be determined by effective laboratory validation, clinical trials, regulatory approval, and cheap access to the ensuing drugs.
(Sources: Treatment for pancreatic cancer? How AI helped target ‘undruggable’ protein, India is the pharmacy of the world. But we are losing the drug development race, As AI enters healthcare, why docs and regulators grapple with safety and oversight)
Points to Ponder
How can AI reduce the time, cost and failure rate associated with identifying potential drug candidates?
Related Previous Year Question
Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare? (2023)
Previous Mains Answer Practice
UPSC Essentials: Mains answer practice — GS 3 (Week 168)
UPSC Essentials: Mains answer practice — GS 3 (Week 167)
UPSC Essentials: Mains answer practice — GS 2 (Week 168)
UPSC Essentials: Mains answer practice — GS 2 (Week 167)
UPSC Essentials: Mains answer practice — GS 1 (Week 167)
UPSC Essentials: Mains answer practice — GS 1 (Week 168)
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Are you preparing for the Civil Services Mains Exam 2026? Attempt a question on the adoption of digital financial services in today's answer-writing practice. (Image generated using AI) 
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