English Language Questions for CLAT | QB Set 26

The India AI Impact Summit 2026 was unfortunately marred by allegations of plagiarism, leading to unseemly optics. The substance, in any case, was always a chimera, a delusion erected on foundations of sand. The chasm between our aspirational rhetoric and our investment reality looks unbridgeable. The Union Budget 2026’s allocation of a mere Rs 1,000 crore for the IndiaAI Mission is, in the context of global capital flows into this domain, tokenism. It is a figure that speaks not of a national mission, but of a pilot project. It is not even enough to subsidise the compute costs for a significant portion of our research community, let alone build the kind of large-scale GPU clusters that would allow Indian researchers to train models comparable to GPT-6 or Claude 4.5.

By way of comparison, China spent USD 98 billion on AI in 2025, out of which the government spend was USD 56 billion. Five US-based private companies — Amazon, Google, Microsoft, Meta and Oracle — will collectively invest USD 700 billion in 2026. The Government of India will spend 1.2 billion over five years on its AI Mission. The message from the finance mandarins is clear: Artificial intelligence, for all the ministerial speeches dedicated to it, remains just another sector, not a core strategic priority. The greatest risk we face is not an AI investment bubble, but insufficient investment, and consequently ceding the technological future to competitors like the US, China and Europe.

Yet, our response to this existential challenge is a budget that has been effectively halved from previous projections, a move that signals to the world, and more importantly, to our own innovators, a comfortable acceptance of the slow lane. This chronic underfunding has a corrosive effect that permeates the ecosystem. The IndiaAI Mission, with its stated goal of building sovereign capabilities, finds its ambition directly contradicted by the ground reality.

Nearly three out of four Indian AI deployments rely on Western proprietary models accessed via APIs. A staggering 65 per cent of GPU computing power among these start-ups is dedicated to inference while only 21 per cent goes toward training new models. The argument that value lies in the application layer is a convenient rationalisation for our failure to compete at the foundational level.

The talent pool, too, is a casualty of this skewed investment. While MeitY underscores that global giants are looking to India to hire AI engineers, the shortage is not in machine learning theory, but in AI Ops — the expertise required to evaluate, optimise, deploy, and monitor complex AI systems in production. Our education system, starved of research funding and world-class computational infrastructure, is producing a workforce highly skilled for service roles but ill-equipped for frontier model development. We are training the global AI economy’s middle management, not its C-suite.

Fiscal timidity becomes even more alarming when viewed through global competition. The Stanford HAI Global AI Power Rankings consistently places India in the second tier. The World Economic Forum ranks India eighth in AI investment. However, the gulf between the top three and the rest remains vast.

The contrast with global frontrunners is stark. The US and China are engaged in a high-stakes competition funded by hundreds of billions of dollars. NASSCOM reports over 86,000 AI patents filed in India between 2010 and 2025. However, patent volume does not equate to AI dominance. The real measure of sovereignty is the ability to build and train state-of-the-art models on homegrown infrastructure using sovereign data.

The Carnegie Endowment identifies the missing pieces in India’s AI ecosystem: talent, data, and R&D funding. While India’s UPI is a world-class digital public infrastructure, AI is different. The models themselves are the product, and countries that control advanced models will set global terms.

So what was the purpose of a grand AI summit? Was it to network start-ups with Western API providers? To showcase a vibrant ecosystem of consumers rather than producers? The summit’s real value lies not in declarations but in catalysing a rethink of fiscal strategy.

The global AI race requires sustained, massive investment in data centres, research grants, and PhD programmes. Without prioritisation, the summit will be remembered not as a turning point, but as a missed opportunity.

The world will smile for the camera, and quietly note that India, for all its grand ambitions, has chosen to remain a consumer in an age of creators.

Q1. Which of the following best captures the author’s central argument regarding India’s AI strategy?

A. India is prioritising AI effectively but facing temporary implementation challenges.
B. India’s AI growth is limited due to lack of private sector interest.
C. India’s AI ambitions are undermined by insufficient financial commitment and strategic prioritisation.
D. India is ahead in AI adoption compared to global competitors.

Q2. Why does the author refer to the budget allocation as “tokenism”?

A. Because the funds are entirely misallocated to irrelevant sectors.
B. Because AI investment is completely absent in the Union Budget.
C. Because the allocation exceeds global spending patterns.
D. Because the allocated amount is too small compared to global AI investments and the stated national ambitions.

Q3. The author argues that relying heavily on Western proprietary models primarily indicates:

A. A lack of sovereign AI capability and foundational model development.
B. India’s strategic integration into global AI markets.
C. Cost efficiency and pragmatic governance.
D. Rapid domestic innovation in application-layer technologies.

Q4. What does the phrase “training the global AI economy’s middle management, not its C-suite” most strongly imply?

A. India is producing AI professionals suited for operational roles rather than leadership in foundational innovation.
B. India’s AI engineers are overqualified for global markets.
C. India is focused entirely on corporate governance structures.
D. Indian professionals dominate executive AI leadership globally.

Q5. According to the passage, what is the primary limitation of measuring AI progress through patent numbers alone?

A. Patent filing processes are legally complex and slow.
B. Patent volume does not necessarily reflect true technological sovereignty or ability to build advanced models.
C. India files fewer patents compared to China and the US.
D. Most patents are rejected by global authorities.

Answer Key

C, D, A, A, B