English Language Questions for CLAT | QB Set 32

“How can India produce AI, instead of becoming its tenant?”

Last week, on a 600-acre site near Visakhapatnam, Google broke ground on what is being dubbed as India’s largest AI hub. The infrastructure partners are AdaniConneX and Airtel’s Nxtra. The silicon will be designed in California and fabricated in Taiwan. The model weights, when the data centre lights up, will be Google’s. India’s contribution is the land, the power, the construction, and the operations.

It is a triumph of sorts. Alas, we have done something like this before.

Thirty years ago, the IT-services boom began with a related bargain. India offered low-cost engineering labour, fluent in English, to American firms that needed someone to write and maintain the systems they had designed. The arrangement worked. Infosys, Wipro and TCS came of age. A middle class emerged. A reputation was earned. Indian services climbed the value chain, and the global capability centres in Bengaluru and Hyderabad now do genuine research and product work for the world’s largest firms.

We were not wrong to celebrate this. But the task was incomplete.

What we did not do was use the proceeds to build the next layer. We did not fund research universities of global rank. We did not raise national R&D intensity from below 1 per cent of GDP, where it has remained for decades. We did not develop deep-tech venture capital, manufacturing depth, or patient state-aligned industrial finance.

Korea built Samsung. Taiwan, smaller than Karnataka, built TSMC, which now sits at the chokepoint of the global AI economy. India built outsourcing. We learned magnificently to deploy systems that other people had designed. We postponed the work of designing them.

The bill on that postponement is now arriving, in the form of artificial intelligence.

The leading edge of AI demands capital, research depth, and concentrated talent at a scale India has not built. A frontier model costs hundreds of millions of dollars to train, draws on decades of accumulated research, and depends on a talent pipeline that runs through a few dozen universities in the United States, China, the United Kingdom, and Canada. India has none of this.

In the past fortnight, two voices have read this moment differently. Ruchir Sharma, at the Express Adda, warned that global investors now see India as the “anti-AI play”. Nandan Nilekani, in a recent essay with Ravi Venkatesan, argued that India’s edge lies in diffusing AI across its small firms and informal economy.

Sharma is reading the high-rent layers and seeing India’s absence. Nilekani is reading the diffusion layer and treating it as India’s destination.

Both stop short of the harder question.

Neither asks how a country with talent and ambition becomes a producer of intelligence rather than a tenant of it.

What we are doing in Vizag, and in Hyderabad and Mumbai and Pune, risks making the same trade we made in the nineties, with stronger lock-in this time. The data centres are real and useful. They will be optimised, for the next decade, to serve foreign models efficiently rather than to train Indian ones at scale.

Every Indian-language inference running on a foreign model from a server in Vizag is a small recurring rent paid to Mountain View, on infrastructure built by Adani.

The arrangement is not new. The technology is.

The stakes go beyond productivity. As AI diffuses into military systems and strategic decision-making, this gap will bear on India’s strategic autonomy in a world where security partnerships cannot be assumed.

India does not need to beat OpenAI at frontier reasoning today, but it needs to learn to compete: Alternative model architectures and approaches at meaningful scale, sovereign compute, and foreign deals structured around expanding Indian capability rather than only power and land.

The Indian state knows how to create a gigawatt of power for a foreign data centre. It needs to learn how to create a research department, a laboratory, a generation of scientists. Concrete it understands. Cognition it does not.

The Economic Survey of 2025-26 noted that only 2 per cent of the world’s AI training-data startups are based in India, against 40 per cent in the United States and 21 per cent in the European Union.

The state knows the gap. The announcements still say far more about infrastructure than about capability.

The deeper danger is that India’s public conversation is too quick to convert constraint into strategy.

Because we cannot yet win at the frontier, we tell ourselves the frontier does not matter. Because we are good at diffusion, we tell ourselves diffusion is enough.

Some of this is an honest assessment of what India can do. Some of it is an alibi for what we will not do.

We must not forget, however, that even diffusion rests on uneven foundations. UPI worked because Indian banking had spent 30 years digitising, Aadhaar because the state had been identifying citizens for 60. Where those rails were missing, in health, in school education, in agricultural extension, the same stack thinking has produced architecture without adoption.

AI diffusion may run into the same problem.

This past week in Visakhapatnam, we poured the foundations of buildings that will host someone else’s intelligence. The buildings will be ours. The intelligence will not.

Whether that is enough this time is the question we should ask before the concrete sets.

Question 1

According to the passage, what was India’s major contribution to the AI hub project near Visakhapatnam?

A. Land, power, construction and operations

B. Designing semiconductor chips

C. Developing frontier AI models

D. Manufacturing AI processors

Question 2

What does the article identify as India’s major failure after the IT-services boom?

A. Failure to attract foreign companies

B. Failure to expand English-speaking talent

C. Failure to build deeper research and innovation capabilities

D. Failure to create software service companies

Question 3

Why does the article describe India as at risk of becoming a “tenant” in the AI economy?

A. India lacks internet connectivity in rural areas

B. India is refusing to adopt AI technologies

C. India is depending entirely on imported hardware only

D. India may mainly host infrastructure for foreign AI systems rather than develop its own intelligence capabilities

Question 4

What comparison does the article make between India and countries like Korea and Taiwan?

A. India spends more on AI research than Taiwan

B. Korea and Taiwan depend heavily on foreign outsourcing

C. Korea and Taiwan built globally influential technology companies, while India mainly built outsourcing capabilities

D. India’s manufacturing ecosystem is stronger than Taiwan’s

Question 5

According to the passage, why did systems like UPI and Aadhaar succeed?

A. They depended entirely on foreign investment

B. They were supported by decades of institutional and digital groundwork

C. They were created by private technology companies alone

D. They avoided state involvement completely

Answers with Detailed Explanation

1. Answer: A. Land, power, construction and operations

The passage clearly states that the silicon would be designed in California, fabricated in Taiwan, and the AI model weights would belong to Google. India’s role was described as providing “the land, the power, the construction, and the operations.” This highlights the author’s concern that India is contributing infrastructure rather than core technological innovation.

2. Answer: C. Failure to build deeper research and innovation capabilities

The article argues that although India succeeded in IT services, it failed to invest sufficiently in research universities, R&D intensity, deep-tech venture capital, manufacturing depth, and industrial finance. According to the authors, India celebrated outsourcing success but did not build the next layer of innovation needed for technological leadership.

3. Answer: D. India may mainly host infrastructure for foreign AI systems rather than develop its own intelligence capabilities

The article repeatedly warns that India risks becoming a “tenant” because foreign firms may own the models, technology, and intelligence, while India only provides physical infrastructure like data centres and energy. The concern is about dependence on foreign AI systems instead of creating sovereign AI capabilities.

4. Answer: C. Korea and Taiwan built globally influential technology companies, while India mainly built outsourcing capabilities

The passage gives the examples of Samsung in Korea and TSMC in Taiwan to show how those countries created globally dominant technology enterprises. In contrast, India focused largely on outsourcing and deploying systems designed elsewhere. This comparison is central to the article’s argument about missed opportunities in innovation.

5. Answer: B. They were supported by decades of institutional and digital groundwork

The authors explain that UPI worked because Indian banking had undergone decades of digitisation, while Aadhaar succeeded because the state had spent decades building identification systems. The passage uses these examples to show that technological diffusion requires strong foundational systems and long-term preparation.