The choice is between AI applications and AI frontiers

Why in the News

India has no competitive frontier artificial intelligence (AI) model and no realistic prospect of producing one without significant policy shifts, at a time when United States and Chinese firms have released a parade of increasingly capable models through the year. The advice India has received from United States industry leaders and academics, supported by sections of the Indian information technology industry, is to concentrate on applications built on foundation models rather than on the frontier itself. The position advanced against that advice is that countries falling behind in frontier AI risk the fate of those that missed the Industrial Revolution, where a small business elite found a niche and prospered while ordinary people were disempowered. The binding constraint identified is not talent or algorithms but computing power, since the IndiaAI mission’s pool of 45,000 graphics processing units (GPUs) is a fraction of what a single United States frontier laboratory controls. The proposal put forward is a compute tax requiring any data centre established in India to reserve a share of its capacity for a publicly administered national pool.

What is a frontier AI model?

1. Frontier model: A frontier model is a foundation model at the leading edge of capability, from which industry specific applications are then built.
2. Scaling laws: The industry has exploited “scaling laws”, which predict how a model’s performance improves with its size and with the computing power used for its training.
3. Compute and data as the decisive input: The algorithms underlying modern AI models are widely understood, so better algorithms improve efficiency while the basic formula for producing a frontier model remains scaling compute and data.

What are the two channels through which AI will matter?

1. Diffusion through the economy: AI will spread by automating some routine jobs, with each industry requiring specialised applications built on foundation models.
2. India’s application start up ecosystem: India has an active start up ecosystem devoted to building such applications, and businesses have rapidly adopted AI tools.
3. The strategic channel is separate: AI will also have a strategic impact on research, cybersecurity and defence, which is not reached by application building.
4. Mathematics and cybersecurity results: AI models have been used to solve some of the most important open problems in mathematics, and Anthropic’s Mythos model has formidable cybersecurity capabilities.

Why is access to foreign frontier models not a durable substitute?

1. Access today is real but conditional: Consumers currently have access to other frontier models, including Chinese open weight models.
2. The most capable model is already withheld: Mythos has not been released publicly and is available only to selected organisations.
3. Export control has already been applied: The United States temporarily imposed export restrictions on Mythos and on a version of Mythos with guardrails called Fable.
4. The stated direction of policy: The United States is likely to restrict and regulate AI to “achieve global dominance”, so present availability cannot be expected to continue indefinitely.

Why is compute the binding constraint for India?

1. The national pool is small: The IndiaAI mission has a pool of 45,000 GPUs, which is only a fraction of the capacity controlled by a single United States frontier laboratory.
2. The flagship allocation is smaller still: The mission allocated 4,096 GPUs to Sarvam AI to train India’s flagship model.
3. The gap is an order of magnitude: That allocation is about 50 times smaller than what is used to train frontier models.
4. Ingenuity does not close it: No amount of ingenuity can compensate for a resource gap of that size, which is why lack of computing power has bottlenecked sovereign Indian model development.

What do the new data centres actually deliver to India?

1. Data centre build out across States: A number of data centres with significant computing capacity are coming up in various States.
2. Capacity reserved for multinational clients: These will primarily serve multinational corporations, and their location in India offers no tangible benefits.
3. The investment goes into equipment: Most of the announced capital investment will be directed to electronic equipment.
4. The employment effect is thin: The employment they create will be limited to a few construction and maintenance jobs.
5. The environmental cost is local: Large data centres have a significant environmental impact, and in India that impact will be borne disproportionately by local communities.

How would a compute tax work?

1. The obligation: Any data centre established in India would be required to reserve a stated share, suggested at 25 per cent, of its computing capacity for a publicly administered national compute pool.
2. The hardware does not move: That capacity would remain physically within the data centre.
3. Allocation is centralised: The reserved capacity would be allocated by a central scheduler to Indian institutions.
4. The bargaining position favours India: Multinational corporations are likely to resist, and their bargaining position is weak given the growing hostility to these installations elsewhere.
5. Limits of the compute tax: Such a tax would not obviate the other data centre concerns, and only together with environmental safeguards and welfare measures would it open a narrow route to building a frontier model in India.

Challenges to a compute tax on data centres

1. Reserved capacity is not the same as usable capacity: Frontier training needs thousands of GPUs interconnected as one cluster, and a quarter of each site’s capacity scattered across many sites does not assemble into that. Eg. The flagship national allocation of 4,096 GPUs already sits far below frontier training scale despite being a single block.
   The Fix: Write the reservation as a contiguous interconnected block within each site, with a minimum cluster size, rather than as a percentage of total capacity.
2. A capacity levy raises the cost of hosting in India: An operator prices the reserved share into its India investment case and can site the facility in a neighbouring jurisdiction instead. Eg. Data centre investment is mobile across countries in a way that manufacturing capacity is not.
   The Fix: Offset the reservation against power tariff and land concessions already given to data centres, so the obligation is priced as a condition of the incentive rather than as an additional charge.
3. A public pool needs an allocation rule it does not yet have: Deciding which institution gets scarce compute, for how long and on what merit is a governance problem that no existing Indian body performs. Eg. The single largest allocation so far went to one start up for the flagship model.
   The Fix: Publish the scheduler’s allocation criteria and a usage register, so grants of compute are contestable in the way research grants are.
4. Compute alone does not produce a model: Frontier training also needs large curated datasets and a small pool of researchers who have trained models at scale, both of which are internationally mobile. Eg. Indian language data is thin compared with the English language corpora frontier models are trained on.
   The Fix: Tie the compute grant to a data contribution obligation, so a recipient returns curated Indian language datasets into the national repository as a condition of access.
5. The environmental burden stays where it was: Reserving capacity changes who uses the machines and not their power draw, water use or siting. Eg. The impact of large installations falls disproportionately on the communities around them.
   The Fix: Attach site level water and power disclosure and a local benefit sharing requirement to the same instrument that creates the reservation.

Conclusion

The question the argument forces is not whether India should build applications, which it already does well, but whether an applications only position is a strategy or a description of the constraint. The claim on the other side is that capability at the frontier has a strategic use in research, security and defence that no amount of downstream product building substitutes for. The compute tax is the first concrete instrument proposed to convert privately owned capacity sited in India into publicly directed capacity, and it is testable against a single question: whether the reserved share can be assembled into a cluster large enough to train anything. The marker to watch is whether any Indian allocation moves from the thousands of GPUs to the tens of thousands, since that is the threshold the gap is actually measured at.

Artificial Intelligence in India

1. AI as a public good: India treats AI as a public good rather than a proprietary luxury, anchored in shared compute infrastructure, open and locally relevant datasets and decentralised talent development.
2. The scale of the ecosystem: Over 6 million people are employed in the technology and AI ecosystem, with more than 1,800 Global Capability Centres of which over 500 are AI focused.
3. Adoption is broad: 87 per cent of enterprises are actively deploying AI solutions, led by industrial and automotive, consumer goods and retail, banking and financial services, and healthcare.
4. The projected economic weight: AI is projected to contribute USD 500 to 600 billion to India’s Gross Domestic Product by 2030.

Government Initiatives for Artificial Intelligence

1. IndiaAI Mission, 2024: Implemented by IndiaAI under the Ministry of Electronics and Information Technology with an outlay of Rs 10,371 crore, on the stated vision of making AI in India and making AI work for India.
2. AIKosh: The national AI dataset repository, carrying over 3,000 datasets and 243 models across 20 sectors.
3. BharatGen: A government funded multimodal large language model initiative designed for AI powered public services and Indian use cases.
4. Digital India Bhashini and Project Vaani: Speech and translation tools across the 22 Scheduled Languages, supported by a 150,000 hour Indian speech dataset.
5. IndiaAI FutureSkills and YUVAi: Fellowships and AI labs concentrated in Tier 2 and Tier 3 cities, and an AI skills initiative for school students in Classes 8 to 12.
6. IndiaAI Safety Institute: The national trust framework covering bias mitigation, privacy, explainability and AI governance.

Matching Previous Year Question

“[2026, GS3, 15 marks] What is agentic Artificial Intelligence (AI)? Explain its working. Describe its applications with suitable examples. Discuss the advantages, risks and challenges associated with agentic AI systems.”