Machinepower Index / Dossiers / India
Rank 9–11 across the scoring rules tested. How this is tested
India
Diffusion Advantage — For nations that cannot build the frontier but can use it faster than anyone: import the models and win on deployment, across the state and the economy, with public consent.
How the Score, Potential and bottleneck cost are worked out: methodology.
The three layers
Energy, grids, data centres and silicon — the physical capacity to run models at scale.
Compute tracked by Epoch AI: India controls 4,400 H100-equivalents, wherever the sites are. Hosted compute is shown, not scored.
Models, alignment capability, standards influence and the talent that produces them.
The capacity to wire AI into the state and the economy while holding public consent.
Where the score comes from
| Layer | Sub-dimension | Basis | Score |
|---|---|---|---|
| Watts | Planning & Permitting | Assessed | 6.2 |
| Firm Power Capacity | Dataset | 7.8 | |
| Sovereign Compute | Dataset | 5.1 | |
| Hardware & Silicon Access | Dataset | 4.9 | |
| Weights | Frontier Model Output | Assessed | 4.5 |
| Alignment Capability | Assessed | 6.8 | |
| Regulatory Standing | Dataset | 6.4 | |
| Talent Density | Dataset | 7.7 | |
| Will | Policy Capacity | Dataset | 9.6 |
| Public Sector Adoption | Dataset | 7.9 | |
| Workforce Transition | Dataset | 5.6 | |
| Public Trust | Dataset | 8.7 |
Assessed marks a measurement we judged rather than took from a dataset: 3 of the 12 here.
Sources
Share of adults agreeing that products and services using AI have more benefits than drawbacks; the Index scores public trust as 1 + share ÷ 10. Survey of 23,532 adults across 32 countries, March to April 2026.
View source →Sarvam released 30B and 105B models trained from scratch in India in February 2026, and BharatGen released Param2, a 17B model covering all 22 scheduled languages; both are open-weight and the sovereign stack is deployed through government partnerships including Aadhaar. A trillion-parameter model is in development; none is yet frontier-class.
View source →The ₹10,372 crore IndiaAI Mission has deployed over 38,000 GPUs on its common compute portal (from an initial 10,000 target), aiming for ~100,000 by end-2026.
View source →GitHub's 2025 Octoverse reports that India has overtaken the United States as the largest open-source contributor base in the world.
View source →Population-scale digital public infrastructure provides the deployment substrate for AI-native state services, a key driver of India's Will score.
View source →Used for the workforce measurement. The IMF combines schooling, pay, labour mobility and active labour-market policies; the Index rescales it so the weakest of 173 economies scores 0 and the strongest 10.
View source →A cross-check for alignment, which stays assessed. The Research Almanac counts English-language research only, so it understates Japanese, Korean and Chinese work.
View source →Fossil, nuclear, hydro and other dispatchable plant; averaged with generation for firm power.
View source →Second source for talent, averaged with Stanford's count.
View source →Second source for public trust, averaged with Ipsos.
View source →Capital is shown beside the Score, not scored.
View source →Half of public trust: support for building more data centres, scored as 1 + share ÷ 10 and averaged with trust in AI.
View source →Bottlenecks
These hold India below its Potential. Its two lowest scores cost it the most: frontier model output at 4.5 (part of Weights) and hardware & silicon access at 4.9 (part of Watts).
Assessment
India commands massive software engineering talent and strong political alignment on sovereign infrastructure (National Supercomputing Mission). Its material power suffers from domestic semiconductor manufacturing delays and grid stability across high-performance compute clusters.
The art of the possible
India's scale is its own art of the possible: 38,000 GPUs today, 100,000 targeted, the world's fastest-growing developer base, and population-scale digital infrastructure nobody else can match. Domestic fabrication and grid reliability would convert the world's largest talent pipeline into a top-three power.
Strategic play — Diffusion Advantage
Make scale the bargaining chip: the largest pool of AI engineers outside the two machinepowers and a market neither can afford to lose, put to work through population-scale digital infrastructure while domestic fabrication and a reliable grid catch up with the 100,000 GPUs it is targeting.
Leverage — the nearest position. Scale is the leverage: the largest pool of AI engineers outside the two machinepowers, and a market neither can afford to lose. Real power, but soft — no government licenses talent.
Open India in the live Index → — every source behind every number, and the comparison tool.