Why India Struggles to Build Frontier AI Models and What It Means for the Future

Introduction

In July 2026, the United States restricted access to Anthropic’s most advanced AI models, Mythos and Fable, cutting off Indian researchers, businesses, and government agencies from frontier intelligence capabilities. The move was not a surprise to security analysts but sent shockwaves through India’s technology ecosystem. For a country that has built world-class IT services, sent missions to Mars, and created digital public infrastructure that powers a billion people, the realization was stark: India does not control its access to the most transformative technology of the century.

The question is no longer whether India can use AI. Indian companies and government agencies deploy artificial intelligence across banking, telecom, healthcare, and governance. The question is whether India can build the foundational models that define the frontier of the technology itself.

This article examines why India, despite its vast talent pool, growing economy, and technological ambitions, has not been able to develop frontier AI models comparable to those from the United States and China. It explores the structural barriers, strategic misalignments, and economic realities that shape India’s position in the global AI race, and what this means for the country’s technological sovereignty and economic future.

The Frontier AI Divide: What India Lacks

Frontier AI models represent the cutting edge of artificial intelligence capability. These are the large language models with hundreds of billions or trillions of parameters that can reason, code, and create at levels approaching human expertise. Today, only a handful of organizations can build them: OpenAI, Google, Anthropic, Meta in the United States, and a growing cohort of state-backed and private labs in China.

India’s absence from this list is not accidental. It is the result of five critical gaps.

Compute Infrastructure: The Hardware Bottleneck

Training a frontier model requires thousands of the most advanced AI accelerators, specialized chips like NVIDIA’s H100 or H200 GPUs, running for months in data centers designed for extreme scale. The United States has the infrastructure. China, despite sanctions, has built its own. India is still catching up.

The IndiaAI Mission, launched in 2024 with a budget of Rs 10,300 crore, has made progress. As of mid 2026, the program has deployed over 38,000 GPUs, nearly four times its original target, with plans to reach 100,000 publicly accessible GPUs by December 2026. Private players are building gigawatt-scale AI data centers, with Larsen and Toubro announcing 30 MW and 40 MW GPU clusters in Chennai and Mumbai, and Yotta Data Services building Shakti Cloud with over 20,000 NVIDIA Blackwell Ultra GPUs.

Yet these numbers pale in comparison to the scale of US operations. A single frontier model training run can require 100,000 GPUs or more. Meta’s most advanced models have used hundreds of thousands of accelerators. The US has multiple such clusters. India, even with its aggressive buildout, is playing catch-up on hardware that is itself controlled by foreign suppliers.

The dependency does not end with chips. The entire semiconductor supply chain, from advanced packaging to memory, is dominated by a handful of companies in the US, Taiwan, South Korea, and Japan. India’s semiconductor mission aims to change this, but building domestic fabrication capacity is a decade-long endeavor.

Research Depth: Beyond Engineering Talent

India produces over 1.5 million engineers annually. Its IITs and IISc are globally respected. But frontier AI requires something more than engineering talent: it requires sustained, high-risk research at scale.

The United States invested decades in AI research before the current breakthroughs. DARPA, NSF, and corporate labs funded the foundational work that led to transformers, deep learning, and large language models. China has poured billions into its AI labs, with state-directed coordination between universities, military research, and private companies.

India’s research investment, by contrast, remains below 1 percent of GDP. The country has brilliant researchers, but they often work in isolation or for foreign firms. The Indian IT services model, which created global giants like TCS, Infosys, and Wipro, was built on labor arbitrage and service delivery, not on deep research and development. Frontier AI labs require a different culture: one that embraces failure, invests in long-term exploration, and can afford to spend years on problems without immediate commercial returns.

As Sumanth Raghavendra, CEO of Presentations.AI, argues, India’s strength lies in systemic adaptation and applied innovation, not in building foundational models from scratch. The country excels at making complex systems work in diverse, constrained environments, witness Aadhaar, UPI, and the India Stack. But these are applications built on existing technological foundations, not the creation of those foundations themselves.

Capital and Concentration: The Economics of Frontier Models

Developing a frontier AI model is one of the most capital-intensive endeavors in technology history. OpenAI’s GPT-4 reportedly cost over 100 million dollars to train. Google’s Gemini models and Anthropic’s Claude series require similar investments. These are not one-time costs. Models need continuous updates, larger datasets, and more compute as capabilities advance.

India’s venture capital ecosystem, while growing, lacks the depth and risk appetite for such investments. The country’s largest technology companies, Infosys, TCS, Wipro, are services businesses, not product companies. Their margins and business models do not support billion-dollar research bets. Startups like Sarvam AI and BharatGen are making progress, but they operate at a fraction of the scale of their American counterparts.

The economic reality is that building a frontier model offers limited direct commercial returns. The real value in AI is increasingly in the applications, not the models themselves. As open-source models like Llama and Mistral improve, the competitive advantage of owning a proprietary frontier model diminishes. This creates a paradox: the models are expensive to build, but their ownership may not confer lasting economic benefits.

Data and Language: The Training Advantage

Frontier models are trained on vast amounts of text, code, and multimedia data. The United States benefits from English language dominance, the world’s largest technology companies, and a culture of open data sharing. China leverages its massive domestic market, state-controlled data resources, and a unified language script.

India’s linguistic diversity is both a strength and a challenge. The country has 22 officially recognized languages and hundreds of dialects. Building models that understand and generate across this linguistic spectrum requires specialized datasets and research. While this creates opportunities for India-specific applications, it also means that Indian models start with a fragmented base compared to the unified English or Chinese language training sets.

Moreover, much of the high-quality training data for AI, scientific papers, code repositories, books, is in English. Indian researchers can access this, but they compete with US and Chinese labs that have better infrastructure and resources to process and utilize this data at scale.

Strategic Coordination: The Ecosystem Gap

The United States and China have coordinated AI strategies. In the US, government, academia, and industry collaborate through initiatives like the AI Safety Institute, DARPA programs, and national AI research centers. China’s approach is more centralized, with the state directing resources and priorities across its technology sector.

India’s AI ecosystem is more fragmented. The IndiaAI Mission is a step in the right direction, but it lacks the depth of coordination seen in the US or the directive power of China’s state-led approach. Indian companies, research institutions, and government agencies often work in silos. The country has not yet developed the institutional frameworks that can sustain a frontier AI effort over the decade-long timeframe required.

The Sovereignty Dilemma: Why Access Matters

The US restrictions on Anthropic’s Mythos model revealed a fundamental vulnerability. India is the second-largest consumer base for Anthropic’s services. Yet when the US government decided to restrict access, Indian customers were cut off regardless of their market importance.

This is not the first time India has faced technology denial. In the 1970s and 1980s, Western countries restricted access to nuclear technology, semiconductor equipment, and advanced computing. India responded by building its own capabilities in these areas. The nuclear program, in particular, became a symbol of technological self-sufficiency.

But AI is different from nuclear technology. Nuclear capabilities are anchored in physical infrastructure that, once built, can operate independently. AI models are software artifacts that depend on continuous access to compute, data, and updates. Even if India builds its own models today, it may still depend on foreign chips, foreign cloud infrastructure, and foreign research tools.

As Amit Kapoor and Sheen Zutshi from the Institute for Competitiveness argue, India needs an AI continuity doctrine. This is not just about building one or two symbolic frontier models. It is about creating the conditions for sustained AI capability, with multiple competing labs, research institutions, and infrastructure providers that can ensure India’s access to frontier intelligence regardless of external restrictions.

The Debate: Should India Even Build Frontier Models?

Not everyone agrees that India needs its own frontier models. A vocal school of thought, represented by technologists like Sumanth Raghavendra, argues that pursuing sovereign LLMs would be a misallocation of national resources.

The Case Against Frontier Models

The arguments are compelling. First, the cost is prohibitive. India would need to invest billions of dollars annually to stay competitive, funds that could be better spent on education, healthcare, and infrastructure.

Second, the strategic value is questionable. As open source models improve, the advantage of owning a proprietary frontier model diminishes. Companies and countries can fine-tune open-source models for their specific needs without the expense of training from scratch.

Third, India’s historical success has come from leveraging global technologies, not creating them. The IT services industry thrived by using foreign software, hardware, and standards. India built digital public infrastructure like Aadhaar and UPI on top of existing technological foundations, not by reinventing the wheel.

Fourth, the real opportunity may lie elsewhere. As foundation models become commodities, the value shifts to the application layer. India’s unique strengths, its understanding of diverse markets, its ability to build resilient systems, its experience with scale, could position it as a leader in applied AI, not foundational AI.

The Case for Frontier Models

Yet the counterarguments are equally compelling. Strategic autonomy matters. Dependence on foreign models creates vulnerability in areas like national security, cyber defense, and critical infrastructure. If the US can cut off access to Mythos, it can cut off access to other models in the future.

Moreover, frontier models are not just about current capabilities. They are about shaping the future of the technology. Countries that build these models influence the direction of AI research, the standards that govern the technology, and the applications that emerge from it. Without its own models, India risks being a rule taker rather than a rule maker in the AI era.

There is also the question of talent retention. India’s best AI researchers currently work for American and Chinese companies. If India does not create its own frontier labs, it will continue to lose its top talent to foreign firms. Building domestic capability is not just about the models themselves, but about creating an ecosystem that can attract and retain the world’s best researchers.

Finally, there is the question of economic value. While the direct returns from frontier models may be limited, the indirect benefits, prestige, influence, technological spillovers, can be significant. The US semiconductor industry, for example, has long been subsidized not just for its direct economic returns, but for its strategic importance.

The Middle Path: What India Is Actually Doing

India is not standing still. The country is pursuing a pragmatic, multi-pronged approach that balances ambition with reality.

Building Compute Infrastructure

The IndiaAI Mission’s Compute Capacity pillar is deploying GPUs at an unprecedented scale. From an initial target of 10,000 GPUs, the program has already deployed over 38,000, with plans to reach 100,000 by the end of 2026. These are accessible to startups, academic institutions, and government agencies at subsidized rates, making AI development more affordable for Indian organizations.

Private sector investment is even more aggressive. Yotta Data Services is building Shakti Cloud with over 20,000 NVIDIA Blackwell Ultra GPUs. Larsen and Toubro is planning gigawatt-scale AI data centers. Microsoft, Google, and other global players are investing billions in Indian AI infrastructure.

Developing Sovereign Models

Indian startups and research institutions are building their own models. Sarvam AI has made progress on sovereign AI and compute models. BharatGen, an initiative from IIT Bombay, is participating in Project Tapestry, a global frontier AI collaboration. The IndiaAI Mission is encouraging the development of indigenous foundation models for India-specific languages and use cases.

These efforts are still in their early stages. Sarvam’s models, for example, are not yet at the frontier level. But they represent important steps toward domestic capability.

Focusing on Applied AI

India is leveraging AI across its economy. The government is using AI for digital governance, from tax administration to social welfare delivery. Private companies are deploying AI in banking, telecom, healthcare, and agriculture. The India Stack, Aadhaar, UPI, DigiLocker, is being extended with AI capabilities.

This focus on applied AI plays to India’s strengths. The country has a long history of using technology to solve complex, large-scale problems. AI is being integrated into this tradition, with applications tailored to India’s unique linguistic, cultural, and infrastructural realities.

Seeking International Partnerships

India is not pursuing AI sovereignty in isolation. The country is seeking trusted partnerships with like-minded nations. Rudra Chaudhuri of the Observer Research Foundation has proposed an India-US Trusted AI Corridor, which would establish protocols for sharing access to frontier models while addressing security concerns.

Such partnerships could provide India with access to advanced models without the full cost of developing them domestically. They could also facilitate technology transfer, joint research, and coordinated standards development.

Investing in Talent and Research

India is strengthening its AI research ecosystem. The IndiaAI Mission includes provisions for skill development, research funding, and the creation of centers of excellence. Indian institutions are increasingly contributing to global AI research, as evidenced by BharatGen’s participation in Project Tapestry.

The country is also working to retain its AI talent. Programs like the IndiaAI Fellowship aim to support researchers working on domestic AI challenges. The goal is to create an ecosystem that can sustain long-term AI research, not just immediate commercial applications.

The Geopolitical Context: AI in a Fragmenting World

The global technology landscape is changing. The era of unrestricted globalization is ending. Export controls are expanding. Semiconductor access is becoming a geopolitical instrument. Software platforms increasingly reflect national interests.

In this environment, AI is emerging as a new frontier of strategic competition. The United States and China are investing heavily in AI not just for economic reasons, but for military and geopolitical advantage. AI capabilities can enhance cyber warfare, autonomous systems, and decision-making in ways that could shift the global balance of power.

India’s position is unique. The country is a major market for American technology firms, a growing center for AI development, and a strategic partner for the United States in its competition with China. Yet India also maintains its own strategic autonomy, pursuing partnerships with multiple nations and developing its own capabilities.

The US restrictions on Anthropic’s models highlight the risks of this position. India is a valuable partner, but it is not a treaty ally. In a crisis, the United States may prioritize its own interests over those of its partners. This creates a dilemma for India: how to benefit from global AI development while ensuring its own technological sovereignty.

The Economic Survey Perspective: A Cautionary Note

The Economic Survey 2025-26 has weighed in on this debate. According to reports, the survey argues that India should not chase frontier AI models. Instead, it should focus on diffusing AI across its economy, building on existing models, and developing applications tailored to its specific needs.

This perspective reflects a pragmatic assessment of India’s capabilities and priorities. Frontier models are expensive, resource-intensive, and may not offer the best return on investment for a developing economy. The survey’s recommendation aligns with the view that India’s comparative advantage lies in applied AI, not foundational AI.

Yet this perspective is not without its critics. Some argue that it underestimates the strategic importance of frontier models and the risks of technological dependence. Others point out that the survey’s focus on diffusion may not account for the long-term benefits of building domestic capability.

The Way Forward: A Balanced Strategy

India’s path in AI will likely be a balanced one, combining elements of sovereignty, partnership, and pragmatism.

Continue Building Compute Infrastructure

India’s aggressive investment in AI infrastructure is essential. The country needs to continue expanding its GPU capacity, data centers, and cloud services. This will not only support domestic AI development but also make India an attractive destination for global AI investment.

Develop Multiple Competing Labs

India should not aim for one or two symbolic frontier models. It should create the conditions for many competing labs, each pushing the boundaries of AI capability. This requires sustained investment in research, talent development, and infrastructure.

As Amit Kapoor and Sheen Zutshi argue, India needs conditions for ten Sarvams, not just one. This means building institutions before the ecosystem is ready, recruiting global talent, and providing the support necessary for long-term research.

Focus on Applied AI and Domain-Specific Models

India’s strength lies in applied AI. The country should continue to focus on developing AI applications for its unique challenges, from healthcare to agriculture to governance. This includes building domain-specific models that can outperform general frontier models in specialized tasks.

Pursue Trusted International Partnerships

India should seek partnerships with like-minded nations to access frontier models, share research, and coordinate standards. The proposed India-US Trusted AI Corridor is a step in the right direction. Such partnerships can provide India with access to advanced capabilities while it builds its own domestic strengths.

Invest in Talent and Education

India’s greatest asset is its people. The country should invest in AI education, research, and talent retention. This includes supporting universities, research institutions, and private sector labs that can attract and develop the world’s best AI researchers.

Address the Semiconductor Gap

Ultimately, AI sovereignty depends on semiconductor sovereignty. India’s semiconductor mission aims to build domestic fabrication capacity, but this is a long-term endeavor. In the meantime, India should work with trusted partners to secure its semiconductor supply chain.

Recommended Readings

For those interested in exploring the topic further, here are some insightful books:

• AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee
  A comprehensive look at the global AI race, with particular focus on the United States and China. Lee, a former president of Google China, provides unique insights into the strategic and economic dimensions of AI development.
• The Age of AI: And Our Human Future by Henry A. Kissinger, Eric Schmidt, and Daniel Huttenlocher
  This book explores the geopolitical, economic, and ethical implications of artificial intelligence. It offers a broad perspective on how AI is reshaping the world and the challenges it poses to nations and societies.
• Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark
  Tegmark, an MIT physicist, examines the future of AI and its potential impact on humanity. The book provides a thoughtful exploration of the opportunities and risks associated with advanced AI, including the question of technological sovereignty.
• The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity by Byron Reese
  This book traces the history of technological revolutions and examines what the AI revolution might mean for the future. It offers insights into how nations can position themselves in the age of intelligent machines.
• AI 2041: Ten Visions for Our Future by Kai Fu Lee and Chen Qiufan
  A collection of science fiction stories that imagine the world in 2041, shaped by artificial intelligence. The book provides a creative exploration of how AI might transform societies, economies, and geopolitics.
• The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil
  Kurzweil’s classic work on the future of technology and intelligence. While speculative, the book offers a vision of how AI might evolve and the implications for nations that lead or lag in its development.
• The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution by Walter Isaacson
  This book provides a historical perspective on technological innovation, with lessons for how nations can foster breakthroughs in areas like artificial intelligence.
• The Code: Silicon Valley and the Remaking of America by Margaret O’Mara
  A history of Silicon Valley and its role in shaping the global technology landscape. The book offers insights into how innovation ecosystems develop and what other nations can learn from the US experience.

Frequently Asked Questions (FAQs)

Conclusion

India’s struggle to build frontier AI models is not a reflection of its technological capability or ambition. It is a reflection of the structural realities of the global AI race. The United States and China have built their AI capabilities over decades, with coordinated strategies, massive investments, and deep research ecosystems. India is still building these foundations.

The US restrictions on Anthropic’s Mythos model were a wake-up call. They revealed that India’s access to frontier AI cannot be assumed. Yet the solution is not simply to build India’s own frontier models. The country must pursue a balanced strategy that combines domestic capability building with international partnerships, applied innovation with foundational research, and economic pragmatism with strategic autonomy.

India’s AI future will not be determined by whether it can build a model as powerful as GPT 5 or Claude 4. It will be determined by whether India can create an AI ecosystem that serves its people, powers its economy, and secures its sovereignty in an era of technological competition. The path is challenging, but the stakes could not be higher.

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