India has emerged as one of the top countries in artificial intelligence (AI) readiness, trailing only the U.S. and China in global rankings, according to a recent J.P. Morgan report titled Semiquincententacles: The U.S. Grip on Global Markets at 250. The report, which highlighted India's unique position in global capital markets and the evolving AI and semiconductor landscape, noted that Stanford University's Global AI Vibrancy Index places the United States first, China second, and India third. However, four separate global indices tell a different story when examining India's AI capabilities across practical implementation, government deployment, and infrastructure development. On the Tortoise Global AI Index from September 2024, which measures practical implementation, innovation and investment, India falls to tenth position. The Oxford Government AI Readiness Index from 2025 ranks India fourteenth in terms of government preparedness to deploy AI in public services. Most significantly, on the IMF's AI Preparedness Index from 2023, which covers digital infrastructure, human capital, innovation and regulation, India places last among the fifteen countries listed, behind China, the UAE, Spain and France. As per the report, the IMF measure, now three years old, predates India's more recent push on digital public infrastructure and AI policy frameworks, and may understate where the country stands today.
The report emphasized India's position among the world's least concentrated equity markets when compared to major global markets. According to the analysis, while the share of the ten largest companies in the S&P 500 has risen sharply from 17% in 2015 to approximately 40% currently, India remains among the three lowest equity concentration figures globally. Only Japan and India have less concentration than the U.S. market structure, highlighting India's more distributed ownership patterns in its capital markets. As the report noted, this contrasts sharply with the U.S. market where the concentration has increased dramatically over the past decade. Recent Bloomberg-compiled data reveals that in both China and India, the ten largest companies now account for about 19% of total market capitalization, down respectively from 26% and 22% a year ago. This trend underscores the absence of dominant AI players in these Asian markets, which is hindering their performance and prompting investors to seek clearer AI associations.
The report underscored the growing dominance of the United States in artificial intelligence and advanced computing, particularly in semiconductors and AI infrastructure. J.P. Morgan noted that whether measuring labor productivity or total factor productivity, the U.S. leads the G10 countries, with productivity growth in information and data-processing sectors accelerating significantly following the launch of generative AI tools. The report emphasized that the U.S. maintains a commanding position in AI-related productivity gains and technological innovation, with the United States being described as the most vibrant and prepared country for AI, with China close behind on some measures. On semiconductors and AI hardware, the report highlighted that Nvidia's share of AI accelerator revenue has declined from 85% in 2023 to an estimated 75% in 2026, according to Silicon Analysts data. That is still an extraordinary degree of market dominance, but custom chips from Google, Amazon, Meta and Microsoft are eating into that share, with hyperscalers reporting total cost of ownership reductions of 30 to 40% compared to merchant Nvidia GPU fleets. J.P. Morgan cites Anthropic's decision to run Claude on Amazon's Trainium chips for the next decade as the strongest third-party endorsement of custom chip economics to date.
The report highlighted rapid advancement of Chinese AI models and growing competition from China within the AI ecosystem. According to the analysis, Chinese models appear as triangles while U.S. models as circles in comparative assessments of AI model performance and operating costs. The report noted that the efficient frontier in intelligence-per-dollar is dominated by China (DeepSeek, MiniMax, Xiaomi, Alibaba), with only a limited presence from U.S. models. By April 2026, leading Chinese open-weight models scored within a few dozen Elo points of closed frontier models and cost 10x-50x less per token. The report cited growing adoption of Chinese AI models by businesses seeking lower costs, with OpenRouter showing a surge in API calls to Chinese models. China's GPU self-sufficiency has risen from 10% in 2021 to 40% today, with projections reaching approximately 80% by 2030, according to Morgan Stanley Asia Technology data cited in the report. If Huawei's claims hold up about scaling compute density through vertical chip logic design, China's AI hardware trajectory looks considerably less constrained than western export controls were intended to ensure.
For India, the report's findings suggest a strengthening position in AI readiness and global market diversification but highlight critical gaps in practical implementation. The report concludes that India has the raw material for AI: the people, the interest, the policy intent, and enough of a research base to register strongly on the Stanford index. However, what it does not yet have in sufficient measure is the implementation layer: cloud infrastructure, business adoption, government deployment capability and the digital foundations that convert AI potential into economic output. The most actionable insight for Indian enterprises concerns the shift to open models, where a smaller open model trained on a company's own proprietary data may outperform a frontier model for specific tasks. Ramp, a financial software company, demonstrated that a Chinese model with 35 billion parameters outperformed Anthropic's Opus 4.6 on its specific financial data tasks at a fraction of the cost. This suggests that a well-resourced Indian enterprise sitting on years of proprietary operational data may not need to wait for frontier model costs to fall - it may already have what it needs to build something competitive on open-weight infrastructure. Despite China's progress in cost-efficient AI models, J.P. Morgan maintained that the United States remains the global leader in AI innovation, infrastructure, and investment, though policy restrictions and supply-chain vulnerabilities could affect its future lead, creating potential opportunities for countries like India to advance in the global AI landscape.
