Bernstein: India Must Break Away from AI Dependence, Build Sovereign Large Models
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Bernstein: India Must Break Away from AI Dependence, Build Sovereign Large Models
With escalating US restrictions on access to frontier AI models, India's strategic risk from reliance on foreign foundational models has come to light. The report recommends India leverage its data advantages in vertical sectors to develop localized specialized large models to ensure technological sovereignty and value capture.
- US recently restricted non-citizen access to latest AI models, warning of India's technology dependence risks.
- India lacks consumer-level platforms similar to China, resulting in the absence of high-quality data ecosystems required to train foundational models.
- India's AI strategy funding allocation is dispersed and volatile; FY26 budget revisions show insufficient investment.
- It is suggested that India focus on data in vertical sectors such as industry and healthcare to build specialized small models with strong defense capabilities.
- AI is transitioning from a commodity to a strategic resource, akin to 'fighter jets', with access coming under strict control.
Report interpretation
Overview
This report deeply explores India's strategic dilemmas in the field of Artificial Intelligence (AI). The report points out that India has long relied on foreign technology stacks, particularly lacking independent capabilities at the foundational Large Language Model (LLM) level. As the US strengthens export controls on frontier AI models (such as restricting Anthropic model access), India faces the risk of being locked in the application layer without capturing core value. The report argues that India cannot rely solely on renting computing power and application-layer innovation; it must establish its own 'DeepSeek moment', meaning developing local sovereign AI capabilities by controlling vertical domain data to safeguard national security and economic interests.
Core views
Technology Dependence and Geopolitical Risks: India's tech ecosystem has long been positioned downstream in the global value chain, heavily dependent on US giants for everything from hardware (CPU/GPU) and operating systems to enterprise software. The report compares foundational AI models to new-era 'fighter jets', noting they have transitioned from ordinary SaaS products to controlled strategic resources. Recent US prohibition of non-citizen access to Anthropic's latest models confirms the trend of restricted technology access. If India continues to outsource core models, it may face risks of application lag or even cut-off access in the future. Structural Reasons for India's Lack of Foundational Models: Despite possessing massive data volumes, India lacks consumer-level platforms (such as search, social media, messaging) like China capable of generating rich, structured datasets. India's IT services model focuses on providing low-cost maintenance and customization for global giants rather than building underlying platforms. This results in a lack of talent pipelines and academic depth required to train advanced AI. Many institutions previously believed India only needed to focus on the application layer, but this was more path dependence than strategic choice. Policy Execution and Funding Challenges: India's current AI strategy suffers from being 'dispersed and weak'. Although ambitious AI missions covering computing power, data, and R&D were proposed, resource allocation is too broad to achieve breakthroughs in any single area. Data shows AI appropriations significantly decreased in FY26 revised budget, and overall public investment scale is far smaller than China's annual hundreds of billions USD investment. Such 'scattering pepper' style investment strategy may lead to inefficiency, with some funds even flowing towards purchasing external technologies rather than enhancing local capabilities. Path Forward: Vertical Data and Specialized Models: The report believes India does not need to replicate China or the US trillion-parameter general large model path. India's true opportunity lies in leveraging its rich proprietary data in industries such as industrial and medical fields. By restricting unlimited access of global platforms to these high-value vertical data, Indian local companies can build defensive specialized small models. These models may not be general-purpose, but hold extremely high value in specific scenarios (such as industrial robot training, medical diagnosis) and can gradually expand to global markets.
Analysis framework
The report adopts a framework combining geopolitics and technology industry analysis. First, by reviewing history (such as dependence during the internet era) and comparing US-China models, it defines India's current structural disadvantages. Second, using a supply chain security perspective, it analyzes the impact of US export controls (such as entity lists, FDPR rules) on AI technology flows, arguing for the urgency of 'technological sovereignty'. Finally, based on comparative advantage theory, it proposes that India should avoid head-on competition with general large models and instead utilize its unique vertical data assets to follow a differentiated, pragmatic AI development path.
Methodology notes
Analyze India's position and value capture capability within the full AI industry chain (hardware, foundational models, application layer)
The report points out that India has long been in the downstream application and service layers, with missing upstream core technologies and data ecosystems, leading to a risk of value loss in the AI era. This method helps understand why relying solely on application-layer innovation is insufficient to support long-term competitiveness.
The gap between market expectations on the effectiveness of India's AI strategy and actual execution capability
The report points out that the market previously believed 'LLMs are unimportant', but this expectation was broken with the introduction of US restriction measures. The report revises the market's optimistic expectations on India's rapid rise in AI by revealing funding dispersion and dependency inertia in policy execution.
Build defensive competitive advantage using proprietary vertical domain data
The report suggests that Indian enterprises should not directly confront US and Chinese giants with general large models, but should use exclusive data in fields such as industry and medicine to build a 'data moat', develop specialized models, and thus gain an irreplaceable position in niche markets.
Key data
- FY25 AI Budget Revision Value1.73 Billion INRSignificantly down revised compared to the budget value of 5.52 Billion INR, indicating execution volatility
- FY26 AI Budget Value8.0 Billion INRBudget allocation, but actual expenditure may be constrained by execution capability
- China AI Public Investment ScaleHundreds of Billions USD per YearAs a comparison, highlighting the relative insufficiency of India's investment scale
Impact & implications
For the Indian tech industry, this means increased pressure to transition from pure IT services outsourcing to owning intellectual property rights-based technical platforms. Local tech companies that can control vertical data and develop specialized models will achieve higher valuation premiums and global competitiveness. For policymakers, a balance needs to be found between open cooperation and technological sovereignty, possibly requiring data localization requirements or targeted subsidies to support local AI stacks. In the long run, this will determine whether India can transform from a 'digital colony' into a value creator in the global AI economy.
Risks
- US further tightens technology export controls to India, including GPUs and foundational model access.
- Indian government policy execution is ineffective, continued dispersed fund allocation leads to lagging local AI capability construction.
- Global AI technology iteration speed is too fast, Indian local models quickly fall behind international mainstream levels in performance.
- Lack of sufficient local high-end AI talent leads to slow R&D progress.
What to watch
- Specific policy implementation status of Indian government on data localization and model localization for key industries (finance, healthcare, defense).
- R&D progress and commercialization cases of Indian local tech companies on vertical domain large models.
- Follow-up actions by US on AI technology export controls, especially restrictive measures targeting non-Chinese countries.
- Actual execution rate and capital usage efficiency of India AI Mission budget.