Where Does India Stand in the Global AI Race? An 8-Layer Framework for Understanding India's AI Strategy

A NOTE ON THIS FRAMEWORK

This AI Note analyses India's AI ecosystem through an eight-layer framework that builds on infrastructure-oriented perspectives, including Jensen Huang's AI stack, while extending the discussion to data, talent, and governance.

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India at a Glance — Key Facts (2024–2026)

Table 1: India's AI Ecosystem — Key Metrics at a Glance (2024–2026)

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Part I — An 8-Layer Framework for National AI Strategy

Why a layered framework? Understanding a nation's AI position requires looking beyond individual breakthroughs or headline investments. It requires examining the full stack — from physical infrastructure at the base to governance at the top — and asking where a nation is strong, where it is building, and where it faces structural risk. This AI Note uses an 8-layer framework for that purpose. It builds on infrastructure-oriented perspectives such as Jensen Huang's AI stack while extending the analysis to data, models, applications, talent, and governance — layers that are particularly relevant when examining emerging AI economies.

On the placement of Data at Layer 5: Although data could be considered at Layer 4 as a prerequisite to models — which holds true in classical machine learning — it is placed here at Layer 5, reflecting its role in the GenAI era as a domain knowledge and application customisation layer. It is worth noting that for domain-specific Small Language Models (SLMs), data remains the foundational training input and the classical sequence applies. Both uses are addressed in the framework.

The 8 Layers — Defined

Table 2: The 8-Layer AI Stack — Definitions and Strategic Significance

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Part II — How Major AI Powers Compare Across the Stack

Examining four major AI economies — the United States, China, the European Union, and India — across each layer reveals distinct strategic profiles. No single nation leads across all eight layers. Each has made different bets, reflecting its resources, political economy, and development priorities.

Global Scorecard

Table 3: Global AI Stack Scorecard (2026). *EU's ASML is the sole supplier of EUV lithography equipment globally — a critical node in the chip supply chain despite limited domestic fab capacity.

Reading this table: India's strongest positions are at L5, L6, and L7 — the layers most directly connected to value creation in the GenAI era. Its current gaps are concentrated at L1, L2, and L3 — layers that require significant capital infrastructure. The US and China lead at capital-intensive layers; India's comparative strength lies in the upper layers. The strategic question is how India leverages upper-layer advantages to progressively build lower-layer capability.

United States — Market-Led, Full-Stack Orientation

Strategic philosophy: Maintain and extend leadership across the entire stack through market forces and active export of the American AI ecosystem as a geopolitical instrument.

NVIDIA dominates AI silicon globally. AWS, Azure, and Google Cloud deliver the majority of AI compute worldwide. The July 2025 White House AI Action Plan ('Winning the Race: America's AI Action Plan', White House, July 2025) codified three pillars: accelerating AI innovation, building AI infrastructure, and leading in international AI diplomacy. A July 2025 Executive Order mandated an American AI Exports Program — promoting export of 'full-stack American AI technology packages' including hardware, cloud, data pipelines, and models as a diplomatic tool.

NOTE FOR INDIA

The US AI export agenda creates both a dependency risk and a partnership opportunity. Pursuing compute and model access partnerships with US providers while building sovereign capability at L4 and L5 is a balance India must actively manage.

China — Sovereign Stack Under Pressure, Efficiency-Led Innovation

Strategic philosophy: Full-stack technological independence, driven by state investment — with algorithmic efficiency emerging as a competitive advantage born of constraint.

US export controls from 2022 onward blocked China's access to NVIDIA's most advanced chips. The response has been an accelerated domestic silicon effort. The most significant recent development is DeepSeek V4 — trained entirely on Huawei's Ascend 950PR processors (Fortune, April 2026) — a production-grade frontier model built on Chinese silicon with no American software dependencies. China's research output is also formidable: its AI research in 2024 matched the combined publications of the US, UK, and EU, with 156 institutions each producing over 50 papers. (Oxford Insights, 2025)

NOTE FOR INDIA

DeepSeek's approach — achieving competitive performance at a fraction of Western compute cost — emerged partly because constraints forced algorithmic innovation. India's resource environment has similar characteristics. Investing in model efficiency, not just raw compute scale, is a viable and practical research direction.

European Union — Governance Leadership, Infrastructure Gaps

Strategic philosophy: Trustworthy, human-centric AI governed by rights-based regulation. The EU leads at L8 and has strong L6 application sectors, while L2 and L5 remain areas of relative weakness.

The EU AI Act — in force August 2024, with prohibited practices enforceable from February 2025 and GPAI model rules from August 2025 (EU AI Act, 2024) — is the world's first comprehensive AI legal framework. It establishes a four-tier risk classification with fines up to €35 million or 7% of global annual turnover. Its influence extends well beyond Europe: companies worldwide must comply to access European markets, making EU governance a de facto global standard in several respects.

NOTE FOR INDIA

India and the EU share a governance orientation towards democratic values and inclusive AI. There is scope for bilateral collaboration on governance frameworks, data standards, and responsible AI principles — particularly as both seek to reduce dependency on US or Chinese infrastructure stacks.

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Part III — India's Position Across the 8 Layers

India's AI position is best understood not by focusing on any single layer but by examining the pattern across all eight. India's infrastructure layers (L1–L3) are in active development. Its model layer (L4) is emerging. Its data layer (L5), application layer (L6), and talent layer (L7) are among the stronger components of its current AI stack. Governance (L8) is at an early but thoughtful stage. This section examines each layer with the evidence available.

L1 — Energy

AI infrastructure is energy-intensive. Training large models can consume electricity equivalent to hundreds of households annually. As India's AI infrastructure scales, energy cost, reliability, and sustainability become meaningful constraints.

India's energy transition is among the most ambitious globally — targeting 500 GW of renewable capacity by 2030 and already among the world's three largest electricity producers. Solar costs in India have fallen to among the lowest globally. Indian states are increasingly competing to attract AI data centre investment, offering power connectivity alongside policy incentives.

OPPORTUNITY

India's solar resource and declining renewable costs position it to build AI compute infrastructure powered by clean energy at competitive price points. As sustainability becomes a factor in where AI investment flows globally, this is worth developing as part of India's broader AI positioning.

L2 — Silicon: The Most Significant Gap

India does not currently manufacture advanced semiconductor chips. Every GPU powering India's AI training runs — H100s, H200s, and A100s in the IndiaAI compute pool — is imported. This represents a structural dependency that, while manageable today, warrants strategic attention.

The government's Production Linked Incentive (PLI) scheme for semiconductors has attracted Micron Technology and Tata Electronics (in partnership with PSMC of Taiwan) — meaningful first steps oriented toward assembly and legacy nodes. A more immediately tractable opportunity lies in chip design. Fabless semiconductor design does not require fabrication facilities — it requires engineering talent, which India has in significant depth. India's existing VLSI design presence through global R&D centres of companies like Qualcomm, ARM, and Intel provides a foundation that could, with focused policy support, develop into domestic AI accelerator design capability.

Table 4: India's Silicon Layer — Current Status and Near-Term Opportunities (2026)

L3 — Compute: Democratised Access

The IndiaAI Mission's compute pillar has moved quickly. As of February 2026, more than 38,000 GPUs have been onboarded for the common compute facility, provided to Indian startups and academia at up to 40% below market rates. (PIB, February 2026) The resulting cost — under ₹100 per hour versus approximately ₹200+ globally — meaningfully lowers barriers to AI development.

The access model is public-private partnership: government provides policy mandate and funding; empanelled cloud providers deliver infrastructure. Critically, the marketplace is open to startups in smaller cities and non-IIT institutions at the same rates as major research centres — a design choice with significant implications for where India's next wave of AI builders will come from.

OPPORTUNITY

As the compute pool expands toward 100,000+ GPUs, the policy question shifts from access to utilisation — ensuring that researchers and startups in Tier 2 and Tier 3 cities have the mentorship and curriculum support to make effective use of available compute, not just access to it.

L4 — Models: Early Progress, Clear Direction

Two years ago, India had no indigenous foundation models. The position has changed, with government investment, startup activity, and academic research now aligned toward building sovereign model capability.

Why sovereign models matter beyond technology: A foundation model trained primarily on English-language Western data will, by default, reflect the assumptions, idioms, and priorities embedded in that data. For a country of India's linguistic and cultural diversity, building models trained on Indian languages, Indian contexts, and Indian domain knowledge is both a practical necessity and a longer-term strategic choice.

Sarvam AI and the Indigenous Model Ecosystem

Sarvam AI — founded in 2023 by Dr. Vivek Raghavan and Dr. Pratyush Kumar from AI4Bharat at IIT Madras — has become the lead vehicle for India's sovereign LLM effort. The trajectory: Sarvam-1 (2B parameters, October 2024) → selected by MeitY as India's Sovereign LLM builder (April 2025) → Sarvam 30B and 105B models (early 2026), with a 120B parameter open-source model in progress. The 105B model is being trained on 4,086 H100 GPUs. (IndianAI.in, April 2025)

Table 5: India's Indigenous AI Model Ecosystem (2026). Sources: IndiaAI Mission, company announcements, NervNow, Rest of World.

The SLM Pathway

While general-purpose frontier model development requires compute budgets that few nations can match, Small Language Models (SLMs) trained on high-quality domain data offer a more tractable pathway for India — and one that leverages its data advantages directly. An SLM trained on Indian agricultural records, Indian clinical data, or Indian legal judgements can substantially outperform a general-purpose model on that specific domain, at a fraction of the compute cost. This is where India's L5 data position directly enables L4 model development — and it is a direction with both near-term feasibility and long-term strategic value.

L5 — Data: A Significant and Distinctive Asset

India's data position is among the strongest components of its current AI stack, for reasons that are structural rather than incidental. Two characteristics make it distinctive: the Digital Public Infrastructure data ecosystem and India's linguistic diversity.

Digital Public Infrastructure as a Data Asset

India has built a Digital Public Infrastructure stack with no close parallel: Aadhaar (1.3 billion biometric identities), UPI (over 170 billion transactions in FY2024–25), DigiLocker, ONDC, and numerous government platforms. These systems generate structured, anonymised data at population scale. The IndiaAI Datasets Platform (AIKosha) is designed to make this accessible to startups and researchers through a unified, non-personal dataset repository. (IndiaAI Mission, 2024)

The financial behaviour data generated by UPI — capturing patterns from hundreds of millions of people, including first-time formal financial participants — is particularly distinctive. Models trained on this data can address financial inclusion, credit assessment, and economic behaviour challenges that no model trained on Western financial data can adequately handle.

Linguistic Diversity as a Data Opportunity

India's 22 official languages and hundreds of dialects represent a data challenge and a data opportunity simultaneously. Approximately 800 million Indians do not use English as their primary medium. AI4Bharat (IIT Madras) has built foundational Indic language infrastructure — IndicBERT, Airavata, IndicLLM datasets, and linguistic benchmarks. BHASHINI provides real-time AI-powered translation. BharatGen is building multimodal Indic language capability. Together, these initiatives are building a data and model infrastructure for Indian languages that will take years to replicate elsewhere.

OPPORTUNITY

The AIKosha platform, if well-resourced and curated, could become a valuable repository of non-English AI training data — useful not only for Indian researchers but for those building AI for Southeast Asian, South Asian, and African language contexts. Positioning this as a resource for the broader research community, while maintaining India's data sovereignty, is an opportunity worth considering at a policy level.

L6 — Applications: A Strong and Distinctive Ecosystem

India's AI application ecosystem is among the stronger components of its current stack, particularly in sectors where Indian conditions create problem framings that differ from those in high-income economies. The combination of English-language technical capability, large-scale domain expertise, and the urgency of solving problems affecting hundreds of millions of people has produced a distinctive application landscape.

Table 6: India's AI Application Layer — Sector Strengths and Opportunities (2026)

OPPORTUNITY

Indian AI applications are built for conditions — low bandwidth, linguistic diversity, resource constraints, large informal economy — that characterise much of the developing world. Agricultural AI built for Indian conditions, financial inclusion tools built on UPI-scale transaction data, and health tools designed for primary care settings all have potential relevance across Southeast Asia, East Africa, and Latin America. This is a market that neither the US nor China is currently optimising for.

L7 — AI Talent: A Long-Horizon Strength

India's talent position in AI is well-supported by data. The Stanford AI Index 2024 places India first globally in AI skill penetration (score: 2.8 vs USA 2.2 and Germany 1.9). AI talent concentration has grown by 263% since 2016. India's annual AI hiring rate of approximately 33% is the highest globally. (Stanford AI Index 2024; Union Minister, Rajya Sabha, December 2025)

India was also the second-largest contributor to AI projects on GitHub in 2024, accounting for 19.9% of all global AI contributions.

The gender dimension is also notable: India leads globally in women's AI skill penetration (score: 1.7 vs USA 1.2 and Israel 0.9). (Stanford AI Index 2024) In a field where gender diversity remains a persistent challenge across most major AI economies, India's position is an underappreciated strength.

Table 7: India's AI Talent Position — Key Metrics (2024–2026)

The talent pipeline is being actively expanded through the IndiaAI FutureSkills programme — supporting 13,500 scholars across undergraduate, postgraduate, and doctoral levels, across disciplines including engineering, medicine, law, and commerce. (IndiaAI Mission, 2025) The multi-disciplinary scope reflects a recognition that addressing India's most significant AI challenges requires domain expertise alongside technical skill.

OPPORTUNITY

The 31 Data and AI Labs now operational in Tier 2 and Tier 3 cities through NIELIT — with 174 ITIs and polytechnics identified for further expansion — are among the more consequential investments in India's AI future. The researchers and builders who will solve distinctively Indian problems are more likely to come from smaller cities than from the major tech hubs. Getting infrastructure, curriculum, and mentorship to those locations is a scaling priority.

L8 — Governance: Principles Established, Framework Developing

India has approached AI governance with a principles-first, consultation-led orientation — appropriate for a stage of development where the infrastructure layers are still being built and over-regulation carries a real cost to innovation. The IndiaAI Mission's Safe & Trusted AI pillar covers bias mitigation, privacy-preserving architectures, deepfake detection, and ethical AI frameworks, with thirteen projects selected and an IndiaAI Safety Institute being established.

India's G20 presidency produced internationally recognised AI governance principles, and India has participated actively in multilateral AI governance conversations. The governance philosophy — inclusive, development-sensitive, rights-aware — reflects India's democratic values and is broadly resonant with other developing economies navigating similar choices.

Table 8: AI Governance Comparison — USA, EU, India (2026)

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Part IV — Strategic Analysis: Opportunities and Challenges

The layer-by-layer picture points to a set of strategic choices India faces. This section moves from description to analysis — examining where the clearest opportunities lie, what the structural challenges are, and what the strategic logic suggests for researchers, entrepreneurs, and policymakers.

4.1 — The Strategic Logic of India's Stack Position

India's upper-layer strengths are not a consolation for lower-layer gaps. They represent a different theory of competitiveness. The US and China have pursued comprehensive stack ownership — investing across all layers simultaneously. India, at its current stage of development, has concentrated strength in the layers that generate direct economic and social value: data (L5), applications (L6), and talent (L7). This is not a random distribution. It reflects decades of investment in education and engineering, the specific advantages created by India's Digital Public Infrastructure, and an entrepreneurial culture oriented toward solving real-world problems at scale.

The strategic implication is that India should not attempt to replicate the US or Chinese approach — seeking dominance across all layers simultaneously. A more productive frame is to ask: how can India's upper-layer strengths be used to build lower-layer capability over time, and where should India seek trusted partnerships rather than trying to build everything domestically?

On silicon (L2), domestic chip design is more achievable in the near term than domestic fabrication, and partnerships with Japan, South Korea, and the EU offer supply chain diversification that reduces dependency on any single source. On compute (L3), the current public-private partnership model is working — the priority is expansion and utilisation, not ownership. On models (L4), the SLM pathway offers a route to genuine sovereign capability without requiring the compute budgets of frontier model development.

4.2 — Opportunities for Researchers

India's research community has scope to make contributions that are globally significant precisely because they address problems where India's structural position creates unique insight and access to unique data.

1. Indic Language and Multimodal AI: Building models, benchmarks, and datasets for India's 22 official languages — particularly lower-resource languages where no global model has invested meaningfully. This is a research area where Indian institutions have genuine first-mover advantage.

2. Domain SLM Development: Specialised language models for Indian agriculture, clinical practice, legal interpretation, and financial behaviour — trained on Indian domain data and designed for Indian deployment conditions. These models address problems no general-purpose model is optimised for.

3. Privacy-Preserving AI at Scale: India's DPI creates research opportunities in federated learning, differential privacy, and secure computation at a scale no other nation can match. This is internationally significant work with India as the primary laboratory.

4. Efficient AI for Constrained Environments: Edge AI, on-device inference, and low-bandwidth AI applications for rural India. Algorithms developed for these conditions are applicable across most of the developing world.

5. AI Fairness for Diverse Populations: Bias detection and mitigation in multilingual AI systems, fairness in AI deployed across extreme income diversity, accountability frameworks for AI in public services. India's diversity makes it a particularly important context for this research.

4.3 — Opportunities for Entrepreneurs

The AI application economy in India is in an early phase. Every major sector is at the beginning of its AI transformation. The compute infrastructure, model availability, and dataset platforms being built through the IndiaAI Mission are substantially lowering barriers to entry. The strategic question for entrepreneurs is not whether opportunities exist — they clearly do — but which ones to prioritise given current market conditions and infrastructure availability.

6. Indic-Language AI Products: Voice interfaces, translation services, vernacular content creation, and regional language business tools for the approximately 800 million Indians whose primary language is not English. This segment is substantially underserved by current AI products.

7. DPI-Integrated Applications: Products that layer intelligence onto Aadhaar, UPI, and ONDC — smart KYC, AI-powered credit assessment, supply chain optimisation, logistics intelligence. The infrastructure rails are built; application development on top of them is an open field.

8. Domain SLMs as Products: Specialised small language models for specific Indian domains, offered as API products or embedded in sector applications. This approach converts India's data advantages directly into intellectual property.

9. Global South Market Development: Taking India-built solutions to Southeast Asia, East Africa, and the Middle East — particularly in agriculture, health, financial inclusion, and education, where Indian conditions resemble target markets more closely than Silicon Valley conditions do.

10. AI Services for Global Capability Centres: India's 1,700+ GCCs are building AI for their parent companies globally. Specialised AI consulting, model fine-tuning, AI product engineering, and AI safety evaluation are growing B2B markets that India's existing IT services ecosystem is well-positioned to address.

4.4 — Structural Challenges

CHALLENGE 1 — SILICON DEPENDENCY

India's dependence on imported AI chips is its most significant structural vulnerability. The IndiaAI compute subsidies address cost — they do not address supply security. In a global environment where AI chip access is increasingly a geopolitical variable, India's long-term strategy requires both supply chain diversification and a credible domestic design programme. Neither is a short-term project, which is precisely why both should begin in earnest now.

CHALLENGE 2 — THE MODEL GAP

Sarvam's 105B model and BharatGen's 17B are meaningful early milestones. They are not yet at the frontier defined by GPT-5 or DeepSeek V4-Pro. The more durable risk is that general-purpose frontier model development moves faster than India's indigenous capability can close the gap. A practical response is to focus on domain-specific excellence — building models that lead in Indian contexts rather than competing head-to-head with general-purpose systems that have substantially larger compute budgets. Domain leadership is both more achievable and more strategically defensible.

CHALLENGE 3 — GOVERNANCE FRAMEWORK

A principles-based approach provides flexibility but creates uncertainty for enterprises deploying AI in India and for international partners considering AI collaboration. A governance sandbox approach — sector-by-sector, iterative, development-sensitive — may suit India's current stage better than a comprehensive omnibus framework. The key is to begin formalising sector-specific rules in high-impact areas such as healthcare AI, financial AI, and public sector AI, rather than waiting for a comprehensive national framework.

CHALLENGE 4 — TALENT DISTRIBUTION

India's AI talent and investment is concentrated in a small number of cities. The 31 AI labs now operational in Tier 2 and Tier 3 cities are a meaningful beginning, but the infrastructure of mentorship, curriculum, and ecosystem support in smaller cities remains thin relative to the scale of opportunity. Distributing not just labs but the broader support ecosystem — faculty development, industry connections, startup support — is the harder and more important part of the talent distribution challenge.

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Part V — Looking Ahead

5.1 — What 2030 Could Look Like

If current investment trajectories are sustained and key strategic choices are made well, here is a plausible picture of India's AI position in 2030.

• A family of sovereign AI models — a flagship multilingual foundation model, a suite of domain SLMs in agriculture, health, legal, and education, and open Indic language models with demonstrated utility across the Global South.
• AI compute infrastructure expanded significantly, with Tier 2 and Tier 3 cities hosting AI data centres, and meaningful progress toward domestically designed AI accelerators.
• AIKosha established as a well-curated, internationally referenced repository of non-English AI training data.
• AI application companies serving markets across Southeast Asia, East Africa, and Latin America — with products built for Global South conditions.
• A functioning, sector-specific AI governance framework that is being referenced by other developing nations as a practical template.
• An AI talent pipeline producing graduates across disciplines, with meaningful representation from smaller cities and sustained leadership in women's AI participation.

ON PROJECTIONS

These are directional scenarios, not predictions. Each depends on policy continuity, private sector commitment, and sustained public investment. The infrastructure being built today — compute, datasets, fellowships, indigenous models — creates the conditions for this trajectory. Whether it is realised will depend on choices made over the next few years.

5.2 — The IndiaAI Mission: Seven Pillars, Eight Layers

It is worth noting how directly the seven pillars of the IndiaAI Mission map onto the 8-layer framework — reflecting a reasonably comprehensive national strategy.

Table 9: IndiaAI Mission Pillars Mapped to the 8-Layer Framework (Sources: PIB, MeitY, IndiaAI Mission reports 2024–2026)

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Conclusion

The 8-layer AI stack offers a structured way to examine a question that is otherwise difficult to answer precisely: where does a nation stand in the global AI landscape, and what should it do about it?

For India, the picture that emerges is one of genuine and specific strengths — in data, in applications, and in the talent pipeline — alongside infrastructure gaps that are real but addressable. The strengths are not uniformly distributed across the stack, but they are concentrated in the layers that are most directly connected to value creation in the GenAI era. The gaps are at layers where capital intensity is highest — and where strategic partnerships and focused domestic programmes can make a meaningful difference over time.

The evidence suggests that India is steadily emerging as one of the world's significant AI ecosystems. India ranks third in Stanford's Global AI Vibrancy Tool (score 21.59), first in AI skill penetration, and second in GitHub AI contributions globally. (Stanford, 2025; GitHub, 2024) The more consequential question is not whether India will participate in the AI era, but how it will shape its own role within it — and whether the AI it builds will reflect Indian priorities, serve Indian languages, and address challenges that matter to Indian communities.

The 8-layer framework does not answer that question. It provides a structure for thinking about it clearly — layer by layer, with evidence, with honesty about gaps, and with attention to where the genuine opportunities lie. The answers will come from the researchers, engineers, entrepreneurs, educators, and policymakers working across India's AI ecosystem today.

The stack is the strategy. How India builds it will define its role in the AI century.

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References

Government & Official Sources

• Cabinet Approval: IndiaAI Mission, ₹10,371.92 Crore. PIB Press Release ID 2012355, March 2024. pib.gov.in
• IndiaAI Mission 24-Month Progress Report. PIB Press Release ID 2227612, February 2026. pib.gov.in
• India's AI Revolution — GPU Infrastructure and Ecosystem. PIB. pib.gov.in/PressReleasePage.aspx?PRID=2108810
• India AI Talent Acquisition. PIB Press Release ID 2206767, December 2025. pib.gov.in
• IndiaAI Compute Capacity Portal. indiaai.gov.in/hub/indiaai-compute-capacity
• Transforming India with AI. PIB Note ID 156786, 2026. pib.gov.in
• EU AI Act — Official Regulatory Framework. European Commission. digital-strategy.ec.europa.eu
• White House AI Action Plan: Winning the Race. July 2025. whitehouse.gov
• Executive Order: Promoting Advanced AI Innovation and Security. June 2026. whitehouse.gov

Academic & Research Sources

• Stanford AI Index Report 2024. Stanford Institute for Human-Centered AI. hai.stanford.edu
• Stanford Global AI Vibrancy Tool 2025. Stanford HAI. hai.stanford.edu
• Oxford Insights Government AI Readiness Index 2025. oxfordinsights.com
• DeepSeek, Huawei, Export Controls, and the Future of the US-China AI Race. CSIS, May 2026. csis.org
• Full Stack: China's Evolving Industrial Policy for AI. RAND Corporation, June 2025. rand.org
• Liljebrunn T. & Catovic A. (2024). The AI Stack: A Framework for a Holistic National AI Strategy. Futurum Strategia.

Industry & News Sources

• Fortune: DeepSeek Unveils V4 Model. April 2026. fortune.com
• NervNow: With Sarvam and Krutrim, Has Make in India in AI Finally Arrived? February 2026. nervnow.com
• Rest of World: India's Frugal AI Startups. April 2026. restofworld.org
• IndianAI.in: India's First Indigenous AI Foundation Model. April 2025. indianai.in
• EU AI Act Compliance Guide 2026. decodethefuture.org

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RESEARCH METHODOLOGY & AI ASSISTANCE

This AI Note was developed through research across academic publications, government reports, industry analyses, and publicly available sources. AI-assisted tools, including Claude, supported literature synthesis, drafting, editorial refinement, and language improvement. The overall structure, analytical framework, interpretations, and conclusions reflect the author's independent judgement and are intended to encourage informed discussion rather than present definitive policy positions.

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About the Author

Bhaveshkumar Choithram Dharmani is the Founder of AIVidhya4Sarvam and works as an AI mentor, researcher, and ecosystem builder. His focus is on AI education, mentorship, and building the conditions for meaningful AI participation across India — in institutions, organisations, and communities that are not yet well-served by the current AI education ecosystem.

AIVidhya4Sarvam (aividhya.in) is an AI mentorship, innovation, and transformation organisation. It works with students, professionals, startups, and institutions to build AI capability with rigour and purpose.

Issue #01 of the India AI Journey series by AIVidhya4Sarvam.

© 2026 AIVidhya4Sarvam. For educational and non-commercial sharing, please attribute the author and source.