India at the Centre of the Global AI and Semiconductor Power Shift

Abstract

The panel examined how artificial intelligence, semiconductor manufacturing, and geopolitics intersect to shape India’s emerging role in the global AI‑semiconductor value chain. Participants explored three thematic pillars – building an intellectual foundation, deepening manufacturing and supply‑chain resilience, and attaining sovereign AI capability. The discussion blended perspectives from government policy, advanced‑computing research, and private‑sector scaling, highlighting concrete programmes (e.g., India AI Mission, data‑center tax incentives, national super‑computing initiatives) and proposing collaborative models to accelerate India’s AI and semiconductor ecosystem.

Detailed Summary

• Contextualisation – AI is no longer a niche technology; it is reshaping economies and national competitiveness.
• Core Thesis – True AI leadership requires the convergence of silicon, software, systems, and policy; no single element can deliver sovereignty alone.
• Three Pillars for the Session
  1. Intellectual Foundations – research, talent, and open knowledge.
  2. Manufacturing Depth & Supply‑Chain Resilience – sustained capital and ecosystem development.
  3. Credible Sovereign AI Capability – secure, locally‑hosted AI services and IP.

---

2. Policy Landscape & Government‑Led Initiatives (Prof. Vivek Kumar Singh)

Key Points	Detail
Strategic Commitment	India has announced a ₹10,000 crore (≈ $1.2 bn) AI Mission for five years, covering ten pillars (data, talent, infrastructure, ethics, etc.).
Fiscal Incentives	Recent tax holidays for AI/data‑center projects to attract capital and accelerate compute deployment.
AI‑Kosh Platform	A national AI data‑exchange intended to house India‑centric datasets for home‑grown AI model development.
Semiconductor Design Ecosystem	India possesses a strong VLSI design talent pool, yet most IP resides abroad. The goal is to grow domestic IP ownership through scale‑driven deployments.
Credibility Over Announcements	Emphasis that credible AI/semiconductor leadership derives from sustained, large‑scale execution, not one‑off policy pronouncements.
Future Outlook	The government sees the AI‑mission and fab‑building plans as complementary levers that will together create a robust ecosystem.

Recommendations (as inferred from the dialogue)

• Prioritise large‑scale deployments of AI compute and data‑center capacity.
• Strengthen IP creation through incentives for domestic design houses.
• Align the AI‑mission pillars with private‑sector road‑maps to avoid silos.

---

3. Private‑Sector Perspective on Manufacturing & Capital (Mr. Rahul Garg)

Theme	Insights
Demand Momentum	Post‑COVID, consumer and industrial demand in India is accelerating; expectations for faster, varied products are rising.
Supply‑Chain Resilience	COVID‑era shocks (e.g., shortage of masks, oxygen concentrators) highlighted the need for “bare‑minimum” domestic manufacturing for critical goods.
Capital Availability	Recent government funds (~₹1 lakh crore, ~$1.2bn)forAI/deep‑tech\ast \ast and\ast \ast private‑sectorpledgesexceeding$100 bn for data‑center localisation. However, the breadth of capital is still limited – early-stage funding is growing but not yet pervasive.
Execution Capacity	Confidence that capital is flowing, but execution speed at scale remains uncertain.
Design vs. Manufacturing Bias	While Moglix focuses on industrial supply‑chain financing, the speaker notes a personal bias toward design and a desire to see more local IP generation.
Strategic Advice – “Try ten things, not just one or two.” Emphasised the need for vertical integration (design → fab → system → product) in India, with the expectation that horizontal capabilities will later emerge.
Fast‑Follower Phenomenon	India has become a rapid adopter of global tech trends – e.g., ChatGPT reached massive Indian user adoption within weeks. The next step is to scale these innovations onto global platforms.

Calls to Action

• Foster public‑private capital pools (government backing to de‑risk, not subsidise, private ventures).
• Encourage multiple parallel experiments across the semiconductor value chain.
• Build a holistic ecosystem: design talent, fab infrastructure, chemical/clean‑room suppliers, packaging & verification services.

---

4. Technical Road‑Map & Near‑Term Value Areas (Dr. Thomas Zacharia)

Area	Strategic Outlook
Sovereign AI	India can lead in residence‑of‑data solutions that are country‑specific (e.g., language‑localised models, sector‑specific use cases).
Startup Landscape	India hosts ~50,000 AI/semiconductor startups; strategic partnerships should focus on the top‑tier (≈ 50) firms to accelerate maturity.
Resilience Through Niche Technologies	Instead of chasing leading‑edge (e.g., 2‑nm) node, India should target critical, high‑volume AI‑infrastructure components such as co‑package optics, advanced interconnects, where global supply is thin.
Supply‑Chain Gaps	Current gaps lie in optics, packaging, verification; potential partners include US, Japan, Malaysia which already dominate niche supply chains.
Public‑Private Partnerships (PPP)	US “Genesis” model – a collaborative framework where government funds grand‑challenge projects, while private firms reap commercial benefits without direct subsidy. India could emulate this to align national super‑computing missions with industry needs.
Strategic Bet for 2030	Focus on open‑standard components (e.g., AMD’s “Helios” ecosystem) where Indian firms can become global leaders by providing cost‑effective, interoperable solutions.
Avoiding the “Fast‑Follower” Trap	Being a perpetual follower caps a nation at “second place”. India should identify domains where it can be world‑leading, rather than trying to dominate every segment.

Key Recommendations

• Create government‑seeded PPPs around “lighthouse” AI/semiconductor challenges.
• Prioritise building niche supply‑chain capabilities (optics, packaging) that have high strategic value.
• Leverage open‑standard architectures to enable Indian vendors to compete globally.

---

5. Education, Skills, & Future Workforce (Prof. Vivek Kumar Singh – follow‑up)

Observation	Implication
Shift from Memory‑Based to Synthetic Learning	Modern curricula need to emphasise creative problem‑solving and AI‑augmented research rather than rote memorisation.
Unlimited Learning Resources	Generative‑AI tools, retrieval‑augmented generation, and recommender systems provide free, personalised up‑skilling (e.g., via NASCOM’s Future Skill Prime).
Startup Ecosystem as Talent Hub	India’s third‑largest global startup ecosystem offers incubators, accelerators, and mentorship – ideal for translating ideas into products.
Reskilling Imperative	Professionals with 20+ years of experience are actively reskilling to stay relevant; the pace of change demands continuous learning.
Policy Recommendation	Strengthen national skilling programmes, expand online‑learning aggregators, and institutionalise pathways from university research to commercial product development.

Strategic Insight – The combination of abundant talent, emerging digital learning platforms, and a vibrant startup culture creates a unique opportunity for India to export skilled AI/semiconductor professionals globally.

---

6. Sustainability as a Core Design Principle (AMD Representative – part of the moderator’s closing)

• AMD’s Sustainability Goal – Flatten the energy curve of its products; design for energy‑efficient compute rather than simply increasing megawatt capacity.
• Humility & Iterative Improvement – Recognises that current solutions are imperfect; commitment to continuous course‑correction as new data emerges.
• Grand‑Challenge Paradox – The 21st‑century challenges (climate, resource scarcity) are often the by‑products of 20th‑century solutions, underscoring the need for sustainability‑first thinking.

Takeaway – Embedding sustainability at the architectural level is essential for long‑term viability of AI/semiconductor infrastructure.

---

7. Closing Reflections & Audience Interaction

• Summative Insight – Momentum alone is insufficient; sequencing, capital discipline, institutional alignment, and deep infrastructure must move in lockstep.
• Final Questions
  • Rahul: Emphasised India’s fast‑follower capability but urged a shift from domestic‑only focus to global‑scale ambition, requiring large, coordinated capital pools (public‑private).
  • Thomas: Reiterated the need for government‑de‑risking via PPPs (e.g., a “Genesis‑type” programme) and highlighted the strategic importance of targeting niche, open‑standard components.
  • Vivek: Stressed the need for bold investment in youth, skilling, and converting university research into marketable products; urged an “ease‑of‑doing‑business” environment.
• Audience Q&A – A professor from IIM raised a sustainability‑first design question; AMD answered by underscoring its energy‑efficiency targets and the broader concept that sustainability should be baked into every design decision.

The session concluded with appreciation for the participants and a brief token of gratitude from the organisers.

Key Takeaways

• India’s AI & semiconductor ambition is backed by a multi‑year ₹10,000 crore AI Mission, data‑center tax incentives, and a national AI‑Kosh data platform.
• Credibility stems from scale‑driven execution, not merely policy announcements.
• Domestic IP creation is critical; India must transition from a design‑service model to owning its semiconductor IP.
• Supply‑chain resilience gained urgency during COVID; a “bare‑minimum” domestic manufacturing base for critical components is now a strategic priority.
• Capital is flowing (government funds + >$100 bn private pledges), but breadth and long‑term commitment remain uneven; public‑private de‑risking mechanisms are essential.
• Strategic focus should be on niche, high‑value semiconductor technologies (e.g., co‑package optics, advanced interconnects) rather than chasing the most advanced process nodes.
• Public‑private partnership models like the US “Genesis” project can align national super‑computing missions with industry, fostering “grand‑challenge” collaborations.
• Education must evolve from memorisation to synthetic, AI‑augmented learning; platforms such as Future Skill Prime provide free, curated up‑skilling at scale.
• India’s massive talent pool and thriving startup ecosystem position it as a global talent exporter and a fast‑follower capable of rapid adoption; the next step is to become a fast leader on selected fronts.
• Sustainability should be a core design pillar for AI and semiconductor products, not a trade‑off; industry players like AMD are committing to energy‑efficient architectures and iterative improvement.
• Ultimate success hinges on coordinated sequencing of policy, capital, infrastructure, and talent development, executed with urgency and clarity.

---

See Also:

• democratizing-ai-resources-in-india
• ai-for-everyone-empowering-people-businesses-and-society
• democratizing-ai-resources-and-building-inclusive-ai-solutions-for-india
• reskilling-for-tomorrow-ai-sustainability-and-indias-jobs-transition
• ai-for-inclusive-societal-development
• ai-for-economic-development-and-social-good
• responsible-ai-at-scale-governance-integrity-and-cyber-readiness-for-a-changing-world