The world is witnessing an AI revolution, and countries are scrambling to build their own foundational AI models. China has DeepSeek, the US has OpenAI’s GPT, and Europe is making strides in AI regulation and research. But where does India stand in this race?
Despite being a global hub for tech talent, India lacks a homegrown AI model that can compete with DeepSeek, GPT, or Bard. The question isn’t just about why we don’t have one yet—it’s about what needs to be done to get there.
India’s startup ecosystem is thriving, its IT sector is world-class, and its digital economy is booming. But building a large-scale AI model requires more than just talent—it needs a strategic vision, deep investments, policy support, and cutting-edge infrastructure.
So, what does India need to do to create its own DeepSeek? Let’s break it down.
1. Solve for Compute Power: The Foundation of AI Innovation
Building large-scale AI models requires massive computing power. Models like GPT-4 and DeepSeek rely on high-performance GPUs (like NVIDIA A100s and H100s) and TPUs, which are expensive and often dominated by Western or Chinese firms.
Challenges India Faces:
Lack of indigenous AI hardware: India does not manufacture its own AI chips at scale.
Heavy reliance on imported GPUs: With global chip shortages, access to high-end GPUs is expensive and limited.
No dedicated AI supercomputers: The US and China have AI-dedicated supercomputers, but India lacks comparable infrastructure.
What India Needs to Do:
✅ Invest in domestic semiconductor manufacturing: India has already launched initiatives like the ₹76,000 crore semiconductor mission, but it needs to accelerate AI chip development.
✅ Build AI supercomputing clusters: A national initiative to develop AI-focused high-performance computing (HPC) centres is crucial.
✅ Partner with global firms for compute access: Until India has its own ecosystem, collaborations with NVIDIA, AMD, and Google could provide interim support.
2. Massive AI-Specific Funding: Where’s the Money?
DeepSeek and GPT-4 didn’t emerge overnight—they were backed by billions of dollars in R&D funding. China’s AI ecosystem thrives because of government-led funding and private capital pouring into AI research.
Challenges India Faces:
Low AI-specific funding: Indian startups raise capital, but AI-specific funding is limited compared to the US or China.
Lack of government AI mega-projects: While India has initiatives like INDIAai and Digital India, there’s no single, dedicated “India AI Fund” that strategically backs foundational AI models.
Risk-averse venture capital (VC) mindset: Most Indian VCs focus on software, SaaS, or fintech, rather than AI infrastructure.
What India Needs to Do:
✅ Create a $10B+ ‘India AI Fund’ to support foundational AI projects, similar to China’s state-backed AI initiatives.
✅ Encourage AI-focused VC funds that specifically target deep-tech startups.
✅ Offer tax incentives & grants for AI
3. Open AI Data Access: Fuel for Training AI Models
AI models like DeepSeek and GPT are trained on massive datasets, often sourced from diverse languages, cultures, and sectors. India has a goldmine of data but struggles with access and standardisation.
Challenges India Faces:
Fragmented data ecosystems: Government and corporate data are siloed and not easily accessible.
Lack of high-quality, labelled Indian datasets: Unlike the US, where data availability is high, India lacks a centralised public AI dataset repository.
Privacy and regulatory hurdles: Data protection laws can make training large AI models complex.
What India Needs to Do:
✅ Launch an ‘India Data Commons’ initiative, where anonymised, high-quality public datasets are made available for AI training.
✅ Encourage private-public collaboration, where Indian startups can train AI on public data in healthcare, education, governance, and agriculture.
✅ Develop AI models trained in regional languages to increase AI accessibility for the next billion users.n to make deep-tech AI research financially viable.
4. AI Talent Pipeline: India Has It, But It’s Drained
India produces some of the world’s best AI engineers and researchers, but many of them work for Google, Meta, OpenAI, or Chinese firms instead of Indian companies. The AI brain drain is real.
Challenges India Faces:
AI talent moving abroad: The best AI minds from IITs, IISc, and research labs often take jobs in the US or China.
Lack of deep-tech research funding: Unlike the US, India doesn’t have an NSF-equivalent for AI funding.
Few incentives for AI researchers: Professors and AI researchers in India don’t have the same financial backing as those in the West.
What India Needs to Do:
✅ Offer AI research grants & fellowships to top Indian AI talent, keeping them in India.
✅ Attract global AI researchers by offering world-class infrastructure in Indian labs.
✅ Encourage IITs and IISc to create AI-first curricula, training students in LLMs, multimodal AI, and AI hardware development.
5. A Strong AI Policy and Regulatory Framework
China’s AI success isn’t just about money—it’s about government-led strategy. India needs a clear AI policy that promotes innovation while ensuring ethical AI deployment.
Challenges India Faces:
No national AI strategy: While INDIAai exists, there’s no comprehensive AI roadmap like China’s 2030 AI Master Plan.
Unclear AI regulation: Privacy laws (like the DPDP Act) exist, but there’s no dedicated AI governance framework.
Slow-moving bureaucracy: AI innovation happens fast, but India’s policy-making can be slow.
What India Needs to Do:
✅ Define a National AI Roadmap, setting clear goals for foundational AI development by 2030.
✅ Encourage AI sandboxes, allowing startups to experiment with AI models without excessive regulation.
✅ Push for India-led AI ethics standards, ensuring AI aligns with Indian values and inclusivity.
The Road to India’s Own DeepSeek
Building an AI model like DeepSeek isn’t just about coding—it’s about vision, investment, and ecosystem-building. India has the talent, data, and ambition to lead in AI, but it needs to overcome key roadblocks in compute power, funding, data access, talent retention, and policy frameworks.
A Winning Formula for India:
✅ Invest in AI infrastructure & chips 🔥
✅ Fund deep-tech AI projects 💰
✅ Make public datasets open for AI training 📊
✅ Retain & attract AI talent 🧠
✅ Develop a national AI strategy 📜
The AI race is on. India can either be a consumer—or a creator. The choice is ours. 🚀 Want more insights on India’s AI future? Stay ahead of the curve—subscribe to AiSutra newsletter today!
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