Dr. Suresh Raikwar (Ph.D. in Information Technology)
 

BLOG-3: Role of the Academia in Urgent Self Resilient Indian AI Ecosystem

Date: 06 September 2025, Time; 02.36 PM

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The U.S. tariff precedent is a wake-up call. In 2022, the U.S. blocked exports of NVIDIA’s A100/H100 GPUs to China, crippling its AI ambitions. Today, India imports 95% of its AI chips (NVIDIA/AMD) and relies on foreign cloud providers (AWS, Azure). A similar U.S. or Western embargo could freeze India’s AI progress overnight [1]. The $5 trillion economy goal of India demands AI Sovereignty. India’s AI market is projected to hit 7.8 billion by 2025 [2], with foreign dependency. Data is on high risk since foreign cloud providers could block access to Indian datasets under geopolitical pressure. Innovation startups waste 30-40% of revenue on foreign SaaS tools [2].

This is the time for India now. Let’s build an AI future as technology users and creators. The Table-1 presents a summarized analysis of the AI services and tools, utilized by India. Further, the reason for self-resiliency is mentioned with a proper road map. The achievements of the India in this roadmap have been highlighted with future gaps to be filled.​​​​​​​​​​​​​​​

 

 

 

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[Note: This table was not getting copied from my original written documents. Thus the image of the table is presented here.] ​

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Key Inferences from Table-1 that:

• Language diversity has been achieved through Bhashini (National Language Translation Mission) supports 22 scheduled languages, including Hindi, Tamil, Bengali, and tribal languages like Gondi and Santhali. Adi Vaani focuses on 8 tribal languages (e.g., Kurukh, Great Andamanese).

• Hardware development is in progress. Vikram-32 (IIT-Madras) is India’s first RISC-V-based processor, designed for edge AI.

• India Semiconductor Mission aims to establish fabs by 2026 (e.g., Tata’s OSAT in Dholera).

• Data Sovereignty may be achieved by Digital Personal Data Protection Act (2023) mandates local storage of critical data, reducing reliance on foreign clouds. BharatBench includes 100+ Indian-language datasets (e.g., medical, agricultural).

• Only 26% of Indian workers are digitally skilled. Startups need 5B+ for scaling.

   

Conclusion: This analysis emphasizes that self Resilient Indian AI Ecosystem is required as soon as possible. Further, the Indian academia can contribute in the following ways to support buiding of Indian AI Ecosystem as early as possible.

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• Promote use of open Indian API like Sarvam, openhathi in the institutions. This will help secure the Indian data and training of the students on Indian APIs.

• Development on the top of these API.

• Suggest capstone projects based on future gaps listed in Table-1. This will help India to achieve the goal in faster manner with lesser Investment.

• Ask students to contribute in Indian Open source libraries to make as rich as possible. Thi way the speed of the development of Indian ecosystem will boost.

   

References: The above information has been obtained from the following links.

[1] US-China Economic and Security Review Commission (2023)

[2] Citation: IBEF AI Report (2024)

[3] IndiaAI Mission: MeitY Press Release (2023)

[4] Bhashini: National Language Translation Mission

[5] Vikram-32: IIT Madras (2024)

[6] BharatBench: NITI Aayog (2025)

[7] Skill Data: NASSCOM Report (2024)

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Note: No use of LLMA in this blog.