While India boasts a thriving ecosystem of AI startups, it has yet to produce its own globally competitive AI engine, sparking questions about the challenges and opportunities in its pursuit of AI leadership.
India, with its vast pool of over 5 million IT professionals and an increasing emphasis on artificial intelligence (AI) in education, would seem to be perfectly positioned to compete in the rapidly evolving global AI race. While the United States established an early lead with ChatGPT in 2023, and China quickly followed with DeepSeek, India has yet to develop a comparable Large Language Model (LLM) capable of emulating human-like communication.
The Burgeoning Indian AI Landscape
Despite the absence of a flagship AI engine, India’s AI sector is experiencing significant growth. Data from Tracxn reveals that the Indian AI landscape encompasses 7,114 startups, collectively securing $23 billion in equity funding. Recognizing the potential of AI, the Indian government has launched the IndiaAI Mission, allocating approximately $1.21 billion to foster the development and deployment of indigenous Large Multimodal Models (LMMs) and domain-specific foundational models across critical sectors.
Navigating the Global AI Arena
According to Abhishek Singh, CEO of IndiaAI Mission, Indian startups must look beyond domestic markets to effectively compete with global AI powerhouses. Speaking at the Accel AI Summit in Bengaluru, Singh emphasized that while initial government support is valuable, long-term success hinges on a global perspective in model training.
The National Association of Software and Service Companies (NASSCOM), representing India’s $283 billion tech industry, acknowledges the complexity and resource intensity of building a globally recognized AI model. Satyaki Maitra, senior manager communications of NASSCOM, underscores the need to move swiftly and establish a unique AI identity.
To bolster AI research capabilities, IndiaAI Mission recently announced the addition of 15,916 Graphics Processing Units (GPUs), essential for parallel processing-intensive AI calculations. This augmentation will increase the total national AI computing capacity to 34,333 GPUs through public-private partnerships.
Cultivating Homegrown AI Innovation
Several startups, including Gan AI, Gnan AI, SarvamAI, and Soket AI, are actively developing foundational models tailored to the Indian context with support from the IndiaAI Mission. Other firms like Sarvam AI, Fractal, and CoRover AI are concentrating on AI innovation in specific areas.
According to Maitra, achieving AI success necessitates collaborative efforts between government, industry, and academia to establish a comprehensive value chain encompassing computer and data governance, model training, and practical deployment.
Overcoming Challenges in India’s AI Ascent
Pawan Duggal, a prominent cybersecurity expert, suggests that India may encounter challenges such as a scarcity of high-end AI hardware, limited access to advanced GPUs, and insufficient cloud computing resources, all of which are vital for training large-scale AI models.
Duggal also points out a significant investment gap compared to global counterparts. While venture capital investment in Indian AI startups has increased, it remains substantially lower than the levels seen in the US and China.
From 2014 to 2023, the US invested $2.34 trillion and China $832 billion in ventures and startups, while India invested $145 billion during the same period, he noted.
Duggal believes that India is making strides toward creating its own AI model but needs to address critical infrastructure, funding, talent, data, and regulatory challenges.
Linguistic Diversity: A Unique Challenge
India’s linguistic diversity presentsa unique hurdle for AI development. English is just one of 22 official languages in the country, which also boasts over 1,600 spoken languages, many of which have limited digital representation.
Yash Shah of Momentum 91, a custom software development company, highlights that the primary use case for an “Indian” LLM lies in its ability to function across various Indian languages. However, this is currently challenging due to the scarcity of quality training data for most Indian languages.
Shah suggests that for English-based LLMs, other companies and countries have a considerable head start that will likely persist.
Key Obstacles to AI Advancement
Utpal Vaishnav of Upsquare Technologies identifies risk-averse investors, inconsistent data regulations, and a constrained GPU supply as major obstacles.
Vaishnav believes that India possesses abundant intellectual capital, with GPUs becoming more accessible and multilingual data awaiting utilization. With patient capital, clear problem definitions, and strategic deployment of talent, a compact, world-class LLM could be launched in two to three years.
Diving Deeper into the Challenges Facing AI Development in India
To truly understand India’s journey toward creating a world-class AI engine, it’s vital to dissect the complex web of challenges hindering its progress.
The Hardware Hurdle: A Critical Bottleneck
As Pawan Duggal emphasized, access to cutting-edge AI hardware, especially advanced GPUs, represents a significant constraint. GPUs are the workhorses of AI, accelerating the computationally intensive tasks of training and running complex AI models. The limited availability of these resources within India poses a direct impediment to rapid AI development and innovation. Indian companies need to find ways to secure access to state-of-the-art hardware resources. This could involve strategic partnerships with international hardware providers, government subsidies for hardware procurement, or the development of indigenous GPU manufacturing capabilities. Without addressing this hardware bottleneck, India’s AI progress will be significantly hampered. Furthermore, the escalating costs of advanced AI hardware necessitate exploring alternative approaches, such as optimized algorithms and distributed training techniques, to mitigate the resource constraints.
The Cloud Capacity Conundrum: Scalability Concerns
Closely related to hardware limitations is the issue of insufficient cloud computing resources. Cloud platforms offer scalable computing power, storage, and services that are essential for handling the massive datasets and computational demands of training large-scale AI models. While cloud adoption is growing in India, the availability of robust and affordable cloud infrastructure tailored for AI workloads lags behind that of leading AI nations. This disparity affects the ability of Indian AI developers to experiment, iterate, and scale their models effectively. Addressing this gap requires investments in expanding cloud infrastructure, promoting the adoption of cloud-native AI development tools, and fostering partnerships with global cloud providers. Moreover, attention should be paid to optimizing cloud usage and resource allocation to maximize efficiency and minimize costs. Open-source cloud platforms and containerization technologies can play a crucial role in enabling efficient and scalable AI deployments.
The Funding Factor: Bridging the Investment Gap
The substantial investment gap between India and global AI leaders like the US and China is a cause for concern. Venture capital fuels the growth of AI startups, enabling them to attract top talent, acquire resources, and pursue ambitious projects. The relative scarcity of AI-focused venture funding in India can stifle innovation and make it difficult for startups to compete on a global scale. Addressing this requires fostering a more conducive investment climate for AI, attracting both domestic and foreign capital. This could involve tax incentives for AI investments, government-backed venture capital funds, and initiatives to connect Indian AI startups with international investors. Furthermore, creating a supportive regulatory environment that encourages AI innovation and minimizes investment risks is crucial. Investor education programs focused on the potential of AI and the specific opportunities in the Indian market can also help bridge the funding gap.
The Talent Tango: Nurturing AI Expertise
While India boasts a large pool of IT professionals, the availability of specialized AI talent remains a challenge. Building and deploying sophisticated AI systems requires a diverse range of skills, including machine learning, deep learning, natural language processing, computer vision, and data science. To bridge this talent gap, India needs to invest in AI-specific education and training programs, attract experienced AI professionals from abroad, and foster a vibrant research community. This includes creating specialized AI curricula at universities and colleges, offering online AI courses and certifications, and supporting research grants and fellowships in AI-related fields. Furthermore, attracting and retaining AI talent requires competitive salaries and benefits, stimulating work environments, and opportunities for professional growth. Collaborations between universities, research institutions, and industry can help bridge the gap between academic research and practical applications, fostering innovation and creating new job opportunities.
Data Deficiencies: Addressing Quantity and Quality
The availability of high-quality, labeled data is the lifeblood of AI. AI models learn patterns and make predictions based on the data they are trained on. The lack of sufficient data in key areas, especially in Indian languages, is a significant obstacle. Furthermore, ensuring data privacy, security, and ethical use is crucial. India needs to develop comprehensive data strategies that address data collection, annotation, governance, and accessibility. This includes creating data sharing platforms, promoting open data initiatives, and investing in data annotation tools and services. Furthermore, addressing the unique challenges of data privacy in India requires developing robust data protection regulations and promoting the adoption of privacy-preserving technologies. Synthesizing datasets for training is also a viable method to approach data insufficiencies. Ensuring data quality is paramount, requiring rigorous data validation and cleaning processes. Developing data governance frameworks that address ethical considerations and promote responsible data use is essential for buildingtrustworthy AI systems.
Regulatory Roadblocks: Navigating Uncertainty
The rapidly evolving nature of AI presents regulatory challenges. Governments around the world are grappling withhow to regulate AI to promote innovation while mitigating potential risks. The absence of clear and consistent AI regulations in India creates uncertainty for AI developers and investors. Establishing well-defined regulatory frameworks that address issues such as data privacy, algorithmic bias, and liability is crucial for fostering responsible AI development. These frameworks should be flexible and adaptable to the evolving landscape of AI, while also providing a level of certainty and predictability for AI developers and investors. Engaging stakeholders from government, industry, academia, and civil society in the regulatory development process is essential for ensuring that the regulations are aligned with the needs and values of the Indian society. Furthermore, promoting international collaboration on AI regulation can help harmonize standards and facilitate cross-border AI innovation. Sandbox environments and regulatory experimentation can also provide valuable insights into the potential impacts of AI and inform the development of effective regulations.
The Opportunities Still Abound: A Vision for the Future
Despite the challenges, India possesses immense potential to become a major player in the global AI landscape. The country’s large population, growing economy, and increasing digital adoption create a fertile ground for AI innovation. To realize this potential, India needs to focus on:
- Strategic Investments: Increasing investment in AI infrastructure, research and development, and education.
- Talent Development: Strengthening AI education and training programs to cultivate a skilled workforce.
- Data Ecosystems: Creating robust data ecosystems that facilitate data collection, sharing, and governance.
- Regulatory Clarity: Establishing clear and consistent AI regulations that promote innovation and mitigate risks.
- Collaborative Partnerships: Fostering collaboration between government, industry, academia, and civil society.
By addressing these challenges and capitalizing on its strengths, India can build a thriving AI ecosystem that drives economic growth, improves quality of life, and contributes to the global AI revolution. The quest for a world-class AI engine may be arduous, but the potential rewards are immense, promising to transform India into an AI powerhouse. The journey involves tackling not only the technological hurdles but also fostering an environment that encourages innovation, collaboration, and responsible AI development. India’s diverse cultural and linguistic landscape presents unique opportunities for creating AI solutions that are tailored to the specific needs of its population. By embracing these opportunities and addressing the challenges head-on, India can emerge as a leader in the global AI landscape, shaping the future of technology and society. This concerted effort requires a long-term vision, unwavering commitment, and a collaborative spirit among all stakeholders. The success of India’s AI quest will not only benefit the country itself but also contribute to the advancement of AI for the benefit of humanity as a whole.