India's journey toward developing foundational AI models presents a complex narrative of ambitious governmental initiatives, significant infrastructure investments, and persistent systemic challenges that continue to impede progress relative to global leaders. Despite being home to one of the world's largest technology workforces and establishing comprehensive policy frameworks like the IndiaAI Mission with a budgetary allocation of ₹10,372 crores, India finds itself in a catch-up position in the race for foundational AI model development. The country has secured the 10th position globally in private AI investment with $1.4 billion, representing a notable achievement for a developing nation, yet this investment pales in comparison to the resources deployed by AI leaders like the United States and China. While the government has initiated the development of India's first government-funded foundational AI model and established infrastructure comprising over 18,000 GPUs, the ecosystem continues to face challenges in talent retention, research output, and the complex requirements of building culturally relevant, multilingual AI systems. This analysis reveals that India's lag in foundational AI model development stems from a confluence of factors including infrastructure limitations, research ecosystem gaps, policy implementation challenges, and the unique complexity of developing AI systems that serve India's linguistic and cultural diversity.
Infrastructure and Computing Resource Challenges
India's foundational AI model development has been significantly constrained by historical limitations in computing infrastructure, though recent governmental initiatives are beginning to address these gaps. The IndiaAI Mission represents the most ambitious attempt to democratize AI computing access, with the establishment of a state-of-the-art infrastructure featuring over 18,000 GPUs distributed across public-private partnerships. This infrastructure includes 12,896 H100, 1,480 H200, and 7,200 MI300X units, representing a computing capacity that significantly exceeds initial targets and provides a foundation comparable to global standards. The government has structured this infrastructure to offer compute services at up to 40% reduced costs to eligible users, including startups, research institutions, and government agencies, addressing one of the primary barriers that previously limited AI research and development in the country.
However, the infrastructure challenge extends beyond mere GPU availability to encompass broader ecosystem considerations. India currently hosts only 150 of the world's 11,000 data centers, despite having over 1,300 AI companies, highlighting a fundamental mismatch between AI ambitions and supporting infrastructure. The data center industry is responding to this challenge, with projections indicating potential revenue generation of $25 billion by 2030 and the addition of 500 megawatts of data center capacity specifically driven by AI applications over the next four years. Companies like CtrlS are investing $2 billion over five years to scale infrastructure for high-density computing and AI workloads, while hyperscalers like AWS and Microsoft Azure are expanding their Indian operations to support the growing demand.
The democratization of computing access through the IndiaAI Compute Portal represents a significant policy innovation, allowing ministries, state governments, and approved entities to request computing capacity through subsidized rates covering approximately 40% of computing costs. This model contrasts sharply with the ecosystem in countries like the United States, where foundational model development has been largely driven by well-funded private companies with access to substantial computing resources. The empanelment of 10 agencies providing diverse AI compute units, including Intel Gaudi 2, AMD MI300X, and NVIDIA H100 series, ensures technological diversity and reduces dependency on single vendors. Despite these advances, the infrastructure scaling remains a work in progress, with technical partners expressing confidence in delivering world-class AI solutions while acknowledging the ongoing nature of capacity expansion.
Research Ecosystem and Talent Dynamics
India's research ecosystem for AI development faces a complex set of challenges that directly impact the country's ability to develop competitive foundational models. The nation's research infrastructure, while substantial in terms of institutional presence through IITs, IIITs, and specialized research centers, has struggled to translate institutional capacity into breakthrough foundational model development. The government's National Strategy for Artificial Intelligence, developed by NITI Aayog, identifies AI as a transformative technology with the potential to increase India's annual growth rate by 1.3 percentage points by 2035, yet the translation of this strategic vision into concrete research outcomes in foundational models has been limited.
A critical challenge highlighted by industry leaders is the phenomenon of "brain drain," though not in the traditional sense of talent migration to Silicon Valley. Anupam Mittal has articulated a concerning trend where India's intellectual capacity is being diverted toward consumption of AI-generated content rather than creation of foundational AI technologies. This observation points to a broader systemic issue where the country's vast human resources are being channeled toward content consumption and platform engagement rather than fundamental research and development in AI technologies. The concern extends to generational impacts, with warnings about raising a generation that is overstimulated by digital content but under-inspired to engage in deep technological innovation.
The research ecosystem's effectiveness is further complicated by the gap between academic research and industry application. While India has established multiple Centers of Excellence for AI and initiated programs to strengthen the connection between research institutions and industry needs, the pace of translating academic research into deployable foundational models remains slower than global competitors. The government's focus on building indigenous AI capabilities includes supporting research in areas specifically relevant to Indian contexts, such as multilingual processing and domain-specific applications for agriculture, healthcare, and governance.However, the research community continues to work within resource constraints that limit the scale and scope of experimental work necessary for foundational model development.
The talent development challenge is being addressed through initiatives like the FutureSkills pillar of the IndiaAI Mission, which aims to create a workforce of 1.25 million to 1.35 million people with AI skills by 2027. This represents a significant scaling effort, yet the focus remains largely on application-level skills rather than the deep research capabilities required for foundational model development. The ecosystem would benefit from increased emphasis on training researchers capable of contributing to fundamental advances in model architectures, training methodologies, and the theoretical foundations of AI systems.
Policy Framework and Regulatory Environment
India's policy framework for AI development presents both enablers and constraints for foundational model development, with recent initiatives showing promise while highlighting implementation challenges. The IndiaAI Mission, approved in March 2024, represents the most comprehensive policy intervention in the AI space, structured around seven key pillars including compute capacity, innovation centers, datasets platform, application development, future skills, startup financing, and safe and trusted AI. The mission's budgetary allocation of ₹10,372 crores over five years demonstrates significant governmental commitment, though the allocation represents a fraction of the investments made by leading AI companies globally for foundational model development.
The policy framework's approach to data governance and privacy presents both opportunities and challenges for foundational model development. The Digital Personal Data Protection Act (DPDP) 2023 establishes comprehensive data protection standards that influence how organizations can collect, process, and utilize data for AI training purposes. While the act provides necessary privacy protections, it also creates compliance requirements that can complicate the large-scale data collection and processing essential for training foundational models. The integration of AI capabilities within the DPDP framework aims to automate compliance monitoring and enhance security, but also requires organizations to navigate complex regulatory requirements that may slow development timelines.
India's regulatory approach contrasts with frameworks like the European Union's AI Act, which establishes risk-based classifications for AI applications and imposes specific requirements based on risk levels. The EU's framework provides clearer guidance for foundational model development, particularly for general-purpose AI systems, while India's approach has been more focused on enabling development through infrastructure provision rather than establishing comprehensive regulatory frameworks. This difference in regulatory philosophy reflects India's position as a developing AI ecosystem seeking to encourage innovation rather than a mature ecosystem focused on managing risks of deployed systems.
The budget allocation patterns within the Ministry of Electronics and Information Technology reveal policy priorities that both support and potentially constrain foundational model development. AI and semiconductors have received the largest financial boost, representing around 60% of the ministry's budget increase in FY25. However, areas like Digital Public Infrastructure and data protection have received comparatively limited funding despite being identified as priorities. The allocation of ₹551 crores to the IndiaAI Mission in its first year represents a significant start, though foundational model development typically requires sustained, multi-year investment commitments that exceed current allocation levels.
Industry Landscape and Innovation Gap
The Indian AI industry landscape reveals a vibrant startup ecosystem alongside established IT services companies, yet both segments face distinct challenges in developing foundational AI models. India is home to more than 140 native AI startups that have collectively raised over $1.5 billion since 2020, demonstrating significant investor confidence in the sector's potential.Leading companies in this space include SarvamAI and Krutrim, which focus specifically on building Indic large language models, and ObserveAI, which has secured over $214 million for AI-powered customer and operational support solutions. The emergence of Krutrim as India's first AI unicorn represents a significant milestone, though the company's focus on Indic languages highlights both the opportunity and challenge of developing foundational models for India's multilingual context.
Established IT services companies like Infosys, TCS, and Wipro have traditionally focused on AI implementation and consulting services rather than foundational model development. Infosys, for example, offers comprehensive AI and automation services including consulting, platform development, and cognitive solutions, but these services primarily involve implementing and customizing existing AI technologies rather than developing new foundational models. This focus reflects the companies' business models, which have historically emphasized service delivery and implementation rather than fundamental research and product development. The transition from service-oriented AI work to foundational model development requires different capabilities, investment patterns, and risk tolerance than traditional IT services business models.
The innovation gap becomes apparent when comparing Indian companies' approaches to those of global leaders. Companies like OpenAI, Google, and Anthropic have made substantial investments in foundational model research, often with funding in the billions of dollars and multi-year research commitments. Indian companies, by contrast, have generally focused on application-specific AI solutions or adaptations of existing models for local markets. The development of Indic LLMs represents an important step toward foundational model development, with companies creating multilingual models that can process content in multiple Indian languages. However, these efforts often build upon existing model architectures rather than developing new foundational approaches to language modeling or AI system design.
The public-private partnership model established through the IndiaAI Mission aims to bridge this innovation gap by providing shared computing resources and funding for foundational model development. The mission's startup financing pillar specifically targets supporting companies working on foundational AI technologies, while the innovation center component aims to facilitate collaboration between industry and research institutions. However, the success of these initiatives depends on the ecosystem's ability to attract and retain talent capable of foundational model development, as well as the willingness of companies to invest in long-term research rather than short-term application development.
Linguistic and Cultural Barriers
The development of foundational AI models for India faces unique challenges related to the country's extraordinary linguistic and cultural diversity, requiring approaches that differ significantly from models developed for primarily English-speaking markets. India recognizes 22 official languages and is home to over 1,600 spoken languages and dialects, creating complexity in data collection, model training, and system evaluation that exceeds most other markets globally. The government's emphasis on developing AI models that understand Indian languages, dialects, and specific industry needs reflects recognition that simply adapting English-language models is insufficient for serving India's diverse population effectively.
The Bhashini project, developed by the Ministry of Electronics and Information Technology under the National Language Translation Mission, represents a significant effort to address linguistic barriers through AI-powered translation and communication tools. Launched in August 2022, Bhashini utilizes natural language processing and AI to enable communication across different language speakers, providing a foundation for multilingual AI development. The platform offers more than 300 pre-trained AI models accessible through Open Bhashini APIs, demonstrating both the scale of linguistic diversity and the technical infrastructure required to support multilingual AI systems. High-profile usage by Prime Minister Modi and Finance Minister Sitharaman for real-time speech translation showcases the platform's capabilities while highlighting the ongoing need for improved accuracy and fluency in machine translation.
The technical challenges of developing multilingual foundational models extend beyond translation to encompass cultural context, regional variations in language usage, and domain-specific terminology across different sectors. Companies like GenVR Research have developed Indic LLMs capable of conversation in 13 Indian languages, including specialized models like the first Large Action Model (LAM) for Indic languages that can detect actions from user chats and perform function calling. These developments represent significant progress, yet they also highlight the complexity of building foundational models that can effectively serve India's linguistic diversity while maintaining performance standards comparable to monolingual models.
The cultural dimension of AI model development presents additional challenges that go beyond linguistic capabilities. Indian AI models must navigate cultural nuances, social contexts, and value systems that may differ significantly from the datasets and training approaches used for global models. The government's focus on developing AI solutions tailored to local needs emphasizes this requirement, recognizing that foundational models must understand not just Indian languages but also the cultural contexts in which those languages are used. This requirement adds complexity to data collection, annotation, and evaluation processes, requiring expertise in cultural and linguistic domains alongside technical AI development capabilities.
Comparative Analysis with Global Leaders
India's position in foundational AI model development becomes clearer when analyzed against the progress and strategies of global leaders, revealing both the scale of the challenge and potential pathways for advancement. The United States leads in foundational model development through companies like OpenAI, Google, and Anthropic, which have invested billions of dollars in research and development over multiple years. OpenAI's ChatGPT, for example, was trained using approximately 25,000 GPUs, while India's entire IndiaAI Mission infrastructure comprises 18,693 GPUs serving the entire ecosystem rather than a single model. This comparison highlights the resource intensity of foundational model development and the scale of investment required to compete at the global level.
China's approach to AI development provides another instructive comparison, particularly in terms of government coordination and strategic planning. China's AI development has been characterized by substantial state investment, coordination between government and private sector entities, and focused efforts on specific AI capabilities deemed strategically important. The Chinese model includes significant investment in computing infrastructure, data collection capabilities, and research institutions, supported by policy frameworks that facilitate rapid deployment and iteration of AI technologies. India's IndiaAI Mission shows similarities in terms of government coordination and infrastructure investment, though at a smaller scale and with different emphasis on democratic access to computing resources.
The European Union's approach through initiatives like the AI Act provides a framework focused on regulatory governance and risk management rather than foundational model development.However, European research institutions and companies have made significant contributions to foundational model research, often through collaborative projects and substantial research funding. The EU's emphasis on open-source model development and regulatory frameworks for responsible AI deployment offers lessons for India's approach to balancing innovation with governance requirements.
Emerging economies that have made progress in AI development offer particularly relevant comparisons for India's strategy. Countries like the UAE have made substantial investments in AI infrastructure and research capabilities, often through targeted initiatives and international partnerships. Israel's success in AI development has been driven by a combination of military research applications, strong academic institutions, and a vibrant startup ecosystem supported by significant venture capital investment. These examples suggest that focused investment, clear strategic priorities, and effective coordination between different ecosystem stakeholders can enable rapid progress even without the scale advantages of larger economies.
The comparative analysis reveals that India's challenges in foundational model development are not unique, but the solutions require adaptation to India's specific context, including linguistic diversity, economic constraints, and the scale of potential impact. India's approach through the IndiaAI Mission emphasizes democratized access to computing resources and inclusive development, which could provide advantages in developing models that serve diverse populations effectively, even if the initial pace of development may be slower than more resource-intensive approaches.
Conclusion
India's lag in foundational AI model development reflects a complex interplay of infrastructure constraints, ecosystem maturity, and the unique challenges of building AI systems for one of the world's most linguistically and culturally diverse populations. While the country has made significant strides through the IndiaAI Mission and substantial budget allocations, the gap with global leaders remains substantial, particularly in terms of resource scale and research output. The government's strategic approach of democratizing AI access through shared computing infrastructure and subsidized resources represents an innovative model that could provide long-term advantages, even if it initially limits the concentration of resources available for any single foundational model development effort.
The path forward requires sustained commitment to several key areas: continued infrastructure scaling beyond current GPU allocations, enhanced coordination between research institutions and industry, and focused talent development programs that emphasize foundational research capabilities rather than just application skills. The linguistic and cultural complexity of developing AI for India presents both challenges and opportunities, potentially enabling the development of foundational models that better serve diverse populations globally. Success will depend on maintaining current investment levels while improving execution efficiency, fostering deeper collaboration between public and private sector entities, and developing evaluation frameworks that capture the unique requirements of multilingual, culturally diverse AI systems.
India's approach to foundational AI model development may ultimately provide a different model from the resource-intensive strategies of global leaders, emphasizing inclusive access, cultural relevance, and democratic participation in AI development. While this approach may initially result in slower progress compared to well-funded private sector initiatives, it could provide sustainable advantages in developing AI systems that serve broader populations effectively and demonstrate alternative pathways for AI development in emerging economies worldwide.