Introduction: Sidelines in the AI Race
In the high-stakes world of artificial intelligence, India finds itself on the sidelines, watching global giants like the US and China dominate the development of large language models (LLMs). As of August 2025, India has not produced a world-class foundational LLM equivalent to models like Grok-4 or GPT-4o. Efforts are mostly limited to smaller, Indic-language models from startups, highlighting a deeper issue: India may be too late to join this technological party. The barriers are multifaceted, involving prolonged timelines, massive investments, and revenue structures ill-suited to India’s economic realities. These challenges are further exacerbated by geopolitical tensions, such as Donald Trump’s recent tariff snub, which underscores India’s inability to counter US dominance either economically or technologically. Shifting this momentum requires not just resources but a profound change in vision and execution, making the path to creating viable LLMs even more daunting.
A Significant Hurdle
The timeline for building a competitive LLM is a significant hurdle. Developing such a model from scratch typically takes 6 to 18 months, involving extensive data collection, training, and iteration. This process demands petabytes of high-quality data and access to thousands of high-performance GPUs, which generate immense computational power. In India, domestic infrastructure for this scale is severely underdeveloped. AI-specific compute capacity is projected to represent only 1-2 percent of global totals by 2030, leaving the country reliant on foreign cloud services that come with high costs and potential data sovereignty issues. Moreover, India’s diverse linguistic landscape—22 official languages and hundreds of dialects—complicates data curation, as datasets must be multilingual and culturally nuanced to avoid biases. Talent shortages compound the delay; while India boasts millions of tech workers, top AI researchers often migrate abroad, drawn by better opportunities in Silicon Valley or Beijing. This brain drain means that even if projects start, they face extended timelines due to skill gaps in advanced machine learning techniques.
An Insurmountable Barrier
Investment scale is another insurmountable barrier. Creating an LLM requires $100 million to $1 billion per model for research, data, and training, plus $5-10 billion for building or accessing data centers capable of handling gigawatt-level energy demands. India’s market investments are dwarfed by global standards. Startups raised just $109 million in equity through July 2025, placing the country 10th worldwide for private AI funding over the past decade, with a cumulative total of around $7-8 billion. In contrast, US companies like xAI secure billions in single funding rounds. Public funding, while present, is insufficient; the government’s allocations are spread thin across multiple sectors, and private venture capital prioritizes quick-return applications over risky foundational research. Without this capital, Indian entities cannot afford the hardware—such as Nvidia’s advanced GPUs—or the energy infrastructure needed, leading to a cycle where prototypes remain small-scale and uncompetitive.
A Fundamental Mismatch
Revenue structures further undermine economic viability. Global LLMs thrive on freemium models, where 1-5 percent of users convert to paid subscribers, generating billions through $20-300 monthly fees despite low ratios. In India, however, affordability reigns supreme, with average revenue per user hovering at $5-10 per month. Consumers favor free or low-cost tools, and paid conversions could dip below 2 percent due to price sensitivity and alternatives from global players. Enterprises offer some potential, but foreign firms capture 80 percent of India’s AI spend, leaving locals with razor-thin margins from service-based customizations. Break-even for an LLM project could stretch beyond a decade, far longer than the 5-7 years seen in the US, where scale and higher ARPU accelerate returns. This mismatch means investments yield diminishing viability, deterring further funding and perpetuating the lag.
A Flawed Framework
India’s policy framework, while ambitious, reveals critical flaws in execution. The IndiaAI Mission, launched in March 2024 with a ₹10,000 crore ($1.2 billion) budget over five years, emphasizes “AI for All” through compute grants, Indic datasets, and foundational model development. Initiatives include a January 2025 request for proposals on multimodal LLMs and plans for an AI Impact Summit in 2026. Eight guiding principles—covering transparency, safety, and equity—draw from international standards. However, disbursal is sluggish, with only 20-30 percent of funds utilized by mid-2025. Regulatory ambiguity persists without a dedicated AI law, relying instead on advisories that create uncertainty for investors. Experts from bodies like NITI Aayog, IITs, and NASSCOM are involved in task forces, bringing academic and industry insights. Yet, critics argue the approach is too bureaucratic, lacking the agility of US models like DARPA, which foster rapid innovation through direct funding and partnerships. The focus on socio-economic applications—in agriculture, healthcare, and education—is laudable but diverts from building core LLM capabilities, resulting in incremental progress rather than breakthroughs.
A Limited Landscape
Private players in India’s AI space demonstrate potential but operate at a disadvantage. Sarvam AI, a Bangalore-based startup, raised $41 million by 2025 and launched an Indic LLM in May, specializing in voice-enabled models for local languages. Krutrim, backed by Ola and valued at $1 billion, introduced a multilingual LLM in 2024, trained on Indic data, with $50 million from investors like Matrix Partners. Tata Elxsi, a public firm, invests around $100 million annually in domain-specific AI for sectors like automotive, leveraging its conglomerate ties. Reliance Jio is building AI data centers with $200 million+ commitments, aiming for infrastructure scale. Other notables include Qure.ai in health AI ($65 million funded) and various startups focusing on chatbots or analytics. However, funding remains fragmented—mostly for applications rather than foundational models—due to perceived risks in a low-margin economy. These players innovate incrementally but lack the capital for global-scale compute, often relying on foreign clouds, which raises sovereignty concerns and increases costs.
Amplifying Headwinds
Geopolitical headwinds, epitomized by Donald Trump’s tariff snub, amplify these challenges. On August 6, 2025, Trump imposed an additional 25 percent tariff on Indian imports—bringing totals to 50 percent—as retaliation for India’s Russian oil purchases, effective after a 20-day grace period. This “reciprocal” penalty targets India’s trade surplus, threatening $200 billion in bilateral trade, including key exports like garments and pharmaceuticals. Trump, self-proclaimed “tariff king,” exploits US economic leverage, underscoring India’s inability to retaliate effectively. Technologically, this extends to AI: US controls critical inputs like advanced chips, and potential export bans could cripple Indian efforts. The snub diverts resources—straining forex reserves and forcing policy shifts away from tech investments toward trade negotiations.
A Compounded Crisis
Blending this with LLM economics paints a grim picture. Tariffs erode fiscal space for AI R&D, while US dominance in hardware and data flows isolates India further. The momentum needed to counter—reversing brain drain, tripling investments, and forging alliances—demands visionary leadership beyond current bureaucratic frameworks. Even if overcome, the 10+ year break-even horizon, amid low conversions and foreign competition, makes LLM creation economically tenuous.
Conclusion: A Narrowing Window
India’s window is narrowing; without radical reforms, projected $500 billion GDP gains from AI by 2030 will remain elusive. The Trump episode serves as a stark reminder: In the AI arena, economic power shapes technological futures, and India must adapt swiftly or risk permanent exclusion.
