By Vivek Chadha, Founder, AccelerateX Ventures
India’s startup ecosystem has seen exponential growth in the past decade, producing thousands of ventures across fintech, edtech, agritech, and more. Yet, when it comes to AI adoption, most of these companies remain behind their global peers, not in usage, but in building. AI tools are being applied, but not created. This imbalance exposes a deeper issue in how Indian startups approach technological evolution.
Consumers, Not Creators of AI
The prime problem is that Indian startups act largely as consumers of AI rather than creators. Tools like OpenAI’s GPT models and Meta’s open-source architectures are widely deployed in Indian applications, but the foundational models, where the real value lies, are still being developed elsewhere. A quick example is Sarvam LLM, India’s homegrown language model, which showcases potential in foundational AI work. Although initiatives like Sarvam LLM are promising steps, they remain isolated examples rather than part of a national deep-tech trend. OpenAI CEO, Sam Altman once acknowledged India’s leadership in AI usage, but also pointed out its absence in foundational development. This signals a consumption-first approach, not an innovation-driven mindset.
Capital Constraints and Short-Term Thinking
Funding is the next roadblock. India’s investment in AI is dwarfed by the financial commitments seen in the US and China. According to the Stanford report, Stanford AI Index 2025, India attracted $1.16 billion in private AI investments in 2024; furthermore, from 2013 to 2024, the total amount of private investments in AI in India reached $11.29 billion, while China touched $120 billion, and the US hit $470 billion. Without serious capital allocation, especially in early-stage research, Indian startups cannot afford to engage in deep-tech development. Investors in India focus too heavily on 3-5 year returns, which discourages risk-taking in AI, where returns often take a decade to materialise.
Venture capital in the US has long embraced the delayed return curve inherent in deep-tech innovation. OpenAI took nearly a decade before commercialising any product. Yet, backers remained patient, understanding the potential upside. In India, that patience is rare. Many funds still treat tech ventures like retail businesses, expecting results on tight timelines. That pressure pushes founders to seek quick integrations of foreign APIs rather than investing in original R&D.
Talent Gaps and Missing Research Ecosystem
Despite India producing large numbers of engineers and having a robust STEM education system, the depth required for AI innovation is lacking. The ecosystem for PhD-level research, specialised AI programs, and collaborative innovation between academia and industry is weak. Most advanced AI work globally is being carried out by individuals with years of research experience, many of them Indians, but working abroad. The ecosystem within the country fails to retain or nurture such talent to the same standard.
This talent gap stems not only from educational limitations but also from the lack of a nurturing innovation environment. AI development requires access to high-end infrastructure, a research-oriented culture, and long-term institutional backing. India has yet to build that at scale. Engineers educated in India often thrive when placed in US labs, but rarely achieve the same breakthroughs within Indian companies. That is not a question of competence, but the surrounding system.
Data Deficiency
India has a massive digital footprint, but most of this data resides with global technology platforms. For AI to mature domestically, startups need access to high-quality, multilingual, India-specific datasets. Without data, there is no model training. And without language models attuned to Indian languages, sectors like governance, healthcare, and education will remain underserved by AI.
Even when data is available, compute infrastructure becomes the next bottleneck. Training large AI models requires high-end GPU clusters, which are expensive and largely unavailable in India. The government has made efforts, like enabling access to 14,000 GPUs, up from 10,000 announced earlier in 2024, through IndiaAI Mission, but this remains a fraction of what is needed to compete globally. Countries like the US and China have been building such infrastructure for years. Indian startups trying to match them without the same foundation are always a few steps behind.
Sectoral Resistance
Sector-specific hesitancy is also holding AI adoption back. In healthcare, for instance, there’s institutional resistance. When technologists suggest that agentic AI can handle diagnostic tasks, doctors push back. The fear of job losses overshadows conversations about efficiency or coverage. This fear-based inertia prevents AI integration into critical sectors. Construction, BFSI, logistics, and other industries have potential for AI-driven transformation but remain conservative in deployment.
Momentum Exists, But It is Not Matching Global Pace
Initiatives like the Sarvam LLM and government-backed missions indicate a policy-level recognition of the AI gap. While these signal intent, their impact remains limited due to delays and underfunding relative to the global race. By the time India builds its LLMs and scales its infrastructure, others may have already moved ahead with next-gen systems.
Solving this problem requires an overhaul in how Indian startups and policymakers treat AI. First, AI investments must be treated with a long-term view. Capital should be made available specifically for foundational AI development, not just SaaS overlays and chatbot layers. Second, the ecosystem needs to attract and retain deep-tech talent. This will need partnerships between academia, private research labs, and government institutions. A superficial push for AI certifications will not suffice. Third, data access must be democratised for startups. Government-led open data frameworks focused on Indian contexts, languages, behaviours, and economic activity can provide startups with the raw material to build competitive AI models. Finally, high-end computing infrastructure has to be made more accessible. Subsidised GPU cloud access or national compute grids can help early-stage companies get started without million-dollar capital requirements. Subsidised GPU cloud access or national compute grids can help early-stage companies get started without million-dollar capital requirements. Public-private partnerships could operationalise this by leveraging India’s existing cloud infrastructure firms.
India’s digital economy is growing. The startup ecosystem is maturing. But unless foundational AI development is prioritised, backed by money, talent, data, and compute, India will remain an AI user, not a builder. What’s missing is a coordinated push to build. If we don’t act now, we will merely watch the next AI revolution unfold from the sidelines.