Small Successes Mask Fundamental Gaps in India’s AI Ecosystem

The growth in India’s AI startup ecosystem, powered by waves of capital and a global-first mindset, often makes the headlines. However, these success stories mask persistent deficiencies in infrastructure, R&D investment, and innovation capacity. 

This systemic gap keeps the AI ecosystem heavily reliant on foreign foundational models, as it lacks sufficient deep research capacity, and struggles to transition from derivative applications to developing core technology. 

Sanchit Vir Gogia, CEO and chief analyst of Greyhound Research, noted that familiar challenges hinder India’s goal to develop GPT-scale models. He said that 58% of decision-makers see the lack of affordable GPU clusters as the main obstacle. In comparison, 52% cite the absence of properly cleared Indic datasets and 49% mention uncertainties regarding model-release regulations and cross-border data flows.

These choke points force most Indian teams to “stick to fine-tuning and distilling imported models rather than attempting true sovereign pre-training,” he said.

The IndiaAI Mission, a ₹10,372 crore initiative, has shifted some focus toward closing these gaps with a call for proposals for indigenous foundational models, robust compute infrastructure, and open data commons. 

But, Gogia emphasises: “Application wins matter, but they need to be anchored to three shared assets: a national compute commons with multi-tenant scheduling, a fully licensed Indic Data Commons, and a transparent and stable regulatory regime for model release and data transfer.”

Applications Over Deeptech

The Times of India reported an “unprecedented” fundraising spree among Indian-origin AI startups employing a global-first approach. Firms like GigaML and Atomicwork have secured early-stage rounds from marquee investors such as Redpoint Ventures, Khosla Ventures, and Lightspeed India. 

The trend is evident: Businesses originating from India are embracing a global-first strategy for entering the market, the TOI noted, with numerous startups attaining substantial annual recurring revenue or securing high valuations while extending their reach beyond India from the outset. This progress indicates both investor trust and an acknowledgement that India’s tech talent is capable of competing on a global stage.

Success stories, however, represent only one layer of India’s AI narrative, where funders favour applications over deeptech. 

India’s VC landscape loves a quick win: much of the capital chases applied AI and rapid commercialisation, chat agents, workflow automation, and analytics tools built on top of Western LLM APIs. 

This productisation-first bias delivers fast adoption and visibility, but as Shreshth Bhatt, a senior research associate at Global Market Insights, noted, “This is not innovation, but the application of existing models … value (innovation) is coming from the West. In the long term, western countries can easily build applications using the foundational models (which they themselves have invented), but India could lag as our innovation relies on the Western technology giants.” 

Without a shift in investor appetite, he warned, “India’s role will remain that of an AI customer, not a creator.”

Data support this trend. In 2024, funding for India’s deeptech sector, including AI, has increased by 78%, yet most of it was directed towards application-level businesses rather than fundamental model research. Funding for early-stage deeptech decreased by 37% as late-stage, market-ready solutions led the transactions.

What’s the Roadblock?

India faces challenges that hinder its innovation, particularly in artificial intelligence. A notable shortage of high-performance AI hardware limits the development of advanced models.

While initiatives like hyperscaler credits and domestic clusters exist, the unpredictable nature of access and system tenancy complicates the efforts for seamless computational support. In addition to hardware constraints, the lack of domain-specific datasets poses a challenge.

Bhatt highlighted that many foundational LLMs in India rely on English-centric datasets, raising concerns about their relevance in a diverse linguistic context.

The current research culture often drives talented individuals abroad, not just for better salaries, but also for improved access to resources, mentorship, and fewer bureaucratic hurdles and infrastructure, he said. Domestic labs typically impose restrictive publication rights and limited collaboration, hindering innovation and talent retention.

Venkata Subramaniam, lead executive of IBM Quantum India, noted that India’s main challenge is that students and early-career researchers seldom get to work on real-world problems at scale. Innovation requires experimentation and iteration, but most talent is limited to theoretical or small projects.

“If a student in a rural college knows exactly what challenge their local farmers face, they should have the computing tools and mentorship to build an AI model to solve it. Today, that bridge simply doesn’t exist,” he added. 

Subramaniam believes the gap in AI development stems from frontier-level infrastructure and training being concentrated in a few corporate labs, leaving most universities and rural areas behind. 

To address this, he suggested implementing shared national GPU clusters for student-led projects focused on India-specific use cases, along with “Build-for-your-community” programs that connect local problem owners with AI talent and a curriculum that integrates domain-specific challenges like crop disease detection with real computing access.

Headline Wins to Global AI Leadership

Gogia aptly summarised: “Public funding, industry matching, and mission-style grants for foundational research must rise together, otherwise India will keep doing the applied work on someone else’s core science.”

Additionally, the establishment of open, rights-cleared domain datasets that accurately represent India’s diverse linguistic and sectoral landscape will be essential. 

A stable and transparent regulatory framework is necessary to facilitate model development, deployment, and cross-border collaboration. Furthermore, implementing long-horizon funding models will safeguard deep R&D initiatives, allowing them to thrive beyond the initial phase of commercial productisation.

Subramanium said the country still hasn’t done enough with productisation to create products that genuinely impact society.  In India, where technology penetration remains low, productisation should be a top priority. “What we lack is the hard engineering needed to take that science to market as real, impactful products,” he said. 

India’s next AI milestone shouldn’t be only another unicorn with a global-first go-to-market strategy, but a homegrown breakthrough in AI science, a sovereign model, algorithm, or dataset that becomes foundational for the world.

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