The optics problem
India does not lack talent in Artificial Intelligence. What it lacks is depth, patience and ownership.
The recent incident of an Indian university presenting imported technology as its own creation should not be dismissed as a minor embarrassment. It reflects a deeper issue. Too often, the focus is on showcasing innovation rather than building long term capability.
India hosts global AI summits and regularly speaks about becoming a global leader. Yet the core layers of the AI ecosystem such as large scale computing, foundational models and advanced infrastructure remain heavily dependent on global technology companies.
Collaboration with global players is important and necessary. The real question is whether India is building enough capability of its own.
India’s strengths and the structural gaps
India’s starting position is strong. The country has built world class digital public infrastructure through Aadhaar, UPI and CoWIN. It produces a large pool of skilled engineers and data professionals. Indian talent plays a key role in global technology companies. The domestic market provides scale, diversity and real world use cases.
However, three structural gaps continue to slow progress.
Access to affordable large scale computing remains limited. Training advanced AI systems requires high performance GPU infrastructure, which is expensive and often controlled by global cloud providers.
Long term research ecosystems are still evolving. Universities need sustained funding, stronger industry collaboration and the flexibility to pursue high risk research over many years.
Deep technology also requires patient capital. Much of the current investment environment still favours quick scaling applications rather than foundational technologies that may take a decade to mature.
Equally important is credibility. Innovation ecosystems grow when claims are matched by real capability and large scale deployment.
Startups that are building real capability
Despite these challenges, several Indian startups are working on foundational and scalable AI.
Sarvam AI is building large language models designed for Indian languages and public use cases and has been selected under the national IndiaAI Mission to strengthen sovereign AI capability.
Krutrim, founded by the Ola group, has developed multilingual AI models supporting more than twenty Indian languages and became India’s first AI focused unicorn, reflecting growing investor confidence in core AI development.
Companies such as Neysa are working to build domestic GPU cloud infrastructure so that Indian enterprises and startups can train and deploy AI systems without relying entirely on foreign hyperscalers.
In sector specific applications, Qure.ai has deployed AI based medical imaging solutions across multiple countries, while Niramai’s breast cancer screening technology is being used in hospitals and outreach programmes. These examples show that Indian innovation can scale globally when supported with the right ecosystem.
The emerging startup landscape shows promise. The next step is enabling these efforts to grow at national scale.
The strategic role of CSR
Corporate Social Responsibility can play a critical role in strengthening the foundations of India’s AI ecosystem.
One of the strongest examples is the Wadhwani Institute for Artificial Intelligence, supported through a thirty million dollar philanthropic commitment. The institute develops AI solutions for agriculture, public health, education and government systems, with a clear focus on large scale social deployment.
CSR support is also helping technologies such as Niramai’s screening platform reach underserved communities through health camps and public programmes. This demonstrates how corporate funding can help move innovation from pilot to real impact.
Indian organisations are also part of global collaborations such as the AI Alliance, which includes members like Infosys, AI4Bharat, Wadhwani AI and Sarvam AI working to advance open and responsible AI development.
The opportunity for CSR is significant. Corporate funds can support university research labs, sponsor fellowships for students and faculty, create shared computing facilities and fund real world pilot deployments. CSR can act as early stage risk capital in areas where commercial investment may be hesitant.
Used strategically, CSR can help build talent pipelines, strengthen research capacity and enable deep tech startups to reach scale.
The policy and ecosystem shift needed
If India wants to move from adoption to leadership, the focus must shift from visibility to outcomes.
The country needs national scale computing infrastructure that universities and startups can access at affordable cost. Public and private capital must support long horizon deep tech investments. Universities need stronger global partnerships and industry led research centres.
Government departments can accelerate innovation by acting as early customers for Indian AI solutions in sectors such as healthcare, agriculture, education and public services. Open datasets and language resources that reflect India’s diversity will also be critical.
Most importantly, progress should be measured through deployments, research output, global products and infrastructure created, not through the number of events or announcements.
The road ahead
India’s advantage is real. The country has talent, scale and a strong digital foundation. Startups are beginning to build foundational models. CSR and philanthropy are supporting social scale deployment. Industry and research collaborations are slowly strengthening.
But leadership in AI will not come from ambition alone. It will come from patient investment, credible execution and a clear shift from showcasing technology to building and owning it.
The choice is straightforward. India can remain one of the world’s largest users of AI. Or it can invest seriously and steadily to become one of the countries that shape its future.
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