AI wrapper and search platform Perplexity AI’s CEO Aravind Srinivas recently said that Infosys co-founder Nandan Nilekani is wrong in pushing Indians to ignore model training skills and focus on building on top of existing models. “Essential to do both,” Srinivas argued in a post on X.
“To be clear: Nandan Nilekani is awesome, and he’s done far more for India than any of us can imagine through Infosys, UPI, etc. But he’s wrong in pushing Indians to ignore model training skills…,” he further said, countering Nilekani’s vision of making India the ‘AI use case capital’ rather than investing in foundational model training.
Nilekani’s argument is that India should prioritise AI applications over competing with the US and China in developing large-scale models. “Our goal should not be to build one more LLM. Let the big boys in the (Silicon) Valley do it, spending billions of dollars,” he said.
He is correct, as India missed the generative AI bus long back.
Notably, Srinivas is too late in the conversation. Others like Ola CEO Bhavish Agarwal, Tech Mahindra CEO CP Gurnani, and HCL co-founder Ajai Chowdhry are, however, optimistic and believe that something like NVIDIA can be born out of the country.
Paras Chopra, founder of Turing’s Dream, an AI research firm, said, “Many AI labs in India focus on local problems, similar to Indian companies. But the internet has no boundaries, so why not aim to be world-class?” He encouraged founders to specialise in niches but strive to be ‘SOTA’ (state-of-the-art) in those areas.
Tech Mahindra is probably the only system integrator to have built its own models from scratch. Other Indian IT firms are relying on existing solutions to create tangible outcomes.
Besides, while startups like Sarvam AI, TWO, and Krutrim are building products, the impact that they have created is minuscule compared to something like OpenAI’s ChatGPT.
“We can’t always have the service mindset, and definitely not when everyone is chasing ASI,” KissanAI CEO Pratik Desai said.
Similarly, Soket AI Lab’s Abhishek Upperwal also believes that India should invest in deep tech startups. “Building LLMs isn’t just about having another AI model in the market. It’s about owning the future. Nations, organisations, and individuals who create these models will dictate industries, policies, and progress.”
India is often dubbed as the use case capital of the world when it comes to most technological advancements. The case against creating newer foundational models is usually because building LLMs from scratch is an expensive, time-consuming, and capital-intensive task.
Last year, Nilkeni said that India’s approach to AI will be different from the global trend of building LLMs. Instead, India should focus on building AI use cases that will reach every citizen. Nilekani emphasised India’s advantage in its population and aspirations, urging the use of existing technology to address challenges rather than waiting for the next $10 trillion model.
“The Indian path in AI is different. We are not in the arms race to build the next LLM…We are here to make a difference, and our aim is to put this technology in the hands of people,” he said.
Perplexity AI is a Wrapper
Commenting on the nature of wrappers, Srinivas said in an earlier podcast, “Wrappers are at all levels; it’s just that they have given you so much value that you do not care.”
The term ‘wrappers’ first came into use in 2023 after the boom of GenAI startups, which were built on existing LLMs, causing a lot of uproar, especially in the VC ecosystem. Srinivas recently met Prime Minister Narendra Modi in the national capital earlier this month.
Many founders and developers build on existing LLMs, creating value at the application layer. Healthify is an interesting case in point, which was featured in OpenAI’s recent DevDay. It demonstrated real-time AI conversations in Hindi using OpenAI APIs. AIM has extensively covered how Indian AI companies, which were built on Llama’s open-source platform in India, delivered great value.
Srinivas Turns to India to Build Foundational Models
“I’m not in a position to run a DeepSeek-like company for India, but I’m happy to help anyone obsessed enough to do it and open-source the models,” Srinivas added. He stressed that India should shift from reusing open-source models to building globally competitive models excelling in both Indic languages and broader benchmarks.
This development comes against the backdrop of China’s DeepSeek lab, taking the internet by storm with the launch of its open-source reasoning models – DeepSeek-R1 and DeepSeek-R1-Zero. These serve as alternatives to proprietary systems like OpenAI’s o1.
“China is currently leading some of the best open-source models (Minimax and DeepSeek V3) and keeps delivering great video models as well (Hailuo and Kling), despite being constrained by US sanctions and cannot purchase the best chip-making tools from ASML (Netherlands) nor the best AI chips,” a user on X commented.
In contrast, Indian innovators face a lack of investment. AIM’s research found that Indian startups require only $10 million to begin foundational AI research, yet funding remains scarce.
Mohandas Pai, head of Aarin Capital, highlighted this issue and said, “Who will give $200 million to a startup in India to build an LLM? Why is nothing like Mistral coming from India?”
Pai called for a government-backed innovation fund, similar to France’s $36 billion France Innovation Fund, which supports startups like Mistral. Such funding, he argued, could enable India to produce foundational models and compete on a global scale.
