Perplexity AI CEO and co-founder Aravind Srinivas has ignited a heated debate within the Indian tech ecosystem by sharing a post on the social media platform X, where he expressed his disagreement with Infosys co-founder Nandan Nilekani's perspective on the country's approach to artificial intelligence (AI) development. Srinivas acknowledged Nilekani's immense contributions to India, particularly his role in co-founding Infosys and driving the development of the United Payments Interface (UPI), but took issue with his advice regarding AI development. Specifically, Srinivas disagreed with Nilekani's suggestion that India should refrain from creating large language models (LLMs) and instead focus on building practical applications on top of existing models.
In his post, Srinivas described Nilekani as "awesome," recognizing the entrepreneur's far-reaching impact on India’s tech and financial sectors. However, Srinivas argued that Nilekani’s stance on AI in India is misguided. While Nilekani advocates for a practical, application-driven approach to AI, Srinivas believes that India must also prioritize developing the foundational capabilities required to train its own AI models. According to Srinivas, both model training and application development are equally important for India to establish itself as a global AI leader. He emphasized that the nation must invest in building its own AI muscle, which involves not just improving existing models, but creating robust models from scratch that can compete internationally.
Srinivas’s comments reflect his frustration with a tendency in India to lean too heavily on open-source models, which may limit innovation and hinder India’s competitiveness in the long run. In a follow-up post, Srinivas expressed: "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 and just focus on building on top of existing models. Essential to do both." This statement underscores his belief that for India to excel in AI, the country must develop expertise in creating its own models and not simply rely on external solutions.
Srinivas went further to demonstrate his commitment to India's AI future by announcing a personal investment offer. He expressed his willingness to invest $1 million of his own money and dedicate five hours per week of his time to help assemble a highly skilled team to focus on AI development in India. "I am ready to invest a $1 million personally and 5 hours/week of my time into the most qualified group of people that can do this right now for making India great again in the context of AI," he wrote. This offer reflects Srinivas's strong belief in the potential for India to lead in AI, provided it takes a bolder, more strategic approach. He also made it clear that the team he hoped to build should be highly focused and dedicated, drawing comparisons to the intensity and commitment of the DeepSeek team, a reference to an AI group known for its remarkable accomplishments.
In addition to his investment offer, Srinivas reflected on his own experiences with Perplexity AI, where he initially believed that training AI models would be prohibitively expensive. However, he now sees an opportunity for India to challenge that notion, urging the country to follow a similar trajectory to the Indian Space Research Organisation (ISRO), which achieved remarkable success in space exploration with relatively modest resources. Srinivas suggested that India should take this opportunity to demonstrate its ability to train competitive AI models, particularly ones that go beyond being tailored for Indian languages and can perform well across global AI benchmarks.
The broader implication of Srinivas's message is that India's AI ecosystem must evolve to become more self-reliant, fostering both innovation and expertise in developing its own models rather than merely relying on external solutions. He believes that this shift in approach would enable India to showcase its technological prowess and take a leadership role in the global AI landscape. As AI technologies continue to advance rapidly worldwide, particularly in the areas of machine learning, deep learning, and natural language processing, Srinivas's call for India to build its own foundational models is a critical point of discussion. This approach, he argues, would not only boost India's competitiveness but also allow the country to create AI applications that are genuinely reflective of its unique needs and challenges.
The debate over whether India should focus on developing its own AI models or build on existing frameworks has taken on increased urgency as the country grapples with its role in the global AI race. While India has made significant strides in tech innovation, including in areas like software development, digital payments, and telecommunications, AI remains a key frontier where the country is seeking to position itself as a major player. Srinivas’s challenge to Nilekani's approach calls for a more ambitious and assertive vision for India’s AI future—one that encourages both foundational research and practical applications. With his willingness to invest in this vision, Srinivas is demonstrating his commitment to not only changing the AI landscape in India but also ensuring that the country plays a leading role in shaping the future of artificial intelligence.
