Narayana Murthy Slams India’s AI Hype: ‘Old Programs Dressed as AI’
At TiE Con Mumbai 2025, Infosys founder N.R. Narayana Murthy criticized the overhyped portrayal of AI in India, calling out companies for branding basic software as AI. He explained the core principles of AI, distinguishing between machine learning, which uses large data for predictions, and deep learning, which mimics the human brain and operates through unsupervised algorithms. Murthy argued that deep learning has greater potential to replicate human behavior, while many so-called AI programs in India are merely outdated software.
He emphasized the importance of differentiating true AI from simple code. Infosys, on the other hand, is working on its own AI models, focusing on Small Language Models (SLM) with open-source and proprietary datasets, aiming to create meaningful AI applications tailored to specific industries.
Narayana Murthy Slams India’s AI Hype: ‘Old Programs Dressed as AI’
At the TiE Con Mumbai 2025 conference, Infosys co-founder N.R. Narayana Murthy voiced significant concerns about the portrayal of artificial intelligence (AI) in India, challenging the widespread practice of repackaging rudimentary software under the guise of cutting-edge AI. During his address, Murthy highlighted a troubling trend where outdated programs are rebranded with flashy AI terminology to capitalize on the hype surrounding the technology. He stressed that this misrepresentation not only misleads the public but also undermines genuine advancements in the field.
Murthy underscored the critical distinction between authentic AI technologies and the superficial labeling prevalent in India’s tech ecosystem. He clarified that true AI encompasses two primary domains: machine learning (ML) and deep learning. Machine learning, he explained, relies on analyzing vast datasets to identify patterns and correlations, enabling systems to make predictions or decisions based on historical data. While powerful, ML operates within predefined parameters and lacks the autonomy to innovate beyond its training.
In contrast, deep learning—a subset of AI inspired by the neural networks of the human brain—leverages unsupervised algorithms to process information, learn independently, and generate novel solutions. This advanced form of AI mimics human cognitive functions, enabling it to adapt, reason, and even create under unfamiliar conditions. Murthy emphasized that deep learning holds far greater potential to replicate human-like intelligence but cautioned that its adoption in India remains limited.
A significant portion of the discourse revolved around the gap between claims and reality in India’s AI landscape. Murthy argued that many products marketed as “AI-driven” are merely legacy systems adorned with modern buzzwords. This practice, he warned, risks stifling innovation by diverting attention and resources from meaningful research. He called for a shift in focus toward developing foundational AI technologies rather than chasing superficial trends. By conflating basic automation with true AI, companies risk eroding public trust and diluting the transformative potential of the technology.
In contrast to these criticisms, Murthy outlined Infosys’s strategic approach to AI development. The company is prioritizing the creation of Small Language Models (SLMs) tailored to specific industry needs. Unlike large language models (LLMs) that demand colossal computational resources and datasets, SLMs are designed for efficiency, leveraging a blend of open-source information and proprietary data. This approach allows Infosys to build specialized tools capable of delivering actionable insights while maintaining scalability and cost-effectiveness. Murthy emphasized that SLMs could address real-world challenges across sectors like healthcare, finance, and logistics by focusing on precision rather than scale.
Infosys’s commitment to ethical AI development was another focal point. Murthy reiterated the importance of transparency, accountability, and privacy in designing AI systems, particularly as they increasingly influence decision-making in critical domains. By integrating diverse data sources and prioritizing user-centric outcomes, Infosys aims to bridge the gap between theoretical AI potential and practical, scalable solutions.
Murthy’s critique extended beyond technical definitions to address systemic issues in India’s tech industry. He urged startups and enterprises to invest in foundational research and talent development, fostering an ecosystem where innovation thrives. This, he argued, requires collaboration between academia, industry, and policymakers to establish frameworks that encourage ethical AI development while curbing hyperbolic claims.
The speech concluded with a call to action for India’s tech community to embrace authenticity. Murthy acknowledged the global race to dominate AI but stressed that India’s contribution must be rooted in substance rather than marketing. By focusing on deep learning advancements and niche applications like SLMs, the country can carve a unique position in the global AI landscape.
In essence, Murthy’s message was a blend of caution and ambition—a reminder that AI’s true value lies not in labels but in its ability to solve complex problems, drive efficiency, and improve lives. As Infosys pioneers targeted AI solutions, the hope is that India’s tech industry will follow suit, prioritizing depth over dazzle in the quest for meaningful innovation.