Infosys Co-Founder Narayana Murthy Defends India’s IT Services Amid Criticism Over Large Language Models (LLMs)
Narayana Murthy, the co-founder of Infosys, has once again come to the defense of India’s information technology (IT) services industry, arguing that it continues to play a crucial role in both the nation’s economic development and its global standing. In a recent interview with Moneycontrol, Murthy responded to ongoing criticisms of India’s IT services, particularly in the context of emerging technologies like artificial intelligence (AI) and large language models (LLMs).
The Debate on India’s Role in AI and LLMs
India’s role in the rapidly advancing field of artificial intelligence has been a subject of intense debate in recent months. Some voices within the country have called for the creation of a homegrown LLM, a technology that has gained considerable traction in the AI sector for its ability to process and generate human-like text. The argument for a local LLM revolves around India’s unique cultural and linguistic needs, which could supposedly be better addressed by a model developed specifically for the Indian context.
However, Murthy, along with other thought leaders in India’s tech ecosystem, including Nandan Nilekani, a fellow Infosys co-founder and the architect of Aadhaar, disagrees with this perspective. Murthy argues that building an entirely new, homegrown LLM may not be the most practical or efficient approach. Instead, he advocates for leveraging and adapting existing, internationally developed LLMs to suit India’s specific needs. His reasoning is based on several practical challenges that India faces, including limited data infrastructure, high development costs, and a relative lack of resources in comparison to global leaders in AI development.
Murthy’s stance is grounded in a long-standing principle that has guided India’s IT services industry: adaptability rather than original invention. In the interview, he pointed out that while India has not traditionally been a leader in inventing entirely new technologies, it excels at adapting foreign innovations to local contexts. According to Murthy, the strength of India’s IT services lies in its ability to take existing global technologies—whether software, hardware, or AI—and tailor them to meet the specific needs of local markets.
India’s Strength: Adapting Global Innovations
Murthy posed a thought-provoking question during the interview: “Which area has India invented? Please give me an example.” His point was not to downplay India’s contributions to the global tech ecosystem but to highlight a key difference between innovation and adaptation. India has long been a leader in applying innovations from other countries to solve local problems, particularly in areas like software development, IT outsourcing, and now, AI.
He elaborated by emphasizing that India’s IT services sector has thrived primarily by offering cost-effective solutions and customized services for companies and governments around the world. Whether it’s creating enterprise software solutions, managing cloud infrastructures, or providing cybersecurity, Indian IT firms have shown an unmatched ability to scale and innovate within existing technological frameworks.
Murthy’s comments reflect a pragmatic outlook that has become increasingly relevant in today’s global tech landscape. Instead of expending vast amounts of resources to replicate or reinvent technologies already available, India can capitalize on its strengths in data processing, skilled labor, and cost efficiency to build on top of existing innovations—particularly in the field of AI.
The Challenges of Building Homegrown AI Models in India
In his defense of the IT services sector, Murthy also discussed the challenges that India would face if it were to embark on creating an indigenous LLM. One of the key obstacles is the country’s traditional reliance on oral knowledge and informal educational systems, which has limited the development of large-scale, written data repositories. These repositories are essential for training large language models, which require vast amounts of high-quality, structured text data.
Unlike countries with long-established written traditions and massive amounts of digital content, India faces a unique hurdle in terms of data availability. India’s linguistic diversity, while a strength in many ways, also complicates the collection and organization of text data in a way that would be conducive to LLM training. Furthermore, training a model from scratch would require enormous computational resources, which would be prohibitively expensive for many Indian firms, especially given the global competition from tech giants such as OpenAI, Google, and Microsoft.
Murthy’s argument is not that India should abandon the idea of AI entirely but that the country should focus on building innovative solutions on top of the existing LLMs. By tailoring these models to meet local needs—whether in regional languages, healthcare, education, or governance—India could create significant value without having to bear the immense costs of developing an entirely new LLM from the ground up.
A Call for Collaboration Rather Than Competition
Murthy’s position also underscores a broader philosophy that has defined India’s IT industry for decades: collaboration over competition. Indian IT services have often been positioned as partners to global firms, providing value through outsourcing and offshoring. Similarly, India’s AI sector can benefit from partnerships with global leaders rather than attempting to outpace them in developing cutting-edge technologies. The focus should be on innovation within the framework of global advancements, making use of external expertise while contributing locally relevant solutions.
In the interview, Murthy stressed the need for India to focus on its existing strengths—such as its tech talent, the thriving IT services sector, and the growing number of AI researchers and startups—and build upon them to create impactful solutions. He also pointed out that India’s recent strides in AI, such as the development of AI tools for agriculture and healthcare, are excellent examples of how the country can adapt and implement foreign technologies in a manner that meets its unique needs.
India’s IT Services Industry: An Underrated Success
Murthy’s comments also serve as a defense of India’s IT services industry, which has often been underrated in comparison to the cutting-edge innovations of Silicon Valley. Over the past few decades, Indian firms have built a reputation as global leaders in IT services, helping multinational companies optimize their business operations and providing technology solutions across industries. Despite the growing focus on AI, cloud computing, and other emerging technologies, India’s IT services sector remains one of the most significant contributors to the country’s economy.
In his view, the IT services sector should not be seen as a laggard but rather as a crucial enabler of India’s tech future. The sector has not only created millions of jobs and contributed significantly to India’s GDP but has also provided a strong foundation for the growth of other tech industries, including AI, blockchain, and cloud computing. Murthy believes that the Indian government and businesses should continue to support and expand the IT services sector, which has proven time and again its ability to adapt and innovate.
Conclusion
In summary, Narayana Murthy’s defense of India’s IT services industry and his pragmatic approach to artificial intelligence offer a refreshing perspective in an era where the push for homegrown technological innovation often dominates discussions. Rather than focusing on creating indigenous LLMs from scratch, Murthy advocates for leveraging existing technologies and adapting them to solve India’s local challenges. His comments underscore the value of India’s long-standing strengths in adaptation, cost-effective solutions, and scalability—traits that have propelled its IT services industry to global prominence.
As the debate over AI continues to unfold, Murthy’s insights remind us that India’s contributions to technology are not always about inventing something entirely new but about applying, adapting, and optimizing existing innovations for the benefit of its people and the world at large.
India’s Oral Tradition and Its Impact on the Development of Large Data Repositories for AI
Narayana Murthy, the co-founder of Infosys, recently offered an insightful perspective on one of the major challenges faced by India in the development of large language models (LLMs)—the cultural preference for oral knowledge over written knowledge. This issue, according to Murthy, has significantly limited India’s ability to create the vast data repositories that are essential for training LLMs, which are at the heart of advancements in artificial intelligence (AI).
Murthy’s remarks shed light on a deeply ingrained aspect of Indian culture that has shaped the country’s history, education, and knowledge systems. While India has made impressive strides in the field of technology, including AI, the country’s oral traditions have created barriers to the creation of large, structured, and digitally accessible datasets, which are critical for training LLMs. In this article, we will explore the role of oral knowledge in Indian society, its impact on AI development, and how India can overcome these challenges to become a leader in the AI age.
The Oral Tradition in India: A Historical Perspective
India’s oral tradition is not merely a modern phenomenon but an ancient practice rooted in the country’s cultural and historical evolution. For centuries, knowledge in India has been passed down through oral means—via storytelling, songs, rituals, and verbal transmission. This oral tradition was especially prominent in religious texts, cultural practices, and even scientific knowledge, which were conveyed through teachers (gurus) and students (shishyas) in an interactive manner, often through memorization and oral recitation.
The significance of this tradition can be traced to India’s rich philosophical, religious, and literary heritage. Many of India’s ancient scriptures, such as the Vedas, Upanishads, and epics like the Ramayana and Mahabharata, were originally passed down orally. This transmission was critical in preserving knowledge for thousands of years, as much of the population lacked access to written materials.
In fact, many Indian intellectual traditions—particularly in fields such as mathematics, astronomy, and medicine—relied on oral instruction and the memory of scholars. For example, in ancient India, mathematicians like Aryabhata and Brahmagupta made monumental contributions, yet their works were shared verbally and through word-of-mouth transmission.
Despite the advent of written scripts and printing presses, the tradition of oral knowledge continued to hold significance in various parts of Indian society, particularly in rural areas where literacy rates were historically low. Even today, oral methods of communication—through stories, songs, folk traditions, and verbal instructions—continue to play a key role in India’s educational and cultural systems.
The Role of Written Knowledge in Modern Technological Advancement
As global technological advancement accelerates, the role of written knowledge has become increasingly important. In the context of AI and machine learning, written knowledge takes on a whole new significance. The data required to train large language models (LLMs)—which form the basis of generative AI, chatbots, and other AI applications—must be written, structured, and digitized.
Unlike oral traditions, which are often transient and non-systematic, written knowledge allows for the organization, preservation, and easy access of vast quantities of data. When training AI models, especially LLMs, a large, structured, and digital dataset is necessary to ensure the accuracy, efficiency, and functionality of the models. These datasets contain millions or even billions of words, text samples, and interactions that are carefully curated and labeled for specific tasks. Whether it’s language translation, sentiment analysis, or text generation, the more diverse and comprehensive the data, the better the model performs.
In countries with strong written traditions and vast repositories of text data, the development of LLMs is relatively easier. For instance, companies like Google, Microsoft, and OpenAI have developed highly sophisticated LLMs by training them on large corpora of text data sourced from books, articles, websites, and other publicly available written materials. These text corpora are not only abundant but are also available in standardized formats that can be processed by algorithms, making it easier to train AI models.
However, India, despite being a leader in IT services and outsourcing, faces unique challenges in this regard due to its historical reliance on oral knowledge systems. The lack of extensive written repositories—especially in regional languages—limits the country’s ability to train AI models on a scale comparable to that of leading AI innovators in the West.
The Limitations of India’s Data Infrastructure for AI
The challenges posed by India’s cultural emphasis on oral over written knowledge are further compounded by the country’s limited data infrastructure. While India has a growing number of digital initiatives, such as e-governance platforms, digital education systems, and AI startups, there is still a lack of structured data repositories that can be used for training LLMs.
India is home to over 2,000 languages and dialects, many of which are not well-represented in digital form. While English is widely spoken and used in the business world, a vast majority of the population speaks languages such as Hindi, Bengali, Telugu, and Tamil—languages that may not have the same level of digital content as English or other major languages. Furthermore, the written records of these languages are often limited in terms of both quantity and quality, making it difficult to develop large, multilingual datasets that can be used for training AI systems.
In addition, the infrastructure to digitize and catalog the vast wealth of oral knowledge that exists in India—whether it’s in the form of ancient scriptures, folk tales, or modern-day conversations—is still underdeveloped. While efforts are being made to create digital libraries and digitize texts in Indian languages, the sheer volume of material that needs to be processed is overwhelming.
To develop LLMs that can understand and process India’s languages, a large-scale effort would be required to digitize and structure data across these languages. For example, there would need to be efforts to transcribe oral knowledge into written formats, label text data for training purposes, and standardize the data so it can be effectively used in machine learning applications.
Murthy’s Vision: Adapting Existing Models Rather Than Reinventing the Wheel
Despite these challenges, Narayana Murthy remains optimistic about India’s future in AI. While he acknowledges the limitations posed by the country’s historical reliance on oral knowledge, he advocates for a pragmatic approach to AI development. Rather than focusing on building indigenous LLMs from scratch, Murthy suggests that India should focus on adapting existing models to suit local needs.
In his view, India’s strength lies not in reinventing the wheel, but in building solutions on top of existing technologies. Instead of attempting to develop new LLMs from the ground up—an expensive and resource-intensive task—India should leverage global models and adapt them to meet the needs of its diverse population. This could include tailoring models to understand multiple Indian languages, recognizing regional dialects, and incorporating local context into AI systems.
Murthy’s argument is rooted in India’s historical strengths. The country has long been a leader in adapting foreign innovations to meet local needs. In the IT services industry, for instance, India has become the world’s largest provider of outsourced technology services by adapting software and technology to meet the specific requirements of businesses around the globe. Similarly, in AI, India could capitalize on existing advancements and use them to solve local challenges, such as improving healthcare access, enhancing education, and providing better government services.
Murthy also emphasizes that AI is not just about creating new models, but about finding practical solutions to real-world problems. India’s need for tailored solutions—whether in language translation, healthcare diagnostics, or financial inclusion—can be met through the creative application of existing AI technologies. By focusing on this kind of adaptation, India can leapfrog some of the challenges that might otherwise hinder its development of homegrown AI systems.
Overcoming the Challenges: Steps Toward a Data-Driven AI Future
While Murthy’s approach is pragmatic, India still faces significant hurdles in terms of data collection, digitization, and infrastructure. To realize the potential of AI, the country will need to address several key areas:
- Building Digital Infrastructure: India needs to invest heavily in digital infrastructure to create large-scale, structured datasets that can be used for AI development. This includes improving internet connectivity, expanding digital literacy, and providing tools for the creation and sharing of data.
- Digitizing Regional Languages: Efforts must be made to digitize Indian languages and make them more accessible to AI systems. This would involve translating oral knowledge into written forms, creating standardized datasets in regional languages, and developing models that can process these languages effectively.
- Collaboration with Global Players: India’s AI ecosystem should also focus on partnerships with global tech companies and institutions. Collaborations could help India gain access to more advanced models and infrastructure, allowing it to apply these technologies to local challenges.
- Encouraging Innovation in Data Science: India has a wealth of AI talent, and fostering innovation in the field of data science is key to overcoming the challenges posed by oral traditions. By encouraging the development of AI tools and platforms that can work with less structured data, India can create new solutions that are uniquely suited to its needs.
Conclusion
Narayana Murthy’s observations about India’s cultural preference for oral knowledge and its impact on AI development provide valuable insights into the country’s challenges and opportunities in the age of artificial intelligence. While the lack of large, structured data repositories poses significant barriers to the development of LLMs, India’s strength in adapting existing technologies offers a pragmatic path forward. By focusing on leveraging global AI models, improving digital infrastructure, and creating tailored solutions for its diverse population, India can position itself as a leader in the global AI landscape.
As Murthy suggests, the future of AI in India may not lie in creating entirely new models but in using the tools that already exist to address the unique challenges of a country with one of the world’s richest cultural and linguistic her
India’s Strength in Adapting Innovations: Narayana Murthy’s Vision on Technological Growth
In a recent interview with Moneycontrol, Narayana Murthy, the co-founder of Infosys, raised a thought-provoking question that struck at the heart of India’s approach to technological development: “Which area has India invented? Please give me an example.” This pointed query, while challenging the perception of India as an inventor of groundbreaking technologies, sheds light on a critical aspect of the country’s technological journey—its ability to adapt and customize foreign innovations to meet local needs.
Murthy’s statement and his broader views highlight a crucial theme in India’s approach to technology: rather than focusing solely on inventing new technologies from scratch, India has excelled in adapting, improving, and scaling existing innovations to address the specific challenges of its diverse population. This adaptability has been a key factor in India’s rise as a global hub for IT services, and it remains central to its aspirations in emerging fields like artificial intelligence (AI), blockchain, and data analytics.
This article will explore Murthy’s perspective in detail, examining how India has leveraged its strengths in adapting foreign technologies, the challenges it faces, and how this approach could shape the future of India’s role in global technological development.
India’s Role in Global Technological Ecosystem
To understand Murthy’s point, it is essential to acknowledge the broader context of India’s role in the global technological ecosystem. India has long been known for its expertise in information technology (IT) and software services. Indian companies like Infosys, Tata Consultancy Services (TCS), and Wipro are major players in the IT services industry, serving clients across the globe. The country has built a reputation as a leader in outsourcing, software development, and systems integration. These services are often based on existing foreign technologies that are adapted and customized to the needs of clients around the world.
India’s strength lies in its capacity to take established technologies, especially those developed in the West, and modify them to suit local conditions. This adaptability has been key to India’s success in the IT industry, where it has helped multinational companies optimize their operations by providing cost-effective and highly skilled solutions.
Murthy’s statement is a reminder that India’s technological prowess is often not about creating entirely new innovations but rather about taking what already exists and making it better suited to the Indian context. This ability to modify, localize, and scale foreign innovations is a central pillar of India’s IT services sector, which continues to thrive by focusing on execution rather than invention.
The Essence of Innovation: Adaptation Rather Than Creation
Murthy’s observation invites us to reconsider the very definition of innovation. Innovation, in the traditional sense, is often associated with inventing something new and disruptive. The Silicon Valley narrative, for example, emphasizes the creation of entirely new technologies that have the potential to change industries or societies. Companies like Google, Apple, and Microsoft are seen as paragons of innovation because they have invented products and platforms that have transformed the way people live, work, and communicate.
However, Murthy’s point challenges this linear view of innovation. In his view, innovation does not always have to involve the creation of something from scratch. The true value of innovation can also lie in the ability to adapt and apply existing technologies in ways that are relevant to specific local or regional contexts. In the case of India, this means taking global innovations and tailoring them to meet the unique needs of its vast and diverse population.
For instance, in the IT services sector, Indian companies have been incredibly successful in taking foreign software platforms and customizing them to meet the requirements of Indian businesses or government bodies. The adaptation process is not limited to translation or simple modification but extends to reengineering solutions to fit local challenges such as resource constraints, infrastructure limitations, and varying consumer preferences.
India’s Strength in IT Outsourcing: A Case Study of Adaptation
One of the clearest examples of India’s strength in adapting foreign innovations can be seen in the IT outsourcing industry. In the 1990s, as globalization and the internet opened up new avenues for business, many Western companies began seeking ways to reduce costs and improve efficiency. India, with its large, English-speaking population and a rapidly developing pool of highly skilled software engineers, emerged as a prime destination for outsourcing.
Rather than creating entirely new technologies, Indian companies capitalized on existing software systems, processes, and frameworks developed in the West. They provided value by customizing these systems for clients in various sectors, including banking, retail, and manufacturing. This approach allowed Indian companies to offer high-quality software development, system integration, and maintenance services at a fraction of the cost of their Western counterparts.
The success of India’s IT outsourcing industry was not about inventing new programming languages or platforms, but about taking existing systems—like enterprise resource planning (ERP) systems or customer relationship management (CRM) software—and adapting them to the specific needs of local clients. This meant integrating these solutions with the unique workflows, regulations, and market conditions of the Indian economy.
Moreover, India’s IT service providers did not just replicate what had been done in the West; they often improved upon it. For example, they introduced process optimizations that allowed global companies to scale more efficiently or made these systems more accessible to small and medium-sized enterprises (SMEs) that could not afford the same level of investment in technology as larger corporations.
This model of adaptation is what has allowed Indian IT services companies to scale globally. They didn’t focus on inventing new technologies but instead focused on providing value through practical, localized applications of existing innovations. This, Murthy argues, is India’s real strength in technology—its ability to adapt, scale, and optimize.
The Role of Adaptation in Emerging Technologies
Murthy’s argument is not limited to India’s past success in IT outsourcing. It has significant implications for India’s future in emerging technologies such as artificial intelligence (AI), machine learning (ML), blockchain, and the Internet of Things (IoT). As these fields continue to evolve, India will face new challenges and opportunities in adopting and adapting the technologies that are reshaping the global landscape.
Take the example of AI and large language models (LLMs). In recent years, there has been growing debate about whether India should focus on creating its own homegrown LLMs or leverage existing models developed by companies like OpenAI or Google. Murthy, along with other prominent industry leaders like Nandan Nilekani, has suggested that India should focus on adapting these existing models rather than trying to reinvent them.
Given the high costs and infrastructure requirements for building large-scale AI models, Murthy’s position is grounded in pragmatism. Instead of attempting to develop an entirely new LLM from scratch, India can focus on tailoring and localizing existing models to meet the unique challenges of its multilingual, diverse society. By leveraging the work already done by global tech companies and adapting it to Indian languages, contexts, and needs, India can make meaningful contributions to the global AI ecosystem.
This approach aligns with India’s broader technological philosophy. Rather than focusing on competing with global giants in the race for innovation, India can take a more collaborative and adaptive approach, leveraging the power of existing technologies to solve real-world problems. This could include creating AI tools that work across multiple Indian languages, improving healthcare and education through AI, and optimizing government services using machine learning models.
Challenges in Adapting Technologies
Of course, the process of adapting foreign technologies is not without its challenges. India’s infrastructure—whether in terms of data availability, computing power, or digital literacy—poses significant hurdles in fully harnessing the potential of AI and other advanced technologies. The country’s digital ecosystem must be expanded and modernized to provide the necessary foundation for scaling up the adaptation of global innovations.
Additionally, India faces a digital divide, where a significant portion of its population lacks access to the internet or modern computing devices. This poses challenges in creating universally accessible solutions, as technologies need to be customized not only for language but also for varying levels of access and literacy.
Finally, while India has a massive pool of skilled engineers, there remains a gap in advanced research and development in certain domains. To truly become a global leader in adapting foreign innovations, India will need to invest more in its research infrastructure and foster a culture of innovation that extends beyond just execution to include invention and scientific discovery.
India’s Future in Global Technological Leadership
Looking ahead, India’s path to global technological leadership will likely continue to be defined by its strength in adapting and localizing foreign innovations. As Murthy suggests, India’s true potential lies not in inventing technologies from scratch, but in its ability to build on and improve what already exists, tailoring these innovations to meet local challenges and needs.
To succeed in the coming decades, India will need to continue nurturing its vast pool of technical talent, invest in digital infrastructure, and focus on creating solutions that are deeply integrated with the needs of its population. By doing so, India can not only become a global hub for technology adaptation but also contribute meaningfully to the development of technologies that can address the world’s most pressing challenges—whether in AI, climate change, healthcare, or education.
In conclusion, Murthy’s comments reflect a deep understanding of India’s role in the global technological ecosystem. By recognizing the value of adaptation over pure invention, India can carve out a unique and powerful position in the future of technology. Whether in IT outsourcing, AI, or emerging technologies, India’s strength lies in its ability to take global innovations and make them work for its people—and, in doing so, make the world a better place.