Beyond the Hype: Why India’s AI Future Hinges on a “Team India” Approach to Jobs 

Beyond the Hype: Why India’s AI Future Hinges on a “Team India” Approach to Jobs 

The global conversation around Artificial Intelligence is often a binary one, oscillating between utopian visions of a frictionless future and dystopian fears of mass obsolescence. But as world leaders, tech moguls, and policymakers gathered in New Delhi for the AI Impact Summit 2026, India’s Chief Economic Advisor, V. Anantha Nageswaran, offered a third, more nuanced path. He didn’t just talk about AI’s potential; he framed it as an urgent national project with a uniquely Indian core: mass employability. 

Speaking at a session on the “Future of Employability and AI,” Nageswaran articulated a vision that is both ambitious and grounded in the country’s socio-economic reality. He declared that India possesses a historic opportunity to become “the first large society where human abundance and machine intelligence reinforce, and not undermine, each other.” This isn’t just a lofty ideal; it’s a strategic imperative. For a nation with a vast and young population, the intersection of AI and employment isn’t a futuristic debate—it is the defining challenge of its present. 

The “Co-Creation” Mandate: Moving Beyond the AI vs. Human Dichotomy 

The dominant narrative in the West, particularly in economies with stagnant or shrinking workforces, often frames AI as a replacement for human labor. Nageswaran’s statement inverts this premise. He posits that India’s “human abundance”—its demographic dividend—can be its greatest asset in the age of AI, provided the two forces are aligned to complement, not compete. 

This concept of “co-creation” is critical. It moves the conversation from one of job displacement to one of task augmentation. The goal, as the CEA implies, is not to protect every existing job from automation—a futile exercise—but to redesign workflows, education, and economic incentives so that humans and machines collaboratively achieve more than either could alone. 

Consider the Indian IT services industry. For decades, its success was built on providing high-volume, cost-effective talent for coding, maintenance, and support. The rise of generative AI threatens this very model by automating many of these entry-level tasks. This is the “conflict” Nageswaran warns against. To achieve “reinforcement,” the industry must pivot. The Indian tech professional of the future won’t just write code; they will be the architect who directs the AI, the ethicist who audits its biases, and the domain expert who interprets its outputs for a local business in Tier-2 city. 

This shift cannot be left to chance. Nageswaran was emphatic on this point: “This will not happen by drift; it will require an urgent say. It will require political will, it will require state capacity, and it will require a clear national commitment.” His words are a call to action, a recognition that market forces alone will not steer this technological revolution toward a socially equitable outcome. The profit motive for a company might be to automate entirely; the national interest must be to augment strategically. 

The First Step: A Pedagogy Revolution, Not Just a Tech Upgrade 

Where does this “national commitment” begin? According to the CEA, it starts with the most fundamental of institutions: our schools. “The first step begins with the reform of our education, pedagogy, and the teaching and imparting of foundational skills,” he stated. “That is where the path to co-creating prosperity with AI begins.” 

This is a profound insight that cuts through the noise of “AI in education” gimmicks. It’s not just about buying laptops for classrooms or teaching students how to use a new chatbot. It’s about rethinking what we teach and how we teach it in a world where knowledge recall is becoming a commodity. 

The current education system, a relic of the industrial age, was designed to produce standardized workers with a fixed set of skills. The AI age demands something radically different: 

• Foundational Skills Over Rote Learning: The ability to read critically, write persuasively, and perform mathematical reasoning will become even more important. These are the building blocks upon which all other learning, including interaction with AI, is built. An AI is only as good as the prompt it’s given; a mind trained in critical thinking will craft infinitely better prompts. 

• Learning to Learn: The half-life of technical skills is shrinking. The most crucial skill for a student today will be the ability to unlearn and relearn continuously. Pedagogy must shift from delivering static content to fostering curiosity, adaptability, and problem-solving. 

• Human-Centric Skills: As AI handles more of the analytical and repetitive tasks, uniquely human skills will skyrocket in value. Empathy, ethical judgment, creativity, collaboration, and complex communication will be the differentiators in a future workforce. Our education system must consciously cultivate these “soft skills” as the hard currency of the future economy. 

This reform is the bedrock. Without a population equipped with these foundational capabilities, any talk of an AI-powered economic miracle is hollow. 

The Startup State: Where Will the Jobs Come From? 

Complementing Nageswaran’s macro vision, Vineet Nayyar, Founder Chairman of Sampark Foundation and a veteran of the Indian IT industry (former CEO of HCL Technologies), offered a grounded, real-world perspective on employment generation. He delivered a reality check that is often missing in corporate boardroom discussions. 

“From an employment point of view… Indian companies, including Indian IT companies, are going to be profit-driven and therefore if you believe that they are going to create employment you must be dreaming,” Nayyar said bluntly. 

This is a critical point. Established corporations, especially in a capital-intensive and globally competitive environment, will optimize for efficiency. In the age of AI, efficiency often means doing more with less—automating processes and reducing headcount for certain tasks. To rely on them as the primary engine for mass job creation is a strategic error. 

Instead, Nayyar argues, the future of employment lies in “mass scale startups.” This isn’t just about a few unicorns in Bengaluru. It’s about fostering a culture of entrepreneurship that permeates the entire country, creating thousands, if not lakhs, of small and medium enterprises that solve local problems. The government’s role, in this view, is to act as a catalyst and an enabler—simplifying regulations, providing access to capital, and building the digital infrastructure that allows small teams to leverage powerful AI tools to build scalable solutions. 

This “startup state” model shifts the focus from being job-seekers to becoming job-creators. The problems to be solved are not “new sets of technology,” as Nayyar put it, but “new sets of problems.” An entrepreneur in rural Maharashtra doesn’t need to build a new large language model; they need to use an existing one to build an app that connects local farmers to fair market prices, predicts weather patterns, or provides vernacular-language veterinary advice. This is where AI’s democratizing power meets India’s grassroots reality. 

The Data Sovereignty Dilemma: A Call for Radical Strategic Thinking 

Nayyar also raised a red flag on a topic that will define geopolitical and economic power in the coming decades: data. He highlighted a profound dilemma facing India. 

“The LLM models which exist worldwide are far superior than the Indian models. Unfortunately, in India, we never develop products… On one side, we have global LLM products which are coming to India and trading on our Indian data. Should we allowed that…? But on the other side if we don’t allow that then we have the data but we don’t have the LLM models.” 

This is the “data sovereignty” dilemma in a nutshell. Indian users generate vast amounts of data, which is the lifeblood for training and refining AI models. Currently, this data often flows to global tech giants, whose models are then trained on this uniquely Indian context and sold back to the Indian market. India gets the service, but loses the value of its data and control over the models that will shape its digital future. 

Nayyar’s call for “radical strategic thinking” is an understatement. The solution is not simple protectionism, which could cut India off from world-class technology. Instead, it requires a multi-pronged strategy: 

1. Aggressive Investment in Foundational Models: Just as the ISRO program demonstrated indigenous technological capability, a concerted, well-funded national mission is needed to develop world-class SLMs (Small Language Models) and LLMs tailored for Indian languages and contexts. This cannot be left solely to the private sector, given the massive initial investment and long gestation period. 

1. Creating a Data Commons: The government could explore creating anonymized, secure public data trusts that allow Indian researchers and startups to build and train models on high-quality Indian data. This would democratize access and foster a vibrant domestic AI ecosystem. 

1. Smart Regulation: Policy must be designed not just to control data flow, but to encourage value creation within India. This could involve incentivizing global companies to partner with Indian firms, train models on Indian soil, and contribute to the local AI talent pool. 

The Summit’s Bigger Picture: India on the World Stage 

The fact that these discussions are taking place at the AI Impact Summit 2026, with leaders like French President Emmanuel Macron, Brazilian President Lula da Silva, and UN Secretary-General Antonio Guterres in attendance, underscores the global significance of India’s approach. The world is watching to see if a large, diverse, and developing democracy can navigate the AI revolution in a way that is inclusive and sustainable. 

If India succeeds, it will offer a powerful counter-narrative to the prevailing models emerging from the US and China. It will demonstrate that AI can be a tool for widespread prosperity, not just concentrated wealth. It will prove that human capital and machine intelligence can be a powerful, symbiotic force. 

But as Nageswaran cautioned, “The window is still open, but it is not indefinite.” The path forward requires a synchronized, “Team India” effort. It demands that policymakers move beyond rhetoric and build “state capacity” to execute complex education reforms. It requires the private sector to look beyond quarterly profits and invest in long-term, human-centric innovation. It requires academia to reimagine curricula and pedagogy for an unpredictable future. And it requires citizens to embrace a mindset of lifelong learning. 

The AI Impact Summit has laid out the vision and the challenge. The real work, the hard work of aligning technology with humanity, now begins. It is a national project that will define not just India’s economic future, but its very social fabric for generations to come.